TY - JOUR AU - Mugasha, Rodney AU - Kwiringira, Andrew AU - Ntono, Vivian AU - Nakiire, Lydia AU - Ayebazibwe, Immaculate AU - Kyozira, Caroline AU - Muruta, Niyonzima Allan AU - Kasule, Namugga Juliet AU - Byonanebye, M. Dathan AU - Nanyondo, Judith AU - Walwema, Richard AU - Kakooza, Francis AU - Lamorde, Mohammed PY - 2025/4/14 TI - Scaling Up and Enhancing the Functionality of the Electronic Integrated Diseases Surveillance and Response System in Uganda, 2020-2022: Description of the Journey, Challenges, and Lessons Learned JO - JMIR Public Health Surveill SP - e59783 VL - 11 KW - electronic Integrated Disease Surveillance and Response KW - eIDSR KW - disease surveillance KW - training of trainers KW - Uganda KW - digital surveillance systems KW - health worker KW - eHealth KW - public health KW - digital health UR - https://publichealth.jmir.org/2025/1/e59783 UR - http://dx.doi.org/10.2196/59783 ID - info:doi/10.2196/59783 ER - TY - JOUR AU - Shereefdeen, Hisba AU - Grant, Elizabeth Lauren AU - Patel, Vayshali AU - MacKay, Melissa AU - Papadopoulos, Andrew AU - Cheng, Leslie AU - Phypers, Melissa AU - McWhirter, Elizabeth Jennifer PY - 2025/4/10 TI - Assessing the Dissemination of Federal Risk Communication by News Media Outlets During Enteric Illness Outbreaks: Canadian Content Analysis JO - JMIR Public Health Surveill SP - e68724 VL - 11 KW - risk communication KW - health communication KW - enteric illness KW - foodborne illness KW - zoonotic disease KW - media KW - content analysis KW - health belief model KW - public health KW - Canada N2 - Background: Effective dissemination of federal risk communication by news media during multijurisdictional enteric illness outbreaks can increase message reach to rapidly contain outbreaks, limit adverse outcomes, and promote informed decision-making by the public. However, dissemination of risk communication from the federal government by mass media has not been evaluated. Objective: This study aimed to describe and assess the dissemination of federal risk communication by news media outlets during multijurisdictional enteric illness outbreaks in Canada. Methods: A comprehensive systematic search of 2 databases, Canadian Newsstream and Canadian Business & Current Affairs, was run using search terms related to the source of enteric illnesses, general outbreak characteristics, and relevant enteric pathogen names to retrieve news media articles issued between 2014 and 2023, corresponding to 46 public health notices (PHNs) communicating information about multijurisdictional enteric illness outbreaks during the same period. A codebook comprised of 3 sections?general characteristics of the article, consistency and accuracy of information presented between PHNs and news media articles, and presence of health belief model constructs?was developed and applied to the dataset. Data were tabulated and visualized using RStudio (Posit). Results: News media communicated about almost all PHNs (44/46, 96%). News media commonly developed their own articles (320/528, 60.6%) to notify the public about an outbreak and its associated product recall (121/320, 37.8%), but rarely communicated about the conclusion of an outbreak (12/320, 3.8%). News media communicated most outbreak characteristics, such as the number of cases (237/319, 74.3%), but the number of deaths was communicated less than half the time (114/260, 43.8%). Benefit and barrier constructs of the health belief model were infrequently present (50/243, 20.6% and 15/243, 6.2%, respectively). Conclusions: Canadian news media disseminated information about most multijurisdictional enteric illness outbreaks. However, differences in coverage of multijurisdictional enteric illness outbreaks by news media were evident. Federal organizations can improve future risk communication of multijurisdictional enteric illness outbreaks by news media by maintaining and strengthening interorganizational connections and ensuring the information quality of PHNs as a key information source for news media. UR - https://publichealth.jmir.org/2025/1/e68724 UR - http://dx.doi.org/10.2196/68724 ID - info:doi/10.2196/68724 ER - TY - JOUR AU - Xu, Huan Richard AU - Liang, Xiao AU - Starcevic, Vladan PY - 2025/4/2 TI - Exploring the Relationship Between Cyberchondria and Suicidal Ideation: Cross-Sectional Mediation Analysis JO - J Med Internet Res SP - e72414 VL - 27 KW - cyberchondria KW - suicidal ideation KW - distress KW - structural equation modeling KW - mediation analysis N2 - Background: The proliferation of internet-based health information has intensified cyberchondria, or anxiety resulting from excessive health-related searches. The relationship between cyberchondria and suicidal ideation remains underexplored, although there are indications that people with high levels of cyberchondria may also be suicidal. Understanding this relationship is critical, given rising digital health-seeking behaviors and the need to mitigate suicide risk. Emerging evidence suggests that psychological distress can mediate the relationship between cyberchondria and suicidal ideation. However, to the best of our knowledge, no research has directly examined these associations. Objective: This study had two aims. The first was to examine the relationship between cyberchondria and suicidal ideation in a sample of the general Chinese population. The second aim was to investigate the possible role of psychological distress, reflecting the symptoms of depression and anxiety, as a mediator in the relationship between cyberchondria and suicidal ideation. Methods: Data were obtained from a cross-sectional and web-based survey conducted in 2024. Structural equation modeling analysis was used to assess the hypothesized association between cyberchondria and suicidal ideation, as well as the mediating effect of psychological distress on this association. The Cyberchondria Severity Scale-12 items, Suicidal Ideation Attributes Scale, and Kessler Psychological Distress Scale-10 items were used to measure cyberchondria, suicidal ideation, and psychological distress, respectively. Standardized (?) estimates, along with their 95% CIs, were calculated for all structural paths, adjusting for participants? background characteristics. Results: A total of 2415 individuals completed the questionnaire (response rate=98.5%). Scores on the Cyberchondria Severity Scale-12 items ranged from 12 to 60, with the mean score being 40 (SD 7.9). The mean score on the Suicidal Ideation Attributes Scale was 12.7 (SD 9.9). Scores on the Kessler Psychological Distress Scale-10 items ranged from 10 to 50, and the mean score was 22 (SD 6.9). Cyberchondria, suicidal ideation, and psychological distress were significantly correlated. Structural equation modeling revealed a significant association between cyberchondria and psychological distress (?=.281; P<.001), between psychological distress and suicidal ideation (?=.504; P<.001), and between cyberchondria and suicidal ideation (?=.107; P<.001). The indirect effect of cyberchondria on suicidal ideation through psychological distress was also significant (?=.142; P<.001). Conclusions: The main contribution of this study is that it highlights an important relationship between cyberchondria and suicidal ideation, with a direct and statistically significant association between these variables. Their relationship is also mediated by psychological distress, which reflects the role of depressive and anxiety symptoms. UR - https://www.jmir.org/2025/1/e72414 UR - http://dx.doi.org/10.2196/72414 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/72414 ER - TY - JOUR AU - Wight, Lisa AU - Tenove, Chris AU - Hirani, Saima AU - Tworek, Heidi PY - 2025/4/2 TI - Mental Health and Coping Strategies of Health Communicators Who Faced Online Abuse During the COVID-19 Pandemic: Mixed Methods Study JO - JMIR Infodemiology SP - e68483 VL - 5 KW - mental health KW - online harassment KW - online abuse KW - coping strategies KW - resilience KW - social media KW - online advocacy KW - public health communication KW - health communication N2 - Background: During the COVID-19 pandemic, health experts used social media platforms to share information and advocate for policies. Many of them faced online abuse, which some reported took a toll on their mental health and well-being. Variation in the impacts of online abuse on mental health, well-being, and professional efficacy suggest that health communicators may differ in their coping strategies and ultimately their resilience to such abuse. Objective: We aimed to explore the impacts of online abuse on health communicators? mental health and well-being as well as their emotion- and problem-focused coping strategies. Methods: We recruited health communicators (public health officials, medical practitioners, and university-based researchers) in Canada who engaged in professional online communication during the COVID-19 pandemic. In phase 1, semistructured interviews were conducted with 35 health communicators. In phase 2, online questionnaires were completed by 34 individuals before participating in workshops. Purposive recruitment resulted in significant inclusion of those who self-identified as racialized or women. Interview and workshop data were subjected to inductive and deductive coding techniques to generate themes. Descriptive statistics were calculated for selected questionnaire questions. Results: In total, 94% (33/35) of interviewees and 82% (28/34) of questionnaire respondents reported experiencing online abuse during the study period (2020-2022). Most health communicators mentioned facing an emotional and psychological toll, including symptoms of depression and anxiety. Racialized and women health communicators faced abuse that emphasized their ethnicity, gender identity, and physical appearance. Health communicators? most common emotion-focused coping strategies were withdrawing from social media platforms, avoiding social media platforms altogether, and accepting online abuse as unavoidable. Common problem-focused coping strategies included blocking or unfriending hostile accounts, changing online behavior, formal help-seeking, and seeking peer support. Due to the impacts of online abuse on participants? mental health and well-being, 41% (14/34) of the questionnaire respondents seriously contemplated quitting health communication, while 53% (18/34) reduced or suspended their online presence. Our findings suggest that health communicators who used problem-focused coping strategies were more likely to remain active online, demonstrating significant professional resilience. Conclusions: Although health communicators in our study implemented various emotion- and problem-focused coping strategies, they still faced challenges in dealing with the impacts of online abuse. Our findings reveal the limitations of individual coping strategies, suggesting the need for effective formal organizational policies to support those who receive online abuse and to sanction those who perpetrate it. Organizational policies could improve long-term outcomes for health communicators? mental health and well-being by mitigating online abuse and supporting its targets. Such policies would bolster professional resilience, ensuring that important health information can still reach the public and is not silenced by online abuse. More research is needed to determine whether gender, race, or other factors shape coping strategies and their effectiveness. UR - https://infodemiology.jmir.org/2025/1/e68483 UR - http://dx.doi.org/10.2196/68483 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/68483 ER - TY - JOUR AU - Kautsar, Prawira Angga AU - Sinuraya, Kurnia Rano AU - van der Schans, Jurjen AU - Postma, Jacobus Maarten AU - Suwantika, A. Auliya PY - 2025/3/27 TI - Exploring Public Sentiment on the Repurposing of Ivermectin for COVID-19 Treatment: Cross-Sectional Study Using Twitter Data JO - JMIR Form Res SP - e50536 VL - 9 KW - COVID-19 KW - ivermectin KW - sentiment analysis KW - Twitter KW - social media KW - public health KW - misinformation KW - geolocation analysis UR - https://formative.jmir.org/2025/1/e50536 UR - http://dx.doi.org/10.2196/50536 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/50536 ER - TY - JOUR AU - John, N. Jennifer AU - Gorman, Sara AU - Scales, David PY - 2025/3/24 TI - Understanding Interventions to Address Infodemics Through Epidemiological, Socioecological, and Environmental Health Models: Framework Analysis JO - JMIR Infodemiology SP - e67119 VL - 5 KW - infodemics KW - misinformation KW - disinformation KW - Covid-19 KW - infodemic management KW - health communication KW - pandemic preparedness N2 - Background: The COVID-19 pandemic was accompanied by a barrage of false, misleading, and manipulated information that inhibited effective pandemic response and led to thousands of preventable deaths. Recognition of the urgent public health threat posed by this infodemic led to the development of numerous infodemic management interventions by a wide range of actors. The need to respond rapidly and with limited information sometimes came at the expense of strategy and conceptual rigor. Given limited funding for public health communication and growing politicization of countermisinformation efforts, responses to future infodemics should be informed by a systematic and conceptually grounded evaluation of the successes and shortcomings of existing interventions to ensure credibility of the field and evidence-based action. Objectives: This study sought to identify gaps and opportunities in existing infodemic management interventions and to assess the use of public health frameworks to structure responses to infodemics. Methods: We expanded a previously developed dataset of infodemic management interventions, spanning guidelines, policies, and tools from governments, academic institutions, nonprofits, media companies, and other organizations, with 379 interventions included in total. We applied framework analysis to describe and interpret patterns within these interventions through their alignment with codes derived from 3 frameworks selected for their prominence in public health and infodemic-related scholarly discourse: the epidemiological model, the socioecological model, and the environmental health framework. Results: The epidemiological model revealed the need for rigorous, transparent risk assessments to triage misinformation. The socioecological model demonstrated an opportunity for greater coordination across levels of influence, with only 11% of interventions receiving multiple socioecological codes, and more robust partnerships with existing organizations. The environmental health framework showed that sustained approaches that comprehensively address all influences on the information environment are needed, representing only 19% of the dataset. Conclusions: Responses to future infodemics would benefit from cross-sector coordination, adoption of measurable and meaningful goals, and alignment with public health frameworks, which provide critical conceptual grounding for infodemic response approaches and ensure comprehensiveness of approach. Beyond individual interventions, a funded coordination mechanism can provide overarching strategic direction and promote collaboration. UR - https://infodemiology.jmir.org/2025/1/e67119 UR - http://dx.doi.org/10.2196/67119 ID - info:doi/10.2196/67119 ER - TY - JOUR AU - Flaherty, Thomas Gerard AU - Mangan, Michael Ryan PY - 2025/3/21 TI - Impact of Social Media Influencers on Amplifying Positive Public Health Messages JO - J Med Internet Res SP - e73062 VL - 27 KW - social media KW - COVID-19 KW - vaccination KW - personal brands KW - public health KW - wellness KW - global health KW - pandemic KW - Twitter KW - tweets KW - vaccine KW - longitudinal design KW - wellness influencers KW - hand annotation KW - antivaccination KW - infodemiology UR - https://www.jmir.org/2025/1/e73062 UR - http://dx.doi.org/10.2196/73062 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/73062 ER - TY - JOUR AU - Alhazzaa, Linah AU - Curcin, Vasa PY - 2025/3/20 TI - Profiling Generalized Anxiety Disorder on Social Networks: Content and Behavior Analysis JO - J Med Internet Res SP - e53399 VL - 27 KW - generalized anxiety disorder KW - mental health KW - Twitter KW - social media analysis KW - natural language processing N2 - Background: Despite a dramatic increase in the number of people with generalized anxiety disorder (GAD), a substantial number still do not seek help from health professionals, resulting in reduced quality of life. With the growth in popularity of social media platforms, individuals have become more willing to express their emotions through these channels. Therefore, social media data have become valuable for identifying mental health status. Objective: This study investigated the social media posts and behavioral patterns of people with GAD, focusing on language use, emotional expression, topics discussed, and engagement to identify digital markers of GAD, such as anxious patterns and behaviors. These insights could help reveal mental health indicators, aiding in digital intervention development. Methods: Data were first collected from Twitter (subsequently rebranded as X) for the GAD and control groups. Several preprocessing steps were performed. Three measurements were defined based on Linguistic Inquiry and Word Count for linguistic analysis. GuidedLDA was also used to identify the themes present in the tweets. Additionally, users? behaviors were analyzed using Twitter metadata. Finally, we studied the correlation between the GuidedLDA-based themes and users? behaviors. Results: The linguistic analysis indicated differences in cognitive style, personal needs, and emotional expressiveness between people with and without GAD. Regarding cognitive style, there were significant differences (P<.001) for all features, such as insight (Cohen d=1.13), causation (Cohen d=1.03), and discrepancy (Cohen d=1.16). Regarding personal needs, there were significant differences (P<.001) in most personal needs categories, such as curiosity (Cohen d=1.05) and communication (Cohen d=0.64). Regarding emotional expressiveness, there were significant differences (P<.001) for most features, including anxiety (Cohen d=0.62), anger (Cohen d=0.72), sadness (Cohen d=0.48), and swear words (Cohen d=2.61). Additionally, topic modeling identified 4 primary themes (ie, symptoms, relationships, life problems, and feelings). We found that all themes were significantly more prevalent for people with GAD than for those without GAD (P<.001), along with significant effect sizes (Cohen d>0.50; P<.001) for most themes. Moreover, studying users? behaviors, including hashtag participation, volume, interaction pattern, social engagement, and reactive behaviors, revealed some digital markers of GAD, with most behavior-based features, such as the hashtag (Cohen d=0.49) and retweet (Cohen d=0.69) ratios, being statistically significant (P<.001). Furthermore, correlations between the GuidedLDA-based themes and users? behaviors were also identified. Conclusions: Our findings revealed several digital markers of GAD on social media. These findings are significant and could contribute to developing an assessment tool that clinicians could use for the initial diagnosis of GAD or the detection of an early signal of worsening in people with GAD via social media posts. This tool could provide ongoing support and personalized coping strategies. However, one limitation of using social media for mental health assessment is the lack of a demographic representativeness analysis. UR - https://www.jmir.org/2025/1/e53399 UR - http://dx.doi.org/10.2196/53399 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53399 ER - TY - JOUR AU - Parveen, Sana AU - Pereira, Garcia Agustin AU - Garzon-Orjuela, Nathaly AU - McHugh, Patricia AU - Surendran, Aswathi AU - Vornhagen, Heike AU - Vellinga, Akke PY - 2025/3/19 TI - COVID-19 Public Health Communication on X (Formerly Twitter): Cross-Sectional Study of Message Type, Sentiment, and Source JO - JMIR Form Res SP - e59687 VL - 9 KW - public health communication KW - surveillance KW - COVID-19 KW - SARS-CoV-2 KW - coronavirus KW - respiratory KW - infectious KW - pulmonary KW - pandemic KW - public health messaging KW - healthcare information KW - social media KW - tweets KW - text mining KW - data mining KW - social marketing KW - infoveillance KW - intervention planning N2 - Background: Social media can be used to quickly disseminate focused public health messages, increasing message reach and interaction with the public. Social media can also be an indicator of people?s emotions and concerns. Social media data text mining can be used for disease forecasting and understanding public awareness of health-related concerns. Limited studies explore the impact of type, sentiment and source of tweets on engagement. Thus, it is crucial to research how the general public reacts to various kinds of messages from different sources. Objective: The objective of this paper was to determine the association between message type, user (source) and sentiment of tweets and public engagement during the COVID-19 pandemic. Methods: For this study, 867,485 tweets were extracted from January 1, 2020 to March 31, 2022 from Ireland and the United Kingdom. A 4-step analytical process was undertaken, encompassing sentiment analysis, bio-classification (user), message classification and statistical analysis. A combination of manual content analysis with abductive coding and machine learning models were used to categorize sentiment, user category and message type for every tweet. A zero-inflated negative binomial model was applied to explore the most engaging content mix. Results: Our analysis resulted in 12 user categories, 6 message categories, and 3 sentiment classes. Personal stories and positive messages have the most engagement, even though not for every user group; known persons and influencers have the most engagement with humorous tweets. Health professionals receive more engagement with advocacy, personal stories/statements and humor-based tweets. Health institutes observe higher engagement with advocacy, personal stories/statements, and tweets with a positive sentiment. Personal stories/statements are not the most often tweeted category (22%) but have the highest engagement (27%). Messages centered on shock/disgust/fear-based (32%) have a 21% engagement. The frequency of informative/educational communications is high (33%) and their engagement is 16%. Advocacy message (8%) receive 9% engagement. Humor and opportunistic messages have engagements of 4% and 0.5% and low frequenciesof 5% and 1%, respectively. This study suggests the optimum mix of message type and sentiment that each user category should use to get more engagement. Conclusions: This study provides comprehensive insight into Twitter (rebranded as X in 2023) users? responses toward various message type and sources. Our study shows that audience engages with personal stories and positive messages the most. Our findings provide valuable guidance for social media-based public health campaigns in developing messages for maximum engagement. UR - https://formative.jmir.org/2025/1/e59687 UR - http://dx.doi.org/10.2196/59687 ID - info:doi/10.2196/59687 ER - TY - JOUR AU - Grygarová, Dominika AU - Havlík, Marek AU - Adámek, Petr AU - Horá?ek, Ji?í AU - Jurí?ková, Veronika AU - Hlinka, Jaroslav AU - Kesner, Ladislav PY - 2025/3/10 TI - Beliefs in Misinformation About COVID-19 and the Russian Invasion of Ukraine Are Linked: Evidence From a Nationally Representative Survey Study JO - JMIR Infodemiology SP - e62913 VL - 5 KW - misinformation KW - COVID-19 KW - war in Ukraine KW - political trust KW - digital media KW - belief rigidity KW - vaccine hesitancy KW - war KW - political KW - trust KW - belief KW - survey KW - questionnaire KW - national KW - false KW - association KW - correlation KW - correlation analysis KW - public opinion KW - media KW - news KW - health information KW - public health KW - COVID KW - propaganda N2 - Background: Detrimental effects of misinformation were observed during the COVID-19 pandemic. Presently, amid Russia?s military aggression in Ukraine, another wave of misinformation is spreading on the web and impacting our daily lives, with many citizens and politicians embracing Russian propaganda narratives. Despite the lack of an objective connection between these 2 societal issues, anecdotal observations suggest that supporters of misinformation regarding COVID-19 (BM-C) have also adopted misinformation about the war in Ukraine (BM-U) while sharing similar media use patterns and political attitudes. Objective: The aim of this study was to determine whether there is a link between respondents? endorsement of the 2 sets of misinformation narratives, and whether some of the selected factors (media use, political trust, vaccine hesitancy, and belief rigidity) are associated with both BM-C and BM-U. Methods: We conducted a survey on a nationally representative sample of 1623 individuals in the Czech Republic. Spearman correlation analysis was performed to identify the relationship between BM-C and BM-U. In addition, multiple linear regression was used to determine associations between the examined factors and both sets of misinformation. Results: We discovered that BM-C and BM-U were moderately correlated (Spearman ?=0.57; P<.001). Furthermore, increased trust in Russia and decreased trust in the local government, public media, and Western allies of the Czech Republic predicted both BM-C and BM-U. Media use indicating frustration with and avoidance of public or mainstream media, consumption of alternative information sources, and participation in web-based discussions indicative of epistemic bubbles predicted beliefs in misinformation narratives. COVID-19 vaccine refusal predicted only BM-C but not BM-U. However, vaccine refusers were overrepresented in the BM-U supporters (64/161, 39.8%) and undecided (128/505, 25.3%) individuals. Both beliefs were associated with belief rigidity. Conclusions: Our study provides empirical evidence that supporters of COVID-19 misinformation were susceptible to ideological misinformation aligning with Russian propaganda. Supporters of both sets of misinformation narratives were primarily linked by their shared trust or distrust in the same geopolitical actors and their distrust in the local government. UR - https://infodemiology.jmir.org/2025/1/e62913 UR - http://dx.doi.org/10.2196/62913 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62913 ER - TY - JOUR AU - Shao, Anqi AU - Chen, Kaiping AU - Johnson, Branden AU - Miranda, Shaila AU - Xing, Qidi PY - 2025/3/10 TI - Ubiquitous News Coverage and Its Varied Effects in Communicating Protective Behaviors to American Adults in Infectious Disease Outbreaks: Time-Series and Longitudinal Panel Study JO - J Med Internet Res SP - e64307 VL - 27 KW - risk communication KW - panel study KW - computational method KW - intermedia agenda setting KW - protective behaviors KW - infectious disease N2 - Background: Effective communication is essential for promoting preventive behaviors during infectious disease outbreaks like COVID-19. While consistent news can better inform the public about these health behaviors, the public may not adopt them. Objective: This study aims to explore the role of different media platforms in shaping public discourse on preventive measures to infectious diseases such as quarantine and vaccination, and how media exposure influences individuals? intentions to adopt these behaviors in the United States. Methods: This study uses data from 3 selected top national newspapers in the United States, Twitter discussions, and a US nationwide longitudinal panel survey from February 2020 to April 2021. We used the Intermedia Agenda-Setting Theory and the Protective Action Decision Model to develop the theoretical framework. Results: We found a 2-way agenda flow between selected national newspapers and the social media platform Twitter, particularly in controversial topics like vaccination (F1,426=16.39; P<.001 for newspapers; F1,426=44.46; P<.001 for Twitter). Exposure to media coverage increased individuals? perceived benefits of certain behaviors like vaccination but did not necessarily translate into behavioral adoption. For example, while individuals? media exposure increased perceived benefits of mask-wearing (?=.057; P<.001 for household benefits; ?=.049; P<.001 for community benefits), it was not consistently linked to higher intentions to wear masks (?=?.026; P=.04). Conclusions: This study integrates media flow across platforms with US national panel survey data, offering a comprehensive view of communication dynamics during the early stage of an infectious disease outbreak. The findings caution against a one-size-fits-all approach in communicating different preventive behaviors, especially where individual and community benefits may not always align. UR - https://www.jmir.org/2025/1/e64307 UR - http://dx.doi.org/10.2196/64307 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64307 ER - TY - JOUR AU - Kim, Kwanho AU - Kim, Soojong PY - 2025/3/4 TI - Large Language Models? Accuracy in Emulating Human Experts? Evaluation of Public Sentiments about Heated Tobacco Products on Social Media: Evaluation Study JO - J Med Internet Res SP - e63631 VL - 27 KW - heated tobacco products KW - artificial intelligence KW - large language models KW - social media KW - sentiment analysis KW - ChatGPT KW - generative pre-trained transformer KW - GPT KW - LLM KW - NLP KW - natural language processing KW - machine learning KW - language model KW - sentiment KW - evaluation KW - tobacco KW - alternative KW - prevention KW - nicotine KW - OpenAI N2 - Background: Sentiment analysis of alternative tobacco products discussed on social media is crucial in tobacco control research. Large language models (LLMs) are artificial intelligence models that were trained on extensive text data to emulate the linguistic patterns of humans. LLMs may hold the potential to streamline the time-consuming and labor-intensive process of human sentiment analysis. Objective: This study aimed to examine the accuracy of LLMs in replicating human sentiment evaluation of social media messages relevant to heated tobacco products (HTPs). Methods: GPT-3.5 and GPT-4 Turbo (OpenAI) were used to classify 500 Facebook (Meta Platforms) and 500 Twitter (subsequently rebranded X) messages. Each set consisted of 200 human-labeled anti-HTPs, 200 pro-HTPs, and 100 neutral messages. The models evaluated each message up to 20 times to generate multiple response instances reporting its classification decisions. The majority of the labels from these responses were assigned as a model?s decision for the message. The models? classification decisions were then compared with those of human evaluators. Results: GPT-3.5 accurately replicated human sentiment evaluation in 61.2% of Facebook messages and 57% of Twitter messages. GPT-4 Turbo demonstrated higher accuracies overall, with 81.7% for Facebook messages and 77% for Twitter messages. GPT-4 Turbo?s accuracy with 3 response instances reached 99% of the accuracy achieved with 20 response instances. GPT-4 Turbo?s accuracy was higher for human-labeled anti- and pro-HTP messages compared with neutral messages. Most of the GPT-3.5 misclassifications occurred when anti- or pro-HTP messages were incorrectly classified as neutral or irrelevant by the model, whereas GPT-4 Turbo showed improvements across all sentiment categories and reduced misclassifications, especially in incorrectly categorized messages as irrelevant. Conclusions: LLMs can be used to analyze sentiment in social media messages about HTPs. Results from GPT-4 Turbo suggest that accuracy can reach approximately 80% compared with the results of human experts, even with a small number of labeling decisions generated by the model. A potential risk of using LLMs is the misrepresentation of the overall sentiment due to the differences in accuracy across sentiment categories. Although this issue could be reduced with the newer language model, future efforts should explore the mechanisms underlying the discrepancies and how to address them systematically. UR - https://www.jmir.org/2025/1/e63631 UR - http://dx.doi.org/10.2196/63631 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053746 ID - info:doi/10.2196/63631 ER - TY - JOUR AU - Salaheddin, Tala AU - Sharma, H. Ramona AU - Fajardo, Marcela AU - Panter, Cameron AU - De Souza, Lauren AU - Matano, Kanyevu Sheila AU - Struik, Laura PY - 2025/3/4 TI - Utilization and Experiences of Using Quit Now, a Nicotine and Tobacco Smoking Cessation Website: Thematic Analysis JO - J Med Internet Res SP - e55592 VL - 27 KW - smoking cessation KW - user experiences KW - nicotine KW - vaping KW - web-based KW - Google Analytics KW - thematic analysis KW - digital health KW - nicotine replacement therapy KW - quit attempts KW - tobacco KW - British Columbia KW - behavioral support KW - pharmacotherapy KW - qualitative interview KW - cessation support KW - QuitNow KW - mobile health KW - mHealth KW - intervention N2 - Background: British Columbia residents have access to a program called QuitNow that provides behavioral support and information about pharmacotherapy to nicotine and tobacco users. Web- or computer-based smoking cessation programs have been shown to yield an abstinence rate about 1.5 times higher when compared to a control. Although quantitative evidence reveals significant promise for web-based services like QuitNow, there is very little qualitative evidence available. Understanding website utilization and the experiences of end users is key to contextualizing the effectiveness of web-based cessation services and providing directions for enhancing these services. Objective: This qualitative interview study aims to delve into users? utilization and experiences of QuitNow, which is supplemented by Google Analytics data. Methods: We interviewed 10 QuitNow users using semistructured interviews to understand what they liked the most and the least about QuitNow. We transcribed these interviews and conducted an inductive thematic analysis using NVivo (QSR International) software to extract common themes about user experiences. We also gathered utilization metrics via Google Analytics (n=13,856 users) to understand which aspects of QuitNow were used the most and which were used the least during the study period. Results: Thematic analysis yielded four major themes: (1) barriers to information access reduce opportunities to take action, (2) lack of clarity around pharmacological options is discouraging, (3) hearing from others is an important part of the journey, and (4) recognizing own agency throughout the quit process. These themes provided context and support for the Google Analytics data, which showed that end user activity, measured by indicators such as page views and average time spent on each page, was highest on pages about how to quit (10,393 page views), pharmacology information (1999 page views), and the community forum (11,560 page views). Conclusions: Results of this study point to several important implications for improving the website, as well as directions for enhancing cessation support services in general. UR - https://www.jmir.org/2025/1/e55592 UR - http://dx.doi.org/10.2196/55592 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053769 ID - info:doi/10.2196/55592 ER - TY - JOUR AU - Stimpson, P. Jim AU - Srivastava, Aditi AU - Tamirisa, Ketan AU - Kaholokula, Keawe?aimoku Joseph AU - Ortega, N. Alexander PY - 2025/3/4 TI - Crisis Communication About the Maui Wildfires on TikTok: Content Analysis of Engagement With Maui Wildfire?Related Posts Over 1 Year JO - JMIR Form Res SP - e67515 VL - 9 KW - social media KW - public health KW - disasters KW - Hawaii KW - media KW - post KW - communication KW - disaster KW - disaster communication KW - wildfire KW - information KW - dissemination KW - engagement KW - content analysis KW - content KW - metrics KW - misinformation KW - community KW - support N2 - Background: The August 2023 wildfire in the town of L?hain? on the island of Maui in Hawai?i caused catastrophic damage, affecting thousands of residents, and killing 102 people. Social media platforms, particularly TikTok, have become essential tools for crisis communication during disasters, providing real-time crisis updates, mobilizing relief efforts, and addressing misinformation. Understanding how disaster-related content is disseminated and engaged with on these platforms can inform strategies for improving emergency communication and community resilience. Objective: Guided by Social-Mediated Crisis Communication theory, this study examined TikTok posts related to the Maui wildfires to assess content themes, public engagement, and the effectiveness of social media in disseminating disaster-related information. Methods: TikTok posts related to the Maui wildfires were collected from August 8, 2023, to August 9, 2024. Using TikTok?s search functionality, we identified and reviewed public posts that contained relevant hashtags. Posts were categorized into 3 periods: during the disaster (August 8 to August 31, 2023), the immediate aftermath (September 1 to December 31, 2023), and the long-term recovery (January 1 to August 9, 2024). Two researchers independently coded the posts into thematic categories, achieving an interrater reliability of 87%. Engagement metrics (likes and shares) were analyzed to assess public interaction with different themes. Multivariable linear regression models were used to examine the associations between log-transformed likes and shares and independent variables, including time intervals, video length, the inclusion of music or effects, content themes, and hashtags. Results: A total of 275 TikTok posts were included in the analysis. Most posts (132/275, 48%) occurred in the immediate aftermath, while 76 (27.6%) were posted during the long-term recovery phase, and 24.4% (n=67) were posted during the event. Posts during the event garnered the highest average number of likes (mean 75,092, SD 252,759) and shares (mean 10,928, SD 55,308). Posts focused on ?Impact & Damage? accounted for the highest engagement, representing 36.8% (4,090,574/11,104,031) of total likes and 61.2% (724,848/1,184,049) of total shares. ?Tourism Impact? (2,172,991/11,104,031, 19.6% of likes; 81,372/1,184,049, 6.9% of shares) and ?Relief Efforts? (509,855/11,104,031, 4.6% of likes; 52,587/1,184,049, 4.4% of shares) were also prominent themes. Regression analyses revealed that videos with ?Misinformation & Fake News? themes had the highest engagement per post, with a 4.55 coefficient for log-shares (95% CI 2.44-6.65), while videos about ?Tourism Impact? and ?Relief Efforts? also showed strong engagement (coefficients for log-likes: 2.55 and 1.76, respectively). Conclusions: TikTok is an influential tool for disaster communication, amplifying both critical disaster updates and misinformation, highlighting the need for strategic content moderation and evidence-based messaging to enhance the platform?s role in crisis response. Public health officials, emergency responders, and policy makers can leverage TikTok?s engagement patterns to optimize communication strategies, improve real-time risk messaging, and support long-term community resilience. UR - https://formative.jmir.org/2025/1/e67515 UR - http://dx.doi.org/10.2196/67515 ID - info:doi/10.2196/67515 ER - TY - JOUR AU - Bazaco, C. Michael AU - Carstens, K. Christina AU - Greenlee, Tiffany AU - Blessington, Tyann AU - Pereira, Evelyn AU - Seelman, Sharon AU - Ivory, Stranjae AU - Jemaneh, Temesgen AU - Kirchner, Margaret AU - Crosby, Alvin AU - Viazis, Stelios AU - van Twuyver, Sheila AU - Gwathmey, Michael AU - Malais, Tanya AU - Ou, Oliver AU - Kenez, Stephanie AU - Nolan, Nichole AU - Karasick, Andrew AU - Punzalan, Cecile AU - Schwensohn, Colin AU - Gieraltowski, Laura AU - Chen Parker, Cary AU - Jenkins, Erin AU - Harris, Stic PY - 2025/2/28 TI - Recent Use of Novel Data Streams During Foodborne Illness Cluster Investigations by the United States Food and Drug Administration: Qualitative Review JO - JMIR Public Health Surveill SP - e58797 VL - 11 KW - foodborne illness surveillance KW - novel data streams KW - outbreak investigations KW - novel data KW - foodborne illness KW - foodborne KW - illness KW - United States KW - public health KW - prevention KW - outbreaks KW - social media KW - product review KW - cluster KW - product information KW - surveillance KW - epidemiology UR - https://publichealth.jmir.org/2025/1/e58797 UR - http://dx.doi.org/10.2196/58797 ID - info:doi/10.2196/58797 ER - TY - JOUR AU - Xie, Jiacheng AU - Zhang, Ziyang AU - Zeng, Shuai AU - Hilliard, Joel AU - An, Guanghui AU - Tang, Xiaoting AU - Jiang, Lei AU - Yu, Yang AU - Wan, Xiufeng AU - Xu, Dong PY - 2025/2/20 TI - Leveraging Large Language Models for Infectious Disease Surveillance?Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis JO - J Med Internet Res SP - e63190 VL - 27 KW - COVID-19 KW - self-reporting data KW - large language model KW - Twitter KW - social media analysis KW - natural language processing KW - machine learning N2 - Background: The emergence of new SARS-CoV-2 variants, the resulting reinfections, and post?COVID-19 condition continue to impact many people?s lives. Tracking websites like the one at Johns Hopkins University no longer report the daily confirmed cases, posing challenges to accurately determine the true extent of infections. Many COVID-19 cases with mild symptoms are self-assessed at home and reported on social media, which provides an opportunity to monitor and understand the progression and evolving trends of the disease. Objective: We aim to build a publicly available database of COVID-19?related tweets and extracted information about symptoms and recovery cycles from self-reported tweets. We have presented the results of our analysis of infection, reinfection, recovery, and long-term effects of COVID-19 on a visualization website that refreshes data on a weekly basis. Methods: We used Twitter (subsequently rebranded as X) to collect COVID-19?related data, from which 9 native English-speaking annotators annotated a training dataset of COVID-19?positive self-reporters. We then used large language models to identify positive self-reporters from other unannotated tweets. We used the Hibert transform to calculate the lead of the prediction curve ahead of the reported curve. Finally, we presented our findings on symptoms, recovery, reinfections, and long-term effects of COVID-19 on the Covlab website. Results: We collected 7.3 million tweets related to COVID-19 between January 1, 2020, and April 1, 2024, including 262,278 self-reported cases. The predicted number of infection cases by our model is 7.63 days ahead of the official report. In addition to common symptoms, we identified some symptoms that were not included in the list from the US Centers for Disease Control and Prevention, such as lethargy and hallucinations. Repeat infections were commonly occurring, with rates of second and third infections at 7.49% (19,644/262,278) and 1.37% (3593/262,278), respectively, whereas 0.45% (1180/262,278) also reported that they had been infected >5 times. We identified 723 individuals who shared detailed recovery experiences through tweets, indicating a substantially reduction in recovery time over the years. Specifically, the average recovery period decreased from around 30 days in 2020 to approximately 12 days in 2023. In addition, geographic information collected from confirmed individuals indicates that the temporal patterns of confirmed cases in states such as California and Texas closely mirror the overall trajectory observed across the United States. Conclusions: Although with some biases and limitations, self-reported tweet data serves as a valuable complement to clinical data, especially in the postpandemic era dominated by mild cases. Our web-based analytic platform can play a significant role in continuously tracking COVID-19, finding new uncommon symptoms, detecting and monitoring the manifestation of long-term effects, and providing necessary insights to the public and decision-makers. UR - https://www.jmir.org/2025/1/e63190 UR - http://dx.doi.org/10.2196/63190 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63190 ER - TY - JOUR AU - Sillence, Elizabeth AU - Branley-Bell, Dawn AU - Moss, Mark AU - Briggs, Pam PY - 2025/2/13 TI - A Model of Trust in Online COVID-19 Information and Advice: Cross-Sectional Questionnaire Study JO - JMIR Infodemiology SP - e59317 VL - 5 KW - eHealth KW - electronic health KW - digital intervention KW - trust KW - online information seeking KW - scientific credibility KW - digital resources KW - COVID-19 KW - SARS-CoV-2 KW - respiratory KW - infectious KW - pulmonary KW - pandemic KW - public health KW - health information KW - global health KW - surveys KW - social media N2 - Background: During the COVID-19 pandemic, many people sought information from websites and social media. Understanding the extent to which these sources were trusted is important in relation to health communication. Objective: This study aims to identify the key factors influencing UK citizens? trust and intention to act on advice about COVID-19 found via digital resources and to test whether an existing model of trust in eHealth provided a good fit for COVID-19?related information seeking online. We also wished to identify any differences between the evaluation of general information and information relating specifically to COVID-19 vaccines. Methods: In total, 525 people completed an online survey in January 2022 encompassing a general web trust questionnaire, measures of information corroboration, coping perceptions, and intention to act. Data were analyzed using principal component analysis and structural equation modeling. The evaluation responses of general information and COVID-19 vaccine information were also compared. Results: The principal component analysis revealed 5 trust factors: (1) credibility and impartiality, (2) familiarity, (3) privacy, (4) usability, and (5) personal experiences. In the final structural equation modeling model, trust had a significant direct effect on intention to act (?=.65; P<.001). Of the trust factors, credibility and impartiality had a significant positive direct effect on trust (?=.82; P<.001). People searching for vaccination information felt less at risk, less anxious, and more optimistic after reading the information. We noted that most people sought information from ?official? sources. Finally, in the context of COVID-19, ?credibility and impartiality? remain a key predictor of trust in eHealth resources, but in comparison with previous models of trust in online health information, checking and corroborating information did not form a significant part of trust evaluations. Conclusions: In times of uncertainty, when faced with a global emergent health concern, people place their trust in familiar websites and rely on the perceived credibility and impartiality of those digital sources above other trust factors. UR - https://infodemiology.jmir.org/2025/1/e59317 UR - http://dx.doi.org/10.2196/59317 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59317 ER - TY - JOUR AU - Fridman, Ilona AU - Boyles, Dahlia AU - Chheda, Ria AU - Baldwin-SoRelle, Carrie AU - Smith, B. Angela AU - Elston Lafata, Jennifer PY - 2025/2/12 TI - Identifying Misinformation About Unproven Cancer Treatments on Social Media Using User-Friendly Linguistic Characteristics: Content Analysis JO - JMIR Infodemiology SP - e62703 VL - 5 KW - linguistic characteristics KW - linguistic features KW - cancer KW - Linguistic Inquiry and Word Count KW - misinformation KW - X KW - Twitter KW - alternative therapy KW - oncology KW - social media KW - natural language processing KW - machine learning KW - synthesis KW - review methodology KW - search KW - literature review N2 - Background: Health misinformation, prevalent in social media, poses a significant threat to individuals, particularly those dealing with serious illnesses such as cancer. The current recommendations for users on how to avoid cancer misinformation are challenging because they require users to have research skills. Objective: This study addresses this problem by identifying user-friendly characteristics of misinformation that could be easily observed by users to help them flag misinformation on social media. Methods: Using a structured review of the literature on algorithmic misinformation detection across political, social, and computer science, we assembled linguistic characteristics associated with misinformation. We then collected datasets by mining X (previously known as Twitter) posts using keywords related to unproven cancer therapies and cancer center usernames. This search, coupled with manual labeling, allowed us to create a dataset with misinformation and 2 control datasets. We used natural language processing to model linguistic characteristics within these datasets. Two experiments with 2 control datasets used predictive modeling and Lasso regression to evaluate the effectiveness of linguistic characteristics in identifying misinformation. Results: User-friendly linguistic characteristics were extracted from 88 papers. The short-listed characteristics did not yield optimal results in the first experiment but predicted misinformation with an accuracy of 73% in the second experiment, in which posts with misinformation were compared with posts from health care systems. The linguistic characteristics that consistently negatively predicted misinformation included tentative language, location, URLs, and hashtags, while numbers, absolute language, and certainty expressions consistently predicted misinformation positively. Conclusions: This analysis resulted in user-friendly recommendations, such as exercising caution when encountering social media posts featuring unwavering assurances or specific numbers lacking references. Future studies should test the efficacy of the recommendations among information users. UR - https://infodemiology.jmir.org/2025/1/e62703 UR - http://dx.doi.org/10.2196/62703 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/62703 ER - TY - JOUR AU - Ivanitskaya, V. Lana AU - Erzikova, Elina PY - 2025/2/11 TI - Visualizing YouTube Commenters? Conceptions of the US Health Care System: Semantic Network Analysis Method for Evidence-Based Policy Making JO - JMIR Infodemiology SP - e58227 VL - 5 KW - social media KW - semantic network KW - health system KW - health policy KW - ideology KW - VOSviewer KW - health care reform KW - health services KW - health care workforce KW - health insurance N2 - Background: The challenge of extracting meaningful patterns from the overwhelming noise of social media to guide decision-makers remains largely unresolved. Objective: This study aimed to evaluate the application of a semantic network method for creating an interactive visualization of social media discourse surrounding the US health care system. Methods: Building upon bibliometric approaches to conducting health studies, we repurposed the VOSviewer software program to analyze 179,193 YouTube comments about the US health care system. Using the overlay-enhanced semantic network method, we mapped the contents and structure of the commentary evoked by 53 YouTube videos uploaded in 2014 to 2023 by right-wing, left-wing, and centrist media outlets. The videos included newscasts, full-length documentaries, political satire, and stand-up comedy. We analyzed term co-occurrence network clusters, contextualized with custom-built information layers called overlays, and performed tests of the semantic network?s robustness, representativeness, structural relevance, semantic accuracy, and usefulness for decision support. We examined how the comments mentioning 4 health system design concepts?universal health care, Medicare for All, single payer, and socialized medicine?were distributed across the network terms. Results: Grounded in the textual data, the macrolevel network representation unveiled complex discussions about illness and wellness; health services; ideology and society; the politics of health care agendas and reforms, market regulation, and health insurance; the health care workforce; dental care; and wait times. We observed thematic alignment between the network terms, extracted from YouTube comments, and the videos that elicited these comments. Discussions about illness and wellness persisted across time, as well as international comparisons of costs of ambulances, specialist care, prescriptions, and appointment wait times. The international comparisons were linked to commentaries with a higher concentration of British-spelled words, underscoring the global nature of the US health care discussion, which attracted domestic and global YouTube commenters. Shortages of nurses, nurse burnout, and their contributing factors (eg, shift work, nurse-to-patient staffing ratios, and corporate greed) were covered in comments with many likes. Comments about universal health care had much higher use of ideological terms than comments about single-payer health systems. Conclusions: YouTube users addressed issues of societal and policy relevance: social determinants of health, concerns for populations considered vulnerable, health equity, racism, health care quality, and access to essential health services. Versatile and applicable to health policy studies, the method presented and evaluated in our study supports evidence-based decision-making and contextualized understanding of diverse viewpoints. Interactive visualizations can help to uncover large-scale patterns and guide strategic use of analytical resources to perform qualitative research. UR - https://infodemiology.jmir.org/2025/1/e58227 UR - http://dx.doi.org/10.2196/58227 UR - http://www.ncbi.nlm.nih.gov/pubmed/39932770 ID - info:doi/10.2196/58227 ER - TY - JOUR AU - Saito, Ryuichi AU - Tsugawa, Sho PY - 2025/2/11 TI - Understanding Citizens? Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI JO - J Med Internet Res SP - e63824 VL - 27 KW - COVID-19 KW - restriction KW - United States KW - X KW - Twitter KW - sentiment analysis KW - large language model KW - LLM KW - GPT-3.5 KW - fine-tuning N2 - Background: The COVID-19 pandemic continues to hold an important place in the collective memory as of 2024. As of March 2024, >676 million cases, 6 million deaths, and 13 billion vaccine doses have been reported. It is crucial to evaluate sociopsychological impacts as well as public health indicators such as these to understand the effects of the COVID-19 pandemic. Objective: This study aimed to explore the sentiments of residents of major US cities toward restrictions on social activities in 2022 during the transitional phase of the COVID-19 pandemic, from the peak of the pandemic to its gradual decline. By illuminating people?s susceptibility to COVID-19, we provide insights into the general sentiment trends during the recovery phase of the pandemic. Methods: To analyze these trends, we collected posts (N=119,437) on the social media platform Twitter (now X) created by people living in New York City, Los Angeles, and Chicago from December 2021 to December 2022, which were impacted by the COVID-19 pandemic in similar ways. A total of 47,111 unique users authored these posts. In addition, for privacy considerations, any identifiable information, such as author IDs and usernames, was excluded, retaining only the text for analysis. Then, we developed a sentiment estimation model by fine-tuning a large language model on the collected data and used it to analyze how citizens? sentiments evolved throughout the pandemic. Results: In the evaluation of models, GPT-3.5 Turbo with fine-tuning outperformed GPT-3.5 Turbo without fine-tuning and Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa)?large with fine-tuning, demonstrating significant accuracy (0.80), recall (0.79), precision (0.79), and F1-score (0.79). The findings using GPT-3.5 Turbo with fine-tuning reveal a significant relationship between sentiment levels and actual cases in all 3 cities. Specifically, the correlation coefficient for New York City is 0.89 (95% CI 0.81-0.93), for Los Angeles is 0.39 (95% CI 0.14-0.60), and for Chicago is 0.65 (95% CI 0.47-0.78). Furthermore, feature words analysis showed that COVID-19?related keywords were replaced with non?COVID-19-related keywords in New York City and Los Angeles from January 2022 onward and Chicago from March 2022 onward. Conclusions: The results show a gradual decline in sentiment and interest in restrictions across all 3 cities as the pandemic approached its conclusion. These results are also ensured by a sentiment estimation model fine-tuned on actual Twitter posts. This study represents the first attempt from a macro perspective to depict sentiment using a classification model created with actual data from the period when COVID-19 was prevalent. This approach can be applied to the spread of other infectious diseases by adjusting search keywords for observational data. UR - https://www.jmir.org/2025/1/e63824 UR - http://dx.doi.org/10.2196/63824 UR - http://www.ncbi.nlm.nih.gov/pubmed/39932775 ID - info:doi/10.2196/63824 ER - TY - JOUR AU - BinHamdan, Hamdan Rahaf AU - Alsadhan, Abdulrahman Salwa AU - Gazzaz, Zohair Arwa AU - AlJameel, Hassan AlBandary PY - 2025/2/10 TI - Social Media Use and Oral Health?Related Misconceptions in Saudi Arabia: Cross-Sectional Study JO - JMIR Form Res SP - e70071 VL - 9 KW - social media KW - oral health KW - health misinformation KW - digital health KW - Saudi Arabia KW - public health KW - Instagram KW - Snapchat KW - TikTok KW - Twitter N2 - Background: Social media has become a central tool in health communication, offering both opportunities and challenges. In Saudi Arabia, where platforms like WhatsApp, Snapchat, and Instagram are widely used, the quality and credibility of oral health information shared digitally remain critical issues. Misconceptions about oral health can negatively influence individuals? behaviors and oral health outcomes. Objective: This study aimed to describe the patterns of social media use and estimate the prevalence of oral health?related misconceptions among adults in Saudi Arabia. Additionally, it assessed the associations between engagement with oral health information, self-reported oral health, and the presence and count of these misconceptions. Methods: A cross-sectional survey was conducted over 10 weeks, targeting adults aged 15 years and older in Saudi Arabia. Data were collected from a total sample size (n=387) via a questionnaire distributed through targeted advertisements on Instagram, TikTok, Snapchat, and X (Twitter). The prevalence of oral health?related misconceptions was estimated using descriptive statistics, including counts and percentages. Chi-square tests described sociodemographic, social media engagement, and self-reported oral health. Logistic and Poisson regression analyses were used to assess associations between engagement and self-reported oral health with misconceptions. Logistic regression models provided odds ratios and adjusted odds ratios with 95% CI to assess the presence of oral health misconceptions. Poisson regression was used to calculate mean ratios and adjusted mean ratios (AMRs) for the count of misconceptions. Results: WhatsApp (n=344, 89.8%) and Instagram (n=304, 78.9%) were the most frequently used social media platforms daily. Common oral health misconceptions included beliefs that ?Pregnancy causes calcium loss in teeth? (n=337, 87%) and ?Dental treatment should be avoided during pregnancy? (n=245, 63.3%). Following dental-specific accounts was significantly associated with lower odds of having any misconceptions (adjusted odds ratio 0.41, 95% CI 0.22-0.78) and a lower count of misconceptions (AMR 0.87, 95% CI 0.77-0.98). Conversely, trust in social media as a source of oral health information was associated with a higher count of misconceptions (AMR 1.16, 95% CI 1.02-1.31). Conclusions: Social media platforms are essential yet double-edged tools for oral health information dissemination in Saudi Arabia. Participants who followed dental-specific accounts had significantly lower misconceptions, while trust in social media as a source of information was linked to higher counts of misconceptions. These findings highlight the importance of promoting credible content from verified sources to combat misconceptions. Strategic collaborations with dental professionals are necessary to enhance the dissemination of accurate oral health information and public awareness and reduce the prevalence of oral health?related misconceptions. UR - https://formative.jmir.org/2025/1/e70071 UR - http://dx.doi.org/10.2196/70071 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/70071 ER - TY - JOUR AU - Alshanik, Farah AU - Khasawneh, Rawand AU - Dalky, Alaa AU - Qawasmeh, Ethar PY - 2025/2/10 TI - Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach JO - JMIR Infodemiology SP - e53434 VL - 5 KW - topic modeling KW - sentiment analysis KW - COVID-19 KW - social media KW - Twitter KW - public discussion N2 - Background: The worldwide effects of the COVID-19 pandemic have been profound, and the Arab world has not been exempt from its wide-ranging consequences. Within this context, social media platforms such as Twitter have become essential for sharing information and expressing public opinions during this global crisis. Careful investigation of Arabic tweets related to COVID-19 can provide invaluable insights into the common topics and underlying sentiments that shape discussions about the COVID-19 pandemic. Objective: This study aimed to understand the concerns and feelings of Twitter users in Arabic-speaking countries about the COVID-19 pandemic. This was accomplished through analyzing the themes and sentiments that were expressed in Arabic tweets about the COVID-19 pandemic. Methods: In this study, 1 million Arabic tweets about COVID-19 posted between March 1 and March 31, 2020, were analyzed. Machine learning techniques, such as topic modeling and sentiment analysis, were applied to understand the main topics and emotions that were expressed in these tweets. Results: The analysis of Arabic tweets revealed several prominent topics related to COVID-19. The analysis identified and grouped 16 different conversation topics that were organized into eight themes: (1) preventive measures and safety, (2) medical and health care aspects, (3) government and social measures, (4) impact and numbers, (5) vaccine development and research, (6) COVID-19 and religious practices, (7) global impact of COVID-19 on sports and countries, and (8) COVID-19 and national efforts. Across all the topics identified, the prevailing sentiments regarding the spread of COVID-19 were primarily centered around anger, followed by disgust, joy, and anticipation. Notably, when conversations revolved around new COVID-19 cases and fatalities, public tweets revealed a notably heightened sense of anger in comparison to other subjects. Conclusions: The study offers valuable insights into the topics and emotions expressed in Arabic tweets related to COVID-19. It demonstrates the significance of social media platforms, particularly Twitter, in capturing the Arabic-speaking community?s concerns and sentiments during the COVID-19 pandemic. The findings contribute to a deeper understanding of the prevailing discourse, enabling stakeholders to tailor effective communication strategies and address specific public concerns. This study underscores the importance of monitoring social media conversations in Arabic to support public health efforts and crisis management during the COVID-19 pandemic. UR - https://infodemiology.jmir.org/2025/1/e53434 UR - http://dx.doi.org/10.2196/53434 UR - http://www.ncbi.nlm.nih.gov/pubmed/39928401 ID - info:doi/10.2196/53434 ER - TY - JOUR AU - Xiong, Xin AU - Xiang, Linghui AU - Chang, Litao AU - Wu, XY Irene AU - Deng, Shuzhen PY - 2025/2/6 TI - Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study JO - J Med Internet Res SP - e66072 VL - 27 KW - mumps KW - deep learning KW - baidu index KW - forecasting KW - incidence prediction KW - time series analysis KW - Yunnan KW - China N2 - Background: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availability of an effective vaccine, mumps outbreaks have reemerged globally, including in China, where it remains a significant public health issue. In Yunnan province, China, the incidence of mumps has fluctuated markedly and is higher than that in mainland China, underscoring the need for improved outbreak prediction methods. Traditional surveillance methods, however, may not be sufficient for timely and accurate outbreak prediction. Objective: Our study aims to leverage the Baidu search index, representing search volumes from China?s most popular search engine, along with environmental data to develop a predictive model for mumps incidence in Yunnan province. Methods: We analyzed mumps incidence in Yunnan Province from 2014 to 2023, and used time series data, including mumps incidence, Baidu search index, and environmental factors, from 2016 to 2023, to develop predictive models based on long short-term memory networks. Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). We constructed four models with different combinations of predictors: (1) model BE, combining the Baidu index and environmental factors data; (2) model IB, combining mumps incidence and Baidu index data; (3) model IE, combining mumps incidence and environmental factors; and (4) model IBE, integrating all 3 data sources. Results: The incidence of mumps in Yunnan showed significant variability, peaking at 37.5 per 100,000 population in 2019. From 2014 to 2023, the proportion of female patients ranged from 41.3% in 2015 to 45.7% in 2020, consistently lower than that of male patients. After excluding variables with a Pearson correlation coefficient of <0.10 or P values of <.05, we included 3 Baidu index search term groups (disease name, symptoms, and treatment) and 6 environmental factors (maximum temperature, minimum temperature, sulfur dioxide, carbon monoxide, particulate matter with a diameter of 2.5 µm or less, and particulate matter with a diameter of 10 µm or less) for model development. DNLM analysis revealed that the relative risks consistently increased with rising Baidu index values, while nonlinear associations between temperature and mumps incidence were observed. Among the 4 models, model IBE exhibited the best performance, achieving the coefficient of determination of 0.72, with mean absolute error, mean absolute percentage error, and root-mean-square error values of 0.33, 15.9%, and 0.43, respectively, in the test set. Conclusions: Our study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. This approach offers a promising tool for improving public health surveillance and enabling rapid responses to mumps outbreaks. UR - https://www.jmir.org/2025/1/e66072 UR - http://dx.doi.org/10.2196/66072 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/66072 ER - TY - JOUR AU - Asaad, Chaimae AU - Khaouja, Imane AU - Ghogho, Mounir AU - Baďna, Karim PY - 2025/2/3 TI - When Infodemic Meets Epidemic: Systematic Literature Review JO - JMIR Public Health Surveill SP - e55642 VL - 11 KW - epidemics KW - social media KW - epidemic surveillance KW - misinformation KW - mental health N2 - Background: Epidemics and outbreaks present arduous challenges, requiring both individual and communal efforts. The significant medical, emotional, and financial burden associated with epidemics creates feelings of distrust, fear, and loss of control, making vulnerable populations prone to exploitation and manipulation through misinformation, rumors, and conspiracies. The use of social media sites has increased in the last decade. As a result, significant amounts of public data can be leveraged for biosurveillance. Social media sites can also provide a platform to quickly and efficiently reach a sizable percentage of the population; therefore, they have a potential role in various aspects of epidemic mitigation. Objective: This systematic literature review aimed to provide a methodical overview of the integration of social media in 3 epidemic-related contexts: epidemic monitoring, misinformation detection, and the relationship with mental health. The aim is to understand how social media has been used efficiently in these contexts, and which gaps need further research efforts. Methods: Three research questions, related to epidemic monitoring, misinformation, and mental health, were conceptualized for this review. In the first PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) stage, 13,522 publications were collected from several digital libraries (PubMed, IEEE Xplore, ScienceDirect, SpringerLink, MDPI, ACM, and ACL) and gray literature sources (arXiv and ProQuest), spanning from 2010 to 2022. A total of 242 (1.79%) papers were selected for inclusion and were synthesized to identify themes, methods, epidemics studied, and social media sites used. Results: Five main themes were identified in the literature, as follows: epidemic forecasting and surveillance, public opinion understanding, fake news identification and characterization, mental health assessment, and association of social media use with psychological outcomes. Social media data were found to be an efficient tool to gauge public response, monitor discourse, identify misleading and fake news, and estimate the mental health toll of epidemics. Findings uncovered a need for more robust applications of lessons learned from epidemic ?postmortem documentation.? A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Conclusions: Harnessing the full potential of social media in epidemic-related tasks requires streamlining the results of epidemic forecasting, public opinion understanding, and misinformation detection, all while keeping abreast of potential mental health implications. Proactive prevention has thus become vital for epidemic curtailment and containment. UR - https://publichealth.jmir.org/2025/1/e55642 UR - http://dx.doi.org/10.2196/55642 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55642 ER - TY - JOUR AU - Chan, J. Garrett AU - Fung, Mark AU - Warrington, Jill AU - Nowak, A. Sarah PY - 2025/1/29 TI - Understanding Health-Related Discussions on Reddit: Development of a Topic Assignment Method and Exploratory Analysis JO - JMIR Form Res SP - e55309 VL - 9 KW - digital health KW - internet KW - open data KW - social networking KW - social media N2 - Background: Social media has become a widely used way for people to share opinions about health care and medical topics. Social media data can be leveraged to understand patient concerns and provide insight into why patients may turn to the internet instead of the health care system for health advice. Objective: This study aimed to develop a method to investigate Reddit posts discussing health-related conditions. Our goal was to characterize these topics and identify trends in these social media?based medical discussions. Methods: Using an initial query, we collected 1 year of Reddit posts containing the phrases ?get tested? and ?get checked.? These posts were manually reviewed, and subreddits containing irrelevant posts were excluded from analysis. This selection of posts was manually read by the investigators to categorize posts into topics. A script was developed to automatically assign topics to additional posts based on keywords. Topic and keyword selections were refined based on manual review for more accurate topic assignment. Topic assignment was then performed on the entire 1-year Reddit dataset containing 347,130 posts. Related topics were grouped into broader medical disciplines. Analysis of the topic assignments was then conducted to assess condition and medical topic frequencies in medical condition?focused subreddits and general subreddits. Results: We created an automated algorithm to assign medical topics to Reddit posts. By iterating through multiple rounds of topic assignment, we improved the accuracy of the algorithm. Ultimately, this algorithm created 82 topics sorted into 17 broader medical disciplines. Of all topics, sexually transmitted infections (STIs), eye disorders, anxiety, and pregnancy had the highest post frequency overall. STIs comprised 7.44% (5876/78,980) of posts, and anxiety comprised 5.43% (4289/78,980) of posts. A total of 34% (28/82) of the topics comprised 80% (63,184/78,980) of all posts. Of the medical disciplines, those with the most posts were psychiatry and mental health; genitourinary and reproductive health; infectious diseases; and endocrinology, nutrition, and metabolism. Psychiatry and mental health comprised 26.6% (21,009/78,980) of posts, and genitourinary and reproductive health comprised 13.6% (10,741/78,980) of posts. Overall, most posts were also classified under these 4 medical disciplines. During analysis, subreddits were also classified as general if they did not focus on a specific health issue and topic-specific if they discussed a specific medical issue. Topics that appeared most frequently in the top 5 in general subreddits included addiction and drug anxiety, attention-deficit/hyperactivity disorder, abuse, and STIs. In topic-specific subreddits, most posts were found to discuss the topic of that subreddit. Conclusions: Certain health topics and medical disciplines are predominant on Reddit. These include topics such as STIs, eye disorders, anxiety, and pregnancy. Most posts were classified under the medical disciplines of psychiatry and mental health, as well as genitourinary and reproductive health. UR - https://formative.jmir.org/2025/1/e55309 UR - http://dx.doi.org/10.2196/55309 UR - http://www.ncbi.nlm.nih.gov/pubmed/39879094 ID - info:doi/10.2196/55309 ER - TY - JOUR AU - Spiegel, Y. Daphna AU - Friesner, D. Isabel AU - Zhang, William AU - Zack, Travis AU - Yan, Gianna AU - Willcox, Julia AU - Prionas, Nicolas AU - Singer, Lisa AU - Park, Catherine AU - Hong, C. Julian PY - 2025/1/28 TI - Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study JO - JMIR Cancer SP - e52886 VL - 11 KW - breast cancer KW - social media KW - patient decision-making KW - natural language processing KW - breast conservation KW - mastectomy N2 - Background: Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information and as a decision tool for patients, and awareness of these conversations is important for patient counseling. Objective: The goal of this study was to compare sentiments and associated emotions in social media discussions surrounding BCS and mastectomy using natural language processing (NLP). Methods: Reddit posts and comments from the Reddit subreddit r/breastcancer and associated metadata were collected using pushshift.io. Overall, 105,231 paragraphs across 59,416 posts and comments from 2011 to 2021 were collected and analyzed. Paragraphs were processed through the Apache Clinical Text Analysis Knowledge Extraction System and identified as discussing BCS or mastectomy based on physician-defined Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concepts. Paragraphs were analyzed with a VADER (Valence Aware Dictionary for Sentiment Reasoning) compound sentiment score (ranging from ?1 to 1, corresponding to negativity or positivity) and GoEmotions scores (0?1) corresponding to the intensity of 27 different emotions and neutrality. Results: Of the 105,231 paragraphs, there were 7306 (6.94% of those analyzed) paragraphs mentioning BCS and mastectomy (2729 and 5476, respectively). Discussion of both increased over time, with BCS outpacing mastectomy. The median sentiment score for all discussions analyzed in aggregate became more positive over time. In specific analyses by topic, positive sentiments for discussions with mastectomy mentions increased over time; however, discussions with BCS-specific mentions did not show a similar trend and remained overall neutral. Compared to BCS, conversations about mastectomy tended to have more positive sentiments. The most commonly identified emotions included neutrality, gratitude, caring, approval, and optimism. Anger, annoyance, disappointment, disgust, and joy increased for BCS over time. Conclusions: Patients are increasingly participating in breast cancer therapy discussions with a web-based community. While discussions surrounding mastectomy became increasingly positive, BCS discussions did not show the same trend. This mirrors national clinical trends in the United States, with the increasing use of mastectomy over BCS in early-stage breast cancer. Recognizing sentiments and emotions surrounding the decision-making process can facilitate patient-centric and emotionally sensitive treatment recommendations. UR - https://cancer.jmir.org/2025/1/e52886 UR - http://dx.doi.org/10.2196/52886 ID - info:doi/10.2196/52886 ER - TY - JOUR AU - Lin, Fu-Huang AU - Chou, Yu-Ching AU - Hsieh, Chi-Jeng AU - Yu, Chia-Peng PY - 2025/1/28 TI - Epidemiological Features, Clinical Symptoms, and Environmental Risk Factors for Notifiable Japanese Encephalitis in Taiwan From 2008 to 2020: Retrospective Study JO - JMIR Public Health Surveill SP - e63053 VL - 11 KW - epidemiology KW - Japanese encephalitis virus KW - domestic KW - environmental factor KW - retrospective study N2 - Background: Japanese encephalitis (JE) is a zoonotic parasitic disease caused by the Japanese encephalitis virus (JEV), and may cause fever, nausea, headache, or meningitis. It is currently unclear whether the epidemiological characteristics of the JEV have been affected by the extreme climatic conditions that have been observed in recent years. Objective: This study aimed to examine the epidemiological characteristics, trends, and potential risk factors of JE in Taiwan from 2008 to 2020. Specifically, the study focused on gender, age, season, residential area, clinical manifestations, high-risk areas, and the impact of environmental and climate factors. Methods: This study reviewed publicly available annual summary data on reported JE cases in the Taiwan Centers for Diseases Control between 2008 and 2020. Results: This study collected 309 confirmed domestic patients and 4 patients with imported JE. There was an increasing trend in the incidence of JE, 0.69?1.57 cases per 1,000,000 people, peaking in 2018. Case fatality rate was 7.7% (24/313). Comparing sex, age, season, and place of residence, the incidence rate was highest in males, 40? to 59-year-old patients, summer, and the Eastern region, with 1.89, 3.27, 1.25, and 12.2 cases per million people, respectively. The average coverage rate of the JE vaccine for children in Taiwan is 94.9%. Additionally, the major clinical manifestations of the cases included fever, unconsciousness, headache, stiff necks, psychological symptoms, vomiting, and meningitis. The major occurrence places of JE included paddy fields, pig farms, pigeon farms, poultry farms, and ponds. For air pollution factors, linear regression analysis showed that SO2 (ppb) concentration was positively associated with JE cases (?=2.184, P=.02), but O3 (ppb) concentration was negatively associated with them (?=?0.157, P=.01). For climate factors, relative humidity (%) was positively associated with JE cases (?=.380, P=.02). Conclusions: This study is the first to report confirmed cases of JE from the surveillance data of the Taiwan Centers for Diseases Control between 2008 and 2020. It identified residence, season, and age as risk factors for JE in Taiwan. Air pollution and climatic factors also influenced the rise in JE cases. This study confirmed that JE remains a prevalent infectious disease in Taiwan, with its epidemic gradually increasing in severity. These findings empower clinicians and health care providers to make informed decisions, guiding their care and resource allocation for patients with JE, a disease that significantly impacts the health and well-being of the Taiwanese population. UR - https://publichealth.jmir.org/2025/1/e63053 UR - http://dx.doi.org/10.2196/63053 ID - info:doi/10.2196/63053 ER - TY - JOUR AU - Kahlawi, Adham AU - Masri, Firas AU - Ahmed, Wasim AU - Vidal-Alaball, Josep PY - 2025/1/27 TI - Cross-Cultural Sense-Making of Global Health Crises: A Text Mining Study of Public Opinions on Social Media Related to the COVID-19 Pandemic in Developed and Developing Economies JO - J Med Internet Res SP - e58656 VL - 27 KW - COVID-19 KW - SARS-CoV-2 KW - pandemic KW - citizen opinion KW - text mining KW - LDA KW - health crisis KW - developing economies KW - Italy KW - Egypt KW - UK KW - dataset KW - content analysis KW - social media KW - twitter KW - tweet KW - sentiment KW - attitude KW - perception KW - perspective KW - machine learning KW - latent Dirichlet allocation KW - vaccine KW - vaccination KW - public health KW - infectious N2 - Background: The COVID-19 pandemic reshaped social dynamics, fostering reliance on social media for information, connection, and collective sense-making. Understanding how citizens navigate a global health crisis in varying cultural and economic contexts is crucial for effective crisis communication. Objective: This study examines the evolution of citizen collective sense-making during the COVID-19 pandemic by analyzing social media discourse across Italy, the United Kingdom, and Egypt, representing diverse economic and cultural contexts. Methods: A total of 755,215 social media posts from X (formerly Twitter) were collected across 3 time periods: the virus' emergence (February 15 to March 31, 2020), strict lockdown (April 1 to May 30, 2020), and the vaccine rollout (December 1, 2020 to January 15, 2021). In total, 284,512 posts from Italy, 261,978 posts from the United Kingdom, and 209,725 posts from Egypt were analyzed using the latent Dirichlet allocation algorithm to identify key thematic topics and track shifts in discourse across time and regions. Results: The analysis revealed significant regional and temporal differences in collective sense-making during the pandemic. In Italy and the United Kingdom, public discourse prominently addressed pragmatic health care measures and government interventions, reflecting higher institutional trust. By contrast, discussions in Egypt were more focused on religious and political themes, highlighting skepticism toward governmental capacity and reliance on alternative frameworks for understanding the crisis. Over time, all 3 countries displayed a shift in discourse toward vaccine-related topics during the later phase of the pandemic, highlighting its global significance. Misinformation emerged as a recurrent theme across regions, demonstrating the need for proactive measures to ensure accurate information dissemination. These findings emphasize the role of cultural, economic, and institutional factors in shaping public responses during health crises. Conclusions: Crisis communication is influenced by cultural, economic, and institutional contexts, as evidenced by regional variations in citizen engagement. Transparent and culturally adaptive communication strategies are essential to combat misinformation and build public trust. This study highlights the importance of tailoring crisis responses to local contexts to improve compliance and collective resilience. UR - https://www.jmir.org/2025/1/e58656 UR - http://dx.doi.org/10.2196/58656 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58656 ER - TY - JOUR AU - Huijgens, Fiorella AU - Kwakman, Pascale AU - Hillen, Marij AU - van Weert, Julia AU - Jaspers, Monique AU - Smets, Ellen AU - Linn, Annemiek PY - 2025/1/16 TI - How Patients With Cancer Use the Internet to Search for Health Information: Scenario-Based Think-Aloud Study JO - JMIR Infodemiology SP - e59625 VL - 5 KW - web-based health information seeking KW - think aloud KW - scenario based KW - cancer KW - patient evaluation KW - information seeking KW - web-based information KW - health information KW - internet KW - pattern KW - motivation KW - cognitive KW - emotional KW - response KW - patient KW - survivor KW - caregiver KW - interview KW - scenario KW - women KW - men N2 - Background: Patients with cancer increasingly use the internet to seek health information. However, thus far, research treats web-based health information seeking (WHIS) behavior in a rather dichotomous manner (ie, approaching or avoiding) and fails to capture the dynamic nature and evolving motivations that patients experience when engaging in WHIS throughout their disease trajectory. Insights can be used to support effective patient-provider communication about WHIS and can lead to better designed web-based health platforms. Objective: This study explored patterns of motivations and emotions behind the web-based information seeking of patients with cancer at various stages of their disease trajectory, as well as the cognitive and emotional responses evoked by WHIS via a scenario-based, think-aloud approach. Methods: In total, 15 analog patients were recruited, representing patients with cancer, survivors, and informal caregivers. Imagining themselves in 3 scenarios?prediagnosis phase (5/15, 33%), treatment phase (5/15, 33%), and survivor phase (5/15, 33%)?patients were asked to search for web-based health information while being prompted to verbalize their thoughts. In total, 2 researchers independently coded the sessions, categorizing the codes into broader themes to comprehend analog patients? experiences during WHIS. Results: Overarching motives for WHIS included reducing uncertainty, seeking reassurance, and gaining empowerment. At the beginning of the disease trajectory, patients mainly showed cognitive needs, whereas this shifted more toward affective needs in the subsequent disease stages. Analog patients? WHIS approaches varied from exploratory to focused or a combination of both. They adapted their search strategy when faced with challenging cognitive or emotional content. WHIS triggered diverse emotions, fluctuating throughout the search. Complex, confrontational, and unexpected information mainly induced negative emotions. Conclusions: This study provides valuable insights into the motivations of patients with cancer underlying WHIS and the emotions experienced at various stages of the disease trajectory. Understanding patients? search patterns is pivotal in optimizing web-based health platforms to cater to specific needs. In addition, these findings can guide clinicians in accommodating patients? specific needs and directing patients toward reliable sources of web-based health information. UR - https://infodemiology.jmir.org/2025/1/e59625 UR - http://dx.doi.org/10.2196/59625 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59625 ER - TY - JOUR AU - Iera, Jessica AU - Isonne, Claudia AU - Seghieri, Chiara AU - Tavoschi, Lara AU - Ceparano, Mariateresa AU - Sciurti, Antonio AU - D'Alisera, Alessia AU - Sane Schepisi, Monica AU - Migliara, Giuseppe AU - Marzuillo, Carolina AU - Villari, Paolo AU - D'Ancona, Fortunato AU - Baccolini, Valentina PY - 2025/1/15 TI - Availability and Key Characteristics of National Early Warning Systems for Emerging Profiles of Antimicrobial Resistance in High-Income Countries: Systematic Review JO - JMIR Public Health Surveill SP - e57457 VL - 11 KW - early warning system KW - surveillance KW - emerging AMR KW - high-income countries KW - antimicrobial resistance N2 - Background: The World Health Organization (WHO) recently advocated an urgent need for implementing national surveillance systems for the timely detection and reporting of emerging antimicrobial resistance (AMR). However, public information on the existing national early warning systems (EWSs) is often incomplete, and a comprehensive overview on this topic is currently lacking. Objective: This review aimed to map the availability of EWSs for emerging AMR in high-income countries and describe their main characteristics. Methods: A systematic review was performed on bibliographic databases, and a targeted search was conducted on national websites. Any article, report, or web page describing national EWSs in high-income countries was eligible for inclusion. EWSs were identified considering the emerging AMR-reporting WHO framework. Results: We identified 7 national EWSs from 72 high-income countries: 2 in the East Asia and Pacific Region (Australia and Japan), 3 in Europe and Central Asia (France, Sweden, and the United Kingdom), and 2 in North America (the United States and Canada). The systems were established quite recently; in most cases, they covered both community and hospital settings, but their main characteristics varied widely across countries in terms of the organization and microorganisms under surveillance, with also different definitions of emerging AMR and alert functioning. A formal system assessment was available only in Australia. Conclusions: A broader implementation and investment of national surveillance systems for the early detection of emerging AMR are still needed to establish EWSs in countries and regions lacking such capabilities. More standardized data collection and reporting are also advisable to improve cooperation on a global scale. Further research is required to provide an in-depth analysis of EWSs, as this study is limited to publicly available data in high-income countries. UR - https://publichealth.jmir.org/2025/1/e57457 UR - http://dx.doi.org/10.2196/57457 ID - info:doi/10.2196/57457 ER - TY - JOUR AU - Wang, Wei AU - Wang, Hui AU - Hu, Xinru AU - Yu, Qian AU - Chen, Fangyi AU - Qiu, Xirui AU - Wang, Xiaoxiao PY - 2025/1/13 TI - The Association Between Posting WeChat Moments and the Risk of Depressive Symptoms Among Middle-Aged and Older Chinese Adults: Prospective National Cohort Study JO - JMIR Public Health Surveill SP - e62730 VL - 11 KW - cohort study KW - depression KW - depressive symptoms KW - mental health KW - middle-aged adults KW - modified Poisson regression KW - older adults KW - WeChat N2 - Background: The association between social media usage and the risk of depressive symptoms has attracted increasing attention. WeChat is a popular social media software in China. The impact of using WeChat and posting WeChat moments on the risk of developing depressive symptoms among community-based middle-aged and older adults in China is unknown. Objective: The objective was to assess the association between using WeChat and posting WeChat moments and the risk of depressive symptoms among middle-aged and older adults in China. Methods: A prospective national cohort study was designed based on the data obtained from the fourth and fifth waves of the China Health and Retirement Longitudinal Study (CHARLS). The strength of association between using WeChat and posting WeChat moments and the risk of depressive symptoms was estimated by modified Poisson regressions. Depressive symptoms were determined using the 10-item Center for Epidemiologic Studies Depression Scale. Meanwhile, the heterogeneity of the associations was explored through multiple subgroup analyses. Moreover, multiple sensitivity analyses were performed to verify the robustness of the associations between the exposures and depressive symptoms. Results: A total of 9670 eligible participants were included in the cohort study, and the incidence rate of depressive symptoms was 19.08% (1845/9670, 95% CI 19.07%?19.09%) from the fourth to fifth waves of the CHARLS. Using WeChat (adjusted relative risk [aRR] 0.691, 95% CI 0.582?0.520) and posting WeChat moments (aRR 0.673, 95% CI 0.552?0.821) reduced the risk of depressive symptoms among middle-aged and older Chinese adults. The association between the exposures and depressive symptoms was robust, proved through multiple sensitivity analyses (all P<.05). However, the associations were heterogeneous in certain subgroup catagories, such as solitude, duration of sleep at night, nap after lunch, physical activity, and having multiple chronic conditions. Conclusions: Using WeChat and especially posting WeChat moments can mitigate the risk of depressive symptoms among community-based middle-aged and older Chinese adults. However, there is likely a need for a longer follow-up period to explore the impact of the exposures on the risk of long-term depressive outcomes. UR - https://publichealth.jmir.org/2025/1/e62730 UR - http://dx.doi.org/10.2196/62730 ID - info:doi/10.2196/62730 ER - TY - JOUR AU - Mendez, R. Samuel AU - Munoz-Najar, Sebastian AU - Emmons, M. Karen AU - Viswanath, Kasisomayajula PY - 2025/1/3 TI - US State Public Health Agencies' Use of Twitter From 2012 to 2022: Observational Study JO - J Med Internet Res SP - e59786 VL - 27 KW - social media KW - health communication KW - Twitter KW - tweet KW - public health KW - state government KW - government agencies KW - information technology KW - data science KW - communication tool KW - COVID-19 pandemic KW - data collection KW - theoretical framework KW - message KW - interaction N2 - Background: Twitter (subsequently rebranded as X) is acknowledged by US health agencies, including the US Centers for Disease Control and Prevention (CDC), as an important public health communication tool. However, there is a lack of data describing its use by state health agencies over time. This knowledge is important amid a changing social media landscape in the wake of the COVID-19 pandemic. Objective: The study aimed to describe US state health agencies? use of Twitter from 2012 through 2022. Furthermore, we organized our data collection and analysis around the theoretical framework of the networked public to contribute to the broader literature on health communication beyond a single platform. Methods: We used Twitter application programming interface data as indicators of state health agencies? engagement with the 4 key qualities of communication in a networked public: scalability, persistence, replicability, and searchability. To assess scalability, we calculated tweet volume and audience engagement metrics per tweet. To assess persistence, we calculated the portion of tweets that were manual retweets or included an account mention. To assess replicability, we calculated the portion of tweets that were retweets or quote tweets. To assess searchability, we calculated the portion of tweets using at least 1 hashtag. Results: We observed a COVID-19 pandemic?era shift in state health agency engagement with scalability. The overall volume of tweets increased suddenly from less than 50,000 tweets in 2019 to over 94,000 in 2020, resulting in an average of 5.3 per day. Though mean tweets per day fell in 2021 and 2022, this COVID-19 pandemic?era low was still higher than the pre?COVID-19 pandemic peak. We also observed a more fragmented approach to searchability aligning with the start of the COVID-19 pandemic. More state-specific hashtags were among the top 10 during the COVID-19 pandemic, compared with more general hashtags related to disease outbreaks and natural disasters in years before. We did not observe such a clear COVID-19 pandemic?era shift in engagement with replicability. The portion of tweets mentioning a CDC account gradually rose and fell around a peak of 7.0% in 2018. Similarly, the rate of retweets of a CDC account rose and fell gradually around a peak of 5.4% in 2018. We did not observe a clear COVID-19 pandemic?era shift in persistence. The portion of tweets mentioning any account reached a maximum of 21% in 2013. It oscillated for much of the study period before dropping off in 2021 and reaching a minimum of 10% in 2022. Before 2018, the top 10 mentioned accounts included at least 2 non-CDC or corporate accounts. From 2018 onward, state agencies were much more prominent. Conclusions: Overall, we observed a more fragmented approach to state health agency communication on Twitter during the pandemic, prioritizing volume over searchability, formally replicating existing messages, and leaving traces of interactions with other accounts. UR - https://www.jmir.org/2025/1/e59786 UR - http://dx.doi.org/10.2196/59786 UR - http://www.ncbi.nlm.nih.gov/pubmed/39752190 ID - info:doi/10.2196/59786 ER - TY - JOUR AU - Srithanaviboonchai, Kriengkrai AU - Yingyong, Thitipong AU - Tasaneeyapan, Theerawit AU - Suparak, Supaporn AU - Jantaramanee, Supiya AU - Roudreo, Benjawan AU - Tanpradech, Suvimon AU - Chuayen, Jarun AU - Kanphukiew, Apiratee AU - Naiwatanakul, Thananda AU - Aungkulanon, Suchunya AU - Martin, Michael AU - Yang, Chunfu AU - Parekh, Bharat AU - Northbrook, Chen Sanny PY - 2024/12/26 TI - Establishment, Implementation, Initial Outcomes, and Lessons Learned from Recent HIV Infection Surveillance Using a Rapid Test for Recent Infection Among Persons Newly Diagnosed With HIV in Thailand: Implementation Study JO - JMIR Public Health Surveill SP - e65124 VL - 10 KW - rapid test KW - surveillance KW - HIV KW - AIDS KW - diagnosis KW - Thailand KW - men who have sex with men KW - RITA KW - human immunodeficiency virus KW - acquired immune deficiency syndrome KW - transgender KW - recent infection testing algorithm N2 - Background: A recent infection testing algorithm (RITA) incorporating case surveillance (CS) with the rapid test for recent HIV infection (RTRI) was integrated into HIV testing services in Thailand as a small-scale pilot project in October 2020. Objective: We aimed to describe the lessons learned and initial outcomes obtained after the establishment of the nationwide recent HIV infection surveillance project from April through August 2022. Methods: We conducted desk reviews, developed a surveillance protocol and manual, selected sites, trained staff, implemented surveillance, and analyzed outcomes. Remnant blood specimens of consenting newly diagnosed individuals were tested using the Asanté HIV-1 Rapid Recency Assay. The duration of HIV infection was classified as RTRI-recent or RTRI-long-term. Individuals testing RTRI-recent with CD4 counts <200 cells/mm3 or those having opportunistic infections were classified as RITA-CS-long-term. Individuals testing RTRI-recent with CD4 counts >200 cells/mm3, no opportunistic infections, and not on antiretroviral treatment were classified as RITA-CS-recent. Results: Two hundred and one hospitals in 14 high-burden HIV provinces participated in the surveillance. Of these, 69 reported ?1 HIV diagnosis during the surveillance period. Of 1053 newly diagnosed cases, 64 (6.1%) were classified as RITA-CS-recent. On multivariate analysis, self-reporting as transgender women (adjusted odds ratio [AOR] 7.41, 95% CI 1.59?34.53) and men who have sex with men (AOR 2.59, 95% CI 1.02?6.56) compared to heterosexual men, and students compared to office workers or employers (AOR 3.76, 95% CI 1.25?11.35) were associated with RITA-CS-recent infection. The proper selection of surveillance sites, utilizing existing surveillance tools and systems, and conducting frequent follow-up and supervision visits were the most commonly cited lessons learned to inform the next surveillance phase. Conclusions: Recent HIV infection surveillance can provide an understanding of current epidemiologic trends to inform HIV prevention interventions to interrupt ongoing or recent HIV transmission. The key success factors of the HIV recent infection surveillance in Thailand include a thorough review of the existing HIV testing service delivery system, a streamlined workflow, strong laboratory and health services, and regular communication between sites and the Provincial Health Offices. UR - https://publichealth.jmir.org/2024/1/e65124 UR - http://dx.doi.org/10.2196/65124 ID - info:doi/10.2196/65124 ER - TY - JOUR AU - Lemieux, Mackenzie AU - Zhou, Cyrus AU - Cary, Caroline AU - Kelly, Jeannie PY - 2024/12/16 TI - Changes in Reproductive Health Information-Seeking Behaviors After the Dobbs Decision: Systematic Search of the Wikimedia Database JO - JMIR Infodemiology SP - e64577 VL - 4 KW - abortion KW - Dobbs KW - internet KW - viewer trends KW - Wikipedia KW - women?s health KW - contraception KW - contraceptive KW - trend KW - information seeking KW - page view KW - reproductive KW - reproduction N2 - Background: After the US Supreme Court overturned Roe v. Wade, confusion followed regarding the legality of abortion in different states across the country. Recent studies found increased Google searches for abortion-related terms in restricted states after the Dobbsv. Jackson Women?s Health Organization decision was leaked. As patients and providers use Wikipedia (Wikimedia Foundation) as a predominant medical information source, we hypothesized that changes in reproductive health information-seeking behavior could be better understood by examining Wikipedia article traffic. Objective: This study aimed to examine trends in Wikipedia usage for abortion and contraception information before and after the Dobbs decision. Methods: Page views of abortion- and contraception-related Wikipedia pages were scraped. Temporal changes in page views before and after the Dobbs decision were then analyzed to explore changes in baseline views, differences in views for abortion-related information in states with restrictive abortion laws versus nonrestrictive states, and viewer trends on contraception-related pages. Results: Wikipedia articles related to abortion topics had significantly increased page views following the leaked and final Dobbs decision. There was a 103-fold increase in the page views for the Wikipedia article Roe v. Wade following the Dobbs decision leak (mean 372,654, SD 135,478 vs mean 3614, SD 248; P<.001) and a 67-fold increase in page views following the release of the final Dobbs decision (mean 8942, SD 402 vs mean 595,871, SD 178,649; P<.001). Articles about abortion in the most restrictive states had a greater increase in page views (mean 40.6, SD 12.7; 18/51, 35% states) than articles about abortion in states with some restrictions or protections (mean 26.8, SD 7.3; 24/51, 47% states; P<.001) and in the most protective states (mean 20.6, SD 5.7; 8/51, 16% states; P<.001). Finally, views to pages about common contraceptive methods significantly increased after the Dobbs decision. ?Vasectomy? page views increased by 183% (P<.001), ?IUD? (intrauterine device) page views increased by 80% (P<.001), ?Combined oral contraceptive pill? page views increased by 24% (P<.001), ?Emergency Contraception? page views increased by 224% (P<.001), and ?Tubal ligation? page views increased by 92% (P<.001). Conclusions: People sought information on Wikipedia about abortion and contraception at increased rates after the Dobbs decision. Increased traffic to abortion-related Wikipedia articles correlated to the restrictiveness of state abortion policies. Increased interest in contraception-related pages reflects the increased demand for contraceptives observed after the Dobbs decision. Our work positions Wikipedia as an important source of reproductive health information and demands increased attention to maintain and improve Wikipedia as a reliable source of health information after the Dobbs decision. UR - https://infodemiology.jmir.org/2024/1/e64577 UR - http://dx.doi.org/10.2196/64577 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64577 ER - TY - JOUR AU - Fan, Lizhou AU - Li, Lingyao AU - Hemphill, Libby PY - 2024/12/12 TI - Toxicity on Social Media During the 2022 Mpox Public Health Emergency: Quantitative Study of Topical and Network Dynamics JO - J Med Internet Res SP - e52997 VL - 26 KW - social media KW - network analysis KW - pandemic risk KW - health care analytics KW - infodemiology KW - infoveillance KW - health communication KW - mpox N2 - Background: Toxicity on social media, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. Its prevalence intensifies during periods of social crises and unrest, eroding a sense of safety and community. Such toxic environments can adversely impact the mental well-being of those exposed and further deepen societal divisions and polarization. The 2022 mpox outbreak, initially called ?monkeypox? but later renamed to reduce stigma and address societal concerns, provides a relevant context for this issue. Objective: In this study, we conducted a comprehensive analysis of the toxic online discourse surrounding the 2022 mpox outbreak. We aimed to dissect its origins, characterize its nature and content, trace its dissemination patterns, and assess its broader societal implications, with the goal of providing insights that can inform strategies to mitigate such toxicity in future crises. Methods: We collected >1.6 million unique tweets and analyzed them with 5 dimensions: context, extent, content, speaker, and intent. Using topic modeling based on bidirectional encoder representations from transformers and social network community clustering, we delineated the toxic dynamics on Twitter. Results: By categorizing topics, we identified 5 high-level categories in the toxic online discourse on Twitter, including disease (20,281/43,521, 46.6%), health policy and health care (8400/43,521, 19.3%), homophobia (10,402/43,521, 23.9%), politics (2611/43,521, 6%), and racism (1784/43,521, 4.1%). Across these categories, users displayed negativity or controversial views on the mpox outbreak, highlighting the escalating political tensions and the weaponization of stigma during this infodemic. Through the toxicity diffusion networks of mentions (17,437 vertices with 3628 clusters), retweets (59,749 vertices with 3015 clusters), and the top users with the highest in-degree centrality, we found that retweets of toxic content were widespread, while influential users rarely engaged with or countered this toxicity through retweets. Conclusions: Our study introduces a comprehensive workflow that combines topical and network analyses to decode emerging social issues during crises. By tracking topical dynamics, we can track the changing popularity of toxic content on the internet, providing a better understanding of societal challenges. Network dynamics highlight key social media influencers and their intentions, suggesting that engaging with these central figures in toxic discourse can improve crisis communication and guide policy making. UR - https://www.jmir.org/2024/1/e52997 UR - http://dx.doi.org/10.2196/52997 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52997 ER - TY - JOUR AU - AbuRaed, Tawfiq Ahmed Ghassan AU - Prikryl, Azuma Emil AU - Carenini, Giuseppe AU - Janjua, Zafar Naveed PY - 2024/12/9 TI - Long COVID Discourse in Canada, the United States, and Europe: Topic Modeling and Sentiment Analysis of Twitter Data JO - J Med Internet Res SP - e59425 VL - 26 KW - long COVID KW - topic modeling KW - sentiment analysis KW - Twitter KW - public perception KW - social media analysis KW - public health N2 - Background: Social media serves as a vast repository of data, offering insights into public perceptions and emotions surrounding significant societal issues. Amid the COVID-19 pandemic, long COVID (formally known as post?COVID-19 condition) has emerged as a chronic health condition, profoundly impacting numerous lives and livelihoods. Given the dynamic nature of long COVID and our evolving understanding of it, effectively capturing people?s sentiments and perceptions through social media becomes increasingly crucial. By harnessing the wealth of data available on social platforms, we can better track the evolving narrative surrounding long COVID and the collective efforts to address this pressing issue. Objective: This study aimed to investigate people?s perceptions and sentiments around long COVID in Canada, the United States, and Europe, by analyzing English-language tweets from these regions using advanced topic modeling and sentiment analysis techniques. Understanding regional differences in public discourse can inform tailored public health strategies. Methods: We analyzed long COVID?related tweets from 2021. Contextualized topic modeling was used to capture word meanings in context, providing coherent and semantically meaningful topics. Sentiment analysis was conducted in a zero-shot manner using Llama 2, a large language model, to classify tweets into positive, negative, or neutral sentiments. The results were interpreted in collaboration with public health experts, comparing the timelines of topics discussed across the 3 regions. This dual approach enabled a comprehensive understanding of the public discourse surrounding long COVID. We used metrics such as normalized pointwise mutual information for coherence and topic diversity for diversity to ensure robust topic modeling results. Results: Topic modeling identified five main topics: (1) long COVID in people including children in the context of vaccination, (2) duration and suffering associated with long COVID, (3) persistent symptoms of long COVID, (4) the need for research on long COVID treatment, and (5) measuring long COVID symptoms. Significant concern was noted across all regions about the duration and suffering associated with long COVID, along with consistent discussions on persistent symptoms and calls for more research and better treatments. In particular, the topic of persistent symptoms was highly prevalent, reflecting ongoing challenges faced by individuals with long COVID. Sentiment analysis showed a mix of positive and negative sentiments, fluctuating with significant events and news related to long COVID. Conclusions: Our study combines natural language processing techniques, including contextualized topic modeling and sentiment analysis, along with domain expert input, to provide detailed insights into public health monitoring and intervention. These findings highlight the importance of tracking public discourse on long COVID to inform public health strategies, address misinformation, and provide support to affected individuals. The use of social media analysis in understanding public health issues is underscored, emphasizing the role of emerging technologies in enhancing public health responses. UR - https://www.jmir.org/2024/1/e59425 UR - http://dx.doi.org/10.2196/59425 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59425 ER - TY - JOUR AU - Béchard, Benoît AU - Gramaccia, A. Julie AU - Gagnon, Dominique AU - Laouan-Sidi, Anassour Elhadji AU - Dubé, Čve AU - Ouimet, Mathieu AU - de Hemptinne, Delphine AU - Tremblay, Sébastien PY - 2024/12/4 TI - The Resilience of Attitude Toward Vaccination: Web-Based Randomized Controlled Trial on the Processing of Misinformation JO - JMIR Form Res SP - e52871 VL - 8 KW - attitude toward vaccination KW - misinformation KW - reinformation KW - confidence KW - perceived tentativeness KW - vaccine hesitancy KW - COVID-19 N2 - Background: Before the COVID-19 pandemic, it was already recognized that internet-based misinformation and disinformation could influence individuals to refuse or delay vaccination for themselves, their families, or their children. Reinformation, which refers to hyperpartisan and ideologically biased content, can propagate polarizing messages on vaccines, thereby contributing to vaccine hesitancy even if it is not outright disinformation. Objective: This study aimed to evaluate the impact of reinformation on vaccine hesitancy. Specifically, the goal was to investigate how misinformation presented in the style and layout of a news article could influence the perceived tentativeness (credibility) of COVID-19 vaccine information and confidence in COVID-19 vaccination. Methods: We conducted a web-based randomized controlled trial by recruiting English-speaking Canadians aged 18 years and older from across Canada through the Qualtrics (Silver Lake) paid opt-in panel system. Participants were randomly assigned to 1 of 4 distinct versions of a news article on COVID-19 vaccines, each featuring variations in writing style and presentation layout. After reading the news article, participants self-assessed the tentativeness of the information provided, their confidence in COVID-19 vaccines, and their attitude toward vaccination in general. Results: The survey included 537 participants, with 12 excluded for not meeting the task completion time. The final sample comprised 525 participants distributed about equally across the 4 news article versions. Chi-square analyses revealed a statistically significant association between general attitude toward vaccination and the perceived tentativeness of the information about COVID-19 vaccines included in the news article (?21=37.8, P<.001). The effect size was small to moderate, with Cramer V=0.27. An interaction was found between vaccine attitude and writing style (?21=6.2, P=.01), with a small effect size, Cramer V=0.11. In addition, a Pearson correlation revealed a significant moderate to strong correlation between perceived tentativeness and confidence in COVID-19 vaccination, r(523)=0.48, P<.001. The coefficient of determination (r2) was 0.23, indicating that 23% of the variance in perceived tentativeness was explained by confidence in COVID-19 vaccines. In comparing participants exposed to a journalistic-style news article with those exposed to an ideologically biased article, Cohen d was calculated to be 0.38, indicating a small to medium effect size for the difference in the perceived tentativeness between these groups. Conclusions: Exposure to a news article conveying misinformation may not be sufficient to change an individual?s level of vaccine hesitancy. The study reveals that the predominant factor in shaping individuals? perceptions of COVID-19 vaccines is their attitude toward vaccination in general. This attitude also moderates the influence of writing style on perceived tentativeness; the stronger one?s opposition to vaccines, the less pronounced the impact of writing style on perceived tentativeness. International Registered Report Identifier (IRRID): RR2-10.2196/41012 UR - https://formative.jmir.org/2024/1/e52871 UR - http://dx.doi.org/10.2196/52871 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/52871 ER - TY - JOUR AU - Xie, Zidian AU - Liu, Xinyi AU - Lou, Xubin AU - Li, Dongmei PY - 2024/12/4 TI - Public Perceptions of Very Low Nicotine Content on Twitter: Observational Study JO - JMIR Form Res SP - e63035 VL - 8 KW - very low nicotine KW - Twitter KW - public perception KW - observational study KW - content analysis N2 - Background: Nicotine is a highly addictive agent in tobacco products. On June 21, 2022, the US Food and Drug Administration (FDA) announced a plan to propose a rule to establish a maximum nicotine level in cigarettes and other combusted tobacco products. Objective: This study aimed to understand public perception and discussion of very low nicotine content (VLNC) on Twitter (rebranded as X in July 2023). Methods: From December 12, 2021, to January 1, 2023, we collected Twitter data using relevant keywords such as ?vln,? ?low nicotine,? and ?reduced nicotine.? After a series of preprocessing steps (such as removing duplicates, retweets, and commercial tweets), we identified 3270 unique noncommercial tweets related to VLNC. We used an inductive method to assess the public perception and discussion of VLNC on Twitter. To establish a codebook, we randomly selected 300 tweets for hand-coding, including the attitudes (positive, neutral, and negative) toward VLNC (including its proposed rule) and major topics (13 topics). The Cohen ? statistic between the 2 human coders reached over 70%, indicating a substantial interrater agreement. The rest of the tweets were single-coded according to the codebook. Results: We observed a significant peak in the discussion of VLNC on Twitter within 4 days of the FDA?s announcement of the proposed rule on June 21, 2022. The proportion of tweets with a negative attitude toward VLNC was significantly lower than those with a positive attitude, 24.5% (801/3270) versus 37.09% (1213/3270) with P<.001 from the 2-proportion z test. Among tweets with a positive attitude, the topic ?Reduce cigarette consumption or help smoking cessation? was dominant (1097/1213, 90.44%). Among tweets with a negative attitude, the topic ?VLNC leads to more smoking? was the most popular topic (227/801, 28.34%), followed by ?Similar toxicity of VLNC as a regular cigarette? (223/801, 27.84%), and ?VLNC is not a good method for quitting smoking? (211/801, 26.34%). Conclusions: There is a more positive attitude toward VLNC than a negative attitude on Twitter, resulting from different opinions about VLNC. Discussions around VLNC mainly focused on whether VLNC could help people quit smoking. UR - https://formative.jmir.org/2024/1/e63035 UR - http://dx.doi.org/10.2196/63035 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63035 ER - TY - JOUR AU - Benjamin, Jennifer AU - Pillow, Tyson AU - MacNeill, Heather AU - Masters, Ken AU - Agrawal, Anoop AU - Mehta, Neil PY - 2024/12/3 TI - Reflections From the Pandemic: Is Connectivism the Panacea for Clinicians? JO - J Med Internet Res SP - e53344 VL - 26 KW - learning theory KW - learning framework KW - connectivism KW - panacea KW - COVID-19 KW - generative artificial intelligence KW - GAI KW - health care community KW - clinician KW - health care KW - airborne disease KW - learning KW - information KW - misinformation KW - autonomy KW - diversity UR - https://www.jmir.org/2024/1/e53344 UR - http://dx.doi.org/10.2196/53344 UR - http://www.ncbi.nlm.nih.gov/pubmed/39625749 ID - info:doi/10.2196/53344 ER - TY - JOUR AU - Baxter-King, Ryan AU - Naeim, Arash AU - Huang, Q. Tina AU - Sepucha, Karen AU - Stanton, Annette AU - Rudkin, Aaron AU - Ryu, Rita AU - Sabacan, Leah AU - Vavreck, Lynn AU - Esserman, Laura AU - Stover Fiscalini, Allison AU - Wenger, S. Neil PY - 2024/12/2 TI - Relationship Between Perceived COVID-19 Risk and Change in Perceived Breast Cancer Risk: Prospective Observational Study JO - JMIR Cancer SP - e47856 VL - 10 KW - breast cancer KW - COVID-19 risk perception KW - cancer screening KW - anxiety KW - cancer KW - COVID-19 KW - prevention KW - medical care KW - screening KW - survey N2 - Background: Whether COVID-19 is associated with a change in risk perception about other health conditions is unknown. Because COVID-19 occurred during a breast cancer study, we evaluated the effect of COVID-19 risk perception on women?s breast cancer risk perception. Objective: This study aims to evaluate the relationship between perceived risk of COVID-19 and change in perceived breast cancer risk. We hypothesized that women who perceived greater COVID-19 risk would evidence increased perceived breast cancer risk and this risk would relate to increased anxiety and missed cancer screening. Methods: Women aged 40-74 years with no breast cancer history were enrolled in a US breast cancer prevention trial in outpatient settings. They had provided breast cancer risk perception and general anxiety before COVID-19. We performed a prospective observational study of the relationship between the perceived risk of COVID-19 and the change in perceived breast cancer risk compared to before the pandemic. Each woman was surveyed up to 4 times about COVID-19 and breast cancer risk perception, general anxiety, and missed medical care early in COVID-19 (May to December 2020). Results: Among 13,002 women who completed a survey, compared to before COVID-19, anxiety was higher during COVID-19 (mean T score 53.5 vs 49.7 before COVID-19; difference 3.8, 95% CI 3.6-4.0; P<.001) and directly related to perceived COVID-19 risk. In survey wave 1, anxiety increased by 2.3 T score points for women with very low perceived COVID-19 risk and 5.2 points for those with moderately or very high perceived COVID-19 risk. Despite no overall difference in breast cancer risk perception (mean 32.5% vs 32.5% before COVID-19; difference 0.24, 95% CI ?0.47 to 0.52; P=.93), there was a direct relationship between change in perceived breast cancer risk with COVID-19 risk perception, ranging in survey wave 4 from a 2.4% decrease in breast cancer risk perception for those with very low COVID-19 risk perception to a 3.4% increase for women with moderately to very high COVID-19 risk perception. This was not explained by the change in anxiety or missed cancer screening. After adjustment for age, race, education, and survey wave, compared to women with very low perceived COVID-19 risk, perceived breast cancer risk increased by 1.54% (95% CI 0.75%-2.33%; P<.001), 4.28% (95% CI 3.30%-5.25%; P<.001), and 3.67% (95% CI 1.94%-5.40%; P<.001) for women with moderately low, neither high nor low, and moderately or very high perceived COVID-19 risk, respectively. Conclusions: Low perceived COVID-19 risk was associated with reduced perceived breast cancer risk, and higher levels of perceived COVID-19 risk were associated with increased perceived breast cancer risk. This natural experiment suggests that a threat such as COVID-19 may have implications beyond the pandemic. Preventive health behaviors related to perceived risk may need attention as COVID-19 becomes endemic. UR - https://cancer.jmir.org/2024/1/e47856 UR - http://dx.doi.org/10.2196/47856 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/47856 ER - TY - JOUR AU - O'Brien, Gabrielle AU - Ganjigunta, Ronith AU - Dhillon, S. Paramveer PY - 2024/11/27 TI - Wellness Influencer Responses to COVID-19 Vaccines on Social Media: A Longitudinal Observational Study JO - J Med Internet Res SP - e56651 VL - 26 KW - social media, COVID-19, vaccination KW - personal brands KW - public health KW - wellness KW - global health KW - pandemic KW - Twitter KW - tweets KW - vaccine KW - longitudinal design KW - wellness influencers KW - hand-annotation KW - anti-vaccination KW - infodemiology N2 - Background: Online wellness influencers (individuals dispensing unregulated health and wellness advice over social media) may have incentives to oppose traditional medical authorities. Their messaging may decrease the overall effectiveness of public health campaigns during global health crises like the COVID-19 pandemic. Objective: This study aimed to probe how wellness influencers respond to a public health campaign; we examined how a sample of wellness influencers on Twitter (rebranded as X in 2023) identified before the COVID-19 pandemic on Twitter took stances on the COVID-19 vaccine during 2020-2022. We evaluated the prevalence of provaccination messaging among wellness influencers compared with a control group, as well as the rhetorical strategies these influencers used when supporting or opposing vaccination. Methods: Following a longitudinal design, wellness influencer accounts were identified on Twitter from a random sample of tweets posted in 2019. Accounts were identified using a combination of topic modeling and hand-annotation for adherence to influencer criteria. Their tweets from 2020-2022 containing vaccine keywords were collected and labeled as pro- or antivaccination stances using a language model. We compared their stances to a control group of noninfluencer accounts that discussed similar health topics before the pandemic using a generalized linear model with mixed effects and a nearest-neighbors classifier. We also used topic modeling to locate key themes in influencer?s pro- and antivaccine messages. Results: Wellness influencers (n=161) had lower rates of provaccination stances in their on-topic tweets (20%, 614/3045) compared with controls (n=242 accounts, with 42% or 3201/7584 provaccination tweets). Using a generalized linear model of tweet stance with mixed effects to model tweets from the same account, the main effect of the group was significant (?1=?2.2668, SE=0.2940; P<.001). Covariate analysis suggests an association between antivaccination tweets and accounts representing individuals (?=?0.9591, SE=0.2917; P=.001) but not social network position. A complementary modeling exercise of stance within user accounts showed a significant difference in the proportion of antivaccination users by group (?21[N=321]=36.1, P<.001). While nearly half of the influencer accounts were labeled by a K-nearest neighbor classifier as predominantly antivaccination (48%, 58/120), only 16% of control accounts were labeled this way (33/201). Topic modeling of influencer tweets showed that the most prevalent antivaccination themes were protecting children, guarding against government overreach, and the corruption of the pharmaceutical industry. Provaccination messaging tended to encourage followers to take action or emphasize the efficacy of the vaccine. Conclusions: Wellness influencers showed higher rates of vaccine opposition compared with other accounts that participated in health discourse before the pandemic. This pattern supports the theory that unregulated wellness influencers have incentives to resist messaging from establishment authorities such as public health agencies. UR - https://www.jmir.org/2024/1/e56651 UR - http://dx.doi.org/10.2196/56651 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56651 ER - TY - JOUR AU - Woods, E. Cindy AU - Furst, Mary-Anne AU - Dissanayake, Manoj AU - Koerner, Jane AU - de Miquel, Carlota AU - Lukersmith, Sue AU - Rosenberg, Sebastian AU - Salvador-Carulla, Luis PY - 2024/11/22 TI - Mental Health Care Navigation Tools in Australia: Infoveillance Study JO - JMIR Public Health Surveill SP - e60079 VL - 10 KW - digital health KW - infoveillance KW - mental health KW - mental health care KW - navigation tools KW - Australia KW - fragmentation KW - digital mental healthcare KW - web-based digital resources KW - diagnostic screening KW - accessibility KW - user friendly N2 - Background: In response to the well-documented fragmentation within its mental health system, Australia has witnessed recently rapid expansion in the availability of digital mental health care navigation tools. These tools focus on assisting consumers to identify and access appropriate mental health care services, the proliferation of such varied web-based resources risks perpetuating further fragmentation and confusion for consumers. There is a pressing need to systematically assess the characteristics, comprehensiveness, and validity of these navigation tools, especially as demand for digital resources continues to escalate. Objective: This study aims to identify and describe the current landscape of Australian digital mental health care navigation tools, with a focus on assessing their comprehensiveness, identifying potential gaps, and the extent to which they meet the needs of various stakeholders. Methods: A comprehensive infoveillance approach was used to identify Australian digital mental health care navigation tools. This process involved a systematic web-based search complemented by consultations with subject matter experts. Identified navigation tools were independently screened by 2 authors, while data extraction was conducted by 3 authors. Extracted data were mapped to key domains and subdomains relevant to navigation tools. Results: From just a handful in 2020, by February 2024 this study identified 102 mental health care navigation tools across Australia. Primary Health Networks (n=37) and state or territory governments (n=21) were the predominant developers of these tools. While the majority of navigation tools were primarily designed for consumer use, many also included resources for health professionals and caregivers. Notably, no navigation tools were specifically designed for mental health care planners. Nearly all tools (except one) featured directories of mental health care services, although their functionalities varied: 27% (n=27) provided referral information, 20% (n=21) offered geolocated service maps, 12% (n=12) included diagnostic screening capabilities, and 7% (n=7) delineated care pathways. Conclusions: The variability of navigation tools designed to facilitate consumer access to mental health services could paradoxically contribute to further confusion. Despite the significant expansion of digital navigation tools in recent years, substantial gaps and challenges remain. These include inconsistencies in tool formats, resulting in variable information quality and validity; a lack of regularly updated service information, including wait times and availability for new clients; insufficient details on program exclusion criteria; and limited accessibility and user-friendliness. Moreover, the inclusion of self-assessment screening tools is infrequent, further limiting the utility of these resources. To address these limitations, we propose the development of a national directory of mental health navigation tools as a centralized resource, alongside a system to guide users toward the most appropriate tool for their individual needs. Addressing these issues will enhance consumer confidence and contribute to the overall accessibility, reliability, and utility of digital navigation tools in Australia?s mental health system. UR - https://publichealth.jmir.org/2024/1/e60079 UR - http://dx.doi.org/10.2196/60079 ID - info:doi/10.2196/60079 ER - TY - JOUR AU - Melo, Lopes Carolina AU - Mageste, Rangel Larissa AU - Guaraldo, Lusiele AU - Paula, Polessa Daniela AU - Wakimoto, Duarte Mayumi PY - 2024/11/18 TI - Use of Digital Tools in Arbovirus Surveillance: Scoping Review JO - J Med Internet Res SP - e57476 VL - 26 KW - arbovirus infections KW - dengue KW - zika virus KW - chikungunya fever KW - public health surveillance KW - digital tool KW - technology N2 - Background: The development of technology and information systems has led to important changes in public health surveillance. Objective: This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance. Methods: The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society). Results: Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance. Conclusions: In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools? data, considering ethical aspects. UR - https://www.jmir.org/2024/1/e57476 UR - http://dx.doi.org/10.2196/57476 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57476 ER - TY - JOUR AU - Owen, David AU - Lynham, J. Amy AU - Smart, E. Sophie AU - Pardińas, F. Antonio AU - Camacho Collados, Jose PY - 2024/11/15 TI - AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges JO - J Med Internet Res SP - e59225 VL - 26 KW - mental health KW - depression KW - anxiety KW - schizophrenia KW - social media KW - natural language processing KW - narrative review N2 - Background: Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care. Objective: This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence?driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness. Methods: A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review. Results: Larger datasets with precise dates of participants? diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone. Conclusions: Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user?s depression and anxiety is merited. UR - https://www.jmir.org/2024/1/e59225 UR - http://dx.doi.org/10.2196/59225 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59225 ER - TY - JOUR AU - Lu, Fangcao AU - Tu, Caixie PY - 2024/11/15 TI - The Impact of Comment Slant and Comment Tone on Digital Health Communication Among Polarized Publics: A Web-Based Survey Experiment JO - J Med Internet Res SP - e57967 VL - 26 KW - comments slant KW - incivility KW - social media KW - influence of presumed influence KW - health compliance KW - mask wearing KW - web survey N2 - Background: Public attitudes toward health issues are becoming increasingly polarized, as seen in social media comments, which vary from supportive to oppositional and frequently include uncivil language. The combined effects of comment slant and comment tone on health behavior among a polarized public need further examination. Objective: This study aims to examine how social media users? prior attitudes toward mask wearing and their exposure to a mask-wearing?promoting post, synchronized with polarized and hostile discussions, affect their compliance with mask wearing. Methods: The study was a web-based survey experiment with participants recruited from Amazon Mechanical Turk. A total of 522 participants provided consent and completed the study. Participants were assigned to read a fictitious mask-wearing?promoting social media post with either civil anti?mask-wearing comments (130/522, 24.9%), civil pro?mask-wearing comments (129/522, 24.7%), uncivil anti?mask-wearing comments (131/522, 25.1%), or uncivil pro?mask-wearing comments (132/522, 25.3%). Following this, the participants were asked to complete self-assessed questionnaires. The PROCESS macro in SPSS (model 12; IBM Corp) was used to test the 3-way interaction effects between comment slant, comment tone, and prior attitudes on participants? presumed influence from the post and their behavioral intention to comply with mask-wearing. Results: Anti?mask-wearing comments led social media users to presume less influence about others? acceptance of masks (B=1.49; P<.001; 95% CI 0.98-2.00) and resulted in decreased mask-wearing intention (B=0.07; P=.03; 95% CI 0.01-0.13). Comment tone with incivility also reduced compliance with mask-wearing (B=?0.44; P=.02; 95% CI ?0.81 to ?0.07). Furthermore, polarized attitudes had a direct impact (B=0.86; P<.001; 95% CI 0.45-1.26) and also interacted with both the slant and tone of comments, influencing mask-wearing intention. Conclusions: Pro?mask-wearing comments enhanced presumed influence and compliance of mask-wearing, but incivility in the comments hindered this positive impact. Antimaskers showed increased compliance when they were unable to find civil support for their opinion in the social media environment. The findings suggest the need to correct and moderate uncivil language and misleading information in online comment sections while encouraging the posting of supportive and civil comments. In addition, information literacy programs are needed to prevent the public from being misled by polarized comments. UR - https://www.jmir.org/2024/1/e57967 UR - http://dx.doi.org/10.2196/57967 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57967 ER - TY - JOUR AU - Yang, Si Myung AU - Taira, Kazuya PY - 2024/11/11 TI - Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study JO - J Med Internet Res SP - e64555 VL - 26 KW - well-being KW - spatial indicator KW - infodemiology KW - search engine KW - public health KW - health policy KW - policy-making KW - Google KW - Japan N2 - Background: In recent years, the adoption of well-being indicators by national governments and international organizations has emerged as an important tool for evaluating state governance and societal progress. Traditionally, well-being has been gauged primarily through economic metrics such as gross domestic product, which fall short of capturing multifaceted well-being, including socioeconomic inequalities, life satisfaction, and health status. Current well-being indicators, including both subjective and objective measures, offer a broader evaluation but face challenges such as high survey costs and difficulties in evaluating at regional levels within countries. The emergence of web log data as an alternative source of well-being indicators offers the potential for more cost-effective, timely, and less biased assessments. Objective: This study aimed to develop a model using internet search data to predict well-being indicators at the regional level in Japan, providing policy makers with a more accessible and cost-effective tool for assessing public well-being and making informed decisions. Methods: This study used the Regional Well-Being Index (RWI) for Japan, which evaluates prefectural well-being across 47 prefectures for the years 2010, 2013, 2016, and 2019, as the outcome variable. The RWI includes a comprehensive approach integrating both subjective and objective indicators across 11 domains, including income, job, and life satisfaction. Predictor variables included z score?normalized relative search volume (RSV) data from Google Trends for words relevant to each domain. Unrelated words were excluded from the analysis to ensure relevance. The Elastic Net methodology was applied to predict RWI using RSVs, with ? balancing ridge and lasso effects and ? regulating their strengths. The model was optimized by cross-validation, determining the best mix and strength of regularization parameters to minimize prediction error. Root mean square errors (RMSE) and coefficients of determination (R2) were used to assess the model?s predictive accuracy and fit. Results: An analysis of Google Trends data yielded 275 words related to the RWI domains, and RSVs were collected for 211 words after filtering out irrelevant terms. The mean search frequencies for these words during 2010, 2013, 2016, and 2019 ranged from ?1.587 to 3.902, with SDs between 3.025 and 0.053. The best Elastic Net model (?=0.1, ?=0.906, RMSE=1.290, and R2=0.904) was built using 2010-2016 training data and 2-13 variables per domain. Applied to 2019 test data, it yielded an RMSE of 2.328 and R2 of 0.665. Conclusions: This study demonstrates the effectiveness of using internet search log data through the Elastic Net machine learning method to predict the RWI in Japanese prefectures with high accuracy, offering a rapid and cost-efficient alternative to traditional survey approaches. This study highlights the potential of this methodology to provide foundational data for evidence-based policy making aimed at enhancing well-being. UR - https://www.jmir.org/2024/1/e64555 UR - http://dx.doi.org/10.2196/64555 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64555 ER - TY - JOUR AU - Uemura, Kosuke AU - Miyagami, Taiju AU - Saita, Mizue AU - Uchida, Takuro AU - Yuasa, Shun AU - Kondo, Keita AU - Miura, Shun AU - Matsushita, Mizuki AU - Shirai, Yuka AU - Misawa, Baku Richard AU - Naito, Toshio PY - 2024/11/11 TI - Trends in Exercise-Related Internet Search Keywords by Sex, Age, and Lifestyle: Infodemiological Study JO - JMIR Form Res SP - e59395 VL - 8 KW - exercise prescriptions KW - sex KW - age KW - lifestyle KW - internet search keywords KW - infodemiology KW - demographic KW - physical activity N2 - Background: Exercise prescription by physicians is beneficial for initiating or intensifying physical activity. However, providing specific exercise prescriptions is challenging; therefore, few physicians prescribe exercise. Objective: This infodemiological study aimed to understand trends in exercise-related internet search keywords based on sex, age, and environmental factors to help doctors prescribe exercise more easily. Methods: Search keyword volume was collected from Yahoo! JAPAN for 2022. Ten exercise-related terms were analyzed to assess exercise interest. Total search activities were analyzed by sex and age. Characteristic scores were based on the Japanese prefecture. By performing hierarchical cluster analysis, regional features were examined, and Kruskal-Wallis tests were used to assess relationships with population and industry data. Results: The top-searched term was ?Pilates? (266,000 queries). Male individuals showed higher interest in activities such as ?running? (25,400/40,700, 62.4%), ?muscle training? (65,800/111,000, 59.3%), and ?hiking? (23,400/40,400, 57.9%) than female individuals. Female individuals exhibited higher interest in ?Pilates? (199,000/266,000, 74.8%), ?yoga? (86,200/117,000, 73.7%), and ?tai chi? (45,300/65,900, 68.7%) than male individuals. Based on age, search activity was highest in the 40-49 years age group for both male and female individuals across most terms. For male individuals, 7 of the 10 searched terms? volume peaked for those in their 40s; ?stretch? was most popular among those in their 50s; and ?tai chi? and ?radio calisthenics? had the highest search volume for those in their 70s. Female individuals in their 40s led the search volume for 9 of the 10 terms, with the exception of ?tai chi,? which peaked for those in their 70s. Hierarchical cluster analysis using a characteristic score as a variable classified prefectures into 4 clusters. The characteristics of these clusters were as follows: cluster 1 had the largest population and a thriving tertiary industry, and individuals tended to search for Pilates and yoga. Following cluster 1, cluster 2, with its substantial population, had a thriving secondary industry, with searches for radio calisthenics and exercise bike. Cluster 4 had a small population, a thriving primary industry, and the lowest search volume for any term. Cluster 3 had a similar population to that of cluster 4 but had a larger secondary industry. Conclusions: Male individuals show more interest in individual activities, such as running, whereas female individuals are interested in group activities, such as Pilates. Despite the high search volume among individuals in their 40s, actual exercise habits are low among those in their 30s to 50s. Search volumes for instructor-led exercises are higher in cluster 1 than in other cluster areas, and the total number of searches decreases as the community size decreases. These results suggest that trends in search behavior depending on sex, age, and environment factors are essential when prescribing exercise for effective behavioral change. UR - https://formative.jmir.org/2024/1/e59395 UR - http://dx.doi.org/10.2196/59395 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59395 ER - TY - JOUR AU - Zhang, Pengfei AU - Kamitaki, K. Brad AU - Do, Phu Thien PY - 2024/11/8 TI - Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene?Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media JO - JMIR Form Res SP - e58176 VL - 8 KW - internet KW - patient reported outcome KW - headache KW - health information KW - Reddit KW - registry KW - monoclonal antibody KW - crowdsourcing KW - postmarketing KW - safety KW - surveillance KW - migraine KW - preventives KW - prevention KW - self-reported KW - calcitonin gene?related peptide KW - calcitonin KW - therapeutics KW - social media KW - medication-related KW - posts KW - propranolol KW - topiramate KW - erenumab KW - fremanezumab KW - cross-sectional KW - surveys N2 - Background: Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene?related peptide (CGRP) signaling for migraine prevention. However, these trials may not accurately reflect the real-world experiences of more diverse and heterogeneous patient populations, who often have higher disease burden and more comorbidities. Therefore, postmarketing safety surveillance is warranted. Regulatory organizations encourage marketing authorization holders to screen digital media for suspected adverse reactions, applying the same requirements as for spontaneous reports. Real-world data from social media platforms constitute a potential venue to capture diverse patient experiences and help detect treatment-related adverse events. However, while social media holds promise for this purpose, its use in pharmacovigilance is still in its early stages. Computational linguistics, which involves the automatic manipulation and quantitative analysis of oral or written language, offers a potential method for exploring this content. Objective: This study aims to characterize adverse events related to monoclonal antibodies targeting CGRP signaling on Reddit, a large online social media forum, by using computational linguistics. Methods: We examined differences in word frequencies from medication-related posts on the Reddit subforum r/Migraine over a 10-year period (2010-2020) using computational linguistics. The study had 2 phases: a validation phase and an application phase. In the validation phase, we compared posts about propranolol and topiramate, as well as posts about each medication against randomly selected posts, to identify known and expected adverse events. In the application phase, we analyzed posts discussing 2 monoclonal antibodies targeting CGRP signaling?erenumab and fremanezumab?to identify potential adverse events for these medications. Results: From 22,467 Reddit r/Migraine posts, we extracted 402 (2%) propranolol posts, 1423 (6.33%) topiramate posts, 468 (2.08%) erenumab posts, and 73 (0.32%) fremanezumab posts. Comparing topiramate against propranolol identified several expected adverse events, for example, ?appetite,? ?weight,? ?taste,? ?foggy,? ?forgetful,? and ?dizziness.? Comparing erenumab against a random selection of terms identified ?constipation? as a recurring keyword. Comparing erenumab against fremanezumab identified ?constipation,? ?depression,? ?vomiting,? and ?muscle? as keywords. No adverse events were identified for fremanezumab. Conclusions: The validation phase of our study accurately identified common adverse events for oral migraine preventive medications. For example, typical adverse events such as ?appetite? and ?dizziness? were mentioned in posts about topiramate. When we applied this methodology to monoclonal antibodies targeting CGRP or its receptor?fremanezumab and erenumab, respectively?we found no definite adverse events for fremanezumab. However, notable flagged words for erenumab included ?constipation,? ?depression,? and ?vomiting.? In conclusion, computational linguistics applied to social media may help identify potential adverse events for novel therapeutics. While social media data show promise for pharmacovigilance, further work is needed to improve its reliability and usability. UR - https://formative.jmir.org/2024/1/e58176 UR - http://dx.doi.org/10.2196/58176 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58176 ER - TY - JOUR AU - Chandrasekaran, Ranganathan AU - Sadiq T, Muhammed AU - Moustakas, Evangelos PY - 2024/11/6 TI - Racial and Demographic Disparities in Susceptibility to Health Misinformation on Social Media: National Survey-Based Analysis JO - J Med Internet Res SP - e55086 VL - 26 KW - health misinformation KW - digital divide KW - racial disparities KW - social media KW - national survey-based analysis KW - health information KW - interventions N2 - Background: Social media platforms have transformed the dissemination of health information, allowing for rapid and widespread sharing of content. However, alongside valuable medical knowledge, these platforms have also become channels for the spread of health misinformation, including false claims and misleading advice, which can lead to significant public health risks. Susceptibility to health misinformation varies and is influenced by individuals? cultural, social, and personal backgrounds, further complicating efforts to combat its spread. Objective: This study aimed to examine the extent to which individuals report encountering health-related misinformation on social media and to assess how racial, ethnic, and sociodemographic factors influence susceptibility to such misinformation. Methods: Data from the Health Information National Trends Survey (HINTS; Cycle 6), conducted by the National Cancer Institute with 5041 US adults between March and November 2022, was used to explore associations between racial and sociodemographic factors (age, gender, race/ethnicity, annual household income, marital status, and location) and susceptibility variables, including encounters with misleading health information on social media, difficulty in assessing information truthfulness, discussions with health providers, and making health decisions based on such information. Results: Over 35.61% (1740/4959) of respondents reported encountering ?a lot? of misleading health information on social media, with an additional 45% (2256/4959) reporting seeing ?some? amount of health misinformation. Racial disparities were evident in comparison with Whites, with non-Hispanic Black (odds ratio [OR] 0.45, 95% CI 0.33-0.6, P<.01) and Hispanic (OR 0.54, 95% CI 0.41-0.71, P<.01) individuals reporting lower odds of finding deceptive information, while Hispanic (OR 1.68, 95% CI 1.48-1.98, P<.05) and non-Hispanic Asian (OR 1.96, 95% CI 1.21-3.18, P<.01) individuals exhibited higher odds in having difficulties in assessing the veracity of health information found on social media. Hispanic and Asian individuals were more likely to discuss with providers and make health decisions based on social media information. Older adults aged ?75 years exhibited challenges in assessing health information on social media (OR 0.63, 95% CI 0.43-0.93, P<.01), while younger adults (18-34) showed increased vulnerability to health misinformation. In addition, income levels were linked to higher exposure to health misinformation on social media: individuals with annual household incomes between US $50,000 and US $75,000 (OR 1.74, 95% CI 1.14-2.68, P<.01), and greater than US $75,000 (OR 1.78, 95% CI 1.20-2.66, P<.01) exhibited greater odds, revealing complexities in decision-making and information access. Conclusions: This study highlights the pervasive presence of health misinformation on social media, revealing vulnerabilities across racial, age, and income groups, underscoring the need for tailored interventions. UR - https://www.jmir.org/2024/1/e55086 UR - http://dx.doi.org/10.2196/55086 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55086 ER - TY - JOUR AU - Bhagavathula, Srikanth Akshaya AU - Dobbs, D. Page PY - 2024/11/5 TI - Online Interest in Elf Bar in the United States: Google Health Trends Analysis JO - J Med Internet Res SP - e50343 VL - 26 KW - e-cigarettes KW - Elf Bar KW - JUUL KW - tobacco KW - Google Trends KW - Google Health Trends N2 - Background: Despite the popularity of JUUL e-cigarettes, other brands (eg, Elf Bar) may be gaining digital attention. Objective: This study compared Google searches for Elf Bar and JUUL from 2022 to 2023 using Google Health Trends Application Programming Interface data. Methods: Using an infodemiology approach, we examined weekly trends in Google searches (per 10 million) for ?Elf Bar? and ?JUUL? at the US national and state levels from January 1, 2022, to December 31, 2023. Joinpoint regression was used to assess statistically significant trends in the search probabilities for ?Elf Bar? and ?JUUL? during the study period. Results: Elf Bar had less online interest than JUUL at the beginning of 2022. When the US Food and Drug Administration denied JUUL marketing authority on June 23, 2022, JUUL searches peaked at 2609.3 × 107 and fell to 83.9 × 107 on September 3, 2023. Elf Bar searches surpassed JUUL on July 10, 2022, and steadily increased, reaching 523.2 × 107 on December 4, 2022. Overall, Elf Bar?s weekly search probability increased by 1.6% (95% CI 1.5%-1.7%; P=.05) from January 2022 to December 2023, with the greatest increase between May 29 and June 19, 2022 (87.7%, 95% CI 35.9%-123.9%; P=.001). Elf Bar searches increased after JUUL?s suspension in Pennsylvania (1010%), Minnesota (872.5%), Connecticut (803.5%), New York (738.1%), and New Jersey (702.9%). Conclusions: Increasing trends in Google searches for Elf Bar indicate that there was a growing online interest in this brand in the United States in 2022. UR - https://www.jmir.org/2024/1/e50343 UR - http://dx.doi.org/10.2196/50343 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/50343 ER - TY - JOUR AU - Ravaut, Mathieu AU - Zhao, Ruochen AU - Phung, Duy AU - Qin, Mengqi Vicky AU - Milovanovic, Dusan AU - Pienkowska, Anita AU - Bojic, Iva AU - Car, Josip AU - Joty, Shafiq PY - 2024/10/30 TI - Targeting COVID-19 and Human Resources for Health News Information Extraction: Algorithm Development and Validation JO - JMIR AI SP - e55059 VL - 3 KW - COVID-19 KW - SARS-CoV-2 KW - summary KW - summarize KW - news articles KW - deep learning KW - classification KW - summarization KW - machine learning KW - extract KW - extraction KW - news KW - media KW - NLP KW - natural language processing N2 - Background: Global pandemics like COVID-19 put a high amount of strain on health care systems and health workers worldwide. These crises generate a vast amount of news information published online across the globe. This extensive corpus of articles has the potential to provide valuable insights into the nature of ongoing events and guide interventions and policies. However, the sheer volume of information is beyond the capacity of human experts to process and analyze effectively. Objective: The aim of this study was to explore how natural language processing (NLP) can be leveraged to build a system that allows for quick analysis of a high volume of news articles. Along with this, the objective was to create a workflow comprising human-computer symbiosis to derive valuable insights to support health workforce strategic policy dialogue, advocacy, and decision-making. Methods: We conducted a review of open-source news coverage from January 2020 to June 2022 on COVID-19 and its impacts on the health workforce from the World Health Organization (WHO) Epidemic Intelligence from Open Sources (EIOS) by synergizing NLP models, including classification and extractive summarization, and human-generated analyses. Our DeepCovid system was trained on 2.8 million news articles in English from more than 3000 internet sources across hundreds of jurisdictions. Results: Rules-based classification with hand-designed rules narrowed the data set to 8508 articles with high relevancy confirmed in the human-led evaluation. DeepCovid?s automated information targeting component reached a very strong binary classification performance of 98.98 for the area under the receiver operating characteristic curve (ROC-AUC) and 47.21 for the area under the precision recall curve (PR-AUC). Its information extraction component attained good performance in automatic extractive summarization with a mean Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score of 47.76. DeepCovid?s final summaries were used by human experts to write reports on the COVID-19 pandemic. Conclusions: It is feasible to synergize high-performing NLP models and human-generated analyses to benefit open-source health workforce intelligence. The DeepCovid approach can contribute to an agile and timely global view, providing complementary information to scientific literature. UR - https://ai.jmir.org/2024/1/e55059 UR - http://dx.doi.org/10.2196/55059 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55059 ER - TY - JOUR AU - Liu, Min AU - Yuan, Shuo AU - Li, Bingyan AU - Zhang, Yuxi AU - Liu, Jia AU - Guan, Cuixia AU - Chen, Qingqing AU - Ruan, Jiayi AU - Xie, Lunfang PY - 2024/10/28 TI - Chinese Public Attitudes and Opinions on Health Policies During Public Health Emergencies: Sentiment and Topic Analysis JO - J Med Internet Res SP - e58518 VL - 26 KW - public health emergencies KW - nucleic acid testing KW - governance strategies KW - sentiment analysis KW - LDA KW - social media KW - COVID-19 KW - opinion analysis N2 - Background: By the end of 2021, the new wave of COVID-19 sparked by the Omicron variant spread rapidly due to its highly contagious nature, affecting more than 170 countries worldwide. Nucleic acid testing became the gold standard for diagnosing novel coronavirus infections. As of July 2022, numerous cities and regions in China have implemented regular nucleic acid testing policies, which have had a significant impact on socioeconomics and people?s lives. This policy has garnered widespread attention on social media platforms. Objective: This study took the newly issued regular nucleic acid testing policy during the COVID-19 pandemic as an example to explore the sentiment responses and fluctuations of netizens toward new policies during public health emergencies. It aimed to propose strategies for managing public opinion on the internet and provide recommendations for policy making and public opinion control. Methods: We collected blog posts related to nucleic acid testing on Weibo from April 1, 2022, to July 31, 2022. We used the topic modeling technique latent Dirichlet allocation (LDA) to identify the most common topics posted by users. We used Bidirectional Encoder Representations from Transformers (BERT) to calculate the sentiment score of each post. We used an autoregressive integrated moving average (ARIMA) model to examine the relationship between sentiment scores and changes over time. We compared the differences in sentiment scores across various topics, as well as the changes in sentiment before and after the announcement of the nucleic acid price reduction policy (May 22) and the lifting of the lockdown policy in Shanghai (June 1). Results: We collected a total of 463,566 Weibo posts, with an average of 3799.72 (SD 1296.06) posts published daily. The LDA topic extraction identified 8 topics, with the most numerous being the Shanghai outbreak, nucleic acid testing price, and transportation. The average sentiment score of the posts was 0.64 (SD 0.31), indicating a predominance of positive sentiment. For all topics, posts with positive sentiment consistently outnumbered those with negative sentiment (?27=24,844.4, P<.001). The sentiment scores of posts related to ?nucleic acid testing price? decreased after May 22 compared with before (t120=3.882, P<.001). Similarly, the sentiment scores of posts related to the ?Shanghai outbreak? decreased after June 1 compared with before (t120=11.943, P<.001). Conclusions: During public health emergencies, the topics of public concern were diverse. Public sentiment toward the regular nucleic acid testing policy was generally positive, but fluctuations occurred following the announcement of key policies. To understand the primary concerns of the public, the government needs to monitor social media posts by citizens. By promptly sharing information on media platforms and engaging in effective communication, the government can bridge the information gap between the public and government agencies, fostering a positive public opinion environment. UR - https://www.jmir.org/2024/1/e58518 UR - http://dx.doi.org/10.2196/58518 UR - http://www.ncbi.nlm.nih.gov/pubmed/39466313 ID - info:doi/10.2196/58518 ER - TY - JOUR AU - Kolis, Jessica AU - Brookmeyer, Kathryn AU - Chuvileva, Yulia AU - Voegeli, Christopher AU - Juma, Sarina AU - Ishizumi, Atsuyoshi AU - Renfro, Katy AU - Wilhelm, Elisabeth AU - Tice, Hannah AU - Fogarty, Hannah AU - Kocer, Irma AU - Helms, Jordan AU - Verma, Anisha PY - 2024/10/24 TI - Infodemics and Vaccine Confidence: Protocol for Social Listening and Insight Generation to Inform Action JO - JMIR Public Health Surveill SP - e51909 VL - 10 KW - infodemic KW - infodemic management KW - vaccine confidence KW - vaccine demand KW - misinformation KW - disinformation KW - infodemiology KW - mixed methods KW - thematic analysis KW - COVID-19 N2 - Background: In the fall of 2020, the COVID-19 infodemic began to affect public confidence in and demand for COVID-19 vaccines in the United States. While polls indicated what consumers felt regarding COVID-19 vaccines, they did not provide an understanding of why they felt that way or the social and informational influences that factored into vaccine confidence and uptake. It was essential for us to better understand how information ecosystems were affecting the confidence in and demand for COVID-19 vaccines in the United States. Objective: The US Centers for Disease Control and Prevention (CDC) established an Insights Unit within the COVID-19 Response?s Vaccine Task Force in January 2021 to assist the agency in acting more swiftly to address the questions, concerns, perceptions, and misinformation that appeared to be affecting uptake of COVID-19 vaccines. We established a novel methodology to rapidly detect and report on trends in vaccine confidence and demand to guide communication efforts and improve programmatic quality in near real time. Methods: We identified and assessed data sources for inclusion through an informal landscape analysis using a snowball method. Selected data sources provided an expansive look at the information ecosystem of the United States regarding COVID-19 vaccines. The CDC?s Vaccinate with Confidence framework and the World Health Organization?s behavioral and social drivers for vaccine decision-making framework were selected as guiding principles for interpreting generated insights and their impact. We used qualitative thematic analysis methods and a consensus-building approach to identify prevailing and emerging themes, assess their potential threat to vaccine confidence, and propose actions to increase confidence and demand. Results: As of August 2022, we have produced and distributed 34 reports to >950 recipients within the CDC and externally. State and local health departments, nonprofit organizations, professional associations, and congressional committees have referenced and used the reports for learning about COVID-19 vaccine confidence and demand, developing communication strategies, and demonstrating how the CDC monitored and responded to misinformation. A survey of the reports? end users found that nearly 75% (40/53) of respondents found them ?very? or ?extremely? relevant and 52% (32/61) used the reports to inform communication strategies. In addition, our methodology underwent continuous process improvement to increase the rigor of the research process, the validity of the findings, and the usability of the reports. Conclusions: This methodology can serve as a diagnostic technique for rapidly identifying opportunities for public health interventions and prevention. As the methodology itself is adaptable, it could be leveraged and scaled for use in a variety of public health settings. Furthermore, it could be considered beyond acute public health crises to support adherence to guidance and recommendations and could be considered within routine monitoring and surveillance systems. UR - https://publichealth.jmir.org/2024/1/e51909 UR - http://dx.doi.org/10.2196/51909 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51909 ER - TY - JOUR AU - Kim, Minji AU - Vassey, Julia AU - Li, Dongmei AU - Galimov, Artur AU - Han, Eileen AU - Kirkpatrick, G. Matthew AU - Stanton, A. Cassandra AU - Ozga, E. Jenny AU - Lee, Sarah AU - Unger, B. Jennifer PY - 2024/10/24 TI - Discussion of Heated Tobacco Products on Twitter Following IQOS?s Modified-Risk Tobacco Product Authorization and US Import Ban: Content Analysis JO - J Med Internet Res SP - e53938 VL - 26 KW - heated tobacco products KW - IQOS KW - social media KW - Twitter KW - tobacco control KW - modified-risk tobacco product authorization KW - MRTP authorization KW - tobacco regulatory science KW - import ban KW - observational study KW - public opinion KW - content analysis N2 - Background: Understanding public opinions about emerging tobacco products is important to inform future interventions and regulatory decisions. Heated tobacco products (HTPs) are an emerging tobacco product category promoted by the tobacco industry as a ?better alternative? to combustible cigarettes. Philip Morris International?s IQOS is leading the global HTP market and recently has been subject to important policy events, including the US Food and Drug Administration?s (FDA) modified-risk tobacco product (MRTP) authorization (July 2020) and the US import ban (November 2021). Although limited in their legal implications outside the United States, these policy events have been quoted in global news outlets and Philip Morris International?s promotional communications, showing how they may potentially impact global tobacco regulation. Given the impending return of IQOS to the US market, understanding how the policy events were received through social media discourse will provide valuable insights to inform global tobacco control policy. Objective: This study aims to examine HTP-related social media discourse around important policy events. Methods: We analyzed HTP-related posts on Twitter during the time period that included IQOS?s MRTP authorization in the United States and the US import ban, examining personal testimonial, news/information, and direct marketing/retail tweets separately. We also examined how the tweets discussed health and policy. A total of 10,454 public English tweets (posted from June 2020 to December 2021) were collected using HTP-related keywords. We randomly sampled 2796 (26.7%) tweets and conducted a content analysis. We used pairwise co-occurrence analyses to evaluate connections across themes. Results: Tweet volumes peaked around IQOS-related policy events. Among all tweets, personal testimonials were the most common (1613/2796, 57.7%), followed by news/information (862/2796, 30.8%) and direct marketing/retail (321/2796, 11%). Among personal testimonials, more tweets were positive (495/1613, 30.7%) than negative (372/1613, 23.1%), often comparing the health risks of HTPs with cigarettes (402/1613, 24.9%) or vaping products (252/1613, 15.6%). Approximately 10% (31/321) of the direct marketing/retail tweets promoted international delivery, suggesting cross-border promotion. More than a quarter of tweets (809/2796, 28.9%) discussed US and global policy, including misinterpretation about IQOS being a ?safer? tobacco product after the US FDA?s MRTP authorization. Neutral testimonials mentioning the IQOS brand (634/1613, 39.3%) and discussing policy (378/1613, 23.4%) showed the largest pairwise co-occurrence. Conclusions: Results suggest the need for careful communication about the meaning of MRTP authorizations and relative risks of tobacco products. Many tweets expressed HTP-favorable opinions referring to reduced health risks, even though the US FDA has denied marketing of the HTP with reduced risk claims. The popularity of social media as an information source with global reach poses unique challenges in health communication and health policies. While many countries restrict tobacco marketing via the web, our results suggest that retailers may circumvent such regulations by operating overseas. UR - https://www.jmir.org/2024/1/e53938 UR - http://dx.doi.org/10.2196/53938 UR - http://www.ncbi.nlm.nih.gov/pubmed/39446431 ID - info:doi/10.2196/53938 ER - TY - JOUR AU - Cassidy, Omni AU - Bragg, Marie AU - Elbel, Brian PY - 2024/10/17 TI - Virtual Reality?Based Food and Beverage Marketing: Potential Implications for Young People of Color, Knowledge Gaps, and Future Research Directions JO - JMIR Public Health Surveill SP - e62807 VL - 10 KW - virtual reality KW - VR KW - digital food and beverage marketing KW - obesity KW - marketing KW - food KW - consumption KW - beverage KW - immersive KW - market KW - consumer UR - https://publichealth.jmir.org/2024/1/e62807 UR - http://dx.doi.org/10.2196/62807 ID - info:doi/10.2196/62807 ER - TY - JOUR AU - Germani, Federico AU - Spitale, Giovanni AU - Biller-Andorno, Nikola PY - 2024/10/15 TI - The Dual Nature of AI in Information Dissemination: Ethical Considerations JO - JMIR AI SP - e53505 VL - 3 KW - AI KW - bioethics KW - infodemic management KW - disinformation KW - artificial intelligence KW - ethics KW - ethical KW - infodemic KW - infodemics KW - public health KW - misinformation KW - information dissemination KW - information literacy UR - https://ai.jmir.org/2024/1/e53505 UR - http://dx.doi.org/10.2196/53505 UR - http://www.ncbi.nlm.nih.gov/pubmed/39405099 ID - info:doi/10.2196/53505 ER - TY - JOUR AU - Kong, Deliang AU - Wu, Chengguo AU - Cui, Yimin AU - Fan, Jun AU - Zhang, Ting AU - Zhong, Jiyuan AU - Pu, Chuan PY - 2024/9/24 TI - Epidemiological Characteristics and Spatiotemporal Clustering of Pulmonary Tuberculosis Among Students in Southwest China From 2016 to 2022: Analysis of Population-Based Surveillance Data JO - JMIR Public Health Surveill SP - e64286 VL - 10 KW - student PTB KW - Southwest China KW - epidemiology KW - visualizing incidence map KW - spatial autocorrelation analysis KW - spatiotemporal clusters KW - pulmonary tuberculosis N2 - Background: Pulmonary tuberculosis (PTB), as a respiratory infectious disease, poses significant risks of covert transmission and dissemination. The high aggregation and close contact among students in Chinese schools exacerbate the transmission risk of PTB outbreaks. Objective: This study investigated the epidemiological characteristics, geographic distribution, and spatiotemporal evolution of student PTB in Chongqing, Southwest China, aiming to delineate the incidence risks and clustering patterns of PTB among students. Methods: PTB case data from students monitored and reported in the Tuberculosis Information Management System within the China Information System for Disease Control and Prevention were used for this study. Descriptive analyses were conducted to characterize the epidemiological features of student PTB. Spatial trend surface analysis, global and local spatial autocorrelation analyses, and disease rate mapping were performed using ArcGIS 10.3. SaTScan 9.6 software was used to identify spatiotemporal clusters of PTB cases. Results: From 2016 to 2022, a total of 9920 student TB cases were reported in Chongqing, Southwest China, with an average incidence rate of 24.89/100,000. The incidence of student TB showed an initial increase followed by a decline, yet it remained relatively high. High school students (age: 13?18 years; 6649/9920, 67.03%) and college students (age: ?19 years; 2921/9920, 29.45%) accounted for the majority of student PTB cases. Patient identification primarily relied on passive detection, with a high proportion of delayed diagnosis and positive etiological results. COVID-19 prevention measures have had some impact on reducing incidence levels, but the primary factor appears to be the implementation of screening measures, which facilitated earlier case detection. Global spatial autocorrelation analysis indicated Moran I values of >0 for all years except 2018, ranging from 0.1908 to 0.4645 (all P values were <.05), suggesting strong positive spatial clustering of student PTB cases across Chongqing. Local spatial autocorrelation identified 7 high-high clusters, 13 low-low clusters, 5 high-low clusters, and 4 low-high clusters. High-high clusters were predominantly located in the southeast and northeast parts of Chongqing, consistent with spatial trend surface analysis and spatiotemporal clustering results. Spatiotemporal scan analysis revealed 4 statistically significant spatiotemporal clusters, with the most likely cluster in the southeast (relative risk [RR]=2.87, log likelihood ratio [LLR]=574.29, P<.001) and a secondary cluster in the northeast (RR=1.99, LLR=234.67, P<.001), indicating higher reported student TB cases and elevated risks of epidemic spread within these regions. Conclusions: Future efforts should comprehensively enhance prevention and control measures in high-risk areas of PTB in Chongqing to mitigate the incidence risk among students. Additionally, implementing proactive screening strategies and enhancing screening measures are crucial for early identification of student patients to prevent PTB outbreaks in schools. UR - https://publichealth.jmir.org/2024/1/e64286 UR - http://dx.doi.org/10.2196/64286 ID - info:doi/10.2196/64286 ER - TY - JOUR AU - Golder, Su AU - O'Connor, Karen AU - Wang, Yunwen AU - Klein, Ari AU - Gonzalez Hernandez, Graciela PY - 2024/9/6 TI - The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review JO - JMIR Public Health Surveill SP - e59167 VL - 10 KW - adverse events KW - pharmacovigilance KW - social media KW - real-world data KW - scoping review N2 - Background: Adverse drug events pose an enormous public health burden, leading to hospitalization, disability, and death. Even the adverse events (AEs) categorized as nonserious can severely impact on patient?s quality of life, adherence, and persistence. Monitoring medication safety is challenging. Web-based patient reports on social media may be a useful supplementary source of real-world data. Despite the growth of sophisticated techniques for identifying AEs using social media data, a consensus has not been reached as to the value of social media in relation to more traditional data sources. Objective: This study aims to evaluate and characterize the utility of social media analysis in adverse drug event detection and pharmacovigilance as compared with other data sources (such as spontaneous reporting systems and the clinical literature). Methods: In this scoping review, we searched 11 bibliographical databases and Google Scholar, followed by handsearching and forward and backward citation searching. Each record was screened by 2 independent reviewers at both the title and abstract stage and the full-text screening stage. Studies were included if they used any type of social media (such as Twitter or patient forums) to detect AEs associated with any drug medication and compared the results ascertained from social media to any other data source. Study information was collated using a piloted data extraction sheet. Data were extracted on the AEs and drugs searched for and included; the methods used (such as machine learning); social media data source; volume of data analyzed; limitations of the methodology; availability of data and code; comparison data source and comparison methods; results, including the volume of AEs, and how the AEs found compared with other data sources in their seriousness, frequencies, and expectedness or novelty (new vs known knowledge); and conclusions. Results: Of the 6538 unique records screened, 73 publications representing 60 studies with a wide variety of extraction methods met our inclusion criteria. The most common social media platforms used were Twitter and online health forums. The most common comparator data source was spontaneous reporting systems, although other comparisons were also made, such as with scientific literature and product labels. Although similar patterns of AE reporting tended to be identified, the frequencies were lower in social media. Social media data were found to be useful in identifying new or unexpected AEs and in identifying AEs in a timelier manner. Conclusions: There is a large body of research comparing AEs from social media to other sources. Most studies advocate the use of social media as an adjunct to traditional data sources. Some studies also indicate the value of social media in understanding patient perspectives such as the impact of AEs, which could be better explored. International Registered Report Identifier (IRRID): RR2-10.2196/47068 UR - https://publichealth.jmir.org/2024/1/e59167 UR - http://dx.doi.org/10.2196/59167 UR - http://www.ncbi.nlm.nih.gov/pubmed/39240684 ID - info:doi/10.2196/59167 ER - TY - JOUR AU - Yan, XiangYu AU - Li, Zhuo AU - Cao, Chunxia AU - Huang, Longxin AU - Li, Yongjie AU - Meng, Xiangbin AU - Zhang, Bo AU - Yu, Maohe AU - Huang, Tian AU - Chen, Jiancheng AU - Li, Wei AU - Hao, Linhui AU - Huang, Dongsheng AU - Yi, Bin AU - Zhang, Ming AU - Zha, Shun AU - Yang, Haijun AU - Yao, Jian AU - Qian, Pengjiang AU - Leung, Kai Chun AU - Fan, Haojun AU - Jiang, Pei AU - Shui, Tiejun PY - 2024/8/30 TI - Characteristics, Influence, Prevention, and Control Measures of the Mpox Infodemic: Scoping Review of Infodemiology Studies JO - J Med Internet Res SP - e54874 VL - 26 KW - mpox KW - infodemic KW - infodemiology KW - information search volume KW - content topic KW - digital health N2 - Background: The mpox pandemic has caused widespread public concern around the world. The spread of misinformation through the internet and social media could lead to an infodemic that poses challenges to mpox control. Objective: This review aims to summarize mpox-related infodemiology studies to determine the characteristics, influence, prevention, and control measures of the mpox infodemic and propose prospects for future research. Methods: The scoping review was conducted based on a structured 5-step methodological framework. A comprehensive search for mpox-related infodemiology studies was performed using PubMed, Web of Science, Embase, and Scopus, with searches completed by April 30, 2024. After study selection and data extraction, the main topics of the mpox infodemic were categorized and summarized in 4 aspects, including a trend analysis of online information search volume, content topics of mpox-related online posts and comments, emotional and sentiment characteristics of online content, and prevention and control measures for the mpox infodemic. Results: A total of 1607 articles were retrieved from the databases according to the keywords, and 61 studies were included in the final analysis. After the World Health Organization?s declaration of an mpox public health emergency of international concern in July 2022, the number of related studies began growing rapidly. Google was the most widely used search engine platform (9/61, 15%), and Twitter was the most used social media app (32/61, 52%) for researchers. Researchers from 33 countries were concerned about mpox infodemic?related topics. Among them, the top 3 countries for article publication were the United States (27 studies), India (9 studies), and the United Kingdom (7 studies). Studies of online information search trends showed that mpox-related online search volume skyrocketed at the beginning of the mpox outbreak, especially when the World Health Organization provided important declarations. There was a large amount of misinformation with negative sentiment and discriminatory and hostile content against gay, bisexual, and other men who have sex with men. Given the characteristics of the mpox infodemic, the studies provided several positive prevention and control measures, including the timely and active publishing of professional, high-quality, and easy-to-understand information online; strengthening surveillance and early warning for the infodemic based on internet data; and taking measures to protect key populations from the harm of the mpox infodemic. Conclusions: This comprehensive summary of evidence from previous mpox infodemiology studies is valuable for understanding the characteristics of the mpox infodemic and for formulating prevention and control measures. It is essential for researchers and policy makers to establish prediction and early warning approaches and targeted intervention methods for dealing with the mpox infodemic in the future. UR - https://www.jmir.org/2024/1/e54874 UR - http://dx.doi.org/10.2196/54874 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54874 ER - TY - JOUR AU - Germani, Federico AU - Spitale, Giovanni AU - Machiri, Varaidzo Sandra AU - Ho, Loon Calvin Wai AU - Ballalai, Isabella AU - Biller-Andorno, Nikola AU - Reis, Alois Andreas PY - 2024/8/29 TI - Ethical Considerations in Infodemic Management: Systematic Scoping Review JO - JMIR Infodemiology SP - e56307 VL - 4 KW - World Health Organization KW - ethics KW - infodemic management KW - social listening KW - review KW - infodemic KW - health emergency KW - health emergencies KW - misinformation KW - disinformation KW - scoping review KW - ethical principles KW - community engagement KW - empowerment KW - data privacy KW - effectiveness N2 - Background: During health emergencies, effective infodemic management has become a paramount challenge. A new era marked by a rapidly changing information ecosystem, combined with the widespread dissemination of misinformation and disinformation, has magnified the complexity of the issue. For infodemic management measures to be effective, acceptable, and trustworthy, a robust framework of ethical considerations is needed. Objective: This systematic scoping review aims to identify and analyze ethical considerations and procedural principles relevant to infodemic management, ultimately enhancing the effectiveness of these practices and increasing trust in stakeholders performing infodemic management practices with the goal of safeguarding public health. Methods: The review involved a comprehensive examination of the literature related to ethical considerations in infodemic management from 2002 to 2022, drawing from publications in PubMed, Scopus, and Web of Science. Policy documents and relevant material were included in the search strategy. Papers were screened against inclusion and exclusion criteria, and core thematic areas were systematically identified and categorized following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We analyzed the literature to identify substantive ethical principles that were crucial for guiding actions in the realms of infodemic management and social listening, as well as related procedural ethical principles. In this review, we consider ethical principles that are extensively deliberated upon in the literature, such as equity, justice, or respect for autonomy. However, we acknowledge the existence and relevance of procedural practices, which we also consider as ethical principles or practices that, when implemented, enhance the efficacy of infodemic management while ensuring the respect of substantive ethical principles. Results: Drawing from 103 publications, the review yielded several key findings related to ethical principles, approaches, and guidelines for practice in the context of infodemic management. Community engagement, empowerment through education, and inclusivity emerged as procedural principles and practices that enhance the quality and effectiveness of communication and social listening efforts, fostering trust, a key emerging theme and crucial ethical principle. The review also emphasized the significance of transparency, privacy, and cybersecurity in data collection. Conclusions: This review underscores the pivotal role of ethics in bolstering the efficacy of infodemic management. From the analyzed body of literature, it becomes evident that ethical considerations serve as essential instruments for cultivating trust and credibility while also facilitating the medium-term and long-term viability of infodemic management approaches. UR - https://infodemiology.jmir.org/2024/1/e56307 UR - http://dx.doi.org/10.2196/56307 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56307 ER - TY - JOUR AU - Rao, K. Varun AU - Valdez, Danny AU - Muralidharan, Rasika AU - Agley, Jon AU - Eddens, S. Kate AU - Dendukuri, Aravind AU - Panth, Vandana AU - Parker, A. Maria PY - 2024/8/23 TI - Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis JO - J Med Internet Res SP - e57885 VL - 26 KW - digital epidemiology KW - BERTtopic KW - Valence Aware Dictionary and Sentiment Reasoner KW - VADER KW - sentiment analysis KW - social media KW - prescription drugs KW - prescription KW - prescriptions KW - drug KW - drugs KW - drug use KW - platform X KW - Twitter KW - tweet KW - tweets KW - latent Dirichlet allocation KW - machine-driven KW - natural language processing KW - NLP KW - brand name KW - logistic regression KW - machine learning KW - health informatics N2 - Background: Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing ?street names? of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, ?brand name? references were more amenable to machine-driven categorization. Objective: This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. Methods: We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet?s engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency?inverse document frequency score. Results: Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. Conclusions: BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non?drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet. UR - https://www.jmir.org/2024/1/e57885 UR - http://dx.doi.org/10.2196/57885 UR - http://www.ncbi.nlm.nih.gov/pubmed/39178036 ID - info:doi/10.2196/57885 ER - TY - JOUR AU - AbouWarda, Horeya AU - Dolata, Mateusz AU - Schwabe, Gerhard PY - 2024/8/19 TI - How Does an Online Mental Health Community on Twitter Empower Diverse Population Levels and Groups? A Qualitative Analysis of #BipolarClub JO - J Med Internet Res SP - e55965 VL - 26 KW - social media KW - Twitter KW - online mental health community KW - OMHC KW - empowerment processes KW - diverse population levels and groups KW - World Health Organization KW - WHO KW - Integrated People-Centred Health Services KW - IPCHS framework (Strategy 1) N2 - Background: Social media, including online health communities (OHCs), are widely used among both healthy people and those with health conditions. Platforms like Twitter (recently renamed X) have become powerful tools for online mental health communities (OMHCs), enabling users to exchange information, express feelings, and socialize. Recognized as empowering processes, these activities could empower mental health consumers, their families and friends, and society. However, it remains unclear how OMHCs empower diverse population levels and groups. Objective: This study aimed to develop an understanding of how empowerment processes are conducted within OMHCs on Twitter by identifying members who shape these communities, detecting the types of empowerment processes aligned with the population levels and groups outlined in Strategy 1 of the Integrated People-Centred Health Services (IPCHS) framework by the World Health Organization (WHO), and clarifying members? involvement tendencies in these processes. Methods: We conducted our analysis on a Twitter OMHC called #bipolarclub. We captured 2068 original tweets using its hashtag #bipolarclub between December 19, 2022, and January 15, 2023. After screening, 547 eligible tweets by 182 authors were analyzed. Using qualitative content analysis, community members were classified by examining the 182 authors? Twitter profiles, and empowerment processes were identified by analyzing the 547 tweets and categorized according to the WHO?s Strategy 1. Members? tendencies of involvement were examined through their contributions to the identified processes. Results: The analysis of #bipolarclub community members unveiled 5 main classifications among the 182 members, with the majority classified as individual members (n=138, 75.8%), followed by health care?related members (n=39, 21.4%). All members declared that they experience mental health conditions, including mental health and general practitioner members, who used the community as consumers and peers rather than for professional services. The analysis of 547 tweets for empowerment processes revealed 3 categories: individual-level processes (6 processes and 2 subprocesses), informal carer processes (1 process for families and 1 process for friends), and society-level processes (1 process and 2 subprocesses). The analysis also demonstrated distinct involvement tendencies among members, influenced by their identities, with individual members engaging in self-expression and family awareness support and health care?related members supporting societal awareness. Conclusions: The examination of the #bipolarclub community highlights the capability of Twitter-based OMHCs to empower mental health consumers (including those from underserved and marginalized populations), their families and friends, and society, aligning with the WHO?s empowerment agenda. This underscores the potential benefits of leveraging Twitter for such objectives. This pioneering study is the very first to analyze how a single OMHC can empower diverse populations, offering various health care stakeholders valuable guidance and aiding them in developing consumer-oriented empowerment programs using such OMHCs. We also propose a structured framework that classifies empowerment processes in OMHCs, inspired by the WHO?s Strategy 1 (IPCHS framework). UR - https://www.jmir.org/2024/1/e55965 UR - http://dx.doi.org/10.2196/55965 UR - http://www.ncbi.nlm.nih.gov/pubmed/39158945 ID - info:doi/10.2196/55965 ER - TY - JOUR AU - Wang, Yunwen AU - O?Connor, Karen AU - Flores, Ivan AU - Berdahl, T. Carl AU - Urbanowicz, J. Ryan AU - Stevens, Robin AU - Bauermeister, A. José AU - Gonzalez-Hernandez, Graciela PY - 2024/8/13 TI - Mpox Discourse on Twitter by Sexual Minority Men and Gender-Diverse Individuals: Infodemiological Study Using BERTopic JO - JMIR Public Health Surveill SP - e59193 VL - 10 KW - mpox KW - monkeypox KW - social media KW - sexual minority KW - SMMGD KW - sexual minority men and gender diverse KW - emerging infectious disease KW - infectious disease outbreak KW - health activism KW - health promotion KW - health stigma KW - stigma prevention KW - health equity KW - natural language processing KW - BERTopic N2 - Background: The mpox outbreak resulted in 32,063 cases and 58 deaths in the United States and 95,912 cases worldwide from May 2022 to March 2024 according to the US Centers for Disease Control and Prevention (CDC). Like other disease outbreaks (eg, HIV) with perceived community associations, mpox can create the risk of stigma, exacerbate homophobia, and potentially hinder health care access and social equity. However, the existing literature on mpox has limited representation of the perspective of sexual minority men and gender-diverse (SMMGD) individuals. Objective: To fill this gap, this study aimed to synthesize themes of discussions among SMMGD individuals and listen to SMMGD voices for identifying problems in current public health communication surrounding mpox to improve inclusivity, equity, and justice. Methods: We analyzed mpox-related posts (N=8688) posted between October 2020 and September 2022 by 2326 users who self-identified on Twitter/X as SMMGD and were geolocated in the United States. We applied BERTopic (a topic-modeling technique) on the tweets, validated the machine-generated topics through human labeling and annotations, and conducted content analysis of the tweets in each topic. Geographic analysis was performed on the size of the most prominent topic across US states in relation to the University of California, Los Angeles (UCLA) lesbian, gay, and bisexual (LGB) social climate index. Results: BERTopic identified 11 topics, which annotators labeled as mpox health activism (n=2590, 29.81%), mpox vaccination (n=2242, 25.81%), and adverse events (n=85, 0.98%); sarcasm, jokes, and emotional expressions (n=1220, 14.04%); COVID-19 and mpox (n=636, 7.32%); government or public health response (n=532, 6.12%); mpox symptoms (n=238, 2.74%); case reports (n=192, 2.21%); puns on the naming of the virus (ie, mpox; n=75, 0.86%); media publicity (n=59, 0.68%); and mpox in children (n=58, 0.67%). Spearman rank correlation indicated significant negative correlation (?=?0.322, P=.03) between the topic size of health activism and the UCLA LGB social climate index at the US state level. Conclusions: Discussions among SMMGD individuals on mpox encompass both utilitarian (eg, vaccine access, case reports, and mpox symptoms) and emotionally charged (ie, promoting awareness, advocating against homophobia, misinformation/disinformation, and health stigma) themes. Mpox health activism is more prevalent in US states with lower LGB social acceptance, suggesting a resilient communicative pattern among SMMGD individuals in the face of public health oppression. Our method for social listening could facilitate future public health efforts, providing a cost-effective way to capture the perspective of impacted populations. This study illuminates SMMGD engagement with the mpox discourse, underscoring the need for more inclusive public health programming. Findings also highlight the social impact of mpox: health stigma. Our findings could inform interventions to optimize the delivery of informational and tangible health resources leveraging computational mixed-method analyses (eg, BERTopic) and big data. UR - https://publichealth.jmir.org/2024/1/e59193 UR - http://dx.doi.org/10.2196/59193 UR - http://www.ncbi.nlm.nih.gov/pubmed/39137013 ID - info:doi/10.2196/59193 ER - TY - JOUR AU - Ramadan, Majed AU - Aboalola, Doaa AU - Aouabdi, Sihem AU - Alghamdi, Tariq AU - Alsolami, Mona AU - Samkari, Alaa AU - Alsiary, Rawiah PY - 2024/8/12 TI - Influence of Breast Cancer Awareness Month on Public Interest of Breast Cancer in High-Income Countries Between 2012 and 2022: Google Trends Analysis JO - JMIR Cancer SP - e49197 VL - 10 KW - Google Trends KW - breast cancer KW - pandemic KW - awareness KW - public interest KW - cancer KW - cancer awareness KW - women KW - mortality rate KW - detection KW - treatment KW - social media KW - tool KW - education KW - support KW - internet users N2 - Background: Breast cancer is the most common cancer among women worldwide. High-income countries have a greater incidence and mortality rate of breast cancer than low-income countries. As a result, raising awareness about breast cancer is crucial in increasing the chances of early detection and treatment. Social media has evolved into an essential tool for Breast Cancer Awareness Month campaigns, allowing people to share their breast cancer stories and experiences while also providing a venue for education and support. Objective: The aim of this study was to assess the level of public interest in searches linked to breast cancer among a sample of high-income nations with a sizable internet user base from 2012 to 2022. We also sought to compare the proportional search volume for breast cancer during Breast Cancer Awareness Month with that during other months of the year. Methods: Google Trends was used to retrieve data on internet user search behaviors in the context of breast cancer from 2012 to 2022. Seven countries were evaluated in this study: Australia, Canada, Ireland, New Zealand, the United Kingdom, Saudi Arabia, and the United States, in addition to global data. Breast cancer relative search volume trends were analyzed annually, monthly, and weekly from 2012 to 2022. The annual percent change (APC) was calculated for each country and worldwide. Monthly and weekly data were used to identify potential trends. Results: A fluctuating pattern in APC rates was observed, with a notable increase in 2018 and a significant decrease in 2020, particularly in Saudi Arabia. Monthly analysis revealed a consistent peak in search volume during October (Breast Cancer Awareness Month) each year. Weekly trends over a 20-year period indicated significant decreases in Australia, Canada, New Zealand, and the United States, while increases were noted in Ireland. Heatmap analysis further highlighted a consistent elevation in median search volume during October across all countries. Conclusions: These findings underscore the impact of Breast Cancer Awareness Month and suggest potential influences of governmental COVID-19 pandemic control measures in 2020 on internet search behavior. UR - https://cancer.jmir.org/2024/1/e49197 UR - http://dx.doi.org/10.2196/49197 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49197 ER - TY - JOUR AU - Yin, Dean-Chen Jason PY - 2024/8/9 TI - Vaccine Hesitancy in Taiwan: Temporal, Multilayer Network Study of Echo Chambers Shaped by Influential Users JO - Online J Public Health Inform SP - e55104 VL - 16 KW - network analysis KW - infodemiology KW - vaccine hesitancy KW - Taiwan KW - multiplex network KW - echo chambers KW - influential users KW - information dissemination KW - health communication KW - Taiwanese data set KW - multilayer network model KW - vaccine hesitant KW - antivaccination KW - infoveillance KW - disease surveillance KW - public health N2 - Background: Vaccine hesitancy is a growing global health threat that is increasingly studied through the monitoring and analysis of social media platforms. One understudied area is the impact of echo chambers and influential users on disseminating vaccine information in social networks. Assessing the temporal development of echo chambers and the influence of key users on their growth provides valuable insights into effective communication strategies to prevent increases in vaccine hesitancy. This also aligns with the World Health Organization?s (WHO) infodemiology research agenda, which aims to propose new methods for social listening. Objective: Using data from a Taiwanese forum, this study aims to examine how engagement patterns of influential users, both within and across different COVID-19 stances, contribute to the formation of echo chambers over time. Methods: Data for this study come from a Taiwanese forum called PTT. All vaccine-related posts on the ?Gossiping? subforum were scraped from January 2021 to December 2022 using the keyword ?vaccine.? A multilayer network model was constructed to assess the existence of echo chambers. Each layer represents either provaccination, vaccine hesitant, or antivaccination posts based on specific criteria. Layer-level metrics, such as average diversity and Spearman rank correlations, were used to measure chambering. To understand the behavior of influential users?or key nodes?in the network, the activity of high-diversity and hardliner nodes was analyzed. Results: Overall, the provaccination and antivaccination layers are strongly polarized. This trend is temporal and becomes more apparent after November 2021. Diverse nodes primarily participate in discussions related to provaccination topics, both receiving comments and contributing to them. Interactions with the antivaccination layer are comparatively minimal, likely due to its smaller size, suggesting that the forum is a ?healthy community.? Overall, diverse nodes exhibit cross-cutting engagement. By contrast, hardliners in the vaccine hesitant and antivaccination layers are more active in commenting within their own communities. This trend is temporal, showing an increase during the Omicron outbreak. Hardliner activity potentially reinforces their stances over time. Thus, there are opposing forces of chambering and cross-cutting. Conclusions: Efforts should be made to moderate hardliner and influential nodes in the antivaccination layer and to support provaccination users engaged in cross-cutting exchanges. There are several limitations to this study. One is the bias of the platform used, and another is the lack of a comprehensive definition of ?influence.? To address these issues, comparative studies across different platforms can be conducted, and various metrics of influence should be explored. Additionally, examining the impact of influential users on network structure and chambering through network simulations and regression analysis provides more robust insights. The study also lacks an explanation for the reasons behind chambering trends. Conducting content analysis can help to understand the nature of engagement and inform interventions to address echo chambers. These approaches align with and further the WHO infodemic research agenda. UR - https://ojphi.jmir.org/2024/1/e55104 UR - http://dx.doi.org/10.2196/55104 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/55104 ER - TY - JOUR AU - Yan, Yifei AU - Li, Jun AU - Liu, Xingyun AU - Li, Qing AU - Yu, Xiaonan Nancy PY - 2024/8/8 TI - Identifying Reddit Users at a High Risk of Suicide and Their Linguistic Features During the COVID-19 Pandemic: Growth-Based Trajectory Model JO - J Med Internet Res SP - e48907 VL - 26 KW - COVID-19 pandemic KW - Reddit KW - suicide risk KW - trajectory N2 - Background: Suicide has emerged as a critical public health concern during the COVID-19 pandemic. With social distancing measures in place, social media has become a significant platform for individuals expressing suicidal thoughts and behaviors. However, existing studies on suicide using social media data often overlook the diversity among users and the temporal dynamics of suicide risk. Objective: By examining the variations in post volume trajectories among users on the r/SuicideWatch subreddit during the COVID-19 pandemic, this study aims to investigate the heterogeneous patterns of change in suicide risk to help identify social media users at high risk of suicide. We also characterized their linguistic features before and during the pandemic. Methods: We collected and analyzed post data every 6 months from March 2019 to August 2022 for users on the r/SuicideWatch subreddit (N=6163). A growth-based trajectory model was then used to investigate the trajectories of post volume to identify patterns of change in suicide risk during the pandemic. Trends in linguistic features within posts were also charted and compared, and linguistic markers were identified across the trajectory groups using regression analysis. Results: We identified 2 distinct trajectories of post volume among r/SuicideWatch subreddit users. A small proportion of users (744/6163, 12.07%) was labeled as having a high risk of suicide, showing a sharp and lasting increase in post volume during the pandemic. By contrast, most users (5419/6163, 87.93%) were categorized as being at low risk of suicide, with a consistently low and mild increase in post volume during the pandemic. In terms of the frequency of most linguistic features, both groups showed increases at the initial stage of the pandemic. Subsequently, the rising trend continued in the high-risk group before declining, while the low-risk group showed an immediate decrease. One year after the pandemic outbreak, the 2 groups exhibited differences in their use of words related to the categories of personal pronouns; affective, social, cognitive, and biological processes; drives; relativity; time orientations; and personal concerns. In particular, the high-risk group was discriminant in using words related to anger (odds ratio [OR] 3.23, P<.001), sadness (OR 3.23, P<.001), health (OR 2.56, P=.005), achievement (OR 1.67, P=.049), motion (OR 4.17, P<.001), future focus (OR 2.86, P<.001), and death (OR 4.35, P<.001) during this stage. Conclusions: Based on the 2 identified trajectories of post volume during the pandemic, this study divided users on the r/SuicideWatch subreddit into suicide high- and low-risk groups. Our findings indicated heterogeneous patterns of change in suicide risk in response to the pandemic. The high-risk group also demonstrated distinct linguistic features. We recommend conducting real-time surveillance of suicide risk using social media data during future public health crises to provide timely support to individuals at potentially high risk of suicide. UR - https://www.jmir.org/2024/1/e48907 UR - http://dx.doi.org/10.2196/48907 UR - http://www.ncbi.nlm.nih.gov/pubmed/39115925 ID - info:doi/10.2196/48907 ER - TY - JOUR AU - Gwon, Nahyun AU - Jeong, Wonjeong AU - Kim, Hyun Jee AU - Oh, Hee Kyoung AU - Jun, Kwan Jae PY - 2024/8/7 TI - Effects of Intervention Timing on Health-Related Fake News: Simulation Study JO - JMIR Form Res SP - e48284 VL - 8 KW - disinformation KW - fenbendazole KW - cancer information KW - simulation KW - fake news KW - online social networking KW - misinformation KW - lung cancer N2 - Background: Fake health-related news has spread rapidly through the internet, causing harm to individuals and society. Despite interventions, a fenbendazole scandal recently spread among patients with lung cancer in South Korea. It is crucial to intervene appropriately to prevent the spread of fake news. Objective: This study investigated the appropriate timing of interventions to minimize the side effects of fake news. Methods: A simulation was conducted using the susceptible-infected-recovered (SIR) model, which is a representative model of the virus spread mechanism. We applied this model to the fake news spread mechanism. The parameters were set similarly to those in the digital environment, where the fenbendazole scandal occurred. NetLogo, an agent-based model, was used as the analytical tool. Results: Fake news lasted 278 days in the absence of interventions. As a result of adjusting and analyzing the timing of the intervention in response to the fenbendazole scandal, we found that faster intervention leads to a shorter duration of fake news (intervention at 54 days = fake news that lasted for 210 days; intervention at 16 days = fake news that lasted for 187 days; and intervention at 10 days = fake news that lasted for 157 days). However, no significant differences were observed when the intervention was performed within 10 days. Conclusions: Interventions implemented within 10 days were effective in reducing the duration of the spread of fake news. Our findings suggest that timely intervention is critical for preventing the spread of fake news in the digital environment. Additionally, a monitoring system that can detect fake news should be developed for a rapid response UR - https://formative.jmir.org/2024/1/e48284 UR - http://dx.doi.org/10.2196/48284 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/48284 ER - TY - JOUR AU - Postma, J. Doerine AU - Heijkoop, A. Magali L. AU - De Smet, M. Peter A. G. AU - Notenboom, Kim AU - Leufkens, M. Hubert G. AU - Mantel-Teeuwisse, K. Aukje PY - 2024/8/6 TI - Identifying Medicine Shortages With the Twitter Social Network: Retrospective Observational Study JO - J Med Internet Res SP - e51317 VL - 26 KW - medicine shortages KW - signal detection KW - social media KW - Twitter social network KW - drug shortage KW - Twitter N2 - Background: Early identification is critical for mitigating the impact of medicine shortages on patients. The internet, specifically social media, is an emerging source of health data. Objective: This study aimed to explore whether a routine analysis of data from the Twitter social network can detect signals of a medicine shortage and serve as an early warning system and, if so, for which medicines or patient groups. Methods: Medicine shortages between January 31 and December 1, 2019, were collected from the Dutch pharmacists? society?s national catalog Royal Dutch Pharmacists Association (KNMP) Farmanco. Posts on these shortages were collected by searching for the name, the active pharmaceutical ingredient, or the first word of the brand name of the medicines in shortage. Posts were then selected based on relevant keywords that potentially indicated a shortage and the percentage of shortages with at least 1 post was calculated. The first posts per shortage were analyzed for their timing (median number of days, including the IQR) versus the national catalog, also stratified by disease and medicine characteristics. The content of the first post per shortage was analyzed descriptively for its reporting stakeholder and the nature of the post. Results: Of the 341 medicine shortages, 102 (29.9%) were mentioned on Twitter. Of these 102 shortages, 18 (5.3% of the total) were mentioned prior to or simultaneous to publication by KNMP Farmanco. Only 4 (1.2%) of these were mentioned on Twitter more than 14 days before. On average, posts were published with a median delay of 37 (IQR 7-81) days to publication by KNMP Farmanco. Shortages mentioned on Twitter affected a greater number of patients and lasted longer than those that were not mentioned. We could not conclusively relate either the presence or absence on Twitter to a disease area or route of administration of the medicine in shortage. The first posts on the 102 shortages were mainly published by patients (n=51, 50.0%) and health care professionals (n=46, 45.1%). We identified 8 categories of nature of content. Sharing personal experience (n=44, 43.1%) was the most common category. Conclusions: The Twitter social network is not a suitable early warning system for medicine shortages. Twitter primarily echoes already-known information rather than spreads new information. However, Twitter or potentially any other social media platform provides the opportunity for future qualitative research in the increasingly important field of medicine shortages that investigates how a larger population of patients is affected by shortages. UR - https://www.jmir.org/2024/1/e51317 UR - http://dx.doi.org/10.2196/51317 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51317 ER - TY - JOUR AU - Varaona, Andrea AU - Alvarez-Mon, Angel Miguel AU - Serrano-Garcia, Irene AU - Díaz-Marsá, Marina AU - Looi, L. Jeffrey C. AU - Molina-Ruiz, M. Rosa PY - 2024/8/1 TI - Exploring the Relationship Between Instagram Use and Self-Criticism, Self-Compassion, and Body Dissatisfaction in the Spanish Population: Observational Study JO - J Med Internet Res SP - e51957 VL - 26 KW - Instagram KW - self-compassion KW - self-esteem KW - self-criticism KW - self-worth KW - body dissatisfaction KW - dissatisfaction KW - satisfaction KW - appearance KW - psychological KW - social media KW - body KW - mental health KW - mental wellbeing KW - Spain KW - Spanish KW - Hispanic KW - depression KW - depressive KW - usage KW - correlation KW - association N2 - Background: The widespread use of online social networks, particularly among the younger demographic, has catalyzed a growing interest in exploring their influence on users? psychological well-being. Instagram (Meta), a visually oriented platform, has garnered significant attention. Prior research has consistently indicated that Instagram usage correlates with heightened levels of perfectionism, body dissatisfaction, and diminished self-esteem. Perfectionism is closely linked to self-criticism, which entails an intense self-scrutiny and is often associated with various psychopathologies. Conversely, self-compassion has been linked to reduced levels of perfectionism and stress, while fostering greater positive affect and overall life satisfaction. Objective: This study investigates the relationship between Instagram usage (time of use and content exposure) and users? levels of self-compassion, self-criticism, and body dissatisfaction. Methods: This study comprised 1051 adult participants aged between 18 and 50 years, either native to Spain or residing in the country for at least a decade. Each participant completed a tailored questionnaire on Instagram usage, along with abbreviated versions of the Self-Compassion Scale, the Body Shape Questionnaire, and the Depressive Experiences Questionnaire, spanning from January 23 to February 25, 2022. Results: A positive correlation was observed between daily Instagram usage and self-criticism scores. Participants of all age groups who spent over 3 hours per day on Instagram exhibited higher self-criticism scores than users who spent less than 1 hour or between 1 and 3 hours per day. Contrary to previous findings, no significant relationship was detected between Instagram usage time and levels of self-compassion or body dissatisfaction. Furthermore, content centered around physical appearance exhibited a positive correlation with self-criticism and body dissatisfaction scores. Among younger participants (aged 18-35 years), those who primarily viewed beauty or fashion content reported higher self-criticism scores than those consuming science-related content. However, this association was not significant for participants aged 35-50 years. Conversely, individuals who predominantly engaged with sports or fitness or family or friends content exhibited higher levels of body dissatisfaction than those focusing on science-related content. No significant associations were observed between self-compassion scores and daily Instagram usage or most-viewed content categories. Conclusions: The findings of this study underscore the considerable impact of Instagram usage on self-criticism and body dissatisfaction?2 variables known to influence users? psychological well-being and be associated with various symptoms and psychological disorders. UR - https://www.jmir.org/2024/1/e51957 UR - http://dx.doi.org/10.2196/51957 UR - http://www.ncbi.nlm.nih.gov/pubmed/39088263 ID - info:doi/10.2196/51957 ER - TY - JOUR AU - Moffett, W. Kenneth AU - Marshall, C. Michael AU - Kim, C. Jae-Eun AU - Dahlen, Heather AU - Denison, Benjamin AU - Kranzler, C. Elissa AU - Meaney, Morgan AU - Hoffman, Blake AU - Pavisic, Ivica AU - Hoffman, Leah PY - 2024/7/29 TI - Analyzing Google COVID-19 Vaccine Intent Search Trends and Vaccine Readiness in the United States: Panel Data Study JO - Online J Public Health Inform SP - e55422 VL - 16 KW - information-seeking behavior KW - COVID-19 KW - internet use KW - vaccination KW - vaccine hesitancy N2 - Background: Factors such as anxiety, worry, and perceptions of insufficient knowledge about a topic motivate individuals to seek web-based health information to guide their health-related decision-making. These factors converged during the COVID-19 pandemic and were linked to COVID-19 vaccination decision-making. While research shows that web-based search relevant to COVID-19 was associated with subsequent vaccine uptake, less is known about COVID-19 vaccine intent search (which assesses vaccine availability, accessibility, and eligibility) as a signal of vaccine readiness. Objective: To increase knowledge about vaccine intent search as a signal of vaccine readiness, we investigated the relationship between COVID-19 vaccine readiness and COVID-19 vaccine intent relative search volume on Google. Methods: We compiled panel data from several data sources in all US counties between January 2021 and April 2023, a time during which those with primary COVID-19 vaccinations increased from <57,000 to >230 million adults. We estimated a random effects generalized least squares regression model with time-fixed effects to assess the relationship between county-level COVID-19 vaccine readiness and COVID-19 vaccine intent relative search volume. We controlled for health care capacity, per capita COVID-19 cases and vaccination doses administered, and sociodemographic indicators. Results: The county-level proportions of unvaccinated adults who reported that they would wait and see before getting a COVID-19 vaccine were positively associated with COVID-19 vaccine intent relative search volume (?=9.123; Z=3.59; P<.001). The county-level proportions of vaccine-enthusiast adults, adults who indicated they were either already vaccinated with a primary COVID-19 vaccine series or planned to complete the vaccine series soon, were negatively associated with COVID-19 vaccine intent relative search volume (?=?10.232; Z=?7.94; P<.001). However, vaccine intent search was higher in counties with high proportions of people who decided to wait and see and lower in counties with high proportions of vaccine enthusiasts. Conclusions: During this period of steep increase in COVID-19 vaccination, web-based search may have signaled differences in county-level COVID-19 vaccine readiness. More vaccine intent searches occurred in high wait-and-see counties, whereas fewer vaccine intent searches occurred in high vaccine-enthusiast counties. Considering previous research that identified a relationship between vaccine intent search and subsequent vaccine uptake, these findings suggest that vaccine intent search aligned with people?s transition from the wait-and-see stage to the vaccine-enthusiast stage. The findings also suggest that web-based search trends may signal localized changes in information seeking and decision-making antecedent to vaccine uptake. Changes in web-based search trends illuminate opportunities for governments and other organizations to strategically allocate resources to increase vaccine uptake. Resource use is part of the larger public policy decisions that influence vaccine uptake, such as efforts to educate the public during evolving public health crises, including future pandemics. UR - https://ojphi.jmir.org/2024/1/e55422 UR - http://dx.doi.org/10.2196/55422 UR - http://www.ncbi.nlm.nih.gov/pubmed/39073868 ID - info:doi/10.2196/55422 ER - TY - JOUR AU - Pascual-Ferrá, Paola AU - Alperstein, Neil AU - Burleson, Julia AU - Jamison, M. Amelia AU - Bhaktaram, Ananya AU - Rath, Sidharth AU - Ganjoo, Rohini AU - Mohanty, Satyanarayan AU - Barnett, J. Daniel AU - Rimal, N. Rajiv PY - 2024/7/26 TI - Assessing Message Deployment During Public Health Emergencies Through Social Media: Empirical Test of Optimizing Content for Effective Dissemination JO - J Med Internet Res SP - e50871 VL - 26 KW - message testing KW - web-based communication KW - user engagement KW - vaccine communication KW - methodology KW - Meta KW - Facebook KW - advertising KW - infodemic KW - communication KW - infodemiology KW - social media advertising tool KW - social media KW - audience KW - engagement KW - rapid message testing at scale KW - mobile phone N2 - Background: During an infodemic, timely, reliable, and accessible information is crucial to combat the proliferation of health misinformation. While message testing can provide vital information to make data-informed decisions, traditional methods tend to be time- and resource-intensive. Recognizing this need, we developed the rapid message testing at scale (RMTS) approach to allow communicators to repurpose existing social media advertising tools and understand the full spectrum of audience engagement. Objective: We had two main objectives: (1) to demonstrate the use of the RMTS approach for message testing, especially when resources and time are limited, and (2) to propose and test the efficacy of an outcome variable that measures engagement along a continuum of viewing experience. Methods: We developed 12 versions of a single video created for a vaccine confidence project in India. We manipulated video length, aspect ratio, and use of subtitles. The videos were tested across 4 demographic groups (women or men, younger or older). We assessed user engagement along a continuum of viewing experience: obtaining attention, sustaining attention, conveying the message, and inspiring action. These were measured by the percentage of video watched and clicks on the call-to-action link. Results: The video advertisements were placed on Facebook for over 4 consecutive days at the cost of US $450 and garnered a total of 3.34 million impressions. Overall, we found that the best-performing video was the shorter version in portrait aspect ratio and without subtitles. There was a significant but small association between the length of the video and users? level of engagement at key points along the continuum of viewing experience (N=1,032,888; ?24=48,261.97; P<.001; V=.22). We found that for the longer video, those with subtitles held viewers longer after 25% video watch time than those without subtitles (n=15,597; ?21=7.33; P=.007; V=.02). While we found some significant associations between the aspect ratio, the use of subtitles, and the number of users watching the video and clicking on the call-to-action link, the effect size for those were extremely small. Conclusions: This test served as a proof of concept for the RMTS approach. We obtained rapid feedback on formal message attributes from a very large sample. The results of this test reinforce the need for platform-specific tailoring of communications. While our data showed a general preference for a short video in portrait orientation and without subtitles among our target audiences on Facebook, that may not necessarily be the case in other social media platforms such as YouTube or TikTok, where users go primarily to watch videos. RMTS testing highlights nuances that communication professionals can address instead of being limited to a ?one size fits all? approach. UR - https://www.jmir.org/2024/1/e50871 UR - http://dx.doi.org/10.2196/50871 UR - http://www.ncbi.nlm.nih.gov/pubmed/38861266 ID - info:doi/10.2196/50871 ER - TY - JOUR AU - Roschke, Kristy AU - Koskan, M. Alexis AU - Sivanandam, Shalini AU - Irby, Jonathan PY - 2024/7/24 TI - Partisan Media, Trust, and Media Literacy: Regression Analysis of Predictors of COVID-19 Knowledge JO - JMIR Form Res SP - e53904 VL - 8 KW - COVID-19 KW - misinformation KW - media literacy KW - news consumption KW - institutional trust KW - media KW - trust KW - prevention KW - control KW - health care professional KW - health care N2 - Background: The COVID-19 pandemic was a devastating public health event that spurred an influx of misinformation. The increase in questionable health content was aided by the speed and scale of digital and social media and certain news agencies? and politicians? active dissemination of misinformation about the virus. The popularity of certain COVID-19 myths created confusion about effective health protocols and impacted trust in the health care and government sectors deployed to manage the pandemic. Objective: This study explored how people?s information habits, their level of institutional trust, the news media outlets they consume and the technologies in which they access it, and their media literacy skills influenced their COVID-19 knowledge. Methods: We administered a web-based survey using Amazon Mechanical Turk (MTurk) to assess US adults? (n=1498) COVID-19 knowledge, media and news habits, media literacy skills, and trust in government and health-related institutions. The data were analyzed using a hierarchical linear regression to examine the association between trust, media literacy, news use, and COVID-19 knowledge. Results: The regression model of demographic variables, political affiliation, trust in institutions, media literacy, and the preference for watching Fox or CNN was statistically significant (R2=0.464; F24,1434=51.653; P<.001; adjusted R2=0.455) in predicting COVID-19 knowledge scores. People who identified as politically conservative, watched Fox News, and reported lower levels of institutional trust and media literacy, scored lower on COVID-19 knowledge questions than those who identified as politically liberal, did not watch Fox News and reported higher levels of institutional trust and media literacy. Conclusions: This study suggests that the media outlets people turn to, their trust in institutions, and their perceived degree of agency to discern credible information can impact people?s knowledge of COVID-19, which has potential implications for managing communication in other public health events. UR - https://formative.jmir.org/2024/1/e53904 UR - http://dx.doi.org/10.2196/53904 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53904 ER - TY - JOUR AU - Jaiswal, Aditi AU - Shah, Aekta AU - Harjadi, Christopher AU - Windgassen, Erik AU - Washington, Peter PY - 2024/7/17 TI - Addendum: Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study JO - JMIR Form Res SP - e59349 VL - 8 UR - https://formative.jmir.org/2024/1/e59349 UR - http://dx.doi.org/10.2196/59349 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59349 ER - TY - JOUR AU - Liu, Pinxin AU - Lou, Xubin AU - Xie, Zidian AU - Shang, Ce AU - Li, Dongmei PY - 2024/7/11 TI - Public Perceptions and Discussions of the US Food and Drug Administration's JUUL Ban Policy on Twitter: Observational Study JO - JMIR Form Res SP - e51327 VL - 8 KW - e-cigarettes KW - JUUL KW - Twitter KW - deep learning KW - FDA KW - Food and Drug Administration KW - vape KW - vaping KW - smoking KW - social media KW - regulation N2 - Background: On June 23, 2022, the US Food and Drug Administration announced a JUUL ban policy, to ban all vaping and electronic cigarette products sold by Juul Labs. Objective: This study aims to understand public perceptions and discussions of this policy using Twitter (subsequently rebranded as X) data. Methods: Using the Twitter streaming application programming interface, 17,007 tweets potentially related to the JUUL ban policy were collected between June 22, 2022, and July 25, 2022. Based on 2600 hand-coded tweets, a deep learning model (RoBERTa) was trained to classify all tweets into propolicy, antipolicy, neutral, and irrelevant categories. A deep learning model (M3 model) was used to estimate basic demographics (such as age and gender) of Twitter users. Furthermore, major topics were identified using latent Dirichlet allocation modeling. A logistic regression model was used to examine the association of different Twitter users with their attitudes toward the policy. Results: Among 10,480 tweets related to the JUUL ban policy, there were similar proportions of propolicy and antipolicy tweets (n=2777, 26.5% vs n=2666, 25.44%). Major propolicy topics included ?JUUL causes youth addition,? ?market surge of JUUL,? and ?health effects of JUUL.? In contrast, major antipolicy topics included ?cigarette should be banned instead of JUUL,? ?against the irrational policy,? and ?emotional catharsis.? Twitter users older than 29 years were more likely to be propolicy (have a positive attitude toward the JUUL ban policy) than those younger than 29 years. Conclusions: Our study showed that the public showed different responses to the JUUL ban policy, which varies depending on the demographic characteristics of Twitter users. Our findings could provide valuable information to the Food and Drug Administration for future electronic cigarette and other tobacco product regulations. UR - https://formative.jmir.org/2024/1/e51327 UR - http://dx.doi.org/10.2196/51327 UR - http://www.ncbi.nlm.nih.gov/pubmed/38990633 ID - info:doi/10.2196/51327 ER - TY - JOUR AU - Neely, Stephen AU - Witkowski, Kaila PY - 2024/7/9 TI - Social Media Authentication and Users? Assessments of Health Information: Random Assignment Survey Experiment JO - JMIR Form Res SP - e52503 VL - 8 KW - social media KW - verification markers KW - vaccine efficacy KW - health communication KW - trust N2 - Background: In an effort to signal the authenticity of user accounts, social networking sites (SNSs) such as Facebook and X, formerly known as Twitter, use visual heuristics (blue checkmarks) to signify whether accounts are verified. While these verification badges are generally well recognized (and often coveted) by SNS users, relatively little is known about how they affect users? perceptions of accuracy or their likelihood of engaging with web-based information. This is particularly true in the case of information posted by medical experts and health care professionals. Objective: This study aims to use an experimental survey design to assess the effect of these verification badges on SNS users? assessments of information accuracy as well as their proclivity to recirculate health information or follow verified medical experts in their social network. Methods: A survey experiment using random assignment was conducted on a representative sample of 534 adult SNS users in Florida, United States. A total of 2 separate experimental scenarios exposed users to vaccine-related posts from verified medical experts on X. In each case, the original post contained a platform-issued verification badge (treatment group), which was subsequently edited out of the image as an experimental control. For each scenario, respondents were randomly assigned to either the treatment or control group, and responses to 3 follow-up questions were assessed through a series of chi-square analyses and 2 logit regression models. Responses were fielded using a stratified quota sampling approach to ensure representativeness of the state?s population based on age, sex, race, ethnicity, and political affiliation. Results: Users? assessments of information accuracy were not significantly impacted by the presence or absence of verification badges, and users exposed to the experimental treatment (verification badge) were not any more likely to repost the message or follow the author. While verification badges did not influence users? assessments or subsequent behaviors, reliance on social media for health-related information and political affiliation were substantial predictors of accuracy assessments in both experimental scenarios. In scenario 1, which included a post addressing COVID-19 vaccine efficacy, users who relied on social media ?a great deal? for health information were 2 times more likely to assess the post as accurate (odds ratio 2.033, 95% CI 1.129-3.661; P=.01). In scenario 2, which included a post about measles vaccines, registered Republicans were nearly 6 times less likely to assess the post as accurate (odds ratio 0.171, 95% CI 0.097-0.299; P<.001). Conclusions: For health professionals and medical experts wishing to leverage social networks to combat misinformation and spread reliable health-related content, account verification appears to offer little by way of added value. On the basis of prior research, other heuristics and communication strategies are likely to yield better results. UR - https://formative.jmir.org/2024/1/e52503 UR - http://dx.doi.org/10.2196/52503 UR - http://www.ncbi.nlm.nih.gov/pubmed/38980714 ID - info:doi/10.2196/52503 ER - TY - JOUR AU - Terada, Marina AU - Okuhara, Tsuyoshi AU - Yokota, Rie AU - Kiuchi, Takahiro AU - Murakami, Kentaro PY - 2024/6/20 TI - Nutrients and Foods Recommended for Blood Pressure Control on Twitter in Japan: Content Analysis JO - J Med Internet Res SP - e49077 VL - 26 KW - Twitter KW - food KW - nutrition KW - misinformation KW - salt KW - content analysis KW - hypertension KW - blood pressure KW - sodium KW - salt reduction N2 - Background: Management and prevention of hypertension are important public health issues. Healthy dietary habits are one of the modifiable factors. As Twitter (subsequently rebranded X) is a digital platform that can influence public eating behavior, there is a knowledge gap regarding the information about foods and nutrients recommended for blood pressure control and who disseminates them on Twitter. Objective: This study aimed to investigate the nature of the information people are exposed to on Twitter regarding nutrients and foods for blood pressure control. Methods: A total of 147,898 Japanese tweets were extracted from January 1, 2022, to December 31, 2022. The final sample of 2347 tweets with at least 1 retweet was manually coded into categories of food groups, nutrients, user characteristics, and themes. The number and percentage of tweets, retweets, and themes in each category were calculated. Results: Of the 2347 tweets, 80% (n=1877) of tweets mentioned foods, which were categorized into 17 different food groups. Seasonings and spices, including salt, were most frequently mentioned (1356/1877, 72.2%). This was followed by vegetable and fruit groups. The 15 kinds of nutrients were mentioned in 1566 tweets, with sodium being the largest proportion at 83.1% (n=1301), followed by potassium at 8.4% (n=132). There was misinformation regarding salt intake for hypertension, accounting for 40.8% (n=531) of tweets referring to salt, including recommendations for salt intake to lower blood pressure. In total, 75% (n=21) of tweets from ?doctors? mentioned salt reduction is effective for hypertension control, while 31.1% (n=74) of tweets from ?health, losing weight, and beauty-related users,? 25.9% (n=429) of tweets from ?general public,? and 23.5% (n=4) tweets from ?dietitian or registered dietitian? denied salt reduction for hypertension. The antisalt reduction tweets accounted for 31.5% (n=106) of the most disseminated tweets related to nutrients and foods for blood pressure control. Conclusions: The large number of tweets in this study indicates a high interest in nutrients and foods for blood pressure control. Misinformation asserting antisalt reduction was posted primarily by the general public and self-proclaimed health experts. The number of tweets from nutritionists, registered dietitians, and doctors who were expected to correct misinformation and promote salt reduction was relatively low, and their messages were not always positive toward salt reduction. There is a need for communication strategies to combat misinformation, promote correct information on salt reduction, and train health care professionals to effectively communicate evidence-based information on this topic. UR - https://www.jmir.org/2024/1/e49077 UR - http://dx.doi.org/10.2196/49077 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49077 ER - TY - JOUR AU - Karapetiantz, Pierre AU - Audeh, Bissan AU - Redjdal, Akram AU - Tiffet, Théophile AU - Bousquet, Cédric AU - Jaulent, Marie-Christine PY - 2024/6/18 TI - Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study JO - J Med Internet Res SP - e46176 VL - 26 KW - pharmacovigilance KW - social media KW - scraper KW - natural language processing KW - signal detection KW - graphical user interface N2 - Background: To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media?s potential remains largely untapped in real-world scenarios. Objective: The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. Methods: To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums? posts extraction, (2) web forums? posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. Results: Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. Conclusions: We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events. UR - https://www.jmir.org/2024/1/e46176 UR - http://dx.doi.org/10.2196/46176 UR - http://www.ncbi.nlm.nih.gov/pubmed/38888956 ID - info:doi/10.2196/46176 ER - TY - JOUR AU - Levin-Rector, Alison AU - Kulldorff, Martin AU - Peterson, R. Eric AU - Hostovich, Scott AU - Greene, K. Sharon PY - 2024/6/11 TI - Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks JO - JMIR Public Health Surveill SP - e50653 VL - 10 KW - communicable diseases KW - disease outbreaks KW - disease surveillance KW - epidemiology KW - infectious disease KW - outbreak detection KW - public health practice KW - SaTScan KW - spatiotemporal KW - urban health UR - https://publichealth.jmir.org/2024/1/e50653 UR - http://dx.doi.org/10.2196/50653 UR - http://www.ncbi.nlm.nih.gov/pubmed/38861711 ID - info:doi/10.2196/50653 ER - TY - JOUR AU - Wang, Hanjing AU - Li, Yupeng AU - Ning, Xuan PY - 2024/6/6 TI - News Coverage of the COVID-19 Pandemic on Social Media and the Public?s Negative Emotions: Computational Study JO - J Med Internet Res SP - e48491 VL - 26 KW - web news coverage KW - emotions KW - social media KW - Facebook KW - COVID-19 N2 - Background: Social media has become an increasingly popular and critical tool for users to digest diverse information and express their perceptions and attitudes. While most studies endeavor to delineate the emotional responses of social media users, there is limited research exploring the factors associated with the emergence of emotions, particularly negative ones, during news consumption. Objective: We aim to first depict the web coverage by news organizations on social media and then explore the crucial elements of news coverage that trigger the public?s negative emotions. Our findings can act as a reference for responsible parties and news organizations in times of crisis. Methods: We collected 23,705 Facebook posts with 1,019,317 comments from the public pages of representative news organizations in Hong Kong. We used text mining techniques, such as topic models and Bidirectional Encoder Representations from Transformers, to analyze news components and public reactions. Beyond descriptive analysis, we used regression models to shed light on how news coverage on social media is associated with the public?s negative emotional responses. Results: Our results suggest that occurrences of issues regarding pandemic situations, antipandemic measures, and supportive actions are likely to reduce the public?s negative emotions, while comments on the posts mentioning the central government and the Government of Hong Kong reveal more negativeness. Negative and neutral media tones can alleviate the rage and interact with the subjects and issues in the news to affect users? negative emotions. Post length is found to have a curvilinear relationship with users? negative emotions. Conclusions: This study sheds light on the impacts of various components of news coverage (issues, subjects, media tone, and length) on social media on the public?s negative emotions (anger, fear, and sadness). Our comprehensive analysis provides a reference framework for efficient crisis communication for similar pandemics at present or in the future. This research, although first extending the analysis between the components of news coverage and negative user emotions to the scenario of social media, echoes previous studies drawn from traditional media and its derivatives, such as web newspapers. Although the era of COVID-19 pandemic gradually brings down the curtain, the commonality of this research and previous studies also contributes to establishing a clearer territory in the field of health crises. UR - https://www.jmir.org/2024/1/e48491 UR - http://dx.doi.org/10.2196/48491 UR - http://www.ncbi.nlm.nih.gov/pubmed/38843521 ID - info:doi/10.2196/48491 ER - TY - JOUR AU - Lu, Qianfeng AU - Schulz, Johannes Peter PY - 2024/6/6 TI - Physician Perspectives on Internet-Informed Patients: Systematic Review JO - J Med Internet Res SP - e47620 VL - 26 KW - internet-informed patients KW - physician-patient communication KW - health information?seeking KW - misinformation KW - digital health N2 - Background: The internet has become a prevalent source of health information for patients. However, its accuracy and relevance are often questionable. While patients seek physicians? expertise in interpreting internet health information, physicians? perspectives on patients? information-seeking behavior are less explored. Objective: This review aims to understand physicians? perceptions of patients? internet health information-seeking behavior as well as their communication strategies and the challenges and needs they face with internet-informed patients. Methods: An initial search in PubMed, Scopus, CINAHL, Communication and Mass Media Complete, and PsycINFO was conducted to collect studies published from January 1990 to August 1, 2022. A subsequent search on December 24, 2023, targeted recent studies published after the initial search cutoff date. Two reviewers independently performed title, abstract, and full-text screening, adhering to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement guidelines. Thematic analysis was then used to identify key themes and systematically categorize evidence from both qualitative and quantitative studies under these themes. Results: A total of 22 qualifying articles were identified after the search and screening process. Physicians were found to hold diverse views on patients? internet searches, which can be viewed as a continuous spectrum of opinions ranging from positive to negative. While some physicians leaned distinctly toward either positive or negative perspectives, a significant number expressed more balanced views. These physicians recognized both the benefits, such as increased patient health knowledge and informed decision-making, and the potential harms, including misinformation and the triggering of negative emotions, such as patient anxiety or confusion, associated with patients? internet health information seeking. Two communicative strategies were identified: the participative and defensive approaches. While the former seeks to guide internet-informed patients to use internet information with physicians? expertise, the latter aims to discourage patients from using the internet to seek health information. Physicians? perceptions were linked to their strategies: those holding positive views tended to adopt a participative approach, while those with negative views favored a defensive strategy. Some physicians claimed to shift between the 2 approaches depending on their interaction with a certain patient. We also identified several challenges and needs of physicians in dealing with internet-informed patients, including the time pressure to address internet-informed patient demands, a lack of structured training, and being uninformed about trustworthy internet sites that can be recommended to internet-informed patients. Conclusions: This review highlights the diverse perceptions that physicians hold toward internet-informed patients, as well as the interplay between their perceptions, communication strategies, and their interactions with individual patients. Incorporating elements into the medical teaching curriculum that introduce physicians to reliable internet health resources for patient guidance, coupled with providing updates on technological advancements, could be instrumental in equipping physicians to more effectively manage internet-informed patients. Trial Registration: PROSPERO CRD42022356317; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=356317 UR - https://www.jmir.org/2024/1/e47620 UR - http://dx.doi.org/10.2196/47620 UR - http://www.ncbi.nlm.nih.gov/pubmed/38842920 ID - info:doi/10.2196/47620 ER - TY - JOUR AU - Roberts-Lewis, Sarah AU - Baxter, Helen AU - Mein, Gill AU - Quirke-McFarlane, Sophia AU - Leggat, J. Fiona AU - Garner, Hannah AU - Powell, Martha AU - White, Sarah AU - Bearne, Lindsay PY - 2024/6/5 TI - Examining the Effectiveness of Social Media for the Dissemination of Research Evidence for Health and Social Care Practitioners: Systematic Review and Meta-Analysis JO - J Med Internet Res SP - e51418 VL - 26 KW - social media KW - dissemination KW - health care KW - social care KW - research evidence KW - practitioners KW - effectiveness KW - meta-analysis KW - systematic review KW - randomized controlled trial KW - RCT N2 - Background: Social media use has potential to facilitate the rapid dissemination of research evidence to busy health and social care practitioners. Objective: This study aims to quantitatively synthesize evidence of the between- and within-group effectiveness of social media for dissemination of research evidence to health and social care practitioners. It also compared effectiveness between different social media platforms, formats, and strategies. Methods: We searched electronic databases for articles in English that were published between January 1, 2010, and January 10, 2023, and that evaluated social media interventions for disseminating research evidence to qualified, postregistration health and social care practitioners in measures of reach, engagement, direct dissemination, or impact. Screening, data extraction, and risk of bias assessments were carried out by at least 2 independent reviewers. Meta-analyses of standardized pooled effects were carried out for between- and within-group effectiveness of social media and comparisons between platforms, formats, and strategies. Certainty of evidence for outcomes was assessed using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework. Results: In total, 50 mixed-quality articles that were heterogeneous in design and outcome were included (n=9, 18% were randomized controlled trials [RCTs]). Reach (measured in number of practitioners, impressions, or post views) was reported in 26 studies. Engagement (measured in likes or post interactions) was evaluated in 21 studies. Direct dissemination (measured in link clicks, article views, downloads, or altmetric attention score) was analyzed in 23 studies (8 RCTs). Impact (measured in citations or measures of thinking and practice) was reported in 13 studies. Included studies almost universally indicated effects in favor of social media interventions, although effect sizes varied. Cumulative evidence indicated moderate certainty of large and moderate between-group effects of social media interventions on direct dissemination (standardized mean difference [SMD] 0.88; P=.02) and impact (SMD 0.76; P<.001). After social media interventions, cumulative evidence showed moderate certainty of large within-group effects on reach (SMD 1.99; P<.001), engagement (SMD 3.74; P<.001), and direct dissemination (SMD 0.82; P=.004) and low certainty of a small within-group effect on impacting thinking or practice (SMD 0.45; P=.02). There was also evidence for the effectiveness of using multiple social media platforms (including Twitter, subsequently rebranded X; and Facebook), images (particularly infographics), and intensive social media strategies with frequent, daily posts and involving influential others. No included studies tested the dissemination of research evidence to social care practitioners. Conclusions: Social media was effective for disseminating research evidence to health care practitioners. More intense social media campaigns using specific platforms, formats, and strategies may be more effective than less intense interventions. Implications include recommendations for effective dissemination of research evidence to health care practitioners and further RCTs in this field, particularly investigating the dissemination of social care research. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42022378793; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=378793 International Registered Report Identifier (IRRID): RR2-10.2196/45684 UR - https://www.jmir.org/2024/1/e51418 UR - http://dx.doi.org/10.2196/51418 UR - http://www.ncbi.nlm.nih.gov/pubmed/38838330 ID - info:doi/10.2196/51418 ER - TY - JOUR AU - Nickel, Brooke AU - Heiss, Raffael AU - Shih, Patti AU - Gram, Grundtvig Emma AU - Copp, Tessa AU - Taba, Melody AU - Moynihan, Ray AU - Zadro, Joshua PY - 2024/6/4 TI - Social Media Promotion of Health Tests With Potential for Overdiagnosis or Overuse: Protocol for a Content Analysis JO - JMIR Res Protoc SP - e56899 VL - 13 KW - social media KW - influencers KW - tests KW - overdiagnosis KW - overuse KW - evidence-based medicine KW - promotion N2 - Background: In recent years, social media have emerged as important spaces for commercial marketing of health tests, which can be used for the screening and diagnosis of otherwise generally healthy people. However, little is known about how health tests are promoted on social media, whether the information provided is accurate and balanced, and if there is transparency around conflicts of interest. Objective: This study aims to understand and quantify how social media is being used to discuss or promote health tests with the potential for overdiagnosis or overuse to generally healthy people. Methods: Content analysis of social media posts on the anti-Mullerian hormone test, whole-body magnetic resonance imaging scan, multicancer early detection, testosterone test, and gut microbe test from influential international social media accounts on Instagram and TikTok. The 5 tests have been identified as having the following criteria: (1) there are evidence-based concerns about overdiagnosis or overuse, (2) there is evidence or concerns that the results of tests do not lead to improved health outcomes for generally healthy people and may cause harm or waste, and (3) the tests are being promoted on social media to generally healthy people. English language text-only posts, images, infographics, articles, recorded videos including reels, and audio-only posts are included. Posts from accounts with <1000 followers as well as stories, live videos, and non-English posts are excluded. Using keywords related to the test, the top posts were searched and screened until there were 100 eligible posts from each platform for each test (total of 1000 posts). Data from the caption, video, and on-screen text are being summarized and extracted into a Microsoft Excel (Microsoft Corporation) spreadsheet and included in the analysis. The analysis will take a combined inductive approach when generating key themes and a deductive approach using a prespecified framework. Quantitative data will be analyzed in Stata SE (version 18.0; Stata Corp). Results: Data on Instagram and TikTok have been searched and screened. Analysis has now commenced. The findings will be disseminated via publications in peer-reviewed international medical journals and will also be presented at national and international conferences in late 2024 and 2025. Conclusions: This study will contribute to the limited evidence base on the nature of the relationship between social media and the problems of overdiagnosis and overuse of health care services. This understanding is essential to develop strategies to mitigate potential harm and plan solutions, with the aim of helping to protect members of the public from being marketed low-value tests, becoming patients unnecessarily, and taking resources away from genuine needs within the health system. International Registered Report Identifier (IRRID): DERR1-10.2196/56899 UR - https://www.researchprotocols.org/2024/1/e56899 UR - http://dx.doi.org/10.2196/56899 UR - http://www.ncbi.nlm.nih.gov/pubmed/38833693 ID - info:doi/10.2196/56899 ER - TY - JOUR AU - Azizi, Mehrnoosh AU - Jamali, Akbar Ali AU - Spiteri, J. Raymond PY - 2024/6/4 TI - Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach JO - JMIR Form Res SP - e49562 VL - 8 KW - machine learning KW - dementia KW - Alzheimer disease KW - COVID-19 KW - X (Twitter) KW - natural language processing N2 - Background: During the pandemic, patients with dementia were identified as a vulnerable population. X (formerly Twitter) became an important source of information for people seeking updates on COVID-19, and, therefore, identifying posts (formerly tweets) relevant to dementia can be an important support for patients with dementia and their caregivers. However, mining and coding relevant posts can be daunting due to the sheer volume and high percentage of irrelevant posts. Objective: The objective of this study was to automate the identification of posts relevant to dementia and COVID-19 using natural language processing and machine learning (ML) algorithms. Methods: We used a combination of natural language processing and ML algorithms with manually annotated posts to identify posts relevant to dementia and COVID-19. We used 3 data sets containing more than 100,000 posts and assessed the capability of various algorithms in correctly identifying relevant posts. Results: Our results showed that (pretrained) transfer learning algorithms outperformed traditional ML algorithms in identifying posts relevant to dementia and COVID-19. Among the algorithms tested, the transfer learning algorithm A Lite Bidirectional Encoder Representations from Transformers (ALBERT) achieved an accuracy of 82.92% and an area under the curve of 83.53%. ALBERT substantially outperformed the other algorithms tested, further emphasizing the superior performance of transfer learning algorithms in the classification of posts. Conclusions: Transfer learning algorithms such as ALBERT are highly effective in identifying topic-specific posts, even when trained with limited or adjacent data, highlighting their superiority over other ML algorithms and applicability to other studies involving analysis of social media posts. Such an automated approach reduces the workload of manual coding of posts and facilitates their analysis for researchers and policy makers to support patients with dementia and their caregivers and other vulnerable populations. UR - https://formative.jmir.org/2024/1/e49562 UR - http://dx.doi.org/10.2196/49562 UR - http://www.ncbi.nlm.nih.gov/pubmed/38833288 ID - info:doi/10.2196/49562 ER - TY - JOUR AU - Pan, Peng AU - Yu, Changhua AU - Li, Tao AU - Dai, Tingting AU - Tian, Hanhan AU - Xiong, Yaozu AU - Lv, Jie AU - Hu, Xiaochu AU - Ma, Weidong AU - Yin, Wenda PY - 2024/5/30 TI - Evaluating the Quality of Cancer-Related WeChat Public Accounts: Cross-Sectional Study JO - JMIR Cancer SP - e52156 VL - 10 KW - cancer KW - big data KW - social media KW - health literacy KW - WeChat KW - China KW - public health N2 - Background: WeChat (Tencent) is one of the most important information sources for Chinese people. Relevantly, various health-related data are constantly transmitted among WeChat users. WeChat public accounts (WPAs) for health are rapidly emerging. Health-related WeChat public accounts have a significant impact on public health. Because of the rise in web-based health-seeking behavior, the general public has grown accustomed to obtaining cancer information from WPAs. Although WPAs make it easy for people to obtain health information, the quality of the information is questionable. Objective: This study aims to assess the quality and suitability of cancer-related WeChat public accounts (CWPAs). Methods: The survey was conducted from February 1 to 28, 2023. Based on the WPA monthly list provided by Qingbo Big Data, 28 CWPAs in the WeChat communication index were selected as the survey sample. Quality assessment of the included CWPAs was performed using the HONcode instrument. Furthermore, suitability was measured by using the Suitability Assessment of Materials. A total of 2 researchers conducted the evaluations independently. Results: Of the 28 CWPAs, 12 (43%) were academic and 16 (57%) were commercial. No statistical difference was found regarding the HONcode scores between the 2 groups (P=.96). The quality of the academic and commercial CWPAs evaluated using the HONcode instrument demonstrated mean scores of 5.58 (SD 2.02) and 5.63 (SD 2.16), respectively, corresponding to a moderate class. All CWPAs? compliance with the HONcode principles was unsatisfactory. A statistically significant difference between the 2 groups was observed in the Suitability Assessment of Materials scores (P=.04). The commercial WPAs reached an overall 55.1% (SD 5.5%) score versus the 50.2% (SD 6.4%) score reached by academic WPAs. The suitability of academic and commercial CWPAs was considered adequate. Conclusions: This study revealed that CWPAs are not sufficiently credible. WPA owners must endeavor to create reliable health websites using approved tools such as the HONcode criteria. However, it is necessary to educate the public about the evaluation tools of health websites to assess their credibility before using the provided content. In addition, improving readability will allow the public to read and understand the content. UR - https://cancer.jmir.org/2024/1/e52156 UR - http://dx.doi.org/10.2196/52156 UR - http://www.ncbi.nlm.nih.gov/pubmed/38814688 ID - info:doi/10.2196/52156 ER - TY - JOUR AU - Comer, Leigha AU - Donelle, Lorie AU - Hiebert, Bradley AU - Smith, J. Maxwell AU - Kothari, Anita AU - Stranges, Saverio AU - Gilliland, Jason AU - Long, Jed AU - Burkell, Jacquelyn AU - Shelley, J. Jacob AU - Hall, Jodi AU - Shelley, James AU - Cooke, Tommy AU - Ngole Dione, Marionette AU - Facca, Danica PY - 2024/5/24 TI - Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review JO - JMIR Public Health Surveill SP - e47154 VL - 10 KW - digital surveillance KW - COVID-19 KW - public health KW - scoping review KW - pandemic KW - digital technologies N2 - Background: The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups). Objective: We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance. Methods: Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines. Results: We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability. Conclusions: A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance. International Registered Report Identifier (IRRID): RR2-https://doi.org/10.1136/bmjopen-2021-053962 UR - https://publichealth.jmir.org/2024/1/e47154 UR - http://dx.doi.org/10.2196/47154 UR - http://www.ncbi.nlm.nih.gov/pubmed/38788212 ID - info:doi/10.2196/47154 ER - TY - JOUR AU - Kirkpatrick, E. Ciera AU - Lawrie, L. LaRissa PY - 2024/5/21 TI - TikTok as a Source of Health Information and Misinformation for Young Women in the United States: Survey Study JO - JMIR Infodemiology SP - e54663 VL - 4 KW - credibility perceptions KW - health information KW - health misinformation KW - information seeking KW - misinformation perceptions KW - public health KW - social media KW - strategic communication KW - third-person effect KW - TikTok N2 - Background: TikTok is one of the most-used and fastest-growing social media platforms in the world, and recent reports indicate that it has become an increasingly popular source of news and information in the United States. These trends have important implications for public health because an abundance of health information exists on the platform. Women are among the largest group of TikTok users in the United States and may be especially affected by the dissemination of health information on TikTok. Prior research has shown that women are not only more likely to look for information on the internet but are also more likely to have their health-related behaviors and perceptions affected by their involvement with social media. Objective: We conducted a survey of young women in the United States to better understand their use of TikTok for health information as well as their perceptions of TikTok?s health information and health communication sources. Methods: A web-based survey of US women aged 18 to 29 years (N=1172) was conducted in April-May 2023. The sample was recruited from a Qualtrics research panel and 2 public universities in the United States. Results: The results indicate that the majority of young women in the United States who have used TikTok have obtained health information from the platform either intentionally (672/1026, 65.5%) or unintentionally (948/1026, 92.4%). Age (959/1026, 93.47%; r=0.30; P<.001), education (959/1026, 93.47%; ?=0.10; P=.001), and TikTok intensity (ie, participants? emotional connectedness to TikTok and TikTok?s integration into their daily lives; 959/1026, 93.47%; r=0.32; P<.001) were positively correlated with overall credibility perceptions of the health information. Nearly the entire sample reported that they think that misinformation is prevalent on TikTok to at least some extent (1007/1026, 98.15%), but a third-person effect was found because the young women reported that they believe that other people are more susceptible to health misinformation on TikTok than they personally are (t1025=21.16; P<.001). Both health professionals and general users were common sources of health information on TikTok: 93.08% (955/1026) of the participants indicated that they had obtained health information from a health professional, and 93.86% (963/1026) indicated that they had obtained health information from a general user. The respondents showed greater preference for health information from health professionals (vs general users; t1025=23.75; P<.001); the respondents also reported obtaining health information from health professionals more often than from general users (t1025=8.13; P<.001), and they were more likely to act on health information from health professionals (vs general users; t1025=12.74; P<.001). Conclusions: The findings suggest that health professionals and health communication scholars need to proactively consider using TikTok as a platform for disseminating health information to young women because young women are obtaining health information from TikTok and prefer information from health professionals. UR - https://infodemiology.jmir.org/2024/1/e54663 UR - http://dx.doi.org/10.2196/54663 UR - http://www.ncbi.nlm.nih.gov/pubmed/38772020 ID - info:doi/10.2196/54663 ER - TY - JOUR AU - Bauer, Brian AU - Norel, Raquel AU - Leow, Alex AU - Rached, Abi Zad AU - Wen, Bo AU - Cecchi, Guillermo PY - 2024/5/16 TI - Using Large Language Models to Understand Suicidality in a Social Media?Based Taxonomy of Mental Health Disorders: Linguistic Analysis of Reddit Posts JO - JMIR Ment Health SP - e57234 VL - 11 KW - natural language processing KW - explainable AI KW - suicide KW - mental health disorders KW - mental health disorder KW - mental health KW - social media KW - online discussions KW - online KW - large language model KW - LLM KW - downstream analyses KW - trauma KW - stress KW - depression KW - anxiety KW - AI KW - artificial intelligence KW - explainable artificial intelligence KW - web-based discussions N2 - Background: Rates of suicide have increased by over 35% since 1999. Despite concerted efforts, our ability to predict, explain, or treat suicide risk has not significantly improved over the past 50 years. Objective: The aim of this study was to use large language models to understand natural language use during public web-based discussions (on Reddit) around topics related to suicidality. Methods: We used large language model?based sentence embedding to extract the latent linguistic dimensions of user postings derived from several mental health?related subreddits, with a focus on suicidality. We then applied dimensionality reduction to these sentence embeddings, allowing them to be summarized and visualized in a lower-dimensional Euclidean space for further downstream analyses. We analyzed 2.9 million posts extracted from 30 subreddits, including r/SuicideWatch, between October 1 and December 31, 2022, and the same period in 2010. Results: Our results showed that, in line with existing theories of suicide, posters in the suicidality community (r/SuicideWatch) predominantly wrote about feelings of disconnection, burdensomeness, hopeless, desperation, resignation, and trauma. Further, we identified distinct latent linguistic dimensions (well-being, seeking support, and severity of distress) among all mental health subreddits, and many of the resulting subreddit clusters were in line with a statistically driven diagnostic classification system?namely, the Hierarchical Taxonomy of Psychopathology (HiTOP)?by mapping onto the proposed superspectra. Conclusions: Overall, our findings provide data-driven support for several language-based theories of suicide, as well as dimensional classification systems for mental health disorders. Ultimately, this novel combination of natural language processing techniques can assist researchers in gaining deeper insights about emotions and experiences shared on the web and may aid in the validation and refutation of different mental health theories. UR - https://mental.jmir.org/2024/1/e57234 UR - http://dx.doi.org/10.2196/57234 ID - info:doi/10.2196/57234 ER - TY - JOUR AU - Liu, Xiaoqi AU - Hu, Qingyuan AU - Wang, Jie AU - Wu, Xusheng AU - Hu, Dehua PY - 2024/5/15 TI - Difference in Rumor Dissemination and Debunking Before and After the Relaxation of COVID-19 Prevention and Control Measures in China: Infodemiology Study JO - J Med Internet Res SP - e48564 VL - 26 KW - new stage KW - public health emergency KW - information epidemic KW - propagation characteristic KW - debunking mechanism KW - China N2 - Background: The information epidemic emerged along with the COVID-19 pandemic. While controlling the spread of COVID-19, the secondary harm of epidemic rumors to social order cannot be ignored. Objective: The objective of this paper was to understand the characteristics of rumor dissemination before and after the pandemic and the corresponding rumor management and debunking mechanisms. This study aimed to provide a theoretical basis and effective methods for relevant departments to establish a sound mechanism for managing network rumors related to public health emergencies such as COVID-19. Methods: This study collected data sets of epidemic rumors before and after the relaxation of the epidemic prevention and control measures, focusing on large-scale network rumors. Starting from 3 dimensions of rumor content construction, rumor propagation, and rumor-refuting response, the epidemic rumors were subdivided into 7 categories, namely, involved subjects, communication content, emotional expression, communication channels, communication forms, rumor-refuting subjects, and verification sources. Based on this framework, content coding and statistical analysis of epidemic rumors were carried out. Results: The study found that the rumor information was primarily directed at a clear target audience. The main themes of rumor dissemination were related to the public?s immediate interests in the COVID-19 field, with significant differences in emotional expression and mostly negative emotions. Rumors mostly spread through social media interactions, community dissemination, and circle dissemination, with text content as the main form, but they lack factual evidence. The preferences of debunking subjects showed differences, and the frequent occurrence of rumors reflected the unsmooth channels of debunking. The ?2 test of data before and after the pandemic showed that the P value was less than .05, indicating that the difference in rumor content before and after the pandemic had statistical significance. Conclusions: This study?s results showed that the themes of rumors during the pandemic are closely related to the immediate interests of the public, and the emotions of the public accelerate the spread of these rumors, which are mostly disseminated through social networks. Therefore, to more effectively prevent and control the spread of rumors during the pandemic and to enhance the capability to respond to public health crises, relevant authorities should strengthen communication with the public, conduct emotional risk assessments, and establish a joint mechanism for debunking rumors. UR - https://www.jmir.org/2024/1/e48564 UR - http://dx.doi.org/10.2196/48564 UR - http://www.ncbi.nlm.nih.gov/pubmed/38748460 ID - info:doi/10.2196/48564 ER - TY - JOUR AU - Zhao, Rui AU - Lu, Xuerong AU - Yang, Jiayi AU - Li, Biao PY - 2024/5/14 TI - Understanding the Impact of Communicating Uncertainty About COVID-19 in the News: Randomized Between-Subjects Factorial Experiment JO - J Med Internet Res SP - e51910 VL - 26 KW - information uncertainty KW - health communication KW - uncertainty management KW - COVID-19 KW - public health perception KW - health information N2 - Background: Whether and how the uncertainty about a public health crisis should be communicated to the general public have been important and yet unanswered questions arising over the past few years. As the most threatening contemporary public health crisis, the COVID-19 pandemic has renewed interest in these unresolved issues by both academic scholars and public health practitioners. Objective: The aim of this study was to investigate the impact of communicating uncertainty about COVID-19?related threats and solutions on individuals? risk perceptions and misinformation vulnerability, as well as the sequential impact of these effects on health information processing and preventative behavioral intentions. Methods: A 2×2 (threat uncertainty [presence vs absence]×solution uncertainty [presence vs absence]) full-fractional between-subjects online experiment was conducted with 371 Chinese adults. Focusing on the discussion of whether the asymptomatic cases detected during the COVID-19 pandemic would further lead to an uncontrolled pandemic, news articles were manipulated in terms of whether the infectiousness of asymptomatic cases and the means to control the transmission are presented in terms of their certainty or uncertainty. Participants were randomly assigned to one of the four experimental conditions, being instructed to read one news article. After reading the news article assigned, participants were asked to respond to a series of questions to assess their cognitive and behavioral responses. Results: Individuals were more susceptible to believing false COVID-19?related information when a certain threat and uncertain solution were framed in the news article. Moreover, individuals? perceptions of crisis severity increased when exposed to news information containing uncertain solutions. Both misinformation vulnerability and perceived severity were positively associated with information processing. Information seeking was positively associated with protective behavioral intention, whereas information avoidance was negatively associated with protective behavioral intention. Conclusions: Our findings imply that uncertainty, depending on its aspect, can be effectively communicated to the public during an emerging public health crisis. These results have theoretical and practical implications for health communicators and journalists. Given its limited influence on individuals? cognitive and behavioral responses, uncertainty related to a health threat should be disseminated to meet the public?s expectation of information transparency. However, caution is advised when communicating uncertainty related to potential solutions, as this factor exhibited a mixed impact on individual responses during a crisis. UR - https://www.jmir.org/2024/1/e51910 UR - http://dx.doi.org/10.2196/51910 UR - http://www.ncbi.nlm.nih.gov/pubmed/38743940 ID - info:doi/10.2196/51910 ER - TY - JOUR AU - Jessiman-Perreault, Genevičve AU - Boucher, Jean-Christophe AU - Kim, Youn So AU - Frenette, Nicole AU - Badami, Abbas AU - Smith, M. Henry AU - Allen Scott, K. Lisa PY - 2024/5/9 TI - The Role of Scientific Research in Human Papillomavirus Vaccine Discussions on Twitter: Social Network Analysis JO - JMIR Infodemiology SP - e50551 VL - 4 KW - human papillomavirus KW - HPV KW - vaccine KW - immunization KW - social media KW - misinformation KW - social network analysis N2 - Background: Attitudes toward the human papillomavirus (HPV) vaccine and accuracy of information shared about this topic in web-based settings vary widely. As real-time, global exposure to web-based discourse about HPV immunization shapes the attitudes of people toward vaccination, the spread of misinformation and misrepresentation of scientific knowledge contribute to vaccine hesitancy. Objective: In this study, we aimed to better understand the type and quality of scientific research shared on Twitter (recently rebranded as X) by vaccine-hesitant and vaccine-confident communities. Methods: To analyze the use of scientific research on social media, we collected tweets and retweets using a list of keywords associated with HPV and HPV vaccines using the Academic Research Product Track application programming interface from January 2019 to May 2021. From this data set, we identified tweets referring to or sharing scientific literature through a Boolean search for any tweets with embedded links, hashtags, or keywords associated with scientific papers. First, we used social network analysis to build a retweet or reply network to identify the clusters of users belonging to either the vaccine-confident or vaccine-hesitant communities. Second, we thematically assessed all shared papers based on typology of evidence. Finally, we compared the quality of research evidence and bibliometrics between the shared papers in the vaccine-confident and vaccine-hesitant communities. Results: We extracted 250 unique scientific papers (including peer-reviewed papers, preprints, and gray literature) from approximately 1 million English-language tweets. Social network maps were generated for the vaccine-confident and vaccine-hesitant communities sharing scientific research on Twitter. Vaccine-hesitant communities share fewer scientific papers; yet, these are more broadly disseminated despite being published in less prestigious journals compared to those shared by the vaccine-confident community. Conclusions: Vaccine-hesitant communities have adopted communication tools traditionally wielded by health promotion communities. Vaccine-confident communities would benefit from a more cohesive communication strategy to communicate their messages more widely and effectively. UR - https://infodemiology.jmir.org/2024/1/e50551 UR - http://dx.doi.org/10.2196/50551 UR - http://www.ncbi.nlm.nih.gov/pubmed/38722678 ID - info:doi/10.2196/50551 ER - TY - JOUR AU - Xue, Jia AU - Shier, L. Micheal AU - Chen, Junxiang AU - Wang, Yirun AU - Zheng, Chengda AU - Chen, Chen PY - 2024/5/8 TI - A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study JO - J Med Internet Res SP - e51698 VL - 26 KW - human service nonprofits KW - sexual assault support centers KW - Canada KW - typology KW - theory KW - Twitter KW - machine learning KW - social media KW - tweet KW - tweets KW - nonprofit KW - nonprofits KW - crisis KW - sexual assault KW - sexual violence KW - sexual abuse KW - support center KW - support centers KW - communication KW - communications KW - organization KW - organizations KW - organizational KW - sentiment analysis KW - business KW - marketing N2 - Background: Nonprofit organizations are increasingly using social media to improve their communication strategies with the broader population. However, within the domain of human service nonprofits, there is hesitancy to fully use social media tools, and there is limited scope among organizational personnel in applying their potential beyond self-promotion and service advertisement. There is a pressing need for greater conceptual clarity to support education and training on the varied reasons for using social media to increase organizational outcomes. Objective: This study leverages the potential of Twitter (subsequently rebranded as X [X Corp]) to examine the online communication content within a sample (n=133) of nonprofit sexual assault (SA) centers in Canada. To achieve this, we developed a typology using a qualitative and supervised machine learning model for the automatic classification of tweets posted by these centers. Methods: Using a mixed methods approach that combines machine learning and qualitative analysis, we manually coded 10,809 tweets from 133 SA centers in Canada, spanning the period from March 2009 to March 2023. These manually labeled tweets were used as the training data set for the supervised machine learning process, which allowed us to classify 286,551 organizational tweets. The classification model based on supervised machine learning yielded satisfactory results, prompting the use of unsupervised machine learning to classify the topics within each thematic category and identify latent topics. The qualitative thematic analysis, in combination with topic modeling, provided a contextual understanding of each theme. Sentiment analysis was conducted to reveal the emotions conveyed in the tweets. We conducted validation of the model with 2 independent data sets. Results: Manual annotation of 10,809 tweets identified seven thematic categories: (1) community engagement, (2) organization administration, (3) public awareness, (4) political advocacy, (5) support for others, (6) partnerships, and (7) appreciation. Organization administration was the most frequent segment, and political advocacy and partnerships were the smallest segments. The supervised machine learning model achieved an accuracy of 63.4% in classifying tweets. The sentiment analysis revealed a prevalence of neutral sentiment across all categories. The emotion analysis indicated that fear was predominant, whereas joy was associated with the partnership and appreciation tweets. Topic modeling identified distinct themes within each category, providing valuable insights into the prevalent discussions surrounding SA and related issues. Conclusions: This research contributes an original theoretical model that sheds light on how human service nonprofits use social media to achieve their online organizational communication objectives across 7 thematic categories. The study advances our comprehension of social media use by nonprofits, presenting a comprehensive typology that captures the diverse communication objectives and contents of these organizations, which provide content to expand training and education for nonprofit leaders to connect and engage with the public, policy experts, other organizations, and potential service users. UR - https://www.jmir.org/2024/1/e51698 UR - http://dx.doi.org/10.2196/51698 UR - http://www.ncbi.nlm.nih.gov/pubmed/38718390 ID - info:doi/10.2196/51698 ER - TY - JOUR AU - Stimpson, P. Jim AU - Park, Sungchul AU - Pruitt, L. Sandi AU - Ortega, N. Alexander PY - 2024/5/8 TI - Variation in Trust in Cancer Information Sources by Perceptions of Social Media Health Mis- and Disinformation and by Race and Ethnicity Among Adults in the United States: Cross-Sectional Study JO - JMIR Cancer SP - e54162 VL - 10 KW - cancer KW - United States KW - cross-sectional study KW - trust KW - consumer health information KW - misinformation KW - disinformation KW - race KW - ethnicity KW - cancer information KW - source KW - sources KW - perception KW - perceptions KW - social media KW - health information KW - cross-sectional data KW - misleading N2 - Background: Mis- and disinformation on social media have become widespread, which can lead to a lack of trust in health information sources and, in turn, lead to negative health outcomes. Moreover, the effect of mis- and disinformation on trust in information sources may vary by racial and ethnic minoritized populations. Objective: We evaluated how trust in multiple sources of cancer information varied by perceptions of health mis- and disinformation on social media and by race and ethnicity. Methods: Cross-sectional, nationally representative survey data from noninstitutionalized adults in the United States from the 2022 Health Information National Trends Survey 6 (HINTS 6) were analyzed (N=4137). The dependent variable measured the level of trust in cancer information sources. The independent variables were perceptions about health mis- and disinformation on social media and race and ethnicity. Multivariable logistic regression models were adjusted for survey weight and design, age, birth gender, race and ethnicity, marital status, urban/rural designation, education, employment status, feelings about household income, frequency of social media visits, and personal and family history of cancer. We also tested the interaction effect between perceptions of social media health mis- and disinformation and participants? self-reported race and ethnicity. Results: Perception of ?a lot of? health mis- and disinformation on social media, relative to perception of ?less than a lot,? was associated with a lower likelihood of high levels of trusting cancer information from government health agencies (odds ratio [OR] 0.60, 95% CI 0.47-0.77), family or friends (OR 0.56, 95% CI 0.44-0.71), charitable organizations (OR 0.78, 95% CI 0.63-0.96), and religious organizations and leaders (OR 0.64, 95% CI 0.52-0.79). Among White participants, those who perceived a lot of health mis- and disinformation on social media were less likely to have high trust in cancer information from government health agencies (margin=61%, 95% CI 57%-66%) and family or friends (margin=49%, 95% CI 43%-55%) compared to those who perceived less than a lot of health mis- and disinformation on social media. Among Black participants, those who perceived a lot of health mis- and disinformation on social media were less likely to have high trust in cancer information from religious organizations and leaders (margin=20%, 95% CI 10%-30%) compared to participants who perceived no or a little health mis- and disinformation on social media. Conclusions: Certain sources of cancer information may need enhanced support against the threat of mis- and disinformation, such as government health agencies, charitable organizations, religious organizations and leaders, and family or friends. Moreover, interventions should partner with racial and ethnically minoritized populations that are more likely to have low trust in certain cancer information sources associated with mis- and disinformation on social media. UR - https://cancer.jmir.org/2024/1/e54162 UR - http://dx.doi.org/10.2196/54162 UR - http://www.ncbi.nlm.nih.gov/pubmed/38717800 ID - info:doi/10.2196/54162 ER - TY - JOUR AU - Gaysynsky, Anna AU - Senft Everson, Nicole AU - Heley, Kathryn AU - Chou, Sylvia Wen-Ying PY - 2024/4/30 TI - Perceptions of Health Misinformation on Social Media: Cross-Sectional Survey Study JO - JMIR Infodemiology SP - e51127 VL - 4 KW - social media KW - misinformation KW - health communication KW - health literacy KW - patient-provider communication N2 - Background: Health misinformation on social media can negatively affect knowledge, attitudes, and behaviors, undermining clinical care and public health efforts. Therefore, it is vital to better understand the public?s experience with health misinformation on social media. Objective: The goal of this analysis was to examine perceptions of the social media information environment and identify associations between health misinformation perceptions and health communication behaviors among US adults. Methods: Analyses used data from the 2022 Health Information National Trends Survey (N=6252). Weighted unadjusted proportions described respondents? perceptions of the amount of false or misleading health information on social media (?perceived misinformation amount?) and how difficult it is to discern true from false information on social media (?perceived discernment difficulty?). Weighted multivariable logistic regressions examined (1) associations of sociodemographic characteristics and subjective literacy measures with misinformation perceptions and (2) relationships between misinformation perceptions and health communication behaviors (ie, sharing personal or general health information on social media and using social media information in health decisions or in discussions with health care providers). Results: Over one-third of social media users (35.61%) perceived high levels of health misinformation, and approximately two-thirds (66.56%) reported high perceived discernment difficulty. Odds of perceiving high amounts of misinformation were lower among non-Hispanic Black/African American (adjusted odds ratio [aOR] 0.407, 95% CI 0.282-0.587) and Hispanic (aOR 0.610, 95% CI 0.449-0.831) individuals compared to White individuals. Those with lower subjective health literacy were less likely to report high perceived misinformation amount (aOR 0.602, 95% CI 0.374-0.970), whereas those with lower subjective digital literacy were more likely to report high perceived misinformation amount (aOR 1.775, 95% CI 1.400-2.251). Compared to White individuals, Hispanic individuals had lower odds of reporting high discernment difficulty (aOR 0.620, 95% CI 0.462-0.831). Those with lower subjective digital literacy (aOR 1.873, 95% CI 1.478-2.374) or numeracy (aOR 1.465, 95% CI 1.047-2.049) were more likely to report high discernment difficulty. High perceived misinformation amount was associated with lower odds of sharing general health information on social media (aOR 0.742, 95% CI 0.568-0.968), using social media information to make health decisions (aOR 0.273, 95% CI 0.156-0.479), and using social media information in discussions with health care providers (aOR 0.460, 95% CI 0.323-0.655). High perceived discernment difficulty was associated with higher odds of using social media information in health decisions (aOR 1.724, 95% CI 1.208-2.460) and health care provider discussions (aOR 1.389, 95% CI 1.035-1.864). Conclusions: Perceptions of high health misinformation prevalence and discernment difficulty are widespread among social media users, and each has unique associations with sociodemographic characteristics, literacy, and health communication behaviors. These insights can help inform future health communication interventions. UR - https://infodemiology.jmir.org/2024/1/e51127 UR - http://dx.doi.org/10.2196/51127 UR - http://www.ncbi.nlm.nih.gov/pubmed/38687591 ID - info:doi/10.2196/51127 ER - TY - JOUR AU - Chadwick, L. Verity AU - Saich, Freya AU - Freeman, Joseph AU - Martiniuk, Alexandra PY - 2024/4/29 TI - Media Discourse Regarding COVID-19 Vaccinations for Children Aged 5 to 11 Years in Australia, Canada, the United Kingdom, and the United States: Comparative Analysis Using the Narrative Policy Framework JO - JMIR Form Res SP - e38761 VL - 8 KW - COVID-19 KW - SARS-CoV-2 KW - vaccine KW - mRNA KW - Pfizer-BioNTech KW - pediatric KW - children KW - media KW - news KW - web-based KW - infodemic KW - disinformation N2 - Background: Media narratives can shape public opinion and actions, influencing the uptake of pediatric COVID-19 vaccines. The COVID-19 pandemic has occurred at a time where infodemics, misinformation, and disinformation are present, impacting the COVID-19 response. Objective: This study aims to investigate how narratives about pediatric COVID-19 vaccines in the media of 4 English-speaking countries: the United States, Australia, Canada, and the United Kingdom. Methods: The Narrative Policy Framework was used to guide the comparative analyses of the major print and web-based news agencies? media regarding COVID-19 vaccines for children aged 5 to 11 years. Data were sought using systematic searching on Factiva (Dow Jones) of 4 key phases of pediatric vaccine approval and rollout. Results: A total of 400 articles (n=287, 71.8% in the United States, n=40, 10% in Australia, n=60, 15% in Canada, and n=13, 3% in the United Kingdom) met the search criteria and were included. Using the Narrative Policy Framework, the following were identified in each article: hero, villain, survivor, and plot. The United States was the earliest country to vaccinate children, and other countries? media often lauded the United States for this. Australian and Canadian media narratives about vaccines for children aged 5 to 11 years were commonly about protecting susceptible people in society, whereas the US and the UK narratives focused more on the vaccine helping children return to school. All 4 countries focused on the vaccines for children aged 5 to 11 years as being key to ?ending? the pandemic. Australian and Canadian narratives frequently compared vaccine rollouts across states or provinces and bemoaned local progress in vaccine delivery compared with other countries globally. Canadian and US narratives highlighted the ?infodemic? about the COVID-19 pandemic and disinformation regarding child vaccines as impeding uptake. All 4 countries?the United States, Australia, the United Kingdom, and Canada?used war imagery in reporting about COVID-19 vaccines for children. The advent of the Omicron variant demonstrated that populations were fatigued by the COVID-19 pandemic, and the media reporting increasingly blamed the unvaccinated. The UK media narrative was unique in describing vaccinating children as a distraction from adult COVID-19 vaccination efforts. The United States and Canada had narratives expressing anger about potential vaccine passports for children. In Australia, general practitioners were labelled as heroes. Finally, the Canadian narrative suggested altruistic forgoing of COVID-19 vaccine ?boosters? as well as pediatric COVID-19 vaccines to benefit those in poorer nations. Conclusions: Public health emergencies require clear; compelling and accurate communication. The stories told during this pandemic are compelling because they contain the classic elements of a narrative; however, they can be reductive and inaccurate. UR - https://formative.jmir.org/2024/1/e38761 UR - http://dx.doi.org/10.2196/38761 UR - http://www.ncbi.nlm.nih.gov/pubmed/36383344 ID - info:doi/10.2196/38761 ER - TY - JOUR AU - Chepo, Macarena AU - Martin, Sam AU - Déom, Noémie AU - Khalid, Firas Ahmad AU - Vindrola-Padros, Cecilia PY - 2024/4/17 TI - Twitter Analysis of Health Care Workers? Sentiment and Discourse Regarding Post?COVID-19 Condition in Children and Young People: Mixed Methods Study JO - J Med Internet Res SP - e50139 VL - 26 KW - COVID-19 KW - postacute sequelae of SARS-CoV-2 KW - PASC KW - post?COVID-19 condition KW - children KW - vaccines KW - social media KW - social network analysis KW - Twitter N2 - Background: The COVID-19 pandemic has had a significant global impact, with millions of cases and deaths. Research highlights the persistence of symptoms over time (post?COVID-19 condition), a situation of particular concern in children and young people with symptoms. Social media such as Twitter (subsequently rebranded as X) could provide valuable information on the impact of the post?COVID-19 condition on this demographic. Objective: With a social media analysis of the discourse surrounding the prevalence of post?COVID-19 condition in children and young people, we aimed to explore the perceptions of health care workers (HCWs) concerning post?COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. This will allow us to contribute to the emerging knowledge on post?COVID-19 condition and identify critical areas and future directions for researchers and policy makers. Methods: From a pragmatic paradigm, we used a mixed methods approach. Through discourse, keyword, sentiment, and image analyses, using Pulsar and InfraNodus, we analyzed the discourse about the experience of post?COVID-19 condition in children and young people in the United Kingdom shared on Twitter between January 1, 2021, and January 31, 2022, from a sample of HCWs with Twitter accounts whose biography identifies them as HCWs. Results: We obtained 300,000 tweets, out of which (after filtering for relevant tweets) we performed an in-depth qualitative sample analysis of 2588 tweets. The HCWs were responsive to announcements issued by the authorities regarding the management of the COVID-19 pandemic in the United Kingdom. The most frequent sentiment expressed was negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing the COVID-19 pandemic and post?COVID-19 condition in children and young people, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences. Conclusions: The perceptions described on Twitter by HCWs concerning the presence of the post?COVID-19 condition in children and young people appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of children and young people with post?COVID-19 condition. UR - https://www.jmir.org/2024/1/e50139 UR - http://dx.doi.org/10.2196/50139 UR - http://www.ncbi.nlm.nih.gov/pubmed/38630514 ID - info:doi/10.2196/50139 ER - TY - JOUR AU - Noh, Jin-Won AU - Cheon, Jooyoung AU - Seong, Hohyun AU - Kwon, Dae Young AU - Yoo, Ki-Bong PY - 2024/4/17 TI - Impacts of Smoking Ban Policies on Billiard Hall Sales in South Korea Using Objective Sales Information of a Credit Card Company: Quasi-Experimental Study JO - JMIR Public Health Surveill SP - e50466 VL - 10 KW - smoking ban policy KW - indoor sports facility KW - South Korea N2 - Background: Smoking ban policies (SBPs) are potent health interventions and offer the potential to influence antismoking behavior. The Korean government completely prohibited smoking in indoor sports facilities, including billiard halls, since the government revised the National Health Promotion Act in December 2017. Objective: This study aimed to examine the impact of the SBP on the economic outcomes of indoor sports facilities, particularly billiard halls. Methods: This study used credit card sales data from the largest card company in South Korea. Data are from January 2017 to December 2018. Monthly sales data were examined across 23 administrative neighborhoods in Seoul, the capital city of South Korea. We conducted the interrupted time series model using the fixed effects model and the linear regression with panel-corrected standard errors (PCSE). Results: The sales and transactions of billiard halls were not significantly changed after the introduction of the SBP in the full PCSE models. The R2 of the full PCSE model was 0.967 for sales and 0.981 for transactions. Conclusions: The introduction of the SBP did not result in substantial economic gains or losses in the sales of billiard halls. In addition to existing price-based policies, the enhanced SBP in public-use facilities, such as billiard halls, can have a positive synergistic effect on reducing smoking prevalence and preventing secondhand smoke. Health policy makers can actively expand the application of SBPs and make an effort to enhance social awareness regarding the necessity and benefits of public SBPs for both smokers and the owners of hospitality facilities. UR - https://publichealth.jmir.org/2024/1/e50466 UR - http://dx.doi.org/10.2196/50466 UR - http://www.ncbi.nlm.nih.gov/pubmed/38630526 ID - info:doi/10.2196/50466 ER - TY - JOUR AU - Bragazzi, Luigi Nicola AU - Garbarino, Sergio PY - 2024/4/16 TI - Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis JO - JMIR Form Res SP - e55762 VL - 8 KW - sleep KW - sleep health KW - sleep-related disbeliefs KW - generative conversational artificial intelligence KW - chatbot KW - ChatGPT KW - misinformation KW - artificial intelligence KW - comparative study KW - expert analysis KW - adequate sleep KW - well-being KW - sleep trackers KW - sleep health education KW - sleep-related KW - chronic disease KW - healthcare cost KW - sleep timing KW - sleep duration KW - presleep behaviors KW - sleep experts KW - healthy behavior KW - public health KW - conversational agents N2 - Background: Adequate sleep is essential for maintaining individual and public health, positively affecting cognition and well-being, and reducing chronic disease risks. It plays a significant role in driving the economy, public safety, and managing health care costs. Digital tools, including websites, sleep trackers, and apps, are key in promoting sleep health education. Conversational artificial intelligence (AI) such as ChatGPT (OpenAI, Microsoft Corp) offers accessible, personalized advice on sleep health but raises concerns about potential misinformation. This underscores the importance of ensuring that AI-driven sleep health information is accurate, given its significant impact on individual and public health, and the spread of sleep-related myths. Objective: This study aims to examine ChatGPT?s capability to debunk sleep-related disbeliefs. Methods: A mixed methods design was leveraged. ChatGPT categorized 20 sleep-related myths identified by 10 sleep experts and rated them in terms of falseness and public health significance, on a 5-point Likert scale. Sensitivity, positive predictive value, and interrater agreement were also calculated. A qualitative comparative analysis was also conducted. Results: ChatGPT labeled a significant portion (n=17, 85%) of the statements as ?false? (n=9, 45%) or ?generally false? (n=8, 40%), with varying accuracy across different domains. For instance, it correctly identified most myths about ?sleep timing,? ?sleep duration,? and ?behaviors during sleep,? while it had varying degrees of success with other categories such as ?pre-sleep behaviors? and ?brain function and sleep.? ChatGPT?s assessment of the degree of falseness and public health significance, on the 5-point Likert scale, revealed an average score of 3.45 (SD 0.87) and 3.15 (SD 0.99), respectively, indicating a good level of accuracy in identifying the falseness of statements and a good understanding of their impact on public health. The AI-based tool showed a sensitivity of 85% and a positive predictive value of 100%. Overall, this indicates that when ChatGPT labels a statement as false, it is highly reliable, but it may miss identifying some false statements. When comparing with expert ratings, high intraclass correlation coefficients (ICCs) between ChatGPT?s appraisals and expert opinions could be found, suggesting that the AI?s ratings were generally aligned with expert views on falseness (ICC=.83, P<.001) and public health significance (ICC=.79, P=.001) of sleep-related myths. Qualitatively, both ChatGPT and sleep experts refuted sleep-related misconceptions. However, ChatGPT adopted a more accessible style and provided a more generalized view, focusing on broad concepts, while experts sometimes used technical jargon, providing evidence-based explanations. Conclusions: ChatGPT-4 can accurately address sleep-related queries and debunk sleep-related myths, with a performance comparable to sleep experts, even if, given its limitations, the AI cannot completely replace expert opinions, especially in nuanced and complex fields such as sleep health, but can be a valuable complement in the dissemination of updated information and promotion of healthy behaviors. UR - https://formative.jmir.org/2024/1/e55762 UR - http://dx.doi.org/10.2196/55762 UR - http://www.ncbi.nlm.nih.gov/pubmed/38501898 ID - info:doi/10.2196/55762 ER - TY - JOUR AU - Haff, L. Priscilla AU - Jacobson, Alli AU - Taylor, M. Madison AU - Schandua, P. Hayden AU - Farris, P. David AU - Doan, Q. Hung AU - Nelson, C. Kelly PY - 2024/4/8 TI - The New Media Landscape and Its Effects on Skin Cancer Diagnostics, Prognostics, and Prevention: Scoping Review JO - JMIR Dermatol SP - e53373 VL - 7 KW - social media KW - communication KW - skin cancer KW - melanoma KW - misinformation KW - scoping review N2 - Background: The wide availability of web-based sources, including social media (SM), has supported rapid, widespread dissemination of health information. This dissemination can be an asset during public health emergencies; however, it can also present challenges when the information is inaccurate or ill-informed. Of interest, many SM sources discuss cancer, specifically cutaneous melanoma and keratinocyte cancers (basal cell and squamous cell carcinoma). Objective: Through a comprehensive and scoping review of the literature, this study aims to gain an actionable perspective of the state of SM information regarding skin cancer diagnostics, prognostics, and prevention. Methods: We performed a scoping literature review to establish the relationship between SM and skin cancer. A literature search was conducted across MEDLINE, Embase, Cochrane Library, Web of Science, and Scopus from January 2000 to June 2023. The included studies discussed SM and its relationship to and effect on skin cancer. Results: Through the search, 1009 abstracts were initially identified, 188 received full-text review, and 112 met inclusion criteria. The included studies were divided into 7 groupings based on a publication?s primary objective: misinformation (n=40, 36%), prevention campaign (n=19, 17%), engagement (n=16, 14%), research (n=12, 11%), education (n=11, 10%), demographics (n=10, 9%), and patient support (n=4, 3%), which were the most common identified themes. Conclusions: Through this review, we gained a better understanding of the SM environment addressing skin cancer information, and we gained insight into the best practices by which SM could be used to positively influence the health care information ecosystem. UR - https://derma.jmir.org/2024/1/e53373 UR - http://dx.doi.org/10.2196/53373 UR - http://www.ncbi.nlm.nih.gov/pubmed/38587890 ID - info:doi/10.2196/53373 ER - TY - JOUR AU - Xuan, Kun AU - Zhang, Ning AU - Li, Tao AU - Pang, Xingya AU - Li, Qingru AU - Zhao, Tianming AU - Wang, Binbing AU - Zha, Zhenqiu AU - Tang, Jihai PY - 2024/4/5 TI - Epidemiological Characteristics of Varicella in Anhui Province, China, 2012-2021: Surveillance Study JO - JMIR Public Health Surveill SP - e50673 VL - 10 KW - varicella KW - incidence KW - epidemiology KW - spatial autocorrelation KW - contagious disease KW - chicken pox KW - varicella zoster virus KW - China N2 - Background: Varicella is a mild, self-limited disease caused by varicella-zoster virus (VZV) infection. Recently, the disease burden of varicella has been gradually increasing in China; however, the epidemiological characteristics of varicella have not been reported for Anhui Province. Objective: The aim of this study was to analyze the epidemiology of varicella in Anhui from 2012 to 2021, which can provide a basis for the future study and formulation of varicella prevention and control policies in the province. Methods: Surveillance data were used to characterize the epidemiology of varicella in Anhui from 2012 to 2021 in terms of population, time, and space. Spatial autocorrelation of varicella was explored using the Moran index (Moran I). The Kulldorff space-time scan statistic was used to analyze the spatiotemporal aggregation of varicella. Results: A total of 276,115 cases of varicella were reported from 2012 to 2021 in Anhui, with an average annual incidence of 44.8 per 100,000, and the highest incidence was 81.2 per 100,000 in 2019. The male-to-female ratio of cases was approximately 1.26, which has been gradually decreasing in recent years. The population aged 5-14 years comprised the high-incidence group, although the incidence in the population 30 years and older has gradually increased. Students accounted for the majority of cases, and the proportion of cases in both home-reared children (aged 0-7 years who are not sent to nurseries, daycare centers, or school) and kindergarten children (aged 3-6 years) has changed slightly in recent years. There were two peaks of varicella incidence annually, except for 2020, and the incidence was typically higher in the winter peak than in summer. The incidence of varicella in southern Anhui was higher than that in northern Anhui. The average annual incidence at the county level ranged from 6.61 to 152.14 per 100,000, and the varicella epidemics in 2018-2021 were relatively severe. The spatial and temporal distribution of varicella in Anhui was not random, with a positive spatial autocorrelation found at the county level (Moran I=0.412). There were 11 districts or counties with high-high clusters, mainly distributed in the south of Anhui, and 3 districts or counties with high-low or low-high clusters. Space-time scan analysis identified five possible clusters of areas, and the most likely cluster was distributed in the southeastern region of Anhui. Conclusions: This study comprehensively describes the epidemiology and changing trend of varicella in Anhui from 2012 to 2021. In the future, preventive and control measures should be strengthened for the key populations and regions of varicella. UR - https://publichealth.jmir.org/2024/1/e50673 UR - http://dx.doi.org/10.2196/50673 UR - http://www.ncbi.nlm.nih.gov/pubmed/38579276 ID - info:doi/10.2196/50673 ER - TY - JOUR AU - Zhang, M. Jueman AU - Wang, Yi AU - Mouton, Magali AU - Zhang, Jixuan AU - Shi, Molu PY - 2024/4/3 TI - Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis JO - J Med Internet Res SP - e53375 VL - 26 KW - HIV KW - vaccine KW - Twitter KW - X platform KW - infodemiology KW - machine learning KW - topic modeling KW - sentiment KW - conspiracy theory KW - COVID-19 N2 - Background: The initiation of clinical trials for messenger RNA (mRNA) HIV vaccines in early 2022 revived public discussion on HIV vaccines after 3 decades of unsuccessful research. These trials followed the success of mRNA technology in COVID-19 vaccines but unfolded amid intense vaccine debates during the COVID-19 pandemic. It is crucial to gain insights into public discourse and reactions about potential new vaccines, and social media platforms such as X (formerly known as Twitter) provide important channels. Objective: Drawing from infodemiology and infoveillance research, this study investigated the patterns of public discourse and message-level drivers of user reactions on X regarding HIV vaccines by analyzing posts using machine learning algorithms. We examined how users used different post types to contribute to topics and valence and how these topics and valence influenced like and repost counts. In addition, the study identified salient aspects of HIV vaccines related to COVID-19 and prominent anti?HIV vaccine conspiracy theories through manual coding. Methods: We collected 36,424 English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022. We used topic modeling and sentiment analysis to uncover latent topics and valence, which were subsequently analyzed across post types in cross-tabulation analyses and integrated into linear regression models to predict user reactions, specifically likes and reposts. Furthermore, we manually coded the 1000 most engaged posts about HIV and COVID-19 to uncover salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti?HIV vaccine conspiracy theories. Results: Topic modeling revealed 3 topics: HIV and COVID-19, mRNA HIV vaccine trials, and HIV vaccine and immunity. HIV and COVID-19 underscored the connections between HIV vaccines and COVID-19 vaccines, as evidenced by subtopics about their reciprocal impact on development and various comparisons. The overall valence of the posts was marginally positive. Compared to self-composed posts initiating new conversations, there was a higher proportion of HIV and COVID-19?related and negative posts among quote posts and replies, which contribute to existing conversations. The topic of mRNA HIV vaccine trials, most evident in self-composed posts, increased repost counts. Positive valence increased like and repost counts. Prominent anti?HIV vaccine conspiracy theories often falsely linked HIV vaccines to concurrent COVID-19 and other HIV-related events. Conclusions: The results highlight COVID-19 as a significant context for public discourse and reactions regarding HIV vaccines from both positive and negative perspectives. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as observed in some anti?HIV vaccine conspiracy theories misleadingly connecting HIV vaccines with COVID-19. These findings have implications for public health communication strategies concerning HIV vaccines. UR - https://www.jmir.org/2024/1/e53375 UR - http://dx.doi.org/10.2196/53375 UR - http://www.ncbi.nlm.nih.gov/pubmed/38568723 ID - info:doi/10.2196/53375 ER - TY - JOUR AU - Singhal, Aditya AU - Neveditsin, Nikita AU - Tanveer, Hasnaat AU - Mago, Vijay PY - 2024/4/3 TI - Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review JO - JMIR Med Inform SP - e50048 VL - 12 KW - fairness, accountability, transparency, and ethics KW - artificial intelligence KW - social media KW - health care N2 - Background: The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application. Objective: This study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation. Methods: Our research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics. Results: Our findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively. Conclusions: Despite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research. UR - https://medinform.jmir.org/2024/1/e50048 UR - http://dx.doi.org/10.2196/50048 UR - http://www.ncbi.nlm.nih.gov/pubmed/38568737 ID - info:doi/10.2196/50048 ER - TY - JOUR AU - Mishra, Vishala AU - Dexter, P. Joseph PY - 2024/4/1 TI - Response of Unvaccinated US Adults to Official Information About the Pause in Use of the Johnson & Johnson?Janssen COVID-19 Vaccine: Cross-Sectional Survey Study JO - J Med Internet Res SP - e41559 VL - 26 KW - Centers for Disease Control and Prevention KW - CDC KW - COVID-19 KW - health communication KW - health information KW - health literacy KW - public health KW - risk perception KW - SARS-CoV-2 KW - vaccine hesitancy KW - web-based surveys UR - https://www.jmir.org/2024/1/e41559 UR - http://dx.doi.org/10.2196/41559 UR - http://www.ncbi.nlm.nih.gov/pubmed/38557597 ID - info:doi/10.2196/41559 ER - TY - JOUR AU - Ahmed, Wasim AU - Aiyenitaju, Opeoluwa AU - Chadwick, Simon AU - Hardey, Mariann AU - Fenton, Alex PY - 2024/3/29 TI - The Influence of Joe Wicks on Physical Activity During the COVID-19 Pandemic: Thematic, Location, and Social Network Analysis of X Data JO - J Med Internet Res SP - e49921 VL - 26 KW - social media KW - social network analysis KW - COVID-19 KW - influencers KW - public health KW - social network KW - physical activity KW - promotion KW - fitness KW - exercise KW - workout KW - Twitter KW - content creation KW - communication N2 - Background:  Social media (SM) was essential in promoting physical activity during the COVID-19 pandemic, especially among people confined to their homes. Joe Wicks, a fitness coach, became particularly popular on SM during this time, posting daily workouts that millions of people worldwide followed. Objective:  This study aims to investigate the influence of Joe Wicks on SM and the impact of his content on physical activity levels among the public. Methods:  We used NodeXL Pro (Social Media Research Foundation) to collect data from X (formerly Twitter) over 54 days (March 23, 2020, to May 15, 2020), corresponding to the strictest lockdowns in the United Kingdom. We collected 290,649 posts, which we analyzed using social network analysis, thematic analysis, time-series analysis, and location analysis. Results:  We found that there was significant engagement with content generated by Wicks, including reposts, likes, and comments. The most common types of posts were those that contained images, videos, and text of young people (school-aged children) undertaking physical activity by watching content created by Joe Wicks and posts from schools encouraging pupils to engage with the content. Other shared posts included those that encouraged others to join the fitness classes run by Wicks and those that contained general commentary. We also found that Wicks? network of influence was extensive and complex. It contained numerous subcommunities and resembled a broadcast network shape. Other influencers added to engagement with Wicks via their networks. Our results show that influencers can create networks of influence that are exhibited in distinctive ways. Conclusions: Our study found that Joe Wicks was a highly influential figure on SM during the COVID-19 pandemic and that his content positively impacted physical activity levels among the public. Our findings suggest that influencers can play an important role in promoting public health and that government officials should consider working with influencers to communicate health messages and promote healthy behaviors. Our study has broader implications beyond the status of fitness influencers. Recognizing the critical role of individuals such as Joe Wicks in terms of health capital should be a critical area of inquiry for governments, public health authorities, and policy makers and mirrors the growing interest in health capital as part of embodied and digital experiences in everyday life. UR - https://www.jmir.org/2024/1/e49921 UR - http://dx.doi.org/10.2196/49921 UR - http://www.ncbi.nlm.nih.gov/pubmed/38551627 ID - info:doi/10.2196/49921 ER - TY - JOUR AU - Ng, Reuben AU - Indran, Nicole PY - 2024/3/29 TI - #ProtectOurElders: Analysis of Tweets About Older Asian Americans and Anti-Asian Sentiments During the COVID-19 Pandemic JO - J Med Internet Res SP - e45864 VL - 26 KW - AAPI KW - anti-Asian hate KW - anti-Asian KW - Asian Americans and Pacific Islanders KW - Asian-American KW - content analysis KW - coronavirus KW - COVID-19 KW - discourse KW - discriminate KW - discrimination KW - discriminatory KW - Pacific Islander KW - racial KW - racism KW - racist KW - SARS-CoV-2 KW - social media KW - tweet KW - Twitter N2 - Background: A silver lining to the COVID-19 pandemic is that it cast a spotlight on a long-underserved group. The barrage of attacks against older Asian Americans during the crisis galvanized society into assisting them in various ways. On Twitter, now known as X, support for them coalesced around the hashtag #ProtectOurElders. To date, discourse surrounding older Asian Americans has escaped the attention of gerontologists?a gap we seek to fill. Our study serves as a reflection of the level of support that has been extended to older Asian Americans, even as it provides timely insights that will ultimately advance equity for them. Objective: This study explores the kinds of discourse surrounding older Asian Americans during the COVID-19 crisis, specifically in relation to the surge in anti-Asian sentiments. The following questions guide this study: What types of discourse have emerged in relation to older adults in the Asian American community and the need to support them? How do age and race interact to shape these discourses? What are the implications of these discourses for older Asian Americans? Methods: We retrieved tweets (N=6099) through 2 search queries. For the first query, we collated tweets with the hashtag #ProtectOurElders. For the second query, we collected tweets with an age-based term, for example, ?elderly? or ?old(er) adults(s)? and either the hashtag #StopAAPIHate or #StopAsianHate. Tweets were posted from January 1, 2020, to August 1, 2023. After applying the exclusion criteria, the final data set contained 994 tweets. Inductive and deductive approaches informed our qualitative content analysis. Results: A total of 4 themes emerged, with 50.1% (498/994) of posts framing older Asian Americans as ?vulnerable and in need of protection? (theme 1). Tweets in this theme either singled them out as a group in need of protection because of their vulnerable status or discussed initiatives aimed at safeguarding their well-being. Posts in theme 2 (309/994, 31%) positioned them as ?heroic and resilient.? Relevant tweets celebrated older Asian Americans for displaying tremendous strength in the face of attack or described them as individuals not to be trifled with. Tweets in theme 3 (102/994, 10.2%) depicted them as ?immigrants who have made selfless contributions and sacrifices.? Posts in this section referenced the immense sacrifices made by older Asian Americans as they migrated to the United States, as well as the systemic barriers they had to overcome. Posts in theme 4 (85/994, 8.5%) venerated older Asian Americans as ?worthy of honor.? Conclusions: The COVID-19 crisis had the unintended effect of garnering greater support for older Asian Americans. It is consequential that support be extended to this group not so much by virtue of their perceived vulnerability but more so in view of their boundless contributions and sacrifices. UR - https://www.jmir.org/2024/1/e45864 UR - http://dx.doi.org/10.2196/45864 UR - http://www.ncbi.nlm.nih.gov/pubmed/38551624 ID - info:doi/10.2196/45864 ER - TY - JOUR AU - Chlabicz, Ma?gorzata AU - Nabo?ny, Aleksandra AU - Koszelew, Jolanta AU - ?aguna, Wojciech AU - Szpakowicz, Anna AU - Sowa, Pawe? AU - Budny, Wojciech AU - Guziejko, Katarzyna AU - Róg-Makal, Magdalena AU - Pancewicz, S?awomir AU - Kondrusik, Maciej AU - Czupryna, Piotr AU - Cudowska, Beata AU - Lebensztejn, Dariusz AU - Moniuszko-Malinowska, Anna AU - Wierzbicki, Adam AU - Kami?ski, A. Karol PY - 2024/3/29 TI - Medical Misinformation in Polish on the World Wide Web During the COVID-19 Pandemic Period: Infodemiology Study JO - J Med Internet Res SP - e48130 VL - 26 KW - infodemic KW - fake news KW - information credibility KW - online health information KW - evidence based medicine KW - EBM KW - false KW - credibility KW - credible KW - health information KW - online information KW - information quality KW - infoveillance KW - infodemiology KW - misinformation KW - disinformation N2 - Background: Although researchers extensively study the rapid generation and spread of misinformation about the novel coronavirus during the pandemic, numerous other health-related topics are contaminating the internet with misinformation that have not received as much attention. Objective: This study aims to gauge the reach of the most popular medical content on the World Wide Web, extending beyond the confines of the pandemic. We conducted evaluations of subject matter and credibility for the years 2021 and 2022, following the principles of evidence-based medicine with assessments performed by experienced clinicians. Methods: We used 274 keywords to conduct web page searches through the BuzzSumo Enterprise Application. These keywords were chosen based on medical topics derived from surveys administered to medical practitioners. The search parameters were confined to 2 distinct date ranges: (1) January 1, 2021, to December 31, 2021; (2) January 1, 2022, to December 31, 2022. Our searches were specifically limited to web pages in the Polish language and filtered by the specified date ranges. The analysis encompassed 161 web pages retrieved in 2021 and 105 retrieved in 2022. Each web page underwent scrutiny by a seasoned doctor to assess its credibility, aligning with evidence-based medicine standards. Furthermore, we gathered data on social media engagements associated with the web pages, considering platforms such as Facebook, Pinterest, Reddit, and Twitter. Results: In 2022, the prevalence of unreliable information related to COVID-19 saw a noteworthy decline compared to 2021. Specifically, the percentage of noncredible web pages discussing COVID-19 and general vaccinations decreased from 57% (43/76) to 24% (6/25) and 42% (10/25) to 30% (3/10), respectively. However, during the same period, there was a considerable uptick in the dissemination of untrustworthy content on social media pertaining to other medical topics. The percentage of noncredible web pages covering cholesterol, statins, and cardiology rose from 11% (3/28) to 26% (9/35) and from 18% (5/28) to 26% (6/23), respectively. Conclusions: Efforts undertaken during the COVID-19 pandemic to curb the dissemination of misinformation seem to have yielded positive results. Nevertheless, our analysis suggests that these interventions need to be consistently implemented across both established and emerging medical subjects. It appears that as interest in the pandemic waned, other topics gained prominence, essentially ?filling the vacuum? and necessitating ongoing measures to address misinformation across a broader spectrum of health-related subjects. UR - https://www.jmir.org/2024/1/e48130 UR - http://dx.doi.org/10.2196/48130 UR - http://www.ncbi.nlm.nih.gov/pubmed/38551638 ID - info:doi/10.2196/48130 ER - TY - JOUR AU - Xue, Jia AU - Zhang, Qiaoru AU - Zhang, Yun AU - Shi, Hong AU - Zheng, Chengda AU - Fan, Jingchuan AU - Zhang, Linxiao AU - Chen, Chen AU - Li, Luye AU - Shier, L. Micheal PY - 2024/3/27 TI - Bridging and Bonding Social Capital by Analyzing the Demographics, User Activities, and Social Network Dynamics of Sexual Assault Centers on Twitter: Mixed Methods Study JO - J Med Internet Res SP - e50552 VL - 26 KW - social media KW - Twitter KW - sexual assault KW - nonprofits KW - Canada KW - violence KW - geolocation KW - communication N2 - Background: Social media platforms have gained popularity as communication tools for organizations to engage with clients and the public, disseminate information, and raise awareness about social issues. From a social capital perspective, relationship building is seen as an investment, involving a complex interplay of tangible and intangible resources. Social media?based social capital signifies the diverse social networks that organizations can foster through their engagement on social media platforms. Literature underscores the great significance of further investigation into the scope and nature of social media use, particularly within sectors dedicated to service delivery, such as sexual assault organizations. Objective: This study aims to fill a research gap by investigating the use of Twitter by sexual assault support agencies in Canada. It seeks to understand the demographics, user activities, and social network structure within these organizations on Twitter, focusing on building social capital. The research questions explore the demographic profile, geographic distribution, and Twitter activity of these organizations as well as the social network dynamics of bridging and bonding social capital. Methods: This study used purposive sampling to investigate sexual assault centers in Canada with active Twitter accounts, resulting in the identification of 124 centers. The Twitter handles were collected, yielding 113 unique handles, and their corresponding Twitter IDs were obtained and validated. A total of 294,350 tweets were collected from these centers, covering >93.54% of their Twitter activity. Preprocessing was conducted to prepare the data, and descriptive analysis was used to determine the center demographics and age. Furthermore, geolocation mapping was performed to visualize the center locations. Social network analysis was used to explore the intricate relationships within the network of sexual assault center Twitter accounts, using various metrics to assess the network structure and connectivity dynamics. Results: The results highlight the substantial presence of sexual assault organizations on Twitter, particularly in provinces such as Ontario, British Columbia, and Quebec, underscoring the importance of tailored engagement strategies considering regional disparities. The analysis of Twitter account creation years shows a peak in 2012, followed by a decline in new account creations in subsequent years. The monthly tweet activity shows November as the most active month, whereas July had the lowest activity. The study also reveals variations in Twitter activity, account creation patterns, and social network dynamics, identifying influential social queens and marginalized entities within the network. Conclusions: This study presents a comprehensive landscape of the demographics and activities of sexual assault centers in Canada on Twitter. This study suggests that future research should explore the long-term consequences of social media use and examine stakeholder perceptions, providing valuable insights to improve communication practices within the nonprofit human services sector and further the missions of these organizations. UR - https://www.jmir.org/2024/1/e50552 UR - http://dx.doi.org/10.2196/50552 UR - http://www.ncbi.nlm.nih.gov/pubmed/38536222 ID - info:doi/10.2196/50552 ER - TY - JOUR AU - Mlambo, Christine Vongai AU - Keller, Eric AU - Mussatto, Caroline AU - Hwang, Gloria PY - 2024/3/27 TI - Development of a Medical Social Media Ethics Scale and Assessment of #IRad, #CardioTwitter, and #MedTwitter Posts: Mixed Methods Study JO - JMIR Infodemiology SP - e47770 VL - 4 KW - ethics KW - social media KW - conflict of interest KW - interventional radiology KW - X KW - Twitter KW - cardiology KW - privacy KW - ethical issues KW - medical social media KW - prevalence KW - professional KW - professionalism N2 - Background: Social media posts by clinicians are not bound by the same rules as peer-reviewed publications, raising ethical concerns that have not been extensively characterized or quantified. Objective: We aim to develop a scale to assess ethical issues on medical social media (SoMe) and use it to determine the prevalence of these issues among posts with 3 different hashtags: #MedTwitter, #IRad, and #CardioTwitter. Methods: A scale was developed based on previous descriptions of professionalism and validated via semistructured cognitive interviewing with a sample of 11 clinicians and trainees, interrater agreement, and correlation of 100 posts. The final scale assessed social media posts in 6 domains. This was used to analyze 1500 Twitter posts, 500 each from the 3 hashtags. Analysis of posts was limited to original Twitter posts in English made by health care professionals in North America. The prevalence of potential issues was determined using descriptive statistics and compared across hashtags using the Fisher exact and ?2 tests with Yates correction. Results: The final scale was considered reflective of potential ethical issues of SoMe by participants. There was good interrater agreement (Cohen ?=0.620, P<.01) and moderate to strong positive interrater correlation (=0.602, P<.001). The 6 scale domains showed minimal to no interrelation (Cronbach ?=0.206). Ethical concerns across all hashtags had a prevalence of 1.5% or less except the conflict of interest concerns on #IRad, which had a prevalence of 3.6% (n=18). Compared to #MedTwitter, posts with specialty-specific hashtags had more patient privacy and conflict of interest concerns. Conclusions: The SoMe professionalism scale we developed reliably reflects potential ethical issues. Ethical issues on SoMe are rare but important and vary in prevalence across medical communities. UR - https://infodemiology.jmir.org/2024/1/e47770 UR - http://dx.doi.org/10.2196/47770 UR - http://www.ncbi.nlm.nih.gov/pubmed/38536206 ID - info:doi/10.2196/47770 ER - TY - JOUR AU - Ullah, Nazifa AU - Martin, Sam AU - Poduval, Shoba PY - 2024/3/26 TI - A Snapshot of COVID-19 Vaccine Discourse Related to Ethnic Minority Communities in the United Kingdom Between January and April 2022: Mixed Methods Analysis JO - JMIR Form Res SP - e51152 VL - 8 KW - COVID-19 KW - ethnic minorities KW - vaccine KW - hesitancy KW - social media KW - discourse KW - minority groups N2 - Background: Existing literature highlights the role of social media as a key source of information for the public during the COVID-19 pandemic and its influence on vaccination attempts. Yet there is little research exploring its role in the public discourse specifically among ethnic minority communities, who have the highest rates of vaccine hesitancy (delay or refusal of vaccination despite availability of services). Objective: This study aims to understand the discourse related to minority communities on social media platforms Twitter and YouTube. Methods: Social media data from the United Kingdom was extracted from Twitter and YouTube using the software Netlytics and YouTube Data Tools to provide a ?snapshot? of the discourse between January and April 2022. A mixed method approach was used where qualitative data were contextualized into codes. Network analysis was applied to provide insight into the most frequent and weighted keywords and topics of conversations. Results: A total of 260 tweets and 156 comments from 4 YouTube videos were included in our analysis. Our data suggests that the most popular topics of conversation during the period sampled were related to communication strategies adopted during the booster vaccine rollout. These were noted to be divisive in nature and linked to wider conversations around racism and historical mistrust toward institutions. Conclusions: Our study suggests a shift in narrative from concerns about the COVID-19 vaccine itself, toward the strategies used in vaccination implementation, in particular the targeting of ethnic minority groups through vaccination campaigns. The implications for public health communication during crisis management in a pandemic context include acknowledging wider experiences of discrimination when addressing ethnic minority communities. UR - https://formative.jmir.org/2024/1/e51152 UR - http://dx.doi.org/10.2196/51152 UR - http://www.ncbi.nlm.nih.gov/pubmed/38530334 ID - info:doi/10.2196/51152 ER - TY - JOUR AU - Molenaar, Annika AU - Lukose, Dickson AU - Brennan, Linda AU - Jenkins, L. Eva AU - McCaffrey, A. Tracy PY - 2024/3/21 TI - Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study JO - J Med Internet Res SP - e47826 VL - 26 KW - food security KW - food insecurity KW - public health KW - sentiment analysis KW - topic modeling KW - natural language processing KW - infodemiology N2 - Background: Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited. Objective: This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security. Methods: A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia-based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years. Results: In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): ?Global production,? ?Food insecurity and health,? ?Use of food banks,? ?Giving to food banks,? ?Family poverty,? ?Food relief provision,? ?Global food insecurity,? ?Climate change,? ?Australian food insecurity,? and ?Human rights.? The topic ?Giving to food banks,? which focused on support and donation, had the highest proportion of positive sentiment, and ?Global food insecurity,? which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues. Conclusions: In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence-based decision-making in this area. UR - https://www.jmir.org/2024/1/e47826 UR - http://dx.doi.org/10.2196/47826 UR - http://www.ncbi.nlm.nih.gov/pubmed/38512326 ID - info:doi/10.2196/47826 ER - TY - JOUR AU - Onie, Sandersan AU - Armstrong, Oliver Susanne AU - Josifovski, Natasha AU - Berlinquette, Patrick AU - Livingstone, Nicola AU - Holland, Sarah AU - Finemore, Coco AU - Gale, Nyree AU - Elder, Emma AU - Laggis, George AU - Heffernan, Cassandra AU - Theobald, Adam AU - Torok, Michelle AU - Shand, Fiona AU - Larsen, Mark PY - 2024/3/19 TI - The Effect of Explicit Suicide Language in Engagement With a Suicide Prevention Search Page Help-Seeking Prompt: Nonrandomized Trial JO - JMIR Ment Health SP - e50283 VL - 11 KW - suicide KW - suicide prevention KW - Google KW - Google Ads KW - internet search KW - explicit wording KW - mental health KW - suicidal KW - advertisement KW - advertisements KW - messaging KW - prevention signage KW - campaign KW - campaigns KW - distress KW - engagement KW - prompt KW - prompts KW - information seeking KW - help seeking KW - searching KW - search N2 - Background: Given that signage, messaging, and advertisements (ads) are the gateway to many interventions in suicide prevention, it is important that we understand what type of messaging works best for whom. Objective: We investigated whether explicitly mentioning suicide increases engagement using internet ads by investigating engagement with campaigns with different categories of keywords searched, which may reflect different cognitive states. Methods: We ran a 2-arm study Australia-wide, with or without ads featuring explicit suicide wording. We analyzed whether there were differences in engagement for campaigns with explicit and nonexplicit ads for low-risk (distressed but not explicitly suicidal), high-risk (explicitly suicidal), and help-seeking for suicide keywords. Results: Our analyses revealed that having explicit wording has opposite effects, depending on the search terms used: explicit wording reduced the engagement rate for individuals searching for low-risk keywords but increased engagement for those using high-risk keywords. Conclusions: The findings suggest that individuals who are aware of their suicidality respond better to campaigns that explicitly use the word ?suicide.? We found that individuals who search for low-risk keywords also respond to explicit ads, suggesting that some individuals who are experiencing suicidality search for low-risk keywords. UR - https://mental.jmir.org/2024/1/e50283 UR - http://dx.doi.org/10.2196/50283 UR - http://www.ncbi.nlm.nih.gov/pubmed/38502162 ID - info:doi/10.2196/50283 ER - TY - JOUR AU - Xian, Xuechang AU - Neuwirth, J. Rostam AU - Chang, Angela PY - 2024/3/19 TI - Government-Nongovernmental Organization (NGO) Collaboration in Macao?s COVID-19 Vaccine Promotion: Social Media Case Study JO - JMIR Infodemiology SP - e51113 VL - 4 KW - COVID-19 KW - government KW - vaccine KW - automated content analysis KW - Granger causality test KW - network agenda setting KW - QAP KW - social media N2 - Background: The COVID-19 pandemic triggered unprecedented global vaccination efforts, with social media being a popular tool for vaccine promotion. Objective: This study probes into Macao?s COVID-19 vaccine communication dynamics, with a focus on the multifaceted impacts of government agendas on social media. Methods: We scrutinized 22,986 vaccine-related Facebook posts from January 2020 to August 2022 in Macao. Using automated content analysis and advanced statistical methods, we unveiled intricate agenda dynamics between government and nongovernment entities. Results: ?Vaccine importance? and ?COVID-19 risk? were the most prominent topics co-occurring in the overall vaccine communication. The government tended to emphasize ?COVID-19 risk? and ?vaccine effectiveness,? while regular users prioritized vaccine safety and distribution, indicating a discrepancy in these agendas. Nonetheless, the government has limited impact on regular users in the aspects of vaccine importance, accessibility, affordability, and trust in experts. The agendas of government and nongovernment users intertwined, illustrating complex interactions. Conclusions: This study reveals the influence of government agendas on public discourse, impacting environmental awareness, public health education, and the social dynamics of inclusive communication during health crises. Inclusive strategies, accommodating public concerns, and involving diverse stakeholders are paramount for effective social media communication during health crises. UR - https://infodemiology.jmir.org/2024/1/e51113 UR - http://dx.doi.org/10.2196/51113 UR - http://www.ncbi.nlm.nih.gov/pubmed/38502184 ID - info:doi/10.2196/51113 ER - TY - JOUR AU - Carboni, Alexa AU - Martini, Olnita AU - Kirk, Jessica AU - Marroquin, A. Nathaniel AU - Ricci, Corinne AU - Cheng, Melissa AU - Szeto, D. Mindy AU - Pulsipher, J. Kayd AU - Dellavalle, P. Robert PY - 2024/3/13 TI - Does Male Skin Care Content on Instagram Neglect Skin Cancer Prevention? JO - JMIR Dermatol SP - e50431 VL - 7 KW - men KW - male KW - male skin care KW - male skincare KW - sunscreen KW - sun protection KW - photoprotection KW - anti-aging KW - skin cancer prevention KW - Instagram KW - social media KW - marketing KW - advertising KW - dermatology KW - dermatologist KW - skin KW - man KW - oncology KW - oncologist UR - https://derma.jmir.org/2024/1/e50431 UR - http://dx.doi.org/10.2196/50431 UR - http://www.ncbi.nlm.nih.gov/pubmed/38477962 ID - info:doi/10.2196/50431 ER - TY - JOUR AU - Jia, Chenjin AU - Li, Pengcheng PY - 2024/3/8 TI - Generation Z?s Health Information Avoidance Behavior: Insights From Focus Group Discussions JO - J Med Internet Res SP - e54107 VL - 26 KW - information avoidance KW - health information KW - Generation Z KW - information overload KW - planned risk information avoidance model N2 - Background: Younger generations actively use social media to access health information. However, research shows that they also avoid obtaining health information online at times when confronted with uncertainty. Objective: This study aims to examine the phenomenon of health information avoidance among Generation Z, a representative cohort of active web users in this era. Methods: Drawing on the planned risk information avoidance model, we adopted a qualitative approach to explore the factors related to information avoidance within the context of health and risk communication. The researchers recruited 38 participants aged 16 to 25 years for the focus group discussion sessions. Results: In this study, we sought to perform a deductive qualitative analysis of the focus group interview content with open, focused, and theoretical coding. Our findings support several key components of the planned risk information avoidance model while highlighting the underlying influence of cognition on emotions. Specifically, socioculturally, group identity and social norms among peers lead some to avoid health information. Cognitively, mixed levels of risk perception, conflicting values, information overload, and low credibility of information sources elicited their information avoidance behaviors. Affectively, negative emotions such as anxiety, frustration, and the desire to stay positive contributed to avoidance. Conclusions: This study has implications for understanding young users? information avoidance behaviors in both academia and practice. UR - https://www.jmir.org/2024/1/e54107 UR - http://dx.doi.org/10.2196/54107 UR - http://www.ncbi.nlm.nih.gov/pubmed/38457223 ID - info:doi/10.2196/54107 ER - TY - JOUR AU - Boatman, Dannell AU - Starkey, Abby AU - Acciavatti, Lori AU - Jarrett, Zachary AU - Allen, Amy AU - Kennedy-Rea, Stephenie PY - 2024/3/8 TI - Using Social Listening for Digital Public Health Surveillance of Human Papillomavirus Vaccine Misinformation Online: Exploratory Study JO - JMIR Infodemiology SP - e54000 VL - 4 KW - human papillomavirus KW - HPV KW - vaccine KW - vaccines KW - vaccination KW - vaccinations KW - sexually transmitted infection KW - STI KW - sexually transmitted disease KW - STD KW - sexual transmission KW - sexually transmitted KW - social media KW - social listening KW - cancer KW - surveillance KW - health communication KW - misinformation KW - artificial intelligence KW - AI KW - infodemiology KW - infoveillance KW - oncology UR - https://infodemiology.jmir.org/2024/1/e54000 UR - http://dx.doi.org/10.2196/54000 UR - http://www.ncbi.nlm.nih.gov/pubmed/38457224 ID - info:doi/10.2196/54000 ER - TY - JOUR AU - Kite, James AU - Grunseit, Anne AU - Mitchell, Glenn AU - Cooper, Pip AU - Chan, Lilian AU - Huang, Bo-Huei AU - Thomas, Margaret AU - O'Hara, Blythe AU - Smith, Abby PY - 2024/3/5 TI - Impact of Traditional and New Media on Smoking Intentions and Behaviors: Secondary Analysis of Tasmania?s Tobacco Control Mass Media Campaign Program, 2019-2021 JO - J Med Internet Res SP - e47128 VL - 26 KW - mass media campaign KW - tobacco control KW - evaluation KW - social media campaign KW - social media KW - digital platform KW - tobacco KW - smoking KW - survey N2 - Background: Tasmania, the smallest state by population in Australia, has a comprehensive tobacco control mass media campaign program that includes traditional (eg, television) and ?new? channels (eg, social media), run by Quit Tasmania. The campaign targets adult smokers, in particular men aged 18-44 years, and people from low socioeconomic areas. Objective: This study assesses the impact of the 2019-2021 campaign program on smokers? awareness of the campaign program, use of Quitline, and smoking-related intentions and behaviors. Methods: We used a tracking survey (conducted 8 times per year, immediately following a burst of campaign activity) to assess campaign recall and recognition, intentions to quit, and behavioral actions taken in response to the campaigns. The sample size was approximately 125 participants at each survey wave, giving a total sample size of 2000 participants over the 2 years. We merged these data with metrics including television target audience rating points, digital and Facebook (Meta) analytics, and Quitline activity data, and conducted regression and time-series modeling. Results: Over the evaluation period, unprompted recall of any Quit Tasmania campaign was 18%, while prompted recognition of the most recent campaign was 50%. Over half (52%) of those who recognized a Quit Tasmania campaign reported that they had performed or considered a quitting-related behavioral action in response to the campaign. In the regression analyses, we found having different creatives within a single campaign burst was associated with higher campaign recall and recognition and an increase in the strength of behavioral actions taken. Higher target audience rating points were associated with higher campaign recall (but not recognition) and an increase in quit intentions, but not an increase in behavioral actions taken. Higher Facebook advertisement reach was associated with lower recall among survey participants, but recognition was higher when digital channels were used. The time-series analyses showed no systematic trends in Quitline activity over the evaluation period, but Quitline activity was higher when Facebook reach and advertisement spending were higher. Conclusions: Our evaluation suggests that a variety of creatives should be used simultaneously and supports the continued use of traditional broadcast channels, including television. However, the impact of television on awareness and behavior may be weakening. Future campaign evaluations should closely monitor the effectiveness of television as a result. We are also one of the first studies to explicitly examine the impact of digital and social media, finding some evidence that they influence quitting-related outcomes. While this evidence is promising for campaign implementation, future evaluations should consider adopting rigorous methods to further investigate this relationship. UR - https://www.jmir.org/2024/1/e47128 UR - http://dx.doi.org/10.2196/47128 UR - http://www.ncbi.nlm.nih.gov/pubmed/38441941 ID - info:doi/10.2196/47128 ER - TY - JOUR AU - Groshon, Laurie AU - Waring, E. Molly AU - Blashill, J. Aaron AU - Dean, Kristen AU - Bankwalla, Sanaya AU - Palmer, Lindsay AU - Pagoto, Sherry PY - 2024/3/4 TI - A Content Analysis of Indoor Tanning Twitter Chatter During COVID-19 Shutdowns: Cross-Sectional Qualitative Study JO - JMIR Dermatol SP - e54052 VL - 7 KW - attitude KW - attitudes KW - content analysis KW - dermatology KW - opinion KW - perception KW - perceptions KW - perspective KW - perspectives KW - sentiment KW - skin KW - social media KW - sun KW - tan KW - tanner KW - tanners KW - tanning KW - tweet KW - tweets KW - Twitter N2 - Background: Indoor tanning is a preventable risk factor for skin cancer. Statewide shutdowns during the COVID-19 pandemic resulted in temporary closures of tanning businesses. Little is known about how tanners reacted to losing access to tanning businesses. Objective: This study aimed to analyze Twitter (subsequently rebranded as X) chatter about indoor tanning during the statewide pandemic shutdowns. Methods: We collected tweets from March 15 to April 30, 2020, and performed a directed content analysis of a random sample of 20% (1165/5811) of tweets from each week. The 2 coders independently rated themes (?=0.67-1.0; 94%-100% agreement). Results: About half (589/1165, 50.6%) of tweets were by people unlikely to indoor tan, and most of these mocked tanners or the act of tanning (562/589, 94.9%). A total of 34% (402/1165) of tweets were posted by users likely to indoor tan, and most of these (260/402, 64.7%) mentioned missing tanning beds, often citing appearance- or mood-related reasons or withdrawal. Some tweets by tanners expressed a desire to purchase or use home tanning beds (90/402, 22%), while only 3.9% (16/402) mentioned tanning alternatives (eg, self-tanner). Very few tweets (29/1165, 2.5%) were public health messages about the dangers of indoor tanning. Conclusions: Findings revealed that during statewide shutdowns, half of the tweets about indoor tanning were mocking tanning bed users and the tanned look, while about one-third were indoor tanners reacting to their inability to access tanning beds. Future work is needed to understand emerging trends in tanning post pandemic. UR - https://derma.jmir.org/2024/1/e54052 UR - http://dx.doi.org/10.2196/54052 UR - http://www.ncbi.nlm.nih.gov/pubmed/38437006 ID - info:doi/10.2196/54052 ER - TY - JOUR AU - Deiner, S. Michael AU - Deiner, A. Natalie AU - Hristidis, Vagelis AU - McLeod, D. Stephen AU - Doan, Thuy AU - Lietman, M. Thomas AU - Porco, C. Travis PY - 2024/3/1 TI - Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study JO - J Med Internet Res SP - e49139 VL - 26 KW - conjunctivitis KW - microblog KW - social media KW - generative large language model KW - Generative Pre-trained Transformers KW - GPT-3.5 KW - GPT-4 KW - epidemic detection KW - Twitter KW - X formerly known as Twitter KW - infectious eye disease N2 - Background: Previous work suggests that Google searches could be useful in identifying conjunctivitis epidemics. Content-based assessment of social media content may provide additional value in serving as early indicators of conjunctivitis and other systemic infectious diseases. Objective: We investigated whether large language models, specifically GPT-3.5 and GPT-4 (OpenAI), can provide probabilistic assessments of whether social media posts about conjunctivitis could indicate a regional outbreak. Methods: A total of 12,194 conjunctivitis-related tweets were obtained using a targeted Boolean search in multiple languages from India, Guam (United States), Martinique (France), the Philippines, American Samoa (United States), Fiji, Costa Rica, Haiti, and the Bahamas, covering the time frame from January 1, 2012, to March 13, 2023. By providing these tweets via prompts to GPT-3.5 and GPT-4, we obtained probabilistic assessments that were validated by 2 human raters. We then calculated Pearson correlations of these time series with tweet volume and the occurrence of known outbreaks in these 9 locations, with time series bootstrap used to compute CIs. Results: Probabilistic assessments derived from GPT-3.5 showed correlations of 0.60 (95% CI 0.47-0.70) and 0.53 (95% CI 0.40-0.65) with the 2 human raters, with higher results for GPT-4. The weekly averages of GPT-3.5 probabilities showed substantial correlations with weekly tweet volume for 44% (4/9) of the countries, with correlations ranging from 0.10 (95% CI 0.0-0.29) to 0.53 (95% CI 0.39-0.89), with larger correlations for GPT-4. More modest correlations were found for correlation with known epidemics, with substantial correlation only in American Samoa (0.40, 95% CI 0.16-0.81). Conclusions: These findings suggest that GPT prompting can efficiently assess the content of social media posts and indicate possible disease outbreaks to a degree of accuracy comparable to that of humans. Furthermore, we found that automated content analysis of tweets is related to tweet volume for conjunctivitis-related posts in some locations and to the occurrence of actual epidemics. Future work may improve the sensitivity and specificity of these methods for disease outbreak detection. UR - https://www.jmir.org/2024/1/e49139 UR - http://dx.doi.org/10.2196/49139 UR - http://www.ncbi.nlm.nih.gov/pubmed/38427404 ID - info:doi/10.2196/49139 ER - TY - JOUR AU - Miller, Tiev AU - Hosseinzadeh, Ali AU - Thordarson, Thomas AU - Kalimullina, Tamila AU - Samejima, Soshi AU - Shackleton, Claire AU - Malik, Raza AU - Calderón-Juárez, Martín AU - Sachdeva, Rahul AU - Krassioukov, Andrei PY - 2024/2/23 TI - Web-Based Information on Spinal Cord Stimulation: Qualitative Assessment of Publicly Accessible Online Resources JO - JMIR Public Health Surveill SP - e50031 VL - 10 KW - access to information KW - consumer health information KW - internet KW - spinal cord stimulation KW - web-based information KW - communication KW - quality KW - readability KW - Google Trends KW - misinformation KW - synthesis N2 - Background: Despite the growing accessibility of web-based information related to spinal cord stimulation (SCS), the content and quality of commonly encountered websites remain unknown. Objective: This study aimed to assess the content and quality of web-based information on SCS. Methods: This qualitative study was prospectively registered in Open Science Framework. Google Trends was used to identify the top trending, SCS-related search queries from 2012 to 2022. Top queried terms were then entered into separate search engines. Information found on websites within the first 2 pages of results was extracted and assessed for quality using the DISCERN instrument, the Journal of the American Medical Association benchmark criteria, and the Health on the Net Foundation code of conduct certification. Website readability and SCS-related information were also assessed. Results: After exclusions, 42 unique sites were identified (scientific resources: n=6, nonprofit: n=12, for-profit: n=20, news or media: n=2, and personal or blog: n=2). Overall, information quality was moderate (DISCERN). Few sites met all the Journal of the American Medical Association benchmark criteria (n=3, 7%) or had Health on the Net Foundation certification (n=7, 16%). On average, information was difficult to read, requiring a 9th- to 10th-grade level of reading comprehension. Sites described SCS subcategories (n=14, 33%), indications (n=38, 90%), contraindications (n=14, 33%), side effects or risks (n=28, 66%), device considerations (n=25, 59%), follow-up (n=22, 52%), expected outcomes (n=31, 73%), provided authorship details (n=20, 47%), and publication dates (n=19, 45%). The proportion of for-profit sites reporting authorship information was comparatively less than other site types (n=3, 15%). Almost all sites focused on surgically implanted SCS (n=37, 88%). On average, nonprofit sites contained the greatest number of peer-reviewed reference citations (n=6, 50%). For-profit sites showed the highest proportion of physician or clinical referrals among site types (n=17, 85%) indicating implicit bias (ie, auto-referral). Conclusions: Overall, our findings suggest the public may be exposed to incomplete or dated information from unidentifiable sources that could put consumers and patient groups at risk. UR - https://publichealth.jmir.org/2024/1/e50031 UR - http://dx.doi.org/10.2196/50031 UR - http://www.ncbi.nlm.nih.gov/pubmed/38393781 ID - info:doi/10.2196/50031 ER - TY - JOUR AU - Davidson, Anne Cara AU - Booth, Richard AU - Jackson, Teresa Kimberley AU - Mantler, Tara PY - 2024/2/23 TI - Toxic Relationships Described by People With Breast Cancer on Reddit: Topic Modeling Study JO - JMIR Cancer SP - e48860 VL - 10 KW - breast cancer KW - intimate partner violence KW - meaning extraction method KW - Reddit KW - sentiment analysis KW - social media KW - social support KW - toxic relationships KW - topic modelling N2 - Background: Social support is essential to promoting optimal health outcomes for women with breast cancer. However, an estimated 12% of women with breast cancer simultaneously experience intimate partner violence (IPV; physical, psychological, or sexual abuse by an intimate partner). Women who experience IPV during breast cancer may lack traditional social support, and thus seek out alternative sources of support. Online community forums, such as Reddit, can provide accessible social connections within breast cancer?specific communities. However, it is largely unknown how women with breast cancer use Reddit to describe and seek support for experiences of IPV. Objective: This study aims to explore how patients with breast cancer describe toxic relationships with their partners and immediate family members on Reddit. Methods: This exploratory, cross-sectional, topic-modeling study analyzed textual data from 96 users in the r/breastcancer subreddit in February 2023. The meaning extraction method, inclusive of principal component analysis, was used to identify underlying components. Components were subjected to sentiment analysis and summative content analysis with emergent categorical development to articulate themes. Results: Seven themes emerged related to toxic relationships: (1) contextualizing storytelling with lymph nodes, (2) toxic behavior and venting emotions, (3) abandonment and abuse following diagnosis, (4) toxic relationships and social-related fears, (5) inner strength and navigating breast cancer over time, (6) assessing social relationships and interactions, and (7) community advice and support. Toxic relationships were commonly characterized by isolation, abandonment, and emotional abuse, which had profound emotional consequences for patients. Reddit facilitated anonymous venting about toxic relationships that helped patients cope with intense feelings and stress. Exchanging advice and support about navigating toxic relationships during breast cancer were core functions of the r/breastcancer community. Conclusions: Findings emphasized the value of Reddit as a source of social support for patients with breast cancer experiencing toxic relationships. Clinicians who understand that many patients with breast cancer experience toxic relationships and considerable psychological sequelae are better prepared to support their patients? holistic well-being. Further investigation of Reddit as a possible resource for advice, information, and support has the potential to help inform clinical practice and subsequently, patient health outcomes. UR - https://cancer.jmir.org/2024/1/e48860 UR - http://dx.doi.org/10.2196/48860 UR - http://www.ncbi.nlm.nih.gov/pubmed/38393769 ID - info:doi/10.2196/48860 ER - TY - JOUR AU - ElSherief, Mai AU - Sumner, Steven AU - Krishnasamy, Vikram AU - Jones, Christopher AU - Law, Royal AU - Kacha-Ochana, Akadia AU - Schieber, Lyna AU - De Choudhury, Munmun PY - 2024/2/23 TI - Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study JO - JMIR Form Res SP - e44726 VL - 8 KW - addiction treatment KW - machine learning KW - misinformation KW - natural language processing KW - opioid use disorder KW - social media KW - substance use N2 - Background: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. Objective: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD. Methods: Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation. Results: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. Conclusions: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content. UR - https://formative.jmir.org/2024/1/e44726 UR - http://dx.doi.org/10.2196/44726 UR - http://www.ncbi.nlm.nih.gov/pubmed/38393772 ID - info:doi/10.2196/44726 ER - TY - JOUR AU - van de Baan, Frank AU - Gifford, Rachel AU - Ruwaard, Dirk AU - Fleuren, Bram AU - Westra, Daan PY - 2024/2/21 TI - Newspaper Coverage of Hospitals During a Prolonged Health Crisis: Longitudinal Mixed Methods Study JO - JMIR Public Health Surveill SP - e48134 VL - 10 KW - health communication KW - news coverage KW - media KW - misinformation KW - accuracy KW - news KW - reporting KW - newspaper KW - knowledge translation KW - COVID-19 KW - dissemination KW - communication N2 - Background: It is important for health organizations to communicate with the public through newspapers during health crises. Although hospitals were a main source of information for the public during the COVID-19 pandemic, little is known about how this information was presented to the public through (web-based) newspaper articles. Objective: This study aims to examine newspaper reporting on the situation in hospitals during the first year of the COVID-19 pandemic in the Netherlands and to assess the degree to which the reporting in newspapers aligned with what occurred in practice. Methods: We used a mixed methods longitudinal design to compare internal data from all hospitals (n=5) located in one of the most heavily affected regions of the Netherlands with the information reported by a newspaper covering the same region. The internal data comprised 763 pages of crisis meeting documents and 635 minutes of video communications. A total of 14,401 newspaper articles were retrieved from the LexisNexis Academic (RELX Group) database, of which 194 (1.3%) articles were included for data analysis. For qualitative analysis, we used content and thematic analyses. For quantitative analysis, we used chi-square tests. Results: The content of the internal data was categorized into 12 themes: COVID-19 capacity; regular care capacity; regional, national, and international collaboration; human resources; well-being; public support; material resources; innovation; policies and protocols; finance; preparedness; and ethics. Compared with the internal documents, the newspaper articles focused significantly more on the themes COVID-19 capacity (P<.001), regular care capacity (P<.001), and public support (P<.001) during the first year of the pandemic, whereas they focused significantly less on the themes material resources (P=.004) and policies and protocols (P<.001). Differences in attention toward themes were mainly observed between the first and second waves of the pandemic and at the end of the third wave. For some themes, the attention in the newspaper articles preceded the attention given to these themes in the internal documents. Reporting was done through various forms, including diary articles written from the perspective of the hospital staff. No indication of the presence of misinformation was found in the newspaper articles. Conclusions: Throughout the first year of the pandemic, newspaper articles provided coverage on the situation of hospitals and experiences of staff. The focus on themes within newspaper articles compared with internal hospital data differed significantly for 5 (42%) of the 12 identified themes. The discrepancies between newspapers and hospitals in their focus on themes could be attributed to their gatekeeping roles. Both parties should be aware of their gatekeeping role and how this may affect information distribution. During health crises, newspapers can be a credible source of information for the public. The information can also be valuable for hospitals themselves, as it allows them to anticipate internal and external developments. UR - https://publichealth.jmir.org/2024/1/e48134 UR - http://dx.doi.org/10.2196/48134 UR - http://www.ncbi.nlm.nih.gov/pubmed/38381496 ID - info:doi/10.2196/48134 ER - TY - JOUR AU - Olsson, Eva Sofia AU - Sreepad, Bhavana AU - Lee, Trevor AU - Fasih, Manal AU - Fijany, Arman PY - 2024/2/20 TI - Public Interest in Acetyl Hexapeptide-8: Longitudinal Analysis JO - JMIR Dermatol SP - e54217 VL - 7 KW - acetyl-hexapeptide-8 KW - anti-aging KW - anti-wrinkle KW - Argireline KW - BoNT KW - botox KW - botulinum neurotoxin KW - cosmetic dermatology KW - cosmetic KW - dermatologist KW - dermatology KW - injectable neurotoxin KW - neurotoxin KW - skin specialist KW - topical agent KW - topical N2 - Background: Acetyl hexapeptide-8, also known as Argireline, is a topical, short-acting, synthetic peptide that has recently gained popularity for its antiwrinkle effects. This agent has emerged as a more accessible alternative to botulinum neurotoxin. Objective: This study evaluates the public interest in acetyl hexapeptide-8 in the United States from 2013 to 2023, as described by search volume on Google, the most-used search engine. Methods: We analyzed the longitudinal relative monthly search volume from January 1, 2013, to January 1, 2023, for acetyl hexapeptide?related terms. We compared the internet search trends for ?Botox? during this period to ?Argireline.? Results: The terms ?Argireline? and ?Botox in a Bottle? both had substantial increases in search volume in 2022. Although its search volume is drastically increasing, ?Argireline? was less searched than ?Botox,? which had a stable, up-trending search volume over the past decade. Conclusions: The increasing interest in acetyl hexapeptide-8 may be due to its cost-effectiveness and use as a botulinum neurotoxin alternative. Affordability, over-the-counter availability, and ease of self-application of the agent suggest its potential to enhance accessibility to cosmetic dermatologic care. UR - https://derma.jmir.org/2024/1/e54217 UR - http://dx.doi.org/10.2196/54217 UR - http://www.ncbi.nlm.nih.gov/pubmed/38376906 ID - info:doi/10.2196/54217 ER - TY - JOUR AU - Murakami, Kentaro AU - Shinozaki, Nana AU - Okuhara, Tsuyoshi AU - McCaffrey, A. Tracy AU - Livingstone, E. M. Barbara PY - 2024/2/14 TI - Prevalence and Correlates of Dietary and Nutrition Information Seeking Through Various Web-Based and Offline Media Sources Among Japanese Adults: Web-Based Cross-Sectional Study JO - JMIR Public Health Surveill SP - e54805 VL - 10 KW - nutrition KW - diet KW - information seeking KW - health literacy KW - food literacy KW - diet quality KW - Japan N2 - Background: The advent of the internet has changed the landscape of available nutrition information. However, little is known about people?s information-seeking behavior toward healthy eating and its potential consequences. Objective: We aimed to examine the prevalence and correlates of nutrition information seeking from various web-based and offline media sources. Methods: This cross-sectional study included 5998 Japanese adults aged 20 to 79 years participating in a web-based questionnaire survey (February and March 2023). The dependent variable was the regular use of web-based and offline media as a reliable source of nutrition information. The main independent variables included health literacy, food literacy, and diet quality, which were assessed using validated tools, as well as sociodemographic factors (sex, age, education level, and nutrition- and health-related occupations). Results: The top source of nutrition information was television (1973/5998, 32.89%), followed by web searches (1333/5998, 22.22%), websites of government and medical manufacturers (997/5998, 16.62%), newspapers (901/5998, 15.02%), books and magazines (697/5998, 11.62%), and video sites (eg, YouTube; 634/5998, 10.57%). Multivariable logistic regression showed that higher health literacy was associated with higher odds of using all the individual sources examined; odds ratios (ORs) for 1-point score increase ranged from 1.27 (95% CI 1.09-1.49) to 1.81 (95% CI 1.57-2.09). By contrast, food literacy was inversely associated with the use of television (OR 0.65, 95% CI 0.55-0.77), whereas it was positively associated with the use of websites of government and medical manufacturers (OR 1.98, 95% CI 1.62-2.44), books and magazines (OR 2.09, 95% CI 1.64-2.66), and video sites (OR 1.53, 95% CI 1.19-1.96). Furthermore, diet quality was positively associated with the use of newspapers (OR 1.02, 95% CI 1.01-1.03) and books and magazines (OR 1.03, 95% CI 1.02-1.04). Being female was associated with using television and books and magazines, whereas being male was associated with using websites of government and medical manufacturers, newspapers, and video sites. Age was positively associated with using newspapers and inversely associated with using websites of government and medical manufacturers and video sites. People with higher education were more likely to refer to websites of government and medical manufacturers and newspapers but less likely to use television and video sites. Dietitians were more likely to use websites of government and medical manufacturers and books and magazines than the general public but less likely to use television and video sites. Conclusions: We identified various web-based and offline media sources regularly used by Japanese adults when seeking nutrition information, and their correlates varied widely. A lack of positive associations between the use of the top 2 major sources (television and web searches) and food literacy or diet quality is highlighted. These findings provide useful insights into the potential for developing and disseminating evidence-based health promotion materials. UR - https://publichealth.jmir.org/2024/1/e54805 UR - http://dx.doi.org/10.2196/54805 UR - http://www.ncbi.nlm.nih.gov/pubmed/38354021 ID - info:doi/10.2196/54805 ER - TY - JOUR AU - Jaiswal, Aditi AU - Washington, Peter PY - 2024/2/14 TI - Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study JO - JMIR Form Res SP - e52660 VL - 8 KW - autism KW - autism spectrum disorder KW - machine learning KW - natural language processing KW - public health KW - sentiment analysis KW - social media analysis KW - Twitter N2 - Background: The increasing use of social media platforms has given rise to an unprecedented surge in user-generated content, with millions of individuals publicly sharing their thoughts, experiences, and health-related information. Social media can serve as a useful means to study and understand public health. Twitter (subsequently rebranded as ?X?) is one such social media platform that has proven to be a valuable source of rich information for both the general public and health officials. We conducted the first study applying Twitter data mining to autism screening. Objective: We aimed to study the feasibility of autism screening from Twitter data and discuss the ethical implications of such models. Methods: We developed a machine learning model to attempt to distinguish individuals with autism from their neurotypical peers based on the textual patterns from their public communications on Twitter. We collected 6,515,470 tweets from users? self-identification with autism using ?#ActuallyAutistic? and a separate control group. To construct the data set, we targeted English-language tweets using the search query ?#ActuallyAutistic? posted from January 1, 2014 to December 31, 2022. We encrypted all user IDs and stripped the tweets of identifiable information such as the associated email address prior to analysis. From these tweets, we identified unique users who used keywords such as ?autism? OR ?autistic? OR ?neurodiverse? in their profile description and collected all the tweets from their timelines. To build the control group data set, we formulated a search query excluding the hashtag ?#ActuallyAutistic? and collected 1000 tweets per day during the same time period. We trained a word2vec model and an attention-based, bidirectional long short-term memory model to validate the performance of per-tweet and per-profile classification models. We deleted the data set and the models after our analysis. Results: Our tweet classifier reached a 73% accuracy, a 0.728 area under the receiver operating characteristic curve score, and an 0.71 F1-score using word2vec representations fed into a logistic regression model, while the user profile classifier achieved an 0.78 area under the receiver operating characteristic curve score and an F1-score of 0.805 using an attention-based, bidirectional long short-term memory model. Conclusions: We have shown that it is feasible to train machine learning models using social media data to predict use of the #ActuallyAutistic hashtag, an imperfect proxy for self-reported autism. While analyzing textual differences in naturalistic text has the potential to help clinicians screen for autism, there remain ethical questions that must be addressed for such research to move forward and to translate into the real world. While machine learning has the potential to improve behavioral research, there are still a plethora of ethical issues in digital phenotyping studies using social media with respect to user consent of marginalized populations. Achieving this requires a more inclusive approach during the model development process that involves the autistic community directly in the ideation and consent processes. UR - https://formative.jmir.org/2024/1/e52660 UR - http://dx.doi.org/10.2196/52660 UR - http://www.ncbi.nlm.nih.gov/pubmed/38354045 ID - info:doi/10.2196/52660 ER - TY - JOUR AU - Castillo, R. Louise I. AU - Tran, Vivian AU - Brachaniec, Mary AU - Chambers, T. Christine AU - Chessie, Kelly AU - Couros, Alec AU - LeRuyet, Andre AU - LeRuyet, Charmayne AU - Thorpe, Lilian AU - Williams, Jaime AU - Wheelwright, Sara AU - Hadjistavropoulos, Thomas PY - 2024/2/8 TI - The #SeePainMoreClearly Phase II Pain in Dementia Social Media Campaign: Implementation and Evaluation Study JO - JMIR Aging SP - e53025 VL - 7 KW - knowledge translation KW - Twitter KW - older adults KW - Facebook KW - knowledge mobilization N2 - Background: Social media platforms have been effective in raising awareness of the underassessment and undertreatment of pain in dementia. Objective: After a successful pilot campaign, we aimed to scale our pain-in-dementia knowledge mobilization pilot initiative (ie, #SeePainMoreClearly) to several social media platforms with the aid of a digital media partner. The goal of the initiative was to increase awareness of the challenges in the assessment and management of pain among people with dementia. A variety of metrics were implemented to evaluate the effort. Through this work, we endeavored to highlight key differences between our pilot initiative (which was a grassroots initiative), focusing largely on Twitter and YouTube, and the current science-media partnership. We also aimed to generate recommendations suitable for other social media campaigns related to health or aging. Methods: Evidence-based information about pain in dementia was summarized into engaging content (eg, videos) tailored to the needs of various knowledge users (eg, health professionals, families, and policy makers). We disseminated information using Facebook (Meta Platforms), Twitter (X Corp), YouTube (Alphabet Inc), Instagram (Meta Platforms), and LinkedIn (LinkedIn Corp) and measured the success of the initiative over a 12-month period (2020 to 2021). The evaluation methods focused on web analytics and questionnaires related to social media content. Knowledge users? web responses about the initiative and semistructured interviews were analyzed using thematic analysis. Results: During the course of the campaign, >700 posts were shared across all platforms. Web analytics showed that we drew >60,000 users from 82 countries to our resource website. Of the social media platforms used, Facebook was the most effective in reaching knowledge users (ie, over 1,300,000 users). Questionnaire responses from users were favorable; interview responses indicated that the information shared throughout the initiative increased awareness of the problem of pain in dementia and influenced respondent behavior. Conclusions: In this investigation, we demonstrated success in directing knowledge users to a resource website with practical information that health professionals could use in patient care along with pain assessment and management information for caregivers and people living with dementia. The evaluation metrics suggested no considerable differences between our pilot campaign and broader initiative when accounting for the length of time of each initiative. The limitations of large-scale health campaigns were noted, and recommendations were outlined for other researchers aiming to leverage social media as a knowledge mobilization tool. UR - https://aging.jmir.org/2024/1/e53025 UR - http://dx.doi.org/10.2196/53025 UR - http://www.ncbi.nlm.nih.gov/pubmed/38329793 ID - info:doi/10.2196/53025 ER - TY - JOUR AU - Ni, Chen-xu AU - Fei, Yi-bo AU - Wu, Ran AU - Cao, Wen-xiang AU - Liu, Wenhao AU - Huang, Fang AU - Shen, Fu-ming AU - Li, Dong-jie PY - 2024/2/7 TI - Tumor Immunotherapy?Related Information on Internet-Based Videos Commonly Used by the Chinese Population: Content Quality Analysis JO - JMIR Form Res SP - e50561 VL - 8 KW - immunotherapy KW - internet videos KW - quality KW - misinformation KW - health informatics KW - Chinese N2 - Background: Tumor immunotherapy is an innovative treatment today, but there are limited data on the quality of immunotherapy information on social networks. Dissemination of misinformation through the internet is a major social issue. Objective: Our objective was to characterize the quality of information and presence of misinformation about tumor immunotherapy on internet-based videos commonly used by the Chinese population. Methods: Using the keyword ?tumor immunotherapy? in Chinese, we searched TikTok, Tencent, iQIYI, and BiliBili on March 5, 2022. We reviewed the 118 screened videos using the Patient Education Materials Assessment Tool?a validated instrument to collect consumer health information. DISCERN quality criteria and the JAMA (Journal of the American Medical Association) Benchmark Criteria were used for assessing the quality and reliability of the health information. The videos? content was also evaluated. Results: The 118 videos about tumor immunotherapy were mostly uploaded by channels dedicated to lectures, health-related animations, and interviews; their median length was 5 minutes, and 79% of them were published in and after 2018. The median understandability and actionability of the videos were 71% and 71%, respectively. However, the quality of information was moderate to poor on the validated DISCERN and JAMA assessments. Only 12 videos contained misinformation (score of >1 out of 5). Videos with a doctor (lectures and interviews) not only were significantly less likely to contain misinformation but also had better quality and a greater forwarding number. Moreover, the results showed that more than half of the videos contain little or no content on the risk factors and management of tumor immunotherapy. Overall, over half of the videos had some or more information on the definition, symptoms, evaluation, and outcomes of tumor immunotherapy. Conclusions: Although the quality of immunotherapy information on internet-based videos commonly used by Chinese people is moderate, these videos have less misinformation and better content. Caution must be exercised when using these videos as a source of tumor immunotherapy?related information. UR - https://formative.jmir.org/2024/1/e50561 UR - http://dx.doi.org/10.2196/50561 UR - http://www.ncbi.nlm.nih.gov/pubmed/38324352 ID - info:doi/10.2196/50561 ER - TY - JOUR AU - Ueda, Ryuichiro AU - Han, Feng AU - Zhang, Hongjian AU - Aoki, Tomohiro AU - Ogasawara, Katsuhiko PY - 2024/2/6 TI - Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users JO - JMIR Infodemiology SP - e37881 VL - 4 KW - COVID-19 KW - sentiment analysis KW - Twitter KW - infodemiology KW - NLP KW - Natural Language Processing N2 - Background: The COVID-19 pandemic prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool for assessing individual and public emotions from text data, gained importance amid the pandemic. This study focuses on Japan?s early public health interventions during COVID-19, utilizing sentiment analysis in infodemiology to gauge public sentiment on social media regarding these interventions. Objective: This study aims to investigate shifts in public emotions and sentiments before and after the first state of emergency was declared in Japan. By analyzing both user-generated tweets and retweets, we aim to discern patterns in emotional responses during this critical period. Methods: We conducted a day-by-day analysis of Twitter (now known as X) data using 4,894,009 tweets containing the keywords ?corona,? ?COVID-19,? and ?new pneumonia? from March 23 to April 21, 2020, approximately 2 weeks before and after the first declaration of a state of emergency in Japan. We also processed tweet data into vectors for each word, employing the Fuzzy-C-Means (FCM) method, a type of cluster analysis, for the words in the sentiment dictionary. We set up 7 sentiment clusters (negative: anger, sadness, surprise, disgust; neutral: anxiety; positive: trust and joy) and conducted sentiment analysis of the tweet groups and retweet groups. Results: The analysis revealed a mix of positive and negative sentiments, with ?joy? significantly increasing in the retweet group after the state of emergency declaration. Negative emotions, such as ?worry? and ?disgust,? were prevalent in both tweet and retweet groups. Furthermore, the retweet group had a tendency to share more negative content compared to the tweet group. Conclusions: This study conducted sentiment analysis of Japanese tweets and retweets to explore public sentiments during the early stages of COVID-19 in Japan, spanning 2 weeks before and after the first state of emergency declaration. The analysis revealed a mix of positive (joy) and negative (anxiety, disgust) emotions. Notably, joy increased in the retweet group after the emergency declaration, but this group also tended to share more negative content than the tweet group. This study suggests that the state of emergency heightened positive sentiments due to expectations for infection prevention measures, yet negative information also gained traction. The findings propose the potential for further exploration through network analysis. UR - https://infodemiology.jmir.org/2024/1/e37881 UR - http://dx.doi.org/10.2196/37881 UR - http://www.ncbi.nlm.nih.gov/pubmed/38127840 ID - info:doi/10.2196/37881 ER - TY - JOUR AU - Stoffel, T. Sandro AU - Law, Hui Jing AU - Kerrison, Robert AU - Brewer, R. Hannah AU - Flanagan, M. James AU - Hirst, Yasemin PY - 2024/2/5 TI - Testing Behavioral Messages to Increase Recruitment to Health Research When Embedded Within Social Media Campaigns on Twitter: Web-Based Experimental Study JO - JMIR Form Res SP - e48538 VL - 8 KW - advertise KW - advertisement KW - advertisements KW - advertising KW - behavior change KW - behavioral KW - behaviour change KW - behavioural KW - campaign KW - campaigns KW - experimental design KW - message KW - messages KW - messaging KW - recruit KW - recruiting KW - recruitment KW - social media KW - social norms KW - Twitter N2 - Background: Social media is rapidly becoming the primary source to disseminate invitations to the public to consider taking part in research studies. There is, however, little information on how the contents of the advertisement can be communicated to facilitate engagement and subsequently promote intentions to participate in research. Objective: This paper describes an experimental study that tested different behavioral messages for recruiting study participants for a real-life observational case-control study. Methods: We included 1060 women in a web-based experiment and randomized them to 1 of 3 experimental conditions: standard advertisement (n=360), patient endorsement advertisement (n=345), and social norms advertisement (n=355). After seeing 1 of the 3 advertisements, participants were asked to state (1) their intention to take part in the advertised case-control study, (2) the ease of understanding the message and study aims, and (3) their willingness to be redirected to the website of the case-control study after completing the survey. Individuals were further asked to suggest ways to improve the messages. Intentions were compared between groups using ordinal logistic regression, reported in percentages, adjusted odds ratio (aOR), and 95% CIs. Results: Those who were in the patient endorsement and social norms?based advertisement groups had significantly lower intentions to take part in the advertised study compared with those in the standard advertisement group (aOR 0.73, 95% CI 0.55-0.97; P=.03 and aOR 0.69, 95% CI 0.52-0.92; P=.009, respectively). The patient endorsement advertisement was perceived to be more difficult to understand (aOR 0.65, 95% CI 0.48-0.87; P=.004) and to communicate the study aims less clearly (aOR 0.72, 95% CI 0.55-0.95; P=.01). While the patient endorsement advertisement had no impact on intention to visit the main study website, the social norms advertisement decreased willingness compared with the standard advertisement group (157/355, 44.2% vs 191/360, 53.1%; aOR 0.74, 95% CI 0.54-0.99; P=.02). The majority of participants (395/609, 64.8%) stated that the messages did not require changes, but some preferred clearer (75/609, 12.3%) and shorter (59/609, 9.7%) messages. Conclusions: The results of this study indicate that adding normative behavioral messages to simulated tweets decreased participant intention to take part in our web-based case-control study, as this made the tweet harder to understand. This suggests that simple messages should be used for participant recruitment through Twitter (subsequently rebranded X). UR - https://formative.jmir.org/2024/1/e48538 UR - http://dx.doi.org/10.2196/48538 UR - http://www.ncbi.nlm.nih.gov/pubmed/38315543 ID - info:doi/10.2196/48538 ER - TY - JOUR AU - Holland, Lena AU - Kanzow, Friederike Amelie AU - Wiegand, Annette AU - Kanzow, Philipp PY - 2024/1/31 TI - Quality of Patient-Centered eHealth Information on Erosive Tooth Wear: Systematic Search and Evaluation of Websites and YouTube Videos JO - J Med Internet Res SP - e49514 VL - 26 KW - consumer health information KW - dental erosion KW - dental sciences KW - digital media KW - erosive tooth wear KW - evidence-based dentistry KW - health education KW - information quality KW - internet KW - shared decision making N2 - Background: Due to the declining prevalence of dental caries, noncarious tooth defects such as erosive tooth wear have gained increased attention over the past decades. While patients more frequently search the internet for health-related information, the quality of patient-centered, web-based health information on erosive tooth wear is currently unknown. Objective: This study aimed to assess the quality of patient-centered, web-based health information (websites and YouTube videos) on erosive tooth wear. Methods: German-language websites were systematically identified through 3 electronic search engines (google.de, bing.de or yahoo.de, and duckduckgo.com) in September 2021. Eligible websites were independently assessed for (1) technical and functional aspects via the LIDA instrument, (2) readability via the Flesch reading-ease score, (3) comprehensiveness of information via a structured checklist, and (4) generic quality and risk of bias via the DISCERN instrument by 2 different reviewers. An overall quality score (ie, higher scores being favored) generated from all 4 domains was used as the primary outcome. Quality scores from each domain were separately analyzed as secondary outcomes and compared by the Friedman test. The effect of practice-specific variables on quality scores of websites from private dental offices was assessed using generalized linear modeling. Eligible YouTube videos were judged based on (1) the comprehensiveness of information, (2) viewers? interaction, and (3) viewing rate. The comprehensiveness of information was compared between websites and YouTube videos using the Wilcoxon rank-sum test. Results: Overall, 231 eligible websites and 7 YouTube videos were identified and assessed. The median overall quality of the websites was 33.6% (IQR 29.8%-39.2%). Secondary outcome scores amounted to 64.3% (IQR 59.8%-69.0%) for technical and functional aspects, 40.0% (IQR 34.0%-49.0%) for readability, 11.5% (IQR 3.9%-26.9%) for comprehensiveness of information, and 16.7% (IQR 8.3%-23.3%) for generic quality. While the comprehensiveness of information and generic quality received low scores, technical and functional aspects as well as readability resulted in higher scores (both Padjusted<.001). Regarding practice-specific variables, websites from private dental offices outside Germany (P=.04; B=?6.64, 95% CI ?12.85 to ?0.42) or from dentists who are a dental society member (P=.049; B=?3.55, 95% CI ?7.09 to ?0.01) resulted in lower readability scores (ie, were more difficult to read), while a shorter time since dentists? examination resulted in higher readability scores (P=.01; B=0.24 per year, 95% CI 0.05-0.43). The comprehensiveness of information from YouTube videos was 34.6% (IQR 13.5%-38.5%). However, the comprehensiveness of information did not vary between websites and YouTube videos (P=.09). Additionally, viewers? interaction (1.7%, IQR 0.7%-3.4%) and viewing rates (101%, IQR 54.6%-112.6%) were low. Conclusions: The quality of German-language, patient-centered, web-based information on erosive tooth wear was limited. Especially, the comprehensiveness and trustworthiness of the available information were insufficient. Web-based information on erosive tooth wear requires improvement to inform patients comprehensively and reliably. UR - https://www.jmir.org/2024/1/e49514 UR - http://dx.doi.org/10.2196/49514 UR - http://www.ncbi.nlm.nih.gov/pubmed/38167299 ID - info:doi/10.2196/49514 ER - TY - JOUR AU - Guo, Feipeng AU - Liu, Zixiang AU - Lu, Qibei AU - Ji, Shaobo AU - Zhang, Chen PY - 2024/1/31 TI - Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media JO - J Med Internet Res SP - e47508 VL - 26 KW - COVID-19 KW - social media public opinion KW - microblog KW - sentiment analysis KW - topic modeling N2 - Background: The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic. Objective: Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion. Methods: First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning logistic regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model. Results: The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive. Conclusions: The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events. UR - https://www.jmir.org/2024/1/e47508 UR - http://dx.doi.org/10.2196/47508 UR - http://www.ncbi.nlm.nih.gov/pubmed/38294856 ID - info:doi/10.2196/47508 ER - TY - JOUR AU - Yin, Shuhua AU - Chen, Shi AU - Ge, Yaorong PY - 2024/1/23 TI - Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study JO - JMIR Infodemiology SP - e49756 VL - 4 KW - infoveillance KW - social media KW - COVID-19 KW - US Centers for Disease Control and Prevention KW - CDC KW - topic modeling KW - multivariate time series analysis N2 - Background: Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC?s social media communications and the actual epidemic metrics to improve public health agencies? communication strategies during health emergencies. Objective: This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC?s digital health communication strategies for future health emergencies. Methods: Two types of data were collected: (1) a total of 17,524 COVID-19?related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19?related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data. Results: Four major topics from the CDC?s COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC?s topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data. Conclusions: Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively. UR - https://infodemiology.jmir.org/2024/1/e49756 UR - http://dx.doi.org/10.2196/49756 UR - http://www.ncbi.nlm.nih.gov/pubmed/38261367 ID - info:doi/10.2196/49756 ER - TY - JOUR AU - Garg, Ashvita AU - Nyitray, G. Alan AU - Roberts, R. James AU - Shungu, Nicholas AU - Ruggiero, J. Kenneth AU - Chandler, Jessica AU - Damgacioglu, Haluk AU - Zhu, Yenan AU - Brownstein, C. Naomi AU - Sterba, R. Katherine AU - Deshmukh, A. Ashish AU - Sonawane, Kalyani PY - 2024/1/15 TI - Consumption of Health-Related Videos and Human Papillomavirus Awareness: Cross-Sectional Analyses of a US National Survey and YouTube From the Urban-Rural Context JO - J Med Internet Res SP - e49749 VL - 26 KW - awareness KW - health awareness KW - health information KW - health videos KW - HINTS KW - HPV vaccine KW - HPV KW - information behavior KW - information behaviors KW - information seeking KW - online information KW - reproductive health KW - rural KW - sexual health KW - sexually transmitted KW - social media KW - STD KW - STI KW - urban KW - video KW - videos KW - YouTube N2 - Background: Nearly 70% of Americans use the internet as their first source of information for health-related questions. Contemporary data on the consumption of web-based videos containing health information among American adults by urbanity or rurality is currently unavailable, and its link with health topic awareness, particularly for human papillomavirus (HPV), is not known. Objective: We aim to describe trends and patterns in the consumption of health-related videos on social media from an urban-rural context, examine the association between exposure to health-related videos on social media and awareness of health topics (ie, HPV and HPV vaccine), and understand public interest in HPV-related video content through search terms and engagement analytics. Methods: We conducted a cross-sectional analysis of the US Health Information National Trends Survey 6, a nationally representative survey that collects data from civilian, noninstitutionalized adults aged 18 years or older residing in the United States. Bivariable analyses were used to estimate the prevalence of consumption of health-related videos on social media among US adults overall and by urbanity or rurality. Multivariable logistic regression models were used to examine the association between the consumption of health-related videos and HPV awareness among urban and rural adults. To provide additional context on the public?s interest in HPV-specific video content, we examined search volumes (quantitative) and related query searches (qualitative) for the terms ?HPV? and ?HPV vaccine? on YouTube. Results: In 2022, 59.6% of US adults (152.3 million) consumed health-related videos on social media, an increase of nearly 100% from 2017 to 2022. Prevalence increased among adults living in both urban (from 31.4% in 2017 to 59.8% in 2022; P<.001) and rural (from 22.4% in 2017 to 58% in 2022; P<.001) regions. Within the urban and rural groups, consumption of health-related videos on social media was most prevalent among adults aged between 18 and 40 years and college graduates or higher-educated adults. Among both urban and rural groups, adults who consumed health-related videos had a significantly higher probability of being aware of HPV and the HPV vaccine compared with those who did not watch health videos on the internet. The term ?HPV? was more frequently searched on YouTube compared with ?HPV vaccine.? Individuals were most commonly searching for videos that covered content about the HPV vaccine, HPV in males, and side effects of the HPV vaccine. Conclusions: The consumption of health-related videos on social media in the United States increased dramatically between 2017 and 2022. The rise was prominent among both urban and rural adults. Watching a health-related video on social media was associated with a greater probability of being aware of HPV and the HPV vaccine. Additional research on designing and developing social media strategies is needed to increase public awareness of health topics. UR - https://www.jmir.org/2024/1/e49749 UR - http://dx.doi.org/10.2196/49749 UR - http://www.ncbi.nlm.nih.gov/pubmed/38224476 ID - info:doi/10.2196/49749 ER - TY - JOUR AU - Pearce, Emily AU - Raj, Hannah AU - Emezienna, Ngozika AU - Gilkey, B. Melissa AU - Lazard, J. Allison AU - Ribisl, M. Kurt AU - Savage, A. Sharon AU - Han, KJ Paul PY - 2024/1/15 TI - The Use of Social Media to Express and Manage Medical Uncertainty in Dyskeratosis Congenita: Content Analysis JO - JMIR Infodemiology SP - e46693 VL - 4 KW - social media KW - medical uncertainty KW - telomere biology disorder KW - dyskeratosis congenita KW - social support N2 - Background: Social media has the potential to provide social support for rare disease communities; however, little is known about the use of social media for the expression of medical uncertainty, a common feature of rare diseases. Objective: This study aims to evaluate the expression of medical uncertainty on social media in the context of dyskeratosis congenita, a rare cancer-prone inherited bone marrow failure and telomere biology disorder (TBD). Methods: We performed a content analysis of uncertainty-related posts on Facebook and Twitter managed by Team Telomere, a patient advocacy group for this rare disease. We assessed the frequency of uncertainty-related posts, uncertainty sources, issues, and management and associations between uncertainty and social support. Results: Across all TBD social media platforms, 45.98% (1269/2760) of posts were uncertainty related. Uncertainty-related posts authored by Team Telomere on Twitter focused on scientific (306/434, 70.5%) or personal (230/434, 53%) issues and reflected uncertainty arising from probability, ambiguity, or complexity. Uncertainty-related posts in conversations among patients and caregivers in the Facebook community group focused on scientific (429/511, 84%), personal (157/511, 30.7%), and practical (114/511, 22.3%) issues, many of which were related to prognostic unknowns. Both platforms suggested uncertainty management strategies that focused on information sharing and community building. Posts reflecting response-focused uncertainty management strategies (eg, emotional regulation) were more frequent on Twitter compared with the Facebook community group (?21=3.9; P=.05), whereas posts reflecting uncertainty-focused management strategies (eg, ordering information) were more frequent in the Facebook community group compared with Twitter (?21=55.1; P<.001). In the Facebook community group, only 36% (184/511) of members created posts during the study period, and those who created posts did so with a low frequency (median 3, IQR 1-7 posts). Analysis of post creator characteristics suggested that most users of TBD social media are White, female, and parents of patients with dyskeratosis congenita. Conclusions: Although uncertainty is a pervasive and multifactorial issue in TBDs, our findings suggest that the discussion of medical uncertainty on TBD social media is largely limited to brief exchanges about scientific, personal, or practical issues rather than ongoing supportive conversation. The nature of uncertainty-related conversations also varied by user group: patients and caregivers used social media primarily to discuss scientific uncertainties (eg, regarding prognosis), form social connections, or exchange advice on accessing and organizing medical care, whereas Team Telomere used social media to express scientific and personal issues of uncertainty and to address the emotional impact of uncertainty. The higher involvement of female parents on TBD social media suggests a potentially greater burden of uncertainty management among mothers compared with other groups. Further research is needed to understand the dynamics of social media engagement to manage medical uncertainty in the TBD community. UR - https://infodemiology.jmir.org/2024/1/e46693 UR - http://dx.doi.org/10.2196/46693 UR - http://www.ncbi.nlm.nih.gov/pubmed/38224480 ID - info:doi/10.2196/46693 ER - TY - JOUR AU - Massey, M. Philip AU - Murray, M. Regan AU - Chiang, C. Shawn AU - Russell, M. Alex AU - Yudell, A. Michael PY - 2023/12/29 TI - Social Media, Public Health Research, and Vulnerability: Considerations to Advance Ethical Guidelines and Strengthen Future Research JO - JMIR Public Health Surveill SP - e49881 VL - 9 KW - research ethics KW - social media KW - vulnerable populations KW - public health KW - ethical guidelines KW - algorithms KW - manipulation UR - https://publichealth.jmir.org/2023/1/e49881 UR - http://dx.doi.org/10.2196/49881 UR - http://www.ncbi.nlm.nih.gov/pubmed/38157235 ID - info:doi/10.2196/49881 ER - TY - JOUR AU - Danias, George AU - Appel, Jacob PY - 2023/12/29 TI - Public Interest in Psilocybin and Psychedelic Therapy in the Context of the COVID-19 Pandemic: Google Trends Analysis JO - JMIR Form Res SP - e43850 VL - 7 KW - psilocybin KW - Google Trends KW - COVID-19 KW - medical informatics KW - depression KW - anxiety KW - substance use KW - social media KW - trend analysis KW - antidepressant N2 - Background: Psychedelic substances have demonstrated promise in the treatment of depression, anxiety, and substance use disorders. Significant media coverage has been dedicated to psychedelic medicine, but it is unclear whether the public associates psilocybin with its potential therapeutic benefits. The COVID-19 pandemic led to an increase in depression, anxiety, and substance abuse in the general population. Objective: This study attempts to link increases in interest in these disorders with increases in interest in psilocybin using Google Trends. Methods: Weekly interest-over-time Google Trends data for 4 years, from the week of March 11, 2018, to the week of March 6, 2022, were obtained for the following terms: ?psilocybin,? ?psychedelic therapy,? ?cannabis,? ?cocaine,? ?antidepressant,? ?depression,? ?anxiety,? and ?addiction.? Important psilocybin-related news and the declaration of the pandemic were noted. Trends data for each of the queried terms were plotted, and multiple regression analysis was performed to determine the slope of the prepandemic and postpandemic data with 95% CIs. Nonparametric Tau-U analysis was performed correcting for baseline trends. Results from this test were used to make inferences about the pre- and postpandemic trends and inferences about the change in overall level of searches between the 2 groups. Results: Tau values for prepandemic data were significant for stable trends, all ranging ?0.4 to 0.4. Tau values for postpandemic data showed positive trends for ?psilocybin,? ?psychedelic therapy,? and ?antidepressant.? All other trends remained stable in the range of ?0.4 to 0.4. When comparing Tau values for pre- and postpandemic data, overall increases in relative search volume (RSV) were seen for ?psilocybin,? ?psychedelic therapy,? and ?anxiety,? and overall decreases in RSV were seen for ?depression,? ?addiction,? and ?cocaine.? Overall RSVs for ?cannabis? and ?antidepressant? remained stable as Tau values ranged between ?0.4 and 0.4. In the immediate aftermath of the declaration of the pandemic, drop-offs in interest were seen for all terms except for ?anxiety? and ?cannabis.? After the initial shock of a global pandemic, ?psilocybin? and ?psychedelic therapy? groups demonstrated increases in interest trends and overall RSV. Conclusions: These data suggest that overall interest in ?psilocybin? and ?psychedelic therapy? increased at higher rates and to higher levels after than before the declaration of the pandemic. This is consistent with our hypothesis that interest increased for these treatments after the pandemic as incidence of depression, anxiety, and addiction increased. However, there may be other drivers of interest for these topics, since interest in antidepressants?the typical pharmacologic treatments for depression and anxiety?followed the expected pattern of drop-off and accelerated interest back to prepandemic levels. Interest in ?psilocybin? and ?psychedelic therapy? may have also been partially driven by popular culture hype and novelty, explaining why interest increased at a higher rate post pandemic and continued to grow, surpassing prior interest. UR - https://formative.jmir.org/2023/1/e43850 UR - http://dx.doi.org/10.2196/43850 UR - http://www.ncbi.nlm.nih.gov/pubmed/38064635 ID - info:doi/10.2196/43850 ER - TY - JOUR AU - Hansen, Rita-Kristin AU - Baiju, Nikita AU - Gabarron, Elia PY - 2023/12/26 TI - Social Media as an Effective Provider of Quality-Assured and Accurate Information to Increase Vaccine Rates: Systematic Review JO - J Med Internet Res SP - e50276 VL - 25 KW - social media KW - vaccines KW - vaccination KW - randomized controlled trials KW - information sources N2 - Background: Vaccination programs are instrumental in prolonging and improving people?s lives by preventing diseases such as measles, diphtheria, tetanus, pertussis, and influenza from escalating into fatal epidemics. Despite the significant impact of these programs, a substantial number of individuals, including 20 million infants annually, lack sufficient access to vaccines. Therefore, it is imperative to raise awareness about vaccination programs. Objective: This study aims to investigate the potential utilization of social media, assessing its scalability and robustness in delivering accurate and reliable information to individuals who are contemplating vaccination decisions for themselves or on behalf of their children. Methods: The protocol for this review is registered in PROSPERO (identifier CRD42022304229) and is being carried out in compliance with the Cochrane Handbook for Systematic Reviews of Interventions. Comprehensive searches have been conducted in databases including MEDLINE, Embase, PsycINFO, CINAHL (Cumulative Index to Nursing and Allied Health), CENTRAL (Cochrane Central Register of Controlled Trials), and Google Scholar. Only randomized controlled trials (RCTs) were deemed eligible for inclusion in this study. The target population encompasses the general public, including adults, children, and adolescents. The defined interventions comprise platforms facilitating 2-way communication for sharing information. These interventions were compared against traditional interventions and teaching methods, referred to as the control group. The outcomes assessed in the included studies encompassed days unvaccinated, vaccine acceptance, and the uptake of vaccines compared with baseline. The studies underwent a risk-of-bias assessment utilizing the Cochrane Risk-of-Bias tool for RCTs, and the certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) assessment. Results: This review included 10 studies, detailed in 12 articles published between 2012 and 2022, conducted in the United States, China, Jordan, Australia, and Israel. The studies involved platforms such as Facebook, Twitter, WhatsApp, and non?general-purpose social media. The outcomes examined in these studies focused on the uptake of vaccines compared with baseline, vaccine acceptance, and the number of days individuals remained unvaccinated. The overall sample size for this review was 26,286, with individual studies ranging from 58 to 21,592 participants. The effect direction plot derived from articles of good and fair quality indicated a nonsignificant outcome (P=.12). Conclusions: The findings suggest that, in a real-world scenario, an equal number of positive and negative results may be expected due to the interventions? impact on the acceptance and uptake of vaccines. Nevertheless, there is a rationale for accumulating experience to optimize the use of social media with the aim of enhancing vaccination rates. Social media can serve as a tool with the potential to disseminate information and boost vaccination rates within a population. However, relying solely on social media is not sufficient, given the complex structures at play in vaccine acceptance. Effectiveness hinges on various factors working in tandem. It is crucial that authorized personnel closely monitor and moderate discussions on social media to ensure responsible and accurate information dissemination. UR - https://www.jmir.org/2023/1/e50276 UR - http://dx.doi.org/10.2196/50276 UR - http://www.ncbi.nlm.nih.gov/pubmed/38147375 ID - info:doi/10.2196/50276 ER - TY - JOUR AU - Bayani, Azadeh AU - Ayotte, Alexandre AU - Nikiema, Noel Jean PY - 2023/12/22 TI - Automated Credibility Assessment of Web-Based Health Information Considering Health on the Net Foundation Code of Conduct (HONcode): Model Development and Validation Study JO - JMIR Form Res SP - e52995 VL - 7 KW - HONcode KW - infodemic KW - natural language processing KW - web-based health information KW - machine learning N2 - Background: An increasing number of users are turning to web-based sources as an important source of health care guidance information. Thus, trustworthy sources of information should be automatically identifiable using objective criteria. Objective: The purpose of this study was to automate the assessment of the Health on the Net Foundation Code of Conduct (HONcode) criteria, enhancing our ability to pinpoint trustworthy health information sources. Methods: A data set of 538 web pages displaying health content was collected from 43 health-related websites. HONcode criteria have been considered as web page and website levels. For the website-level criteria (confidentiality, transparency, financial disclosure, and advertising policy), a bag of keywords has been identified to assess the criteria using a rule-based model. For the web page?level criteria (authority, complementarity, justifiability, and attribution) several machine learning (ML) approaches were used. In total, 200 web pages were manually annotated until achieving a balanced representation in terms of frequency. In total, 3 ML models?random forest, support vector machines (SVM), and Bidirectional Encoder Representations from Transformers (BERT)?were trained on the initial annotated data. A second step of training was implemented for the complementarity criterion using the BERT model for multiclass classification of the complementarity sentences obtained by annotation and data augmentation (positive, negative, and noncommittal sentences). Finally, the remaining web pages were classified using the selected model and 100 sentences were randomly selected for manual review. Results: For web page?level criteria, the random forest model showed a good performance for the attribution criterion while displaying subpar performance in the others. BERT and SVM had a stable performance across all the criteria. BERT had a better area under the curve (AUC) of 0.96, 0.98, and 1.00 for neutral sentences, justifiability, and attribution, respectively. SVM had the overall better performance for the classification of complementarity with the AUC equal to 0.98. Finally, SVM and BERT had an equal AUC of 0.98 for the authority criterion. For the website level criteria, the rule-based model was able to retrieve web pages with an accuracy of 0.97 for confidentiality, 0.82 for transparency, and 0.51 for both financial disclosure and advertising policy. The final evaluation of the sentences determined 0.88 of precision and the agreement level of reviewers was computed at 0.82. Conclusions: Our results showed the potential power of automating the HONcode criteria assessment using ML approaches. This approach could be used with different types of pretrained models to accelerate the text annotation, and classification and to improve the performance in low-resource cases. Further work needs to be conducted to determine how to assign different weights to the criteria, as well as to identify additional characteristics that should be considered for consolidating these criteria into a comprehensive reliability score. UR - https://formative.jmir.org/2023/1/e52995 UR - http://dx.doi.org/10.2196/52995 UR - http://www.ncbi.nlm.nih.gov/pubmed/38133919 ID - info:doi/10.2196/52995 ER - TY - JOUR AU - Frennesson, Felicia Nessie AU - McQuire, Cheryl AU - Aijaz Khan, Saher AU - Barnett, Julie AU - Zuccolo, Luisa PY - 2023/12/20 TI - Evaluating Messaging on Prenatal Health Behaviors Using Social Media Data: Systematic Review JO - J Med Internet Res SP - e44912 VL - 25 KW - acceptability KW - design KW - development KW - effectiveness KW - health behavior KW - health messaging KW - messaging KW - prenatal health KW - prenatal KW - social media data KW - social media KW - tool N2 - Background: Social media platforms are increasingly being used to disseminate messages about prenatal health. However, to date, we lack a systematic assessment of how to evaluate the impact of official prenatal health messaging and campaigns using social media data. Objective: This study aims to review both the published and gray literature on how official prenatal health messaging and campaigns have been evaluated to date in terms of impact, acceptability, effectiveness, and unintended consequences, using social media data. Methods: A total of 6 electronic databases were searched and supplemented with the hand-searching of reference lists. Both published and gray literature were eligible for review. Data were analyzed using content analysis for descriptive data and a thematic synthesis approach to summarize qualitative evidence. A quality appraisal tool, designed especially for use with social media data, was used to assess the quality of the included articles. Results: A total of 11 studies were eligible for the review. The results showed that the most common prenatal health behavior targeted was alcohol consumption, and Facebook was the most commonly used source of social media data. The majority (n=6) of articles used social media data for descriptive purposes only. The results also showed that there was a lack of evaluation of the effectiveness, acceptability, and unintended consequences of the prenatal health message or campaign. Conclusions: Social media is a widely used and potentially valuable resource for communicating and evaluating prenatal health messaging. However, this review suggests that there is a need to develop and adopt sound methodology on how to evaluate prenatal health messaging using social media data, for the benefit of future research and to inform public health practice. UR - https://www.jmir.org/2023/1/e44912 UR - http://dx.doi.org/10.2196/44912 UR - http://www.ncbi.nlm.nih.gov/pubmed/38117557 ID - info:doi/10.2196/44912 ER - TY - JOUR AU - Song, Junxian AU - Cui, Yuxia AU - Song, Jing AU - Lee, Chongyou AU - Wu, Manyan AU - Chen, Hong PY - 2023/12/19 TI - Evaluation of the Needs and Experiences of Patients with Hypertriglyceridemia: Social Media Listening Infosurveillance Study JO - J Med Internet Res SP - e44610 VL - 25 KW - social media listening KW - hypertriglyceridemia KW - infosurveillance study KW - disease cognition KW - lifestyle intervention KW - lipid disorder KW - awareness KW - online search KW - telemedicine KW - self-medication KW - Chinese medicine KW - natural language processing KW - cardiovascular disease KW - stroke KW - online platform KW - self-management KW - Q&A search platform KW - social media N2 - Background: Hypertriglyceridemia is a risk factor for cardiovascular diseases. Internet usage in China is increasing, giving rise to large-scale data sources, especially to access, disseminate, and discuss medical information. Social media listening (SML) is a new approach to analyze and monitor online discussions related to various health-related topics in diverse diseases, which can generate insights into users? experiences and expectations. However, to date, no studies have evaluated the utility of SML to understand patients? cognizance and expectations pertaining to the management of hypertriglyceridemia. Objective: The aim of this study was to utilize SML to explore the disease cognition level of patients with hypertriglyceridemia, choice of intervention measures, and the status quo of online consultations and question-and-answer (Q&A) search platforms. Methods: An infosurveillance study was conducted wherein a disease-specific comprehensive search was performed between 2004 and 2020 in Q&A search and online consultation platforms. Predefined single and combined keywords related to hypertriglyceridemia were used in the search, including disease, symptoms, diagnosis, and treatment indicators; lifestyle interventions; and therapeutic agents. The search output was aggregated using an aggregator tool and evaluated. Results: Disease-specific consultation data (n=69,845) and corresponding response data (n=111,763) were analyzed from 20 data sources (6 Q&A search platforms and 14 online consultation platforms). Doctors from inland areas had relatively high voice volumes and appear to exert a substantial influence on these platforms. Patients with hypertriglyceridemia engaging on the internet have an average level of cognition about the disease and its intervention measures. However, a strong demand for the concept of the disease and ?how to treat it? was observed. More emphasis on the persistence of the disease and the safety of medications was observed. Young patients have a lower willingness for drug interventions, whereas patients with severe hypertriglyceridemia have a clearer intention to use drug intervention and few patients have a strong willingness for the use of traditional Chinese medicine. Conclusions: Findings from this disease-specific SML study revealed that patients with hypertriglyceridemia in China actively seek information from both online Q&A search and consultation platforms. However, the integrity of internet doctors? suggestions on lifestyle interventions and the accuracy of drug intervention recommendations still need to be improved. Further, a combined prospective qualitative study with SML is required for added rigor and confirmation of the relevance of the findings. UR - https://www.jmir.org/2023/1/e44610 UR - http://dx.doi.org/10.2196/44610 UR - http://www.ncbi.nlm.nih.gov/pubmed/38113100 ID - info:doi/10.2196/44610 ER - TY - JOUR AU - Kim, Seoyun AU - Cha, Junyeop AU - Kim, Dongjae AU - Park, Eunil PY - 2023/11/30 TI - Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts JO - J Med Internet Res SP - e49074 VL - 25 KW - mental health KW - sentiment analysis KW - mental disorder KW - text analysis KW - NLP KW - natural language processing KW - clustering N2 - Background: Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits. Objective: In this study, we analyzed the distinctive linguistic characteristics in users? posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. Methods: Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health?related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. Results: We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. Conclusions: By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients? mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online. UR - https://www.jmir.org/2023/1/e49074 UR - http://dx.doi.org/10.2196/49074 UR - http://www.ncbi.nlm.nih.gov/pubmed/38032730 ID - info:doi/10.2196/49074 ER - TY - JOUR AU - Sigalo, Nekabari AU - Frias-Martinez, Vanessa PY - 2023/11/30 TI - Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology Study JO - JMIR Infodemiology SP - e43700 VL - 3 KW - social media KW - Twitter KW - COVID-19 KW - vaccine KW - surveys KW - SARS-CoV-2 KW - vaccinations KW - hesitancy N2 - Background: Traditionally, surveys are conducted to answer questions related to public health but can be costly to execute. However, the information that researchers aim to extract from surveys could potentially be retrieved from social media, which possesses data that are highly accessible and lower in cost to collect. Objective: This study aims to evaluate whether attitudes toward COVID-19 vaccines collected from the Household Pulse Survey (HPS) could be predicted using attitudes extracted from Twitter (subsequently rebranded X). Ultimately, this study aimed to determine whether Twitter can provide us with similar information to that observed in traditional surveys or whether saving money comes at the cost of losing rich data. Methods: COVID-19 vaccine attitudes were extracted from the HPS conducted between January 6 and May 25, 2021. Twitter?s streaming application programming interface was used to collect COVID-19 vaccine tweets during the same period. A sentiment and emotion analysis of tweets was conducted to examine attitudes toward the COVID-19 vaccine on Twitter. Generalized linear models and generalized linear mixed models were used to evaluate the ability of COVID-19 vaccine attitudes on Twitter to predict vaccine attitudes in the HPS. Results: The results revealed that vaccine perceptions expressed on Twitter performed well in predicting vaccine perceptions in the survey. Conclusions: These findings suggest that the information researchers aim to extract from surveys could potentially also be retrieved from a more accessible data source, such as Twitter. Leveraging Twitter data alongside traditional surveys can provide a more comprehensive and nuanced understanding of COVID-19 vaccine perceptions, facilitating evidence-based decision-making and tailored public health strategies. UR - https://infodemiology.jmir.org/2023/1/e43700 UR - http://dx.doi.org/10.2196/43700 UR - http://www.ncbi.nlm.nih.gov/pubmed/37903294 ID - info:doi/10.2196/43700 ER - TY - JOUR AU - Tin, Jason AU - Stevens, Hannah AU - Rasul, Ehab Muhammad AU - Taylor, D. Laramie PY - 2023/11/29 TI - Incivility in COVID-19 Vaccine Mandate Discourse and Moral Foundations: Natural Language Processing Approach JO - JMIR Form Res SP - e50367 VL - 7 KW - incivility KW - vaccine hesitancy KW - moral foundations KW - COVID-19 KW - vaccines KW - morality KW - social media KW - natural language processing KW - machine learning N2 - Background: Vaccine hesitancy poses a substantial threat to efforts to mitigate the harmful effects of the COVID-19 pandemic. To combat vaccine hesitancy, officials in the United States issued vaccine mandates, which were met with strong antivaccine discourse on social media platforms such as Reddit. The politicized and polarized nature of COVID-19 on social media has fueled uncivil discourse related to vaccine mandates, which is known to decrease confidence in COVID-19 vaccines. Objective: This study examines the moral foundations underlying uncivil COVID-19 vaccine discourse. Moral foundations theory poses that individuals make decisions to express approval or disapproval (ie, uncivil discourse) based on innate moral values. We examine whether moral foundations are associated with dimensions of incivility. Further, we explore whether there are any differences in the presence of incivility between the r/coronaviruscirclejerk and r/lockdownskepticism subreddits. Methods: Natural language processing methodologies were leveraged to analyze the moral foundations underlying uncivil discourse in 2 prominent antivaccine subreddits, r/coronaviruscirclejerk and r/lockdownskepticism. All posts and comments from both of the subreddits were collected since their inception in March 2022. This was followed by filtering the data set for key terms associated with the COVID-19 vaccine (eg, ?vaccinate? and ?Pfizer?) and mandates (eg, ?forced? and ?mandating?). These key terms were selected based on a review of existing literature and because of their salience in both of the subreddits. A 10% sample of the filtered key terms was used for the final analysis. Results: Findings suggested that moral foundations play a role in the psychological processes underlying uncivil vaccine mandate discourse. Specifically, we found substantial associations between all moral foundations (ie, care and harm, fairness and cheating, loyalty and betrayal, authority and subversion, and sanctity and degradation) and dimensions of incivility (ie, toxicity, insults, profanity, threat, and identity attack) except for the authority foundation. We also found statistically significant differences between r/coronaviruscirclejerk and r/lockdownskepticism for the presence of the dimensions of incivility. Specifically, the mean of identity attack, insult, toxicity, profanity, and threat in the r/lockdownskepticism subreddit was significantly lower than that in the r/coronaviruscirclejerk subreddit (P<.001). Conclusions: This study shows that moral foundations may play a substantial role in the presence of incivility in vaccine discourse. On the basis of the findings of the study, public health practitioners should tailor messaging by addressing the moral values underlying the concerns people may have about vaccines, which could manifest as uncivil discourse. Another way to tailor public health messaging could be to direct it to parts of social media platforms with increased uncivil discourse. By integrating moral foundations, public health messaging may increase compliance and promote civil discourse surrounding COVID-19. UR - https://formative.jmir.org/2023/1/e50367 UR - http://dx.doi.org/10.2196/50367 UR - http://www.ncbi.nlm.nih.gov/pubmed/38019581 ID - info:doi/10.2196/50367 ER - TY - JOUR AU - Scales, David AU - Hurth, Lindsay AU - Xi, Wenna AU - Gorman, Sara AU - Radhakrishnan, Malavika AU - Windham, Savannah AU - Akunne, Azubuike AU - Florman, Julia AU - Leininger, Lindsey AU - Gorman, Jack PY - 2023/11/14 TI - Addressing Antivaccine Sentiment on Public Social Media Forums Through Web-Based Conversations Based on Motivational Interviewing Techniques: Observational Study JO - JMIR Infodemiology SP - e50138 VL - 3 KW - anti-vaccine KW - digital environment KW - engagement KW - health misinformation KW - infodemic KW - infodemiology KW - information environment KW - medical misinformation KW - misinformation KW - observational study KW - social media engagement metrics KW - social media N2 - Background: Health misinformation shared on social media can have negative health consequences; yet, there is a dearth of field research testing interventions to address health misinformation in real time, digitally, and in situ on social media. Objective: We describe a field study of a pilot program of ?infodemiologists? trained with evidence-informed intervention techniques heavily influenced by principles of motivational interviewing. Here we provide a detailed description of the nature of infodemiologists? interventions on posts sharing misinformation about COVID-19 vaccines, present an initial evaluation framework for such field research, and use available engagement metrics to quantify the impact of these in-group messengers on the web-based threads on which they are intervening. Methods: We monitored Facebook (Meta Platforms, Inc) profiles of news organizations marketing to 3 geographic regions (Newark, New Jersey; Chicago, Illinois; and central Texas). Between December 2020 and April 2021, infodemiologists intervened in 145 Facebook news posts that generated comments containing either false or misleading information about vaccines or overt antivaccine sentiment. Engagement (emojis plus replies) data were collected on Facebook news posts, the initial comment containing misinformation (level 1 comment), and the infodemiologist?s reply (level 2 reply comment). A comparison-group evaluation design was used, with numbers of replies, emoji reactions, and engagements for level 1 comments compared with the median metrics of matched comments using the Wilcoxon signed rank test. Level 2 reply comments (intervention) were also benchmarked against the corresponding metric of matched reply comments (control) using the Wilcoxon signed rank test (paired at the level 1 comment level). Infodemiologists? level 2 reply comments (intervention) and matched reply comments (control) were further compared using 3 Poisson regression models. Results: In total, 145 interventions were conducted on 132 Facebook news posts. The level 1 comments received a median of 3 replies, 3 reactions, and 7 engagements. The matched comments received a median of 1.5 (median of IQRs 3.75) engagements. Infodemiologists made 322 level 2 reply comments, precipitating 189 emoji reactions and a median of 0.5 (median of IQRs IQR 0) engagements. The matched reply comments received a median of 1 (median of IQRs 2.5) engagement. Compared to matched comments, level 1 comments received more replies, emoji reactions, and engagements. Compared to matched reply comments, level 2 reply comments received fewer and narrower ranges of replies, reactions, and engagements, except for the median comparison for replies. Conclusions: Overall, empathy-first communication strategies based on motivational interviewing garnered less engagement relative to matched controls. One possible explanation is that our interventions quieted contentious, misinformation-laden threads about vaccines on social media. This work reinforces research on accuracy nudges and cyberbullying interventions that also reduce engagement. More research leveraging field studies of real-time interventions is needed, yet data transparency by technology platforms will be essential to facilitate such experiments. UR - https://infodemiology.jmir.org/2023/1/e50138 UR - http://dx.doi.org/10.2196/50138 UR - http://www.ncbi.nlm.nih.gov/pubmed/37962940 ID - info:doi/10.2196/50138 ER - TY - JOUR AU - Al-Rawi, Ahmed AU - Blackwell, Breanna AU - Zemenchik, Kiana AU - Lee, Kelley PY - 2023/11/10 TI - Twitter Misinformation Discourses About Vaping: Systematic Content Analysis JO - J Med Internet Res SP - e49416 VL - 25 KW - vaping KW - e-cigarette KW - smoking KW - misinformation KW - fact checking KW - social media KW - Twitter KW - nicotine KW - content analysis KW - fact-checking KW - disinformation KW - weaponized KW - health risk KW - risk KW - health education KW - education KW - communication KW - electronic nicotine delivery systems KW - ENDS N2 - Background: While there has been substantial analysis of social media content deemed to spread misinformation about electronic nicotine delivery systems use, the strategic use of misinformation accusations to undermine opposing views has received limited attention. Objective: This study aims to fill this gap by analyzing how social media users discuss the topic of misinformation related to electronic nicotine delivery systems, notably vaping products. Additionally, this study identifies and analyzes the actors commonly blamed for spreading such misinformation and how these claims support both the provaping and antivaping narratives. Methods: Using Twitter?s (subsequently rebranded as X) academic application programming interface, we collected tweets referencing #vape and #vaping and keywords associated with fake news and misinformation. This study uses systematic content analysis to analyze the tweets and identify common themes and actors who discuss or possibly spread misinformation. Results: This study found that provape users dominate the platform regarding discussions about misinformation about vaping, with provaping tweets being more frequent and having higher overall user engagement. The most common narrative for provape tweets surrounds the conversation of vaping being perceived as safe. On the other hand, the most common topic from the antivape narrative is that vaping is indeed harmful. This study also points to a general distrust in authority figures, with news outlets, public health authorities, and political actors regularly accused of spreading misinformation, with both placing blame. However, specific actors differ depending on their positionalities. The vast number of accusations from provaping advocates is found to shape what is considered misinformation and works to silence other narratives. Additionally, allegations against reliable and proven sources, such as public health authorities, work to discredit assessments about the health impacts, which is detrimental to public health overall for both provaping and antivaping advocates. Conclusions: We conclude that the spread of misinformation and the accusations of misinformation dissemination using terms such as ?fact check,? ?misinformation,? ?fake news,? and ?disinformation? have become weaponized and co-opted by provaping actors to delegitimize criticisms about vaping and to increase confusion about the potential health risks. The study discusses the mixed types of impact of vaping on public health for both smokers and nonsmokers. Additionally, we discuss the implications for effective health education and communication about vaping and how misinformation claims can affect evidence-based discourse on Twitter as well as informed vaping decisions. UR - https://www.jmir.org/2023/1/e49416 UR - http://dx.doi.org/10.2196/49416 UR - http://www.ncbi.nlm.nih.gov/pubmed/37948118 ID - info:doi/10.2196/49416 ER - TY - JOUR AU - Pan, Xiaogao AU - Hounye, Houssou Alphonse AU - Zhao, Yuqi AU - Cao, Cong AU - Wang, Jiaoju AU - Abidi, Venunye Mimi AU - Hou, Muzhou AU - Xiong, Li AU - Chai, Xiangping PY - 2023/11/6 TI - A Digital Mask-Voiceprint System for Postpandemic Surveillance and Tracing Based on the STRONG Strategy JO - J Med Internet Res SP - e44795 VL - 25 KW - COVID-19 KW - surveillance KW - digital tracing KW - mask management KW - voiceprint KW - Spatiotemporal Reporting Over Network and GPS KW - STRONG KW - STRONG strategy KW - living with the virus KW - dynamic clearance KW - digital surveillance KW - pandemic KW - vaccine KW - public health KW - mental KW - social KW - communication technology KW - communication KW - tracing UR - https://www.jmir.org/2023/1/e44795 UR - http://dx.doi.org/10.2196/44795 UR - http://www.ncbi.nlm.nih.gov/pubmed/37856760 ID - info:doi/10.2196/44795 ER - TY - JOUR AU - Leslie, Abimbola AU - Okunromade, Omolola AU - Sarker, Abeed PY - 2023/11/3 TI - Public Perceptions About Monkeypox on Twitter: Thematic Analysis JO - JMIR Form Res SP - e48710 VL - 7 KW - monkeypox KW - social media KW - public health KW - Twitter KW - perception KW - digital platform KW - infectious disease KW - outbreak KW - awareness KW - analyses KW - misinformation N2 - Background: Social media has emerged as an important source of information generated by large segments of the population, which can be particularly valuable during infectious disease outbreaks. The recent outbreak of monkeypox led to an increase in discussions about the topic on social media, thus presenting the opportunity to conduct studies based on the generated data. Objective: By analyzing posts from Twitter (subsequently rebranded X), we aimed to identify the topics of public discourse as well as knowledge and opinions about the monkeypox virus during the 2022 outbreak. Methods: We collected data from Twitter focusing on English-language posts containing key phrases like ?monkeypox,? ?mpoxvirus,? and ?monkey pox,? as well as their hashtag equivalents from August to October 2022. We preprocessed the data using natural language processing to remove duplicates and filter out noise. We then selected a random sample from the collected posts. Three annotators reviewed a sample of the posts and created a guideline for coding based on discussion. Finally, the annotators analyzed, coded, and manually categorized them first into topics and then into coarse-grained themes. Disagreements were resolved via discussion among all authors. Results: A total of 128,615 posts were collected over a 3-month period, and 200 tweets were selected and included for manual analyses. The following 8 themes were generated from the Twitter posts: monkeypox doubts, media, monkeypox transmission, effect of monkeypox, knowledge of monkeypox, politics, monkeypox vaccine, and general comments. The most common themes from our study were monkeypox doubts and media, each accounting for 22% (44/200) of the posts. The posts represented a mix of useful information reflecting emerging knowledge on the topic as well as misinformation. Conclusions: Social networks, such as Twitter, are useful sources of information in the early stages of outbreaks. Close to real-time identification and analyses of misinformation may help authorities take the necessary steps in a timely manner. UR - https://formative.jmir.org/2023/1/e48710 UR - http://dx.doi.org/10.2196/48710 UR - http://www.ncbi.nlm.nih.gov/pubmed/37921866 ID - info:doi/10.2196/48710 ER - TY - JOUR AU - Dai, Jing AU - Lyu, Fang AU - Yu, Lin AU - He, Yunyu PY - 2023/11/2 TI - Temporal and Emotional Variations in People?s Perceptions of Mass Epidemic Infectious Disease After the COVID-19 Pandemic Using Influenza A as an Example: Topic Modeling and Sentiment Analysis Based on Weibo Data JO - J Med Internet Res SP - e49300 VL - 25 KW - mass epidemic infections KW - sentiment analysis KW - text mining KW - spatial differences KW - temporal differences KW - influenza A KW - COVID-19 N2 - Background: The COVID-19 pandemic has had profound impacts on society, including public health, the economy, daily life, and social interactions. Social distancing measures, travel restrictions, and the influx of pandemic-related information on social media have all led to a significant shift in how individuals perceive and respond to health crises. In this context, there is a growing awareness of the role that social media platforms such as Weibo, among the largest and most influential social media sites in China, play in shaping public sentiment and influencing people?s behavior during public health emergencies. Objective: This study aims to gain a comprehensive understanding of the sociospatial impact of mass epidemic infectious disease by analyzing the spatiotemporal variations and emotional orientations of the public after the COVID-19 pandemic. We use the outbreak of influenza A after the COVID-19 pandemic as a case study. Through temporal and spatial analyses, we aim to uncover specific variations in the attention and emotional orientations of people living in different provinces in China regarding influenza A. We sought to understand the societal impact of large-scale infectious diseases and the public?s stance after the COVID-19 pandemic to improve public health policies and communication strategies. Methods: We selected Weibo as the data source and collected all influenza A?related Weibo posts from November 1, 2022, to March 31, 2023. These data included user names, geographic locations, posting times, content, repost counts, comments, likes, user types, and more. Subsequently, we used latent Dirichlet allocation topic modeling to analyze the public?s focus as well as the bidirectional long short-term memory model to conduct emotional analysis. We further classified the focus areas and emotional orientations of different regions. Results: The research findings indicate that, compared with China?s western provinces, the eastern provinces exhibited a higher volume of Weibo posts, demonstrating a greater interest in influenza A. Moreover, inland provinces displayed elevated levels of concern compared with coastal regions. In addition, female users of Weibo exhibited a higher level of engagement than male users, with regular users comprising the majority of user types. The public?s focus was categorized into 23 main themes, with the overall emotional sentiment predominantly leaning toward negativity (making up 7562 out of 9111 [83%] sentiments). Conclusions: The results of this study underscore the profound societal impact of the COVID-19 pandemic. People tend to be pessimistic toward new large-scale infectious diseases, and disparities exist in the levels of concern and emotional sentiments across different regions. This reflects diverse societal responses to health crises. By gaining an in-depth understanding of the public?s attitudes and focal points regarding these infectious diseases, governments and decision makers can better formulate policies and action plans to cater to the specific needs of different regions and enhance public health awareness. UR - https://www.jmir.org/2023/1/e49300 UR - http://dx.doi.org/10.2196/49300 UR - http://www.ncbi.nlm.nih.gov/pubmed/37917144 ID - info:doi/10.2196/49300 ER - TY - JOUR AU - Christodoulakis, Nicolette AU - Abdelkader, Wael AU - Lokker, Cynthia AU - Cotterchio, Michelle AU - Griffith, E. Lauren AU - Vanderloo, M. Leigh AU - Anderson, N. Laura PY - 2023/11/2 TI - Public Health Surveillance of Behavioral Cancer Risk Factors During the COVID-19 Pandemic: Sentiment and Emotion Analysis of Twitter Data JO - JMIR Form Res SP - e46874 VL - 7 KW - cancer risk factors KW - Twitter KW - sentiment analysis KW - emotion analysis KW - social media KW - physical inactivity KW - poor nutrition KW - alcohol KW - smoking N2 - Background: The COVID-19 pandemic and its associated public health mitigation strategies have dramatically changed patterns of daily life activities worldwide, resulting in unintentional consequences on behavioral risk factors, including smoking, alcohol consumption, poor nutrition, and physical inactivity. The infodemic of social media data may provide novel opportunities for evaluating changes related to behavioral risk factors during the pandemic. Objective: We explored the feasibility of conducting a sentiment and emotion analysis using Twitter data to evaluate behavioral cancer risk factors (physical inactivity, poor nutrition, alcohol consumption, and smoking) over time during the first year of the COVID-19 pandemic. Methods: Tweets during 2020 relating to the COVID-19 pandemic and the 4 cancer risk factors were extracted from the George Washington University Libraries Dataverse. Tweets were defined and filtered using keywords to create 4 data sets. We trained and tested a machine learning classifier using a prelabeled Twitter data set. This was applied to determine the sentiment (positive, negative, or neutral) of each tweet. A natural language processing package was used to identify the emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) based on the words contained in the tweets. Sentiments and emotions for each of the risk factors were evaluated over time and analyzed to identify keywords that emerged. Results: The sentiment analysis revealed that 56.69% (51,479/90,813) of the tweets about physical activity were positive, 16.4% (14,893/90,813) were negative, and 26.91% (24,441/90,813) were neutral. Similar patterns were observed for nutrition, where 55.44% (27,939/50,396), 15.78% (7950/50,396), and 28.79% (14,507/50,396) of the tweets were positive, negative, and neutral, respectively. For alcohol, the proportions of positive, negative, and neutral tweets were 46.85% (34,897/74,484), 22.9% (17,056/74,484), and 30.25% (22,531/74,484), respectively, and for smoking, they were 41.2% (11,628/28,220), 24.23% (6839/28,220), and 34.56% (9753/28,220), respectively. The sentiments were relatively stable over time. The emotion analysis suggests that the most common emotion expressed across physical activity and nutrition tweets was trust (69,495/320,741, 21.67% and 42,324/176,564, 23.97%, respectively); for alcohol, it was joy (49,147/273,128, 17.99%); and for smoking, it was fear (23,066/110,256, 20.92%). The emotions expressed remained relatively constant over the observed period. An analysis of the most frequent words tweeted revealed further insights into common themes expressed in relation to some of the risk factors and possible sources of bias. Conclusions: This analysis provided insight into behavioral cancer risk factors as expressed on Twitter during the first year of the COVID-19 pandemic. It was feasible to extract tweets relating to all 4 risk factors, and most tweets had a positive sentiment with varied emotions across the different data sets. Although these results can play a role in promoting public health, a deeper dive via qualitative analysis can be conducted to provide a contextual examination of each tweet. UR - https://formative.jmir.org/2023/1/e46874 UR - http://dx.doi.org/10.2196/46874 UR - http://www.ncbi.nlm.nih.gov/pubmed/37917123 ID - info:doi/10.2196/46874 ER - TY - JOUR AU - Dou, Xuelin AU - Liu, Yang AU - Liao, Aijun AU - Zhong, Yuping AU - Fu, Rong AU - Liu, Lihong AU - Cui, Canchan AU - Wang, Xiaohong AU - Lu, Jin PY - 2023/11/2 TI - Patient Journey Toward a Diagnosis of Light Chain Amyloidosis in a National Sample: Cross-Sectional Web-Based Study JO - JMIR Form Res SP - e44420 VL - 7 KW - systemic light chain amyloidosis KW - AL amyloidosis KW - rare disease KW - big data KW - network analysis KW - machine model KW - natural language processing KW - web-based N2 - Background: Systemic light chain (AL) amyloidosis is a rare and multisystem disease associated with increased morbidity and a poor prognosis. Delayed diagnoses are common due to the heterogeneity of the symptoms. However, real-world insights from Chinese patients with AL amyloidosis have not been investigated. Objective: This study aimed to describe the journey to an AL amyloidosis diagnosis and to build an in-depth understanding of the diagnostic process from the perspective of both clinicians and patients to obtain a correct and timely diagnosis. Methods: Publicly available disease-related content from social media platforms between January 2008 and April 2021 was searched. After performing data collection steps with a machine model, a series of disease-related posts were extracted. Natural language processing was used to identify the relevance of variables, followed by further manual evaluation and analysis. Results: A total of 2204 valid posts related to AL amyloidosis were included in this study, of which 1968 were posted on haodf.com. Of these posts, 1284 were posted by men (median age 57, IQR 46-67 years); 1459 posts mentioned renal-related symptoms, followed by heart (n=833), liver (n=491), and stomach (n=368) symptoms. Furthermore, 1502 posts mentioned symptoms related to 2 or more organs. Symptoms for AL amyloidosis most frequently mentioned by suspected patients were nonspecific weakness (n=252), edema (n=196), hypertrophy (n=168), and swelling (n=140). Multiple physician visits were common, and nephrologists (n=265) and hematologists (n=214) were the most frequently visited specialists by suspected patients for initial consultation. Additionally, interhospital referrals were also commonly seen, centralizing in tertiary hospitals. Conclusions: Chinese patients with AL amyloidosis experienced referrals during their journey toward accurate diagnosis. Increasing awareness of the disease and early referral to a specialized center with expertise may reduce delayed diagnosis and improve patient management. UR - https://formative.jmir.org/2023/1/e44420 UR - http://dx.doi.org/10.2196/44420 UR - http://www.ncbi.nlm.nih.gov/pubmed/37917132 ID - info:doi/10.2196/44420 ER - TY - JOUR AU - Carabot, Federico AU - Fraile-Martínez, Oscar AU - Donat-Vargas, Carolina AU - Santoma, Javier AU - Garcia-Montero, Cielo AU - Pinto da Costa, Mariana AU - Molina-Ruiz, M. Rosa AU - Ortega, A. Miguel AU - Alvarez-Mon, Melchor AU - Alvarez-Mon, Angel Miguel PY - 2023/10/31 TI - Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study JO - J Med Internet Res SP - e50013 VL - 25 KW - awareness KW - epidemic KW - fentanyl KW - health communication KW - infodemiology KW - machine learning KW - opioids KW - recreational use KW - social media listening KW - Twitter KW - user N2 - Background: Opioids are used for the treatment of refractory pain, but their inappropriate use has detrimental consequences for health. Understanding the current experiences and perceptions of patients in a spontaneous and colloquial environment regarding the key drugs involved in the opioid crisis is of utmost significance. Objective: The study aims to analyze Twitter content related to opioids, with objectives including characterizing users participating in these conversations, identifying prevalent topics and gauging public perception, assessing opinions on drug efficacy and tolerability, and detecting discussions related to drug dispensing, prescription, or acquisition. Methods: In this cross-sectional study, we gathered public tweets concerning major opioids posted in English or Spanish between January 1, 2019, and December 31, 2020. A total of 256,218 tweets were collected. Approximately 27% (69,222/256,218) were excluded. Subsequently, 7000 tweets were subjected to manual analysis based on a codebook developed by the researchers. The remaining databases underwent analysis using machine learning classifiers. In the codebook, the type of user was the initial classification domain. We differentiated between patients, family members and friends, health care professionals, and institutions. Next, a distinction was made between medical and nonmedical content. If it was medical in nature, we classified it according to whether it referred to the drug?s efficacy or adverse effects. In nonmedical content tweets, we analyzed whether the content referred to management issues (eg, pharmacy dispensation, medical appointment prescriptions, commercial advertisements, or legal aspects) or the trivialization of the drug. Results: Among the entire array of scrutinized pharmaceuticals, fentanyl emerged as the predominant subject, featuring in 27% (39,997/148,335 posts) of the tweets. Concerning user categorization, roughly 70% (101,259/148,335) were classified as patients. Nevertheless, tweets posted by health care professionals obtained the highest number of retweets (37/16,956, 0.2% of their posts received over 100 retweets). We found statistically significant differences in the distribution concerning efficacy and side effects among distinct drug categories (P<.001). Nearly 60% (84,401/148,335) of the posts were devoted to nonmedical subjects. Within this category, legal facets and recreational use surfaced as the most prevalent themes, while in the medical discourse, efficacy constituted the most frequent topic, with over 90% (45,621/48,777) of instances characterizing it as poor or null. The opioid with the greatest proportion of tweets concerning legal considerations was fentanyl. Furthermore, fentanyl was the drug most frequently offered for sale on Twitter, while methadone generated the most tweets about pharmacy delivery. Conclusions: The opioid crisis is present on social media, where tweets discuss legal and recreational use. Opioid users are the most active participants, prioritizing medication efficacy over side effects. Surprisingly, health care professionals generate the most engagement, indicating their positive reception. Authorities must monitor web-based opioid discussions to detect illicit acquisitions and recreational use. UR - https://www.jmir.org/2023/1/e50013 UR - http://dx.doi.org/10.2196/50013 UR - http://www.ncbi.nlm.nih.gov/pubmed/37906234 ID - info:doi/10.2196/50013 ER - TY - JOUR AU - Luo, Tingyan AU - Zhou, Jie AU - Yang, Jing AU - Xie, Yulan AU - Wei, Yiru AU - Mai, Huanzhuo AU - Lu, Dongjia AU - Yang, Yuecong AU - Cui, Ping AU - Ye, Li AU - Liang, Hao AU - Huang, Jiegang PY - 2023/10/30 TI - Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis JO - J Med Internet Res SP - e49400 VL - 25 KW - scarlet fever KW - Baidu search index KW - autoregressive integrated moving average KW - ARIMA KW - warning KW - prediction N2 - Background: Internet-derived data and the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models are extensively used for infectious disease surveillance. However, the effectiveness of the Baidu search index (BSI) in predicting the incidence of scarlet fever remains uncertain. Objective: Our objective was to investigate whether a low-cost BSI monitoring system could potentially function as a valuable complement to traditional scarlet fever surveillance in China. Methods: ARIMA and ARIMAX models were developed to predict the incidence of scarlet fever in China using data from the National Health Commission of the People?s Republic of China between January 2011 and August 2022. The procedures included establishing a keyword database, keyword selection and filtering through Spearman rank correlation and cross-correlation analyses, construction of the scarlet fever comprehensive search index (CSI), modeling with the training sets, predicting with the testing sets, and comparing the prediction performances. Results: The average monthly incidence of scarlet fever was 4462.17 (SD 3011.75) cases, and annual incidence exhibited an upward trend until 2019. The keyword database contained 52 keywords, but only 6 highly relevant ones were selected for modeling. A high Spearman rank correlation was observed between the scarlet fever reported cases and the scarlet fever CSI (rs=0.881). We developed the ARIMA(4,0,0)(0,1,2)(12) model, and the ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0) and ARIMAX(1,0,2)(2,0,0)(12) models were combined with the BSI. The 3 models had a good fit and passed the residuals Ljung-Box test. The ARIMA(4,0,0)(0,1,2)(12), ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0), and ARIMAX(1,0,2)(2,0,0)(12) models demonstrated favorable predictive capabilities, with mean absolute errors of 1692.16 (95% CI 584.88-2799.44), 1067.89 (95% CI 402.02-1733.76), and 639.75 (95% CI 188.12-1091.38), respectively; root mean squared errors of 2036.92 (95% CI 929.64-3144.20), 1224.92 (95% CI 559.04-1890.79), and 830.80 (95% CI 379.17-1282.43), respectively; and mean absolute percentage errors of 4.33% (95% CI 0.54%-8.13%), 3.36% (95% CI ?0.24% to 6.96%), and 2.16% (95% CI ?0.69% to 5.00%), respectively. The ARIMAX models outperformed the ARIMA models and had better prediction performances with smaller values. Conclusions: This study demonstrated that the BSI can be used for the early warning and prediction of scarlet fever, serving as a valuable supplement to traditional surveillance systems. UR - https://www.jmir.org/2023/1/e49400 UR - http://dx.doi.org/10.2196/49400 UR - http://www.ncbi.nlm.nih.gov/pubmed/37902815 ID - info:doi/10.2196/49400 ER - TY - JOUR AU - Chi, Yu AU - Chen, Huai-yu PY - 2023/10/25 TI - Investigating Substance Use via Reddit: Systematic Scoping Review JO - J Med Internet Res SP - e48905 VL - 25 KW - substance use KW - systematic scoping review KW - Reddit KW - social media KW - drug use KW - tobacco use KW - alcohol use N2 - Background: Reddit?s (Reddit Inc) large user base, diverse communities, and anonymity make it a useful platform for substance use research. Despite a growing body of literature on substance use on Reddit, challenges and limitations must be carefully considered. However, no systematic scoping review has been conducted on the use of Reddit as a data source for substance use research. Objective: This review aims to investigate the use of Reddit for studying substance use by examining previous studies? objectives, reasons, limitations, and methods for using Reddit. In addition, we discuss the implications and contributions of previous studies and identify gaps in the literature that require further attention. Methods: A total of 7 databases were searched using keyword combinations including Reddit and substance-related keywords in April 2022. The initial search resulted in 456 articles, and 227 articles remained after removing duplicates. All included studies were peer reviewed, empirical, available in full text, and pertinent to Reddit and substance use, and they were all written in English. After screening, 60 articles met the eligibility criteria for the review, with 57 articles identified from the initial database search and 3 from the ancestry search. A codebook was developed, and qualitative content analysis was performed to extract relevant evidence related to the research questions. Results: The use of Reddit for studying substance use has grown steadily since 2015, with a sharp increase in 2021. The primary objective was to identify tendencies and patterns in various types of substance use discussions (52/60, 87%). Reddit was also used to explore unique user experiences, propose methodologies, investigate user interactions, and develop interventions. A total of 9 reasons for using Reddit to study substance use were identified, such as the platform?s anonymity, its widespread popularity, and the explicit topics of subreddits. However, 7 limitations were noted, including the platform?s low representativeness of the general population with substance use and the lack of demographic information. Most studies use application programming interfaces for data collection and quantitative approaches for analysis, with few using qualitative approaches. Machine learning algorithms are commonly used for natural language processing tasks. The theoretical, methodological, and practical implications and contributions of the included articles are summarized and discussed. The most prevalent practical implications are investigating prevailing topics in Reddit discussions, providing recommendations for clinical practices and policies, and comparing Reddit discussions on substance use across various sources. Conclusions: This systematic scoping review provides an overview of Reddit?s use as a data source for substance use research. Although the limitations of Reddit data must be considered, analyzing them can be useful for understanding patterns and user experiences related to substance use. Our review also highlights gaps in the literature and suggests avenues for future research. UR - https://www.jmir.org/2023/1/e48905 UR - http://dx.doi.org/10.2196/48905 UR - http://www.ncbi.nlm.nih.gov/pubmed/37878361 ID - info:doi/10.2196/48905 ER - TY - JOUR AU - Ball, J. Katelin AU - Muse, W. Brandon AU - Cook, Bailey AU - Quinn, P. Alyssa AU - Brooks, D. Benjamin PY - 2023/10/24 TI - Hell?s Itch: A Unique Reaction to UV Exposure JO - JMIR Dermatol SP - e48669 VL - 6 KW - Hell?s Itch KW - social media KW - sunburn KW - sun KW - survey KW - skin KW - dermatology KW - dermatological KW - itch KW - itchiness KW - itchy KW - symptoms KW - experience KW - ultraviolet KW - UV KW - dermatologist KW - teledermatology KW - hair KW - nails KW - scratch UR - https://derma.jmir.org/2023/1/e48669 UR - http://dx.doi.org/10.2196/48669 UR - http://www.ncbi.nlm.nih.gov/pubmed/37874633 ID - info:doi/10.2196/48669 ER - TY - JOUR AU - Pathak, Nitin Gaurav AU - Chandy, John Rithi AU - Naini, Vidisha AU - Razi, Shazli AU - Feldman, R. Steven PY - 2023/10/19 TI - A Social Media Analysis of Pemphigus JO - JMIR Dermatol SP - e50011 VL - 6 KW - pemphigus KW - social media KW - pemphigus vulgaris KW - Facebook KW - YouTube KW - Twitter KW - Instagram KW - dissemination KW - medical information KW - autoimmune disease KW - diagnosis KW - engagement KW - educational KW - content KW - awareness UR - https://derma.jmir.org/2023/1/e50011 UR - http://dx.doi.org/10.2196/50011 UR - http://www.ncbi.nlm.nih.gov/pubmed/37856177 ID - info:doi/10.2196/50011 ER - TY - JOUR AU - Lane, Hakan AU - Walker, Mark PY - 2023/10/19 TI - The Impact of Temperature, Humidity, and Sunshine on Internet Search Volumes Related to Psoriasis JO - JMIR Dermatol SP - e49901 VL - 6 KW - psoriasis KW - infodemiology KW - internet search KW - internet searching KW - web search KW - information seeking KW - information search behavior KW - information search behaviour KW - dermatology KW - skin KW - weather KW - temperature KW - humidity KW - sunshine UR - https://derma.jmir.org/2023/1/e49901 UR - http://dx.doi.org/10.2196/49901 UR - http://www.ncbi.nlm.nih.gov/pubmed/37856189 ID - info:doi/10.2196/49901 ER - TY - JOUR AU - Chau, Brian AU - Taba, Melody AU - Dodd, Rachael AU - McCaffery, Kirsten AU - Bonner, Carissa PY - 2023/10/18 TI - Twitch Data in Health Promotion Research: Protocol for a Case Study Exploring COVID-19 Vaccination Views Among Young People JO - JMIR Res Protoc SP - e48641 VL - 12 KW - twitch KW - social media KW - COVID-19 KW - vaccination communication KW - video gaming KW - gaming KW - health promotion KW - streaming N2 - Background: Social media platforms have emerged as a useful channel for health promotion communication, offering different channels to reach targeted populations. For example, social media has recently been used to disseminate information about COVID-19 vaccination across various demographics. Traditional modes of health communication such as television, health events, and newsletters may not reach all groups within a community. Health communications for younger generations are increasingly disseminated through social media to reflect key information sources. This paper explores a social media gaming platform as an alternative way to reach young people in health promotion research. Objective: This protocol study aimed to pilot-test the potential of Twitch, a live streaming platform initially designed for video gaming, to conduct health promotion research with young people. We used COVID-19 vaccination as a topical case study that was recommended by Australian health authorities at the time of the research. Methods: The research team worked with a Twitch Account Manager to design and test a case study within the guidelines and ethics protocols required by Twitch, identify suitable streamers to approach and establish a protocol for conducting research on the platform. This involved conducting a poll to initiate discussion about COVID-19 vaccination, monitoring the chat in 3 live Twitch sessions with 2 streamers to pilot the protocol, and briefly analyze Twitch chat logs to observe the range of response types that may be acquired from this methodology. Results: The Twitch streams provided logs and videos on demand that were derived from the live session. These included demographics of viewers, chat logs, and polling results. The results of the poll showed a range of engagement in health promotion for the case study topic: the majority of participants had received their vaccination by the time of the poll; however, there was still a proportion that had not received their vaccination yet or had decided to not be vaccinated. Analysis of the Twitch chat logs demonstrated a range of both positive and negative themes regarding health promotion for the case study topic. This included irrelevant comments, misinformation (compared to health authority information at the time of this study), comedic and conspiracy responses, as well as vaccine status, provaccine comments, and vaccine-hesitant comments. Conclusions: This study developed and tested a protocol for using Twitch data for health promotion research with young people. With live polling, open text discussion between participants and immediate responses to questions, Twitch can be used to collect both quantitative and qualitative research data from demographics that use social media. The platform also presents some challenges when engaging with independent streamers and sensitive health topics. This study provides an initial protocol for future researchers to use and build on. International Registered Report Identifier (IRRID): RR1-10.2196/48641 UR - https://www.researchprotocols.org/2023/1/e48641 UR - http://dx.doi.org/10.2196/48641 UR - http://www.ncbi.nlm.nih.gov/pubmed/37851494 ID - info:doi/10.2196/48641 ER - TY - JOUR AU - Yang, Liuyang AU - Zhang, Ting AU - Han, Xuan AU - Yang, Jiao AU - Sun, Yanxia AU - Ma, Libing AU - Chen, Jialong AU - Li, Yanming AU - Lai, Shengjie AU - Li, Wei AU - Feng, Luzhao AU - Yang, Weizhong PY - 2023/10/17 TI - Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study JO - J Med Internet Res SP - e45085 VL - 25 KW - early warning KW - epidemic intelligence KW - infectious disease KW - influenza-like illness KW - surveillance N2 - Background: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. Objective: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. Methods: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. Results: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Conclusions: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models. UR - https://www.jmir.org/2023/1/e45085 UR - http://dx.doi.org/10.2196/45085 UR - http://www.ncbi.nlm.nih.gov/pubmed/37847532 ID - info:doi/10.2196/45085 ER - TY - JOUR AU - Laison, Elolo Elda Kokoe AU - Hamza Ibrahim, Mohamed AU - Boligarla, Srikanth AU - Li, Jiaxin AU - Mahadevan, Raja AU - Ng, Austen AU - Muthuramalingam, Venkataraman AU - Lee, Yi Wee AU - Yin, Yijun AU - Nasri, R. Bouchra PY - 2023/10/16 TI - Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis JO - J Med Internet Res SP - e47014 VL - 25 KW - Lyme disease KW - Twitter KW - BERT KW - Bidirectional Encoder Representations from Transformers KW - emojis KW - machine learning KW - natural language processing N2 - Background: Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals, and accurate laboratory testing and interpretation for clinical diagnosis validation. A lack of these can lead to delayed diagnosis and treatment, which can exacerbate the severity of Lyme disease symptoms. Therefore, there is a need to improve the monitoring of Lyme disease by using other data sources, such as web-based data. Objective: We analyzed global Twitter data to understand its potential and limitations as a tool for Lyme disease surveillance. We propose a transformer-based classification system to identify potential Lyme disease cases using self-reported tweets. Methods: Our initial sample included 20,000 tweets collected worldwide from a database of over 1.3 million Lyme disease tweets. After preprocessing and geolocating tweets, tweets in a subset of the initial sample were manually labeled as potential Lyme disease cases or non-Lyme disease cases using carefully selected keywords. Emojis were converted to sentiment words, which were then replaced in the tweets. This labeled tweet set was used for the training, validation, and performance testing of DistilBERT (distilled version of BERT [Bidirectional Encoder Representations from Transformers]), ALBERT (A Lite BERT), and BERTweet (BERT for English Tweets) classifiers. Results: The empirical results showed that BERTweet was the best classifier among all evaluated models (average F1-score of 89.3%, classification accuracy of 90.0%, and precision of 97.1%). However, for recall, term frequency-inverse document frequency and k-nearest neighbors performed better (93.2% and 82.6%, respectively). On using emojis to enrich the tweet embeddings, BERTweet had an increased recall (8% increase), DistilBERT had an increased F1-score of 93.8% (4% increase) and classification accuracy of 94.1% (4% increase), and ALBERT had an increased F1-score of 93.1% (5% increase) and classification accuracy of 93.9% (5% increase). The general awareness of Lyme disease was high in the United States, the United Kingdom, Australia, and Canada, with self-reported potential cases of Lyme disease from these countries accounting for around 50% (9939/20,000) of the collected English-language tweets, whereas Lyme disease?related tweets were rare in countries from Africa and Asia. The most reported Lyme disease?related symptoms in the data were rash, fatigue, fever, and arthritis, while symptoms, such as lymphadenopathy, palpitations, swollen lymph nodes, neck stiffness, and arrythmia, were uncommon, in accordance with Lyme disease symptom frequency. Conclusions: The study highlights the robustness of BERTweet and DistilBERT as classifiers for potential cases of Lyme disease from self-reported data. The results demonstrated that emojis are effective for enrichment, thereby improving the accuracy of tweet embeddings and the performance of classifiers. Specifically, emojis reflecting sadness, empathy, and encouragement can reduce false negatives. UR - https://www.jmir.org/2023/1/e47014 UR - http://dx.doi.org/10.2196/47014 UR - http://www.ncbi.nlm.nih.gov/pubmed/37843893 ID - info:doi/10.2196/47014 ER - TY - JOUR AU - Oudat, Qutaibah AU - Bakas, Tamilyn PY - 2023/10/11 TI - Merits and Pitfalls of Social Media as a Platform for Recruitment of Study Participants JO - J Med Internet Res SP - e47705 VL - 25 KW - recruitment KW - social media KW - review KW - study participant KW - methods UR - https://www.jmir.org/2023/1/e47705 UR - http://dx.doi.org/10.2196/47705 UR - http://www.ncbi.nlm.nih.gov/pubmed/37819692 ID - info:doi/10.2196/47705 ER - TY - JOUR AU - Tanner, P. Joshua AU - Takats, Courtney AU - Lathan, Stuart Hannah AU - Kwan, Amy AU - Wormer, Rachel AU - Romero, Diana AU - Jones, E. Heidi PY - 2023/10/4 TI - Approaches to Research Ethics in Health Research on YouTube: Systematic Review JO - J Med Internet Res SP - e43060 VL - 25 KW - data anonymization KW - research ethics KW - ethics KW - informed consent KW - public health KW - research KW - social media KW - YouTube N2 - Background: YouTube has become a popular source of health care information, reaching an estimated 81% of adults in 2021; approximately 35% of adults in the United States have used the internet to self-diagnose a condition. Public health researchers are therefore incorporating YouTube data into their research, but guidelines for best practices around research ethics using social media data, such as YouTube, are unclear. Objective: This study aims to describe approaches to research ethics for public health research implemented using YouTube data. Methods: We implemented a systematic review of articles found in PubMed, SocINDEX, Web of Science, and PsycINFO following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. To be eligible to be included, studies needed to be published in peer-reviewed journals in English between January 1, 2006, and October 31, 2019, and include analyses on publicly available YouTube data on health or public health topics; studies using primary data collection, such as using YouTube for study recruitment, interventions, or dissemination evaluations, were not included. We extracted data on the presence of user identifying information, institutional review board (IRB) review, and informed consent processes, as well as research topic and methodology. Results: This review includes 119 articles from 88 journals. The most common health and public health topics studied were in the categories of chronic diseases (44/119, 37%), mental health and substance use (26/119, 21.8%), and infectious diseases (20/119, 16.8%). The majority (82/119, 68.9%) of articles made no mention of ethical considerations or stated that the study did not meet the definition of human participant research (16/119, 13.4%). Of those that sought IRB review (15/119, 12.6%), 12 out of 15 (80%) were determined to not meet the definition of human participant research and were therefore exempt from IRB review, and 3 out of 15 (20%) received IRB approval. None of the 3 IRB-approved studies contained identifying information; one was explicitly told not to include identifying information by their ethics committee. Only 1 study sought informed consent from YouTube users. Of 119 articles, 33 (27.7%) contained identifying information about content creators or video commenters, one of which attempted to anonymize direct quotes by not including user information. Conclusions: Given the variation in practice, concrete guidelines on research ethics for social media research are needed, especially around anonymizing and seeking consent when using identifying information. Trial Registration: PROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=148170 UR - https://www.jmir.org/2023/1/e43060 UR - http://dx.doi.org/10.2196/43060 UR - http://www.ncbi.nlm.nih.gov/pubmed/37792443 ID - info:doi/10.2196/43060 ER - TY - JOUR AU - Lustig, Andrew AU - Brookes, Gavin PY - 2023/9/29 TI - Corpus-Based Discourse Analysis of a Reddit Community of Users of Crystal Methamphetamine: Mixed Methods Study JO - JMIR Infodemiology SP - e48189 VL - 3 KW - methamphetamine KW - social media KW - substance-related disorders KW - discourse analysis KW - mental health KW - mixed methods KW - corpus analysis KW - web-based health N2 - Background: Methamphetamine is a highly addictive stimulant that affects the central nervous system. Crystal methamphetamine is a form of the drug resembling glass fragments or shiny bluish-white rocks that can be taken through smoking, swallowing, snorting, or injecting the powder once it has been dissolved in water or alcohol. Objective: The objective of this study is to examine how identities are socially (discursively) constructed by people who use methamphetamine within a subreddit for people who regularly use crystal meth. Methods: Using a mixed methods approach, we analyzed 1000 threads (318,422 words) from a subreddit for regular crystal meth users. The qualitative component of the analysis used concordancing and corpus-based discourse analysis to identify discursive themes informed by assemblage theory. The quantitative portion of the analysis used corpus linguistic techniques including keyword analysis to identify words occurring with statistically marked frequency in the corpus and collocation analysis to analyze their discursive context. Results: Our findings reveal that the subreddit contributors use a rich and varied lexicon to describe crystal meth and other substances, ranging from a neuroscientific register (eg, methamphetamine and dopamine) to informal vernacular (eg, meth, dope, and fent) and commercial appellations (eg, Adderall and Seroquel). They also use linguistic resources to construct symbolic boundaries between different types of methamphetamine users, differentiating between the esteemed category of ?functional addicts? and relegating others to the stigmatized category of ?tweakers.? In addition, contributors contest the dominant view that methamphetamine use inevitably leads to psychosis, arguing instead for a more nuanced understanding that considers the interplay of factors such as sleep deprivation, poor nutrition, and neglected hygiene. Conclusions: The subreddit contributors? discourse offers a ?set and setting? perspective, which provides a fresh viewpoint on drug-induced psychosis and can guide future harm reduction strategies and research. In contrast to this view, many previous studies overlook the real-world complexities of methamphetamine use, perhaps due to the use of controlled experimental settings. Actual drug use, intoxication, and addiction are complex, multifaceted, and elusive phenomena that defy straightforward characterization. UR - https://infodemiology.jmir.org/2023/1/e48189 UR - http://dx.doi.org/10.2196/48189 UR - http://www.ncbi.nlm.nih.gov/pubmed/37773617 ID - info:doi/10.2196/48189 ER - TY - JOUR AU - Uhawenimana, Claudien Thierry AU - Musabwasoni, Sandra Marie Grace AU - Nsengiyumva, Richard AU - Mukamana, Donatilla PY - 2023/9/27 TI - Sexuality and Sexual and Reproductive Health Depiction in Social Media: Content Analysis of Kinyarwanda YouTube Channels JO - J Med Internet Res SP - e46488 VL - 25 KW - sexuality KW - sexual and reproductive health KW - Kinyarwanda YouTube channels KW - content analysis KW - social media KW - media platform KW - COVID-19 N2 - Background: Social media platforms such as YouTube can be used to educate people of reproductive age about healthy and nonrisky sexual and reproductive health (SRH) practices and behaviors. However, there is a paucity of evidence to ascertain the authenticity of sexuality and SRH content on Kinyarwanda YouTube, making it difficult to determine the extent to which these topics are covered, the characteristics of available videos, and the themes covered by these videos. Objective: The aims of this study were (1) to determine the extent to which YouTube channels in Kinyarwanda-language videos address sexuality and SRH issues, identify the general characteristics of the available videos (type of video, when published, intention for the audience, and content focus), and the aspects of sexuality and SRH covered; and (2) to identify the themes covered by retrieved Kinyarwanda videos, and the extent to which the channels have been used to communicate issues of sexuality and SRH during the COVID-19 pandemic. Methods: Using a content analysis approach, we searched Kinyarwanda YouTube channels to analyze videos about sexuality and SRH. The adopted framework for data collection from social media platforms builds on three key steps: (1) development, (2) application, and (3) assessment of search filters. To be included, an audio and/or visual video had to be in Kinyarwanda and the video had to be directed to the general public. Descriptive statistics (frequency and percentages) were computed to characterize the basic characteristics of retrieved channels, portrayal of the videos, and presentation of sexuality and SRH themes that emerged from retrieved videos. Further analysis involved cross-tabulations to explore associations between the focus of the channel and the date when the channel was opened and the focus of the channel and who was involved in the video. Results: The YouTube search retrieved 21,506 videos that tackled sexuality and SRH topics. During the COVID-19 pandemic, there was a 4-fold increase (from 7.2% to 30.6%) in channels that solely focused on sexually explicit content. The majority of the 1369 retrieved channels (n=1150, 84.0%) tackled the topic of sexuality, with sexually explicit content predominantly found in the majority of these videos (n=1082, 79%), and only 16% (n=287) of the videos covered SRH topics. Conclusions: This is the first study to analyze the use of YouTube in communicating about sexuality and SRH in the Kinyarwanda language. This study relied on videos that appeared online. Further research should gather information about who accesses the videos, and how channel owners and individuals involved in the videos perceive the impact of their videos on the Rwandan community?s sexuality and SRH. UR - https://www.jmir.org/2023/1/e46488 UR - http://dx.doi.org/10.2196/46488 UR - http://www.ncbi.nlm.nih.gov/pubmed/37756040 ID - info:doi/10.2196/46488 ER - TY - JOUR AU - Faviez, Carole AU - Talmatkadi, Manissa AU - Foulquié, Pierre AU - Mebarki, Adel AU - Schück, Stéphane AU - Burgun, Anita AU - Chen, Xiaoyi PY - 2023/9/25 TI - Assessment of the Early Detection of Anosmia and Ageusia Symptoms in COVID-19 on Twitter: Retrospective Study JO - JMIR Infodemiology SP - e41863 VL - 3 KW - social media KW - COVID-19 KW - anosmia KW - ageusia KW - infodemiology KW - symptom KW - Twitter KW - psychological KW - tweets KW - pandemic KW - rapid stage KW - epidemic KW - information KW - knowledge KW - online health KW - misinformation KW - education KW - online education KW - ehealth KW - qualitative N2 - Background: During the unprecedented COVID-19 pandemic, social media has been extensively used to amplify the spread of information and to express personal health-related experiences regarding symptoms, including anosmia and ageusia, 2 symptoms that have been reported later than other symptoms. Objective: Our objective is to investigate to what extent Twitter users reported anosmia and ageusia symptoms in their tweets and if they connected them to COVID-19, to evaluate whether these symptoms could have been identified as COVID-19 symptoms earlier using Twitter rather than the official notice. Methods: We collected French tweets posted between January 1, 2020, and March 31, 2020, containing anosmia- or ageusia-related keywords. Symptoms were detected using fuzzy matching. The analysis consisted of 3 parts. First, we compared the coverage of anosmia and ageusia symptoms in Twitter and in traditional media to determine if the association between COVID-19 and anosmia or ageusia could have been identified earlier through Twitter. Second, we conducted a manual analysis of anosmia- and ageusia-related tweets to obtain quantitative and qualitative insights regarding their nature and to assess when the first associations between COVID-19 and these symptoms were established. We randomly annotated tweets from 2 periods: the early stage and the rapid spread stage of the epidemic. For each tweet, each symptom was annotated regarding 3 modalities: symptom (yes or no), associated with COVID-19 (yes, no, or unknown), and whether it was experienced by someone (yes, no, or unknown). Third, to evaluate if there was a global increase of tweets mentioning anosmia or ageusia in early 2020, corresponding to the beginning of the COVID-19 epidemic, we compared the tweets reporting experienced anosmia or ageusia between the first periods of 2019 and 2020. Results: In total, 832 (respectively 12,544) tweets containing anosmia (respectively ageusia) related keywords were extracted over the analysis period in 2020. The comparison to traditional media showed a strong correlation without any lag, which suggests an important reactivity of Twitter but no earlier detection on Twitter. The annotation of tweets from 2020 showed that tweets correlating anosmia or ageusia with COVID-19 could be found a few days before the official announcement. However, no association could be found during the first stage of the pandemic. Information about the temporality of symptoms and the psychological impact of these symptoms could be found in the tweets. The comparison between early 2020 and early 2019 showed no difference regarding the volumes of tweets. Conclusions: Based on our analysis of French tweets, associations between COVID-19 and anosmia or ageusia by web users could have been found on Twitter just a few days before the official announcement but not during the early stage of the pandemic. Patients share qualitative information on Twitter regarding anosmia or ageusia symptoms that could be of interest for future analyses. UR - https://infodemiology.jmir.org/2023/1/e41863 UR - http://dx.doi.org/10.2196/41863 UR - http://www.ncbi.nlm.nih.gov/pubmed/37643302 ID - info:doi/10.2196/41863 ER - TY - JOUR AU - Li, Ziyu AU - Wu, Xiaoqian AU - Xu, Lin AU - Liu, Ming AU - Huang, Cheng PY - 2023/9/21 TI - Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling JO - J Med Internet Res SP - e45019 VL - 25 KW - topic model KW - health rumors KW - social media KW - WeChat official account KW - content analysis KW - public health KW - machine learning KW - Twitter KW - social network KW - misinformation KW - users KW - disease KW - diet N2 - Background: Social networks have become one of the main channels for obtaining health information. However, they have also become a source of health-related misinformation, which seriously threatens the public?s physical and mental health. Governance of health-related misinformation can be implemented through topic identification of rumors on social networks. However, little attention has been paid to studying the types and routes of dissemination of health rumors on the internet, especially rumors regarding health-related information in Chinese social media. Objective: This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends and to analyze the modeling results of the text by using the Latent Dirichlet Allocation model. Methods: We used a web crawler tool to capture health rumor?dispelling articles on WeChat rumor-dispelling public accounts. We collected information from health-debunking articles posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model called Latent Dirichlet Allocation was used to identify and generalize the most common topics. The proportion distribution of the themes was calculated, and the negative impact of various health rumors in different periods was analyzed. Additionally, the prevalence of health rumors was analyzed by the number of health rumors generated at each time point. Results: We collected 9366 rumor-refuting articles from January 1, 2016, to August 31, 2022, from WeChat official accounts. Through topic modeling, we divided the health rumors into 8 topics, that is, rumors on prevention and treatment of infectious diseases (1284/9366, 13.71%), disease therapy and its effects (1037/9366, 11.07%), food safety (1243/9366, 13.27%), cancer and its causes (946/9366, 10.10%), regimen and disease (1540/9366, 16.44%), transmission (914/9366, 9.76%), healthy diet (1068/9366, 11.40%), and nutrition and health (1334/9366, 14.24%). Furthermore, we summarized the 8 topics under 4 themes, that is, public health, disease, diet and health, and spread of rumors. Conclusions: Our study shows that topic modeling can provide analysis and insights into health rumor governance. The rumor development trends showed that most rumors were on public health, disease, and diet and health problems. Governments still need to implement relevant and comprehensive rumor management strategies based on the rumors prevalent in their countries and formulate appropriate policies. Apart from regulating the content disseminated on social media platforms, the national quality of health education should also be improved. Governance of social networks should be clearly implemented, as these rapidly developed platforms come with privacy issues. Both disseminators and receivers of information should ensure a realistic attitude and disseminate health information correctly. In addition, we recommend that sentiment analysis?related studies be conducted to verify the impact of health rumor?related topics. UR - https://www.jmir.org/2023/1/e45019 UR - http://dx.doi.org/10.2196/45019 UR - http://www.ncbi.nlm.nih.gov/pubmed/37733396 ID - info:doi/10.2196/45019 ER - TY - JOUR AU - Taguchi, Kazuho AU - Matsoso, Precious AU - Driece, Roland AU - da Silva Nunes, Tovar AU - Soliman, Ahmed AU - Tangcharoensathien, Viroj PY - 2023/9/20 TI - Effective Infodemic Management: A Substantive Article of the Pandemic Accord JO - JMIR Infodemiology SP - e51760 VL - 3 KW - Pandemic Accord KW - infodemic KW - infodemic management KW - COVID-19 KW - social media KW - Intergovernmental Negotiating Body KW - INB KW - INB Bureau KW - World Health Organization KW - WHO KW - misinformation KW - disinformation KW - public health UR - https://infodemiology.jmir.org/2023/1/e51760 UR - http://dx.doi.org/10.2196/51760 UR - http://www.ncbi.nlm.nih.gov/pubmed/37728969 ID - info:doi/10.2196/51760 ER - TY - JOUR AU - Adebesin, Funmi AU - Smuts, Hanlie AU - Mawela, Tendani AU - Maramba, George AU - Hattingh, Marie PY - 2023/9/20 TI - The Role of Social Media in Health Misinformation and Disinformation During the COVID-19 Pandemic: Bibliometric Analysis JO - JMIR Infodemiology SP - e48620 VL - 3 KW - bibliometric analysis KW - COVID-19 KW - fake news KW - health disinformation KW - health misinformation KW - social media N2 - Background: The use of social media platforms to seek information continues to increase. Social media platforms can be used to disseminate important information to people worldwide instantaneously. However, their viral nature also makes it easy to share misinformation, disinformation, unverified information, and fake news. The unprecedented reliance on social media platforms to seek information during the COVID-19 pandemic was accompanied by increased incidents of misinformation and disinformation. Consequently, there was an increase in the number of scientific publications related to the role of social media in disseminating health misinformation and disinformation at the height of the COVID-19 pandemic. Health misinformation and disinformation, especially in periods of global public health disasters, can lead to the erosion of trust in policy makers at best and fatal consequences at worst. Objective: This paper reports a bibliometric analysis aimed at investigating the evolution of research publications related to the role of social media as a driver of health misinformation and disinformation since the start of the COVID-19 pandemic. Additionally, this study aimed to identify the top trending keywords, niche topics, authors, and publishers for publishing papers related to the current research, as well as the global collaboration between authors on topics related to the role of social media in health misinformation and disinformation since the start of the COVID-19 pandemic. Methods: The Scopus database was accessed on June 8, 2023, using a combination of Medical Subject Heading and author-defined terms to create the following search phrases that targeted the title, abstract, and keyword fields: (?Health*? OR ?Medical?) AND (?Misinformation? OR ?Disinformation? OR ?Fake News?) AND (?Social media? OR ?Twitter? OR ?Facebook? OR ?YouTube? OR ?WhatsApp? OR ?Instagram? OR ?TikTok?) AND (?Pandemic*? OR ?Corona*? OR ?Covid*?). A total of 943 research papers published between 2020 and June 2023 were analyzed using Microsoft Excel (Microsoft Corporation), VOSviewer (Centre for Science and Technology Studies, Leiden University), and the Biblioshiny package in Bibliometrix (K-Synth Srl) for RStudio (Posit, PBC). Results: The highest number of publications was from 2022 (387/943, 41%). Most publications (725/943, 76.9%) were articles. JMIR published the most research papers (54/943, 5.7%). Authors from the United States collaborated the most, with 311 coauthored research papers. The keywords ?Covid-19,? ?social media,? and ?misinformation? were the top 3 trending keywords, whereas ?learning systems,? ?learning models,? and ?learning algorithms? were revealed as the niche topics on the role of social media in health misinformation and disinformation during the COVID-19 outbreak. Conclusions: Collaborations between authors can increase their productivity and citation counts. Niche topics such as ?learning systems,? ?learning models,? and ?learning algorithms? could be exploited by researchers in future studies to analyze the influence of social media on health misinformation and disinformation during periods of global public health emergencies. UR - https://infodemiology.jmir.org/2023/1/e48620 UR - http://dx.doi.org/10.2196/48620 UR - http://www.ncbi.nlm.nih.gov/pubmed/37728981 ID - info:doi/10.2196/48620 ER - TY - JOUR AU - Dolatabadi, Elham AU - Moyano, Diana AU - Bales, Michael AU - Spasojevic, Sofija AU - Bhambhoria, Rohan AU - Bhatti, Junaid AU - Debnath, Shyamolima AU - Hoell, Nicholas AU - Li, Xin AU - Leng, Celine AU - Nanda, Sasha AU - Saab, Jad AU - Sahak, Esmat AU - Sie, Fanny AU - Uppal, Sara AU - Vadlamudi, Khatri Nirma AU - Vladimirova, Antoaneta AU - Yakimovich, Artur AU - Yang, Xiaoxue AU - Kocak, Akinli Sedef AU - Cheung, M. Angela PY - 2023/9/19 TI - Using Social Media to Help Understand Patient-Reported Health Outcomes of Post?COVID-19 Condition: Natural Language Processing Approach JO - J Med Internet Res SP - e45767 VL - 25 KW - long COVID KW - post?COVID-19 condition KW - PCC KW - social media KW - natural language processing KW - transformer models KW - bidirectional encoder representations from transformers KW - machine learning KW - Twitter KW - Reddit KW - PRO KW - patient-reported outcome KW - patient-reported symptom KW - health outcome KW - symptom KW - entity extraction KW - entity normalization N2 - Background: While scientific knowledge of post?COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. Objective: In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline?s potential as a surveillance tool. Methods: We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. Results: UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. Conclusions: The outcome of our social media?derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient?s journey that can help health care providers anticipate future needs. International Registered Report Identifier (IRRID): RR2-10.1101/2022.12.14.22283419 UR - https://www.jmir.org/2023/1/e45767 UR - http://dx.doi.org/10.2196/45767 UR - http://www.ncbi.nlm.nih.gov/pubmed/37725432 ID - info:doi/10.2196/45767 ER - TY - JOUR AU - Bizzotto, Nicole AU - Schulz, Johannes Peter AU - de Bruijn, Gert-Jan PY - 2023/9/18 TI - The ?Loci? of Misinformation and Its Correction in Peer- and Expert-Led Online Communities for Mental Health: Content Analysis JO - J Med Internet Res SP - e44656 VL - 25 KW - online communities KW - social media KW - mental health KW - misinformation KW - empowerment KW - content analysis KW - online community KW - infodemiology KW - information seeking KW - help seeking KW - information behavior KW - online search KW - search query KW - information quality KW - information accuracy N2 - Background: Mental health problems are recognized as a pressing public health issue, and an increasing number of individuals are turning to online communities for mental health to search for information and support. Although these virtual platforms have the potential to provide emotional support and access to anecdotal experiences, they can also present users with large amounts of potentially inaccurate information. Despite the importance of this issue, limited research has been conducted, especially on the differences that might emerge due to the type of content moderation of online communities: peer-led or expert-led. Objective: We aim to fill this gap by examining the prevalence, the communicative context, and the persistence of mental health misinformation on Facebook online communities for mental health, with a focus on understanding the mechanisms that enable effective correction of inaccurate information and differences between expert-led and peer-led groups. Methods: We conducted a content analysis of 1534 statements (from 144 threads) in 2 Italian-speaking Facebook groups. Results: The study found that an alarming number of comments (26.1%) contained medically inaccurate information. Furthermore, nearly 60% of the threads presented at least one misinformation statement without any correction attempt. Moderators were more likely to correct misinformation than members; however, they were not immune to posting content containing misinformation, which was an unexpected finding. Discussions about aspects of treatment (including side effects or treatment interruption) significantly increased the probability of encountering misinformation. Additionally, the study found that misinformation produced in the comments of a thread, rather than as the first post, had a lower probability of being corrected, particularly in peer-led communities. Conclusions: The high prevalence of misinformation in online communities, particularly when left uncorrected, underscores the importance of conducting additional research to identify effective mechanisms to prevent its spread. This is especially important given the study?s finding that misinformation tends to be more prevalent around specific ?loci? of discussion that, once identified, can serve as a starting point to develop strategies for preventing and correcting misinformation within them. UR - https://www.jmir.org/2023/1/e44656 UR - http://dx.doi.org/10.2196/44656 UR - http://www.ncbi.nlm.nih.gov/pubmed/37721800 ID - info:doi/10.2196/44656 ER - TY - JOUR AU - Ng, Margaret Yee Man AU - Hoffmann Pham, Katherine AU - Luengo-Oroz, Miguel PY - 2023/9/15 TI - Exploring YouTube?s Recommendation System in the Context of COVID-19 Vaccines: Computational and Comparative Analysis of Video Trajectories JO - J Med Internet Res SP - e49061 VL - 25 KW - algorithmic auditing KW - antivaccine sentiment KW - crowdsourcing KW - recommendation systems KW - watch history KW - YouTube N2 - Background: Throughout the COVID-19 pandemic, there has been a concern that social media may contribute to vaccine hesitancy due to the wide availability of antivaccine content on social media platforms. YouTube has stated its commitment to removing content that contains misinformation on vaccination. Nevertheless, such claims are difficult to audit. There is a need for more empirical research to evaluate the actual prevalence of antivaccine sentiment on the internet. Objective: This study examines recommendations made by YouTube?s algorithms in order to investigate whether the platform may facilitate the spread of antivaccine sentiment on the internet. We assess the prevalence of antivaccine sentiment in recommended videos and evaluate how real-world users? experiences are different from the personalized recommendations obtained by using synthetic data collection methods, which are often used to study YouTube?s recommendation systems. Methods: We trace trajectories from a credible seed video posted by the World Health Organization to antivaccine videos, following only video links suggested by YouTube?s recommendation system. First, we gamify the process by asking real-world participants to intentionally find an antivaccine video with as few clicks as possible. Having collected crowdsourced trajectory data from respondents from (1) the World Health Organization and United Nations system (nWHO/UN=33) and (2) Amazon Mechanical Turk (nAMT=80), we next compare the recommendations seen by these users to recommended videos that are obtained from (3) the YouTube application programming interface?s RelatedToVideoID parameter (nRTV=40) and (4) from clean browsers without any identifying cookies (nCB=40), which serve as reference points. We develop machine learning methods to classify antivaccine content at scale, enabling us to automatically evaluate 27,074 video recommendations made by YouTube. Results: We found no evidence that YouTube promotes antivaccine content; the average share of antivaccine videos remained well below 6% at all steps in users? recommendation trajectories. However, the watch histories of users significantly affect video recommendations, suggesting that data from the application programming interface or from a clean browser do not offer an accurate picture of the recommendations that real users are seeing. Real users saw slightly more provaccine content as they advanced through their recommendation trajectories, whereas synthetic users were drawn toward irrelevant recommendations as they advanced. Rather than antivaccine content, videos recommended by YouTube are likely to contain health-related content that is not specifically related to vaccination. These videos are usually longer and contain more popular content. Conclusions: Our findings suggest that the common perception that YouTube?s recommendation system acts as a ?rabbit hole? may be inaccurate and that YouTube may instead be following a ?blockbuster? strategy that attempts to engage users by promoting other content that has been reliably successful across the platform. UR - https://www.jmir.org/2023/1/e49061 UR - http://dx.doi.org/10.2196/49061 UR - http://www.ncbi.nlm.nih.gov/pubmed/37713243 ID - info:doi/10.2196/49061 ER - TY - JOUR AU - Malhotra, Kashish AU - Kempegowda, Punith PY - 2023/9/11 TI - Appraising Unmet Needs and Misinformation Spread About Polycystic Ovary Syndrome in 85,872 YouTube Comments Over 12 Years: Big Data Infodemiology Study JO - J Med Internet Res SP - e49220 VL - 25 KW - polycystic ovary syndrome KW - PCOS KW - public KW - YouTube KW - global health KW - online trends KW - global equity KW - infodemiology KW - big data KW - comments KW - sentiment KW - network analysis KW - contextualization KW - word association KW - misinformation KW - endocrinopathy KW - women KW - gender KW - users KW - treatment KW - fatigue KW - pain KW - motherhood N2 - Background: Polycystic ovary syndrome (PCOS) is the most common endocrinopathy in women, resulting in substantial burden related to metabolic, reproductive, and psychological complications. While attempts have been made to understand the themes and sentiments of the public regarding PCOS at the local and regional levels, no study has explored worldwide views, mainly due to financial and logistical limitations. YouTube is one of the largest sources of health-related information, where many visitors share their views as questions or comments. These can be used as a surrogate to understand the public?s perceptions. Objective: We analyzed the comments of all videos related to PCOS published on YouTube from May 2011 to April 2023 and identified trends over time in the comments, their context, associated themes, gender-based differences, and underlying sentiments. Methods: After extracting all the comments using the YouTube application programming interface, we contextually studied the keywords and analyzed gender differences using the Benjamini-Hochberg procedure. We applied a multidimensional approach to analyzing the content via association mining using Mozdeh. We performed network analysis to study associated themes using the Fruchterman-Reingold algorithm and then manually screened the comments for content analysis. The sentiments associated with YouTube comments were analyzed using SentiStrength. Results: A total of 85,872 comments from 940 PCOS videos on YouTube were extracted. We identified a specific gender for 13,106 comments. Of these, 1506 were matched to male users (11.5%), and 11,601 comments to female users (88.5%). Keywords including diagnosing PCOS, symptoms of PCOS, pills for PCOS (medication), and pregnancy were significantly associated with female users. Keywords such as herbal treatment, natural treatment, curing PCOS, and online searches were significantly associated with male users. The key themes associated with female users were symptoms of PCOS, positive personal experiences (themes such as helpful and love), negative personal experiences (fatigue and pain), motherhood (infertility and trying to conceive), self-diagnosis, and use of professional terminology detailing their journey. The key themes associated with male users were misinformation regarding the ?cure? for PCOS, using natural and herbal remedies to cure PCOS, fake testimonies from spammers selling their courses and consultations, finding treatment for PCOS, and sharing perspectives of female family members. The overall average positive sentiment was 1.6651 (95% CI 1.6593-1.6709), and the average negative sentiment was 1.4742 (95% CI 1.4683-1.4802) with a net positive difference of 0.1909. Conclusions: There may be a disparity in views on PCOS between women and men, with the latter associated with non?evidence-based approaches and misinformation. The improving sentiment noticed with YouTube comments may reflect better health care services. Prioritizing and promoting evidence-based care and disseminating pragmatic online coverage is warranted to improve public sentiment and limit misinformation spread. UR - https://www.jmir.org/2023/1/e49220 UR - http://dx.doi.org/10.2196/49220 UR - http://www.ncbi.nlm.nih.gov/pubmed/37695666 ID - info:doi/10.2196/49220 ER - TY - JOUR AU - Chu, MY Amanda AU - Chong, Y. Andy C. AU - Lai, T. Nick H. AU - Tiwari, Agnes AU - So, P. Mike K. PY - 2023/9/7 TI - Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19 JO - JMIR Public Health Surveill SP - e42446 VL - 9 KW - internet search volumes KW - network analysis KW - pandemic risk KW - health care analytics KW - network connectedness KW - infodemiology KW - infoveillance KW - mobile phone KW - COVID-19 N2 - Background: The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT?s normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks. Objective: This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk. Methods: We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated. Results: Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores. Conclusions: The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet. UR - https://publichealth.jmir.org/2023/1/e42446 UR - http://dx.doi.org/10.2196/42446 UR - http://www.ncbi.nlm.nih.gov/pubmed/37676701 ID - info:doi/10.2196/42446 ER - TY - JOUR AU - Kureyama, Nari AU - Terada, Mitsuo AU - Kusudo, Maho AU - Nozawa, Kazuki AU - Wanifuchi-Endo, Yumi AU - Fujita, Takashi AU - Asano, Tomoko AU - Kato, Akiko AU - Mori, Makiko AU - Horisawa, Nanae AU - Toyama, Tatsuya PY - 2023/9/6 TI - Fact-Checking Cancer Information on Social Media in Japan: Retrospective Study Using Twitter JO - JMIR Form Res SP - e49452 VL - 7 KW - cancer KW - fact-check KW - misinformation KW - social media KW - twitter N2 - Background: The widespread use of social media has made it easier for patients to access cancer information. However, a large amount of misinformation and harmful information that could negatively impact patients? decision-making is also disseminated on social media platforms. Objective: We aimed to determine the actual amount of misinformation and harmful information as well as trends in the dissemination of cancer-related information on Twitter, a representative social media platform. Our findings can support decision-making among Japanese patients with cancer. Methods: Using the Twitter app programming interface, we extracted tweets containing the term ?cancer? in Japanese that were posted between August and September of 2022. The eligibility criteria were the cancer-related tweets with the following information: (1) reference to the occurrence or prognosis of cancer, (2) recommendation or nonrecommendation of actions, (3) reference to the course of cancer treatment or adverse events, (4) results of cancer research, and (5) other cancer-related knowledge and information. Finally, we selected the top 100 tweets with the highest number of ?likes.? For each tweet, 2 independent reviewers evaluated whether the information was factual or misinformation, and whether it was harmful or safe with the reasons for the decisions on the misinformation and harmful tweets. Additionally, we examined the frequency of information dissemination using the number of retweets for the top 100 tweets and investigated trends in the dissemination of information. Results: The extracted tweets totaled 69,875. Of the top 100 cancer-related tweets with the most ?likes? that met the eligibility criteria, 44 (44%) contained misinformation, 31 (31%) contained harmful information, and 30 (30%) contained both misinformation and harmful information. Misinformation was described as Unproven (29/94, 40.4%), Disproven (19/94, 20.2%), Inappropriate application (4/94, 4.3%), Strength of evidence mischaracterized (14/94, 14.9%), Misleading (18/94, 18%), and Other misinformation (1/94, 1.1%). Harmful action was described as Harmful action (9/59, 15.2%), Harmful inaction (43/59, 72.9%), Harmful interactions (3/59, 5.1%), Economic harm (3/59, 5.1%), and Other harmful information (1/59, 1.7%). Harmful information was liked more often than safe information (median 95, IQR 43-1919 vs 75.0 IQR 43-10,747; P=.04). The median number of retweets for the leading 100 tweets was 13.5 (IQR 0-2197). Misinformation was retweeted significantly more often than factual information (median 29.0, IQR 0-502 vs 7.5, IQR 0-2197; P=.01); harmful information was also retweeted significantly more often than safe information (median 35.0, IQR 0-502 vs 8.0, IQR 0-2197; P=.002). Conclusions: It is evident that there is a prevalence of misinformation and harmful information related to cancer on Twitter in Japan and it is crucial to increase health literacy and awareness regarding this issue. Furthermore, we believe that it is important for government agencies and health care professionals to continue providing accurate medical information to support patients and their families in making informed decisions. UR - https://formative.jmir.org/2023/1/e49452 UR - http://dx.doi.org/10.2196/49452 UR - http://www.ncbi.nlm.nih.gov/pubmed/37672310 ID - info:doi/10.2196/49452 ER - TY - JOUR AU - Hirabayashi, Mai AU - Shibata, Daisaku AU - Shinohara, Emiko AU - Kawazoe, Yoshimasa PY - 2023/9/5 TI - Influence of Tweets Indicating False Rumors on COVID-19 Vaccination: Case Study JO - JMIR Form Res SP - e45867 VL - 7 KW - coronavirus KW - correlation KW - COVID-19 KW - disinformation KW - false information KW - infodemiology KW - misinformation KW - rumor KW - rumor-indication KW - SARS-CoV-2 KW - social media KW - tweet KW - Twitter KW - vaccination KW - vaccine N2 - Background: As of December 2022, the outbreak of COVID-19 showed no sign of abating, continuing to impact people?s lives, livelihoods, economies, and more. Vaccination is an effective way to achieve mass immunity. However, in places such as Japan, where vaccination is voluntary, there are people who choose not to receive the vaccine, even if an effective vaccine is offered. To promote vaccination, it is necessary to clarify what kind of information on social media can influence attitudes toward vaccines. Objective: False rumors and counterrumors are often posted and spread in large numbers on social media, especially during emergencies. In this paper, we regard tweets that contain questions or point out errors in information as counterrumors. We analyze counterrumors tweets related to the COVID-19 vaccine on Twitter. We aimed to answer the following questions: (1) what kinds of COVID-19 vaccine?related counterrumors were posted on Twitter, and (2) are the posted counterrumors related to social conditions such as vaccination status? Methods: We use the following data sets: (1) counterrumors automatically collected by the ?rumor cloud? (18,593 tweets); and (2) the number of COVID-19 vaccine inoculators from September 27, 2021, to August 15, 2022, published on the Prime Minister?s Office?s website. First, we classified the contents contained in counterrumors. Second, we counted the number of COVID-19 vaccine?related counterrumors from data set 1. Then, we examined the cross-correlation coefficients between the numbers of data sets 1 and 2. Through this verification, we examined the correlation coefficients for the following three periods: (1) the same period of data; (2) the case where the occurrence of the suggestion of counterrumors precedes the vaccination (negative time lag); and (3) the case where the vaccination precedes the occurrence of counterrumors (positive time lag). The data period used for the validation was from October 4, 2021, to April 18, 2022. Results: Our classification results showed that most counterrumors about the COVID-19 vaccine were negative. Moreover, the correlation coefficients between the number of counterrumors and vaccine inoculators showed significant and strong positive correlations. The correlation coefficient was over 0.7 at ?8, ?7, and ?1 weeks of lag. Results suggest that the number of vaccine inoculators tended to increase with an increase in the number of counterrumors. Significant correlation coefficients of 0.5 to 0.6 were observed for lags of 1 week or more and 2 weeks or more. This implies that an increase in vaccine inoculators increases the number of counterrumors. These results suggest that the increase in the number of counterrumors may have been a factor in inducing vaccination behavior. Conclusions: Using quantitative data, we were able to reveal how counterrumors influence the vaccination status of the COVID-19 vaccine. We think that our findings would be a foundation for considering countermeasures of vaccination. UR - https://formative.jmir.org/2023/1/e45867 UR - http://dx.doi.org/10.2196/45867 UR - http://www.ncbi.nlm.nih.gov/pubmed/37669092 ID - info:doi/10.2196/45867 ER - TY - JOUR AU - Esener, Yildiz AU - McCall, Terika AU - Lakdawala, Adnan AU - Kim, Heejun PY - 2023/8/31 TI - Seeking and Providing Social Support on Twitter for Trauma and Distress During the COVID-19 Pandemic: Content and Sentiment Analysis JO - J Med Internet Res SP - e46343 VL - 25 KW - COVID-19 KW - social support KW - trauma KW - distress KW - posttraumatic stress disorder KW - PTSD KW - Twitter KW - social media KW - mental health N2 - Background: The COVID-19 pandemic can be recognized as a traumatic event that led to stressors, resulting in trauma or distress among the general population. Social support is vital in the management of these stressors, especially during a traumatic event, such as the COVID-19 pandemic. Because of the limited face-to-face interactions enforced by physical distancing regulations during the pandemic, people sought solace on social media platforms to connect with, and receive support from, one another. Hence, it is crucial to investigate the ways in which people seek and offer support on social media for mental health management. Objective: The research aimed to examine the types of social support (eg, emotional, informational, instrumental, and appraisal) sought and provided for trauma or distress on Twitter during the COVID-19 pandemic. In addition, this study aimed to gain insight into the difficulties and concerns of people during the pandemic by identifying the associations between terms representing the topics of interest related to trauma or distress and their corresponding sentiments. Methods: The study methods included content analysis to investigate the type of social support people sought for trauma or distress during the pandemic. Sentiment analysis was also performed to track the negative and positive sentiment tweets posted between January 1, 2020, and March 15, 2021. Association rule mining was used to uncover associations between terms and sentiments in tweets. In addition, the research used Kruskal-Wallis and Mann-Whitney U tests to determine whether the retweet count and like count varied based on the social support type. Results: Most Twitter users who indicated trauma or distress sought emotional support. Regarding sentiment, Twitter users mostly posted negative sentiment tweets, particularly in January 2021. An intriguing observation was that wearing masks could trigger and exacerbate trauma or distress. The results revealed that people mostly sought and provided emotional support on Twitter regarding difficulties with wearing masks, mental health status, financial hardships, and treatment methods for trauma or distress. In addition, tweets regarding emotional support received the most endorsements from other users, highlighting the critical role of social support in fostering a sense of community and reducing the feelings of isolation during the pandemic. Conclusions: This study demonstrates the potential of social media as a platform to exchange social support during challenging times and to identify the specific concerns (eg, wearing masks and exacerbated symptoms) of individuals with self-reported trauma or distress. The findings provide insights into the types of support that were most beneficial for those struggling with trauma or distress during the pandemic and may inform policy makers and health organizations regarding better practices for pandemic response and special considerations for groups with a history of trauma or distress. UR - https://www.jmir.org/2023/1/e46343 UR - http://dx.doi.org/10.2196/46343 UR - http://www.ncbi.nlm.nih.gov/pubmed/37651178 ID - info:doi/10.2196/46343 ER - TY - JOUR AU - Dobbs, D. Page AU - Boykin, Ames Allison AU - Ezike, Nnamdi AU - Myers, J. Aaron AU - Colditz, B. Jason AU - Primack, A. Brian PY - 2023/8/31 TI - Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis JO - JMIR Form Res SP - e50346 VL - 7 KW - social media KW - Twitter KW - Tobacco 21 KW - mixed methods KW - tobacco policy KW - sentiment KW - tweet KW - tweets KW - tobacco KW - smoke KW - smoking KW - smoker KW - policy KW - policies KW - law KW - regulation KW - regulations KW - laws KW - attitude KW - attitudes KW - opinion KW - opinions N2 - Background: On December 20, 2019, the US ?Tobacco 21? law raised the minimum legal sales age of tobacco products to 21 years. Initial research suggests that misinformation about Tobacco 21 circulated via news sources on Twitter and that sentiment about the law was associated with particular types of tobacco products and included discussions about other age-related behaviors. However, underlying themes about this sentiment as well as temporal trends leading up to enactment of the law have not been explored. Objective: This study sought to examine (1) sentiment (pro-, anti-, and neutral policy) about Tobacco 21 on Twitter and (2) volume patterns (number of tweets) of Twitter discussions leading up to the enactment of the federal law. Methods: We collected tweets related to Tobacco 21 posted between September 4, 2019, and December 31, 2019. A 2% subsample of tweets (4628/231,447) was annotated by 2 experienced, trained coders for policy-related information and sentiment. To do this, a codebook was developed using an inductive procedure that outlined the operational definitions and examples for the human coders to annotate sentiment (pro-, anti-, and neutral policy). Following the annotation of the data, the researchers used a thematic analysis to determine emergent themes per sentiment category. The data were then annotated again to capture frequencies of emergent themes. Concurrently, we examined trends in the volume of Tobacco 21?related tweets (weekly rhythms and total number of tweets over the time data were collected) and analyzed the qualitative discussions occurring at those peak times. Results: The most prevalent category of tweets related to Tobacco 21 was neutral policy (514/1113, 46.2%), followed by antipolicy (432/1113, 38.8%); 167 of 1113 (15%) were propolicy or supportive of the law. Key themes identified among neutral tweets were news reports and discussion of political figures, parties, or government involvement in general. Most discussions were generated from news sources and surfaced in the final days before enactment. Tweets opposing Tobacco 21 mentioned that the law was unfair to young audiences who were addicted to nicotine and were skeptical of the law?s efficacy and importance. Methods used to evade the law were found to be represented in both neutral and antipolicy tweets. Propolicy tweets focused on the protection of youth and described the law as a sensible regulatory approach rather than a complete ban of all products or flavored products. Four spikes in daily volume were noted, 2 of which corresponded with political speeches and 2 with the preparation and passage of the legislation. Conclusions: Understanding themes of public sentiment?as well as when Twitter activity is most active?will help public health professionals to optimize health promotion activities to increase community readiness and respond to enforcement needs including education for retailers and the general public. UR - https://formative.jmir.org/2023/1/e50346 UR - http://dx.doi.org/10.2196/50346 UR - http://www.ncbi.nlm.nih.gov/pubmed/37651169 ID - info:doi/10.2196/50346 ER - TY - JOUR AU - Lu, Chang AU - Hu, Bo AU - Li, Qiang AU - Bi, Chao AU - Ju, Xing-Da PY - 2023/8/29 TI - Psychological Inoculation for Credibility Assessment, Sharing Intention, and Discernment of Misinformation: Systematic Review and Meta-Analysis JO - J Med Internet Res SP - e49255 VL - 25 KW - psychological inoculation KW - misinformation KW - discernment KW - sharing KW - meta-analysis N2 - Background: The prevalence of misinformation poses a substantial threat to individuals? daily lives, necessitating the deployment of effective remedial approaches. One promising strategy is psychological inoculation, which pre-emptively immunizes individuals against misinformation attacks. However, uncertainties remain regarding the extent to which psychological inoculation effectively enhances the capacity to differentiate between misinformation and real information. Objective: To reduce the potential risk of misinformation about digital health, this study aims to examine the effectiveness of psychological inoculation in countering misinformation with a focus on several factors, including misinformation credibility assessment, real information credibility assessment, credibility discernment, misinformation sharing intention, real information sharing intention, and sharing discernment. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we conducted a meta-analysis by searching 4 databases (Web of Science, APA PsycINFO, Proquest, and PubMed) for empirical studies based on inoculation theory and outcome measure?related misinformation published in the English language. Moderator analyses were used to examine the differences in intervention strategy, intervention type, theme, measurement time, team, and intervention design. Results: Based on 42 independent studies with 42,530 subjects, we found that psychological inoculation effectively reduces misinformation credibility assessment (d=?0.36, 95% CI ?0.50 to ?0.23; P<.001) and improves real information credibility assessment (d=0.20, 95% CI 0.06-0.33; P=.005) and real information sharing intention (d=0.09, 95% CI 0.03-0.16; P=.003). However, psychological inoculation does not significantly influence misinformation sharing intention (d=?0.35, 95% CI ?0.79 to 0.09; P=.12). Additionally, we find that psychological inoculation effectively enhances credibility discernment (d=0.20, 95% CI 0.13-0.28; P<.001) and sharing discernment (d=0.18, 95% CI 0.12-0.24; P<.001). Regarding health misinformation, psychological inoculation effectively decreases misinformation credibility assessment and misinformation sharing intention. The results of the moderator analyses showed that content-based, passive inoculation was more effective in increasing credibility and sharing intention. The theme of climate change demonstrates a stronger effect on real information credibility. Comparing intervention types showed that pre-post interventions are more effective for misinformation credibility assessment, while post-only interventions are better for credibility discernment. Conclusions: This study indicated that psychological inoculation enhanced individuals? ability to discern real information from misinformation and share real information. Incorporating psychological inoculation to cultivate an informed public is crucial for societal resilience against misinformation threats in an age of information proliferation. As a scalable and cost-effective intervention strategy, institutions can apply psychological inoculation to mitigate potential misinformation crises. UR - https://www.jmir.org/2023/1/e49255 UR - http://dx.doi.org/10.2196/49255 UR - http://www.ncbi.nlm.nih.gov/pubmed/37560816 ID - info:doi/10.2196/49255 ER - TY - JOUR AU - Jones, M. Christopher AU - Diethei, Daniel AU - Schöning, Johannes AU - Shrestha, Rehana AU - Jahnel, Tina AU - Schüz, Benjamin PY - 2023/8/24 TI - Impact of Social Reference Cues on Misinformation Sharing on Social Media: Series of Experimental Studies JO - J Med Internet Res SP - e45583 VL - 25 KW - misinformation KW - social media KW - health literacy KW - COVID-19 KW - fake news KW - Twitter KW - tweet KW - infodemiology KW - information behavior KW - information sharing KW - sharing behavior KW - behavior change KW - social cue KW - social reference KW - flag N2 - Background: Health-related misinformation on social media is a key challenge to effective and timely public health responses. Existing mitigation measures include flagging misinformation or providing links to correct information, but they have not yet targeted social processes. Current approaches focus on increasing scrutiny, providing corrections to misinformation (debunking), or alerting users prospectively about future misinformation (prebunking and inoculation). Here, we provide a test of a complementary strategy that focuses on the social processes inherent in social media use, in particular, social reinforcement, social identity, and injunctive norms. Objective: This study aimed to examine whether providing balanced social reference cues (ie, cues that provide information on users sharing and, more importantly, not sharing specific content) in addition to flagging COVID-19?related misinformation leads to reductions in sharing behavior and improvement in overall sharing quality. Methods: A total of 3 field experiments were conducted on Twitter?s native social media feed (via a newly developed browser extension). Participants? feed was augmented to include misleading and control information, resulting in 4 groups: no-information control, Twitter?s own misinformation warning (misinformation flag), social cue only, and combined misinformation flag and social cue. We tracked the content shared or liked by participants. Participants were provided with social information by referencing either their personal network on Twitter or all Twitter users. Results: A total of 1424 Twitter users participated in 3 studies (n=824, n=322, and n=278). Across all 3 studies, we found that social cues that reference users? personal network combined with a misinformation flag reduced the sharing of misleading but not control information and improved overall sharing quality. We show that this improvement could be driven by a change in injunctive social norms (study 2) but not social identity (study 3). Conclusions: Social reference cues combined with misinformation flags can significantly and meaningfully reduce the amount of COVID-19?related misinformation shared and improve overall sharing quality. They are a feasible and scalable way to effectively curb the sharing of COVID-19?related misinformation on social media. UR - https://www.jmir.org/2023/1/e45583 UR - http://dx.doi.org/10.2196/45583 UR - http://www.ncbi.nlm.nih.gov/pubmed/37616030 ID - info:doi/10.2196/45583 ER - TY - JOUR AU - Vargas Meza, Xanat AU - Park, Woo Han PY - 2023/8/23 TI - Information Circulation Among Spanish-Speaking and Caribbean Communities Related to COVID-19: Social Media?Based Multidimensional Analysis JO - J Med Internet Res SP - e42669 VL - 25 KW - COVID-19 KW - social media KW - Spanish KW - multidimensional analysis KW - Caribbean KW - accessibility N2 - Background: Scienti?c studies from North America and Europe tend to predominate the internet and bene?t English-speaking users. Meanwhile, the COVID-19 death rate was high at the onset of the pandemic in Spanish-speaking countries, and information about nearby Caribbean countries was rarely highlighted. Given the rise in social media use in these regions, the web-based dissemination of scientific information related to COVID-19 must be thoroughly examined. Objective: This study aimed to provide a multidimensional analysis of peer-reviewed information circulation related to COVID-19 in Spanish-speaking and Caribbean regions. Methods: COVID-19?related, peer-reviewed resources shared by web-based accounts located in Spanish-speaking and Caribbean regions were identified through the Altmetric website, and their information was collected. A multidimensional model was used to examine these resources, considering time, individuality, place, activity, and relations. Time was operationalized as the 6 dates of data collection, individuality as the knowledge area and accessibility level, place as the publication venue and affiliation countries, activity as the Altmetric score and number of mentions in the selected regions, and relations as coauthorship between countries and types of social media users who disseminated COVID-19?related information. Results: The highest information circulation peaks in Spanish-speaking countries were from April 2020 to August 2020 and from December 2020 to April 2021, whereas the highest peaks in Caribbean regions were from December 2019 to April 2020. Regarding Spanish-speaking regions, at the onset of the pandemic, scientific expertise was concentrated on a few peer-reviewed sources written in English. The top scienti?c journals mentioned were from English-speaking, westernized regions, whereas the top scienti?c authorships were from China. The most mentioned scientific resources were about breakthrough findings in the medical and health sciences area, written in highly technical language. The top relationships were self-loops in China, whereas international collaborations were between China and the United States. Argentina had high closeness and betweenness, and Spain had high closeness. On the basis of social media data, a combination of media outlets; educational institutions; and expert associations, particularly from Panama, influenced the diffusion of peer-reviewed information. Conclusions: We determined the diffusion patterns of peer-reviewed resources in Spanish-speaking countries and Caribbean territories. This study aimed to advance the management and analysis of web-based public data from non-white people to improve public health communication in their regions. UR - https://www.jmir.org/2023/1/e42669 UR - http://dx.doi.org/10.2196/42669 UR - http://www.ncbi.nlm.nih.gov/pubmed/37402284 ID - info:doi/10.2196/42669 ER - TY - JOUR AU - Lungu, Adrian Daniel AU - Rřislien, Jo AU - Berg, Hilde Siv AU - Smeets, Ionica AU - Shortt, Therese Marie AU - Thune, Henriette AU - Brřnnick, Kallesten Kolbjřrn PY - 2023/8/23 TI - Assessing the Effect of Nonvisual Information Factors in Pandemic-Related Video Communication: Randomized Controlled Between-Subjects Experiment JO - J Med Internet Res SP - e42528 VL - 25 KW - video communication KW - COVID-19 KW - trust KW - comprehension KW - intentions KW - behavior KW - visual KW - pandemic KW - risk KW - communication KW - policy KW - effect KW - video KW - experiment N2 - Background: Videos have been an important medium for providing health and risk communication to the public during the COVID-19 pandemic. Public health officials, health care professionals, and policy makers have used videos to communicate pandemic-related content to large parts of the population. Evidence regarding the outcomes of such communication, along with their determinants, is however limited. Objective: The aim of this study was to test the impact of nonvisual information factors of video communication on 4 outcomes: trust, comprehension, intentions, and behavior. Methods: Twelve short health communication videos related to pandemics were produced and shown to a large sample of participants, applying a randomized controlled between-subjects design. Three factors were included in the creation of the videos: the topic (exponential growth, handwashing, and burden of pandemics on the health care system), the source (expert and nonexpert), and a call to action (present or absent). Participants were randomly assigned to 1 video intervention, and 1194 valid replies were collected. The data were analyzed using factorial ANOVA. Results: The 3 pandemic-related topics did not affect trust, comprehension, intentions, or behavior. Trust was positively influenced by an expert source (2.5%), whereas a nonexpert source instead had a positive effect on the proxy for behavior (5.7%) compared with the expert source. The inclusion of a call to action had a positive effect on both trust (4.1%) and comprehension (15%). Conclusions: Trust and comprehension in pandemic-related video communication can be enhanced by using expert sources and by including a call to action, irrespective of the topic being communicated. Intentions and behavior appear to be affected to a small extent by the 3 factors tested in this study. International Registered Report Identifier (IRRID): RR2-10.2196/34275 UR - https://www.jmir.org/2023/1/e42528 UR - http://dx.doi.org/10.2196/42528 UR - http://www.ncbi.nlm.nih.gov/pubmed/37610820 ID - info:doi/10.2196/42528 ER - TY - JOUR AU - Du, Min AU - Yan, Wenxin AU - Zhu, Lin AU - Liang, Wannian AU - Liu, Min AU - Liu, Jue PY - 2023/8/23 TI - Trends in the Baidu Index in Search Activity Related to Mpox at Geographical and Economic Levels and Associated Factors in China: National Longitudinal Analysis JO - JMIR Form Res SP - e44031 VL - 7 KW - mpox KW - internet attention KW - emergency KW - disparities KW - China N2 - Background: Research assessing trends in online search activity related to mpox in China is scarce. Objective: We aimed to provide evidence for an overview of online information searching during an infectious disease outbreak by analyzing trends in online search activity related to mpox at geographical and economic levels in China and explore influencing factors. Methods: We used the Baidu index to present online search activity related to mpox from May 19 to September 19, 2022. Segmented interrupted time-series analysis was used to estimate trends in online search activity. Factors influencing these trends were analyzed using a general linear regression (GLM) model. We calculated the concentration index to measure economic-related inequality in online search activity and related trends. Results: Online search activity was highest on the day the first imported case of mpox appeared in Chongqing compared to 3 other cutoff time points. After the day of the first imported mpox case in Taiwan, the declaration of a public health emergency of international concern, the first imported mpox case in Hong Kong, and the first imported mpox case in Chongqing, national online search activity increased by 0.642%, 1.035%, 1.199%, and 2.023%, respectively. The eastern regions had higher increases than the central and western regions. Across 31 provinces, municipalities, and autonomous regions, the top 3 areas with higher increases were Beijing, Shanghai, and Tianjin at 3 time points, with the exception of the day of the first imported mpox case in Chongqing (Chongqing replaced Tianjin on that day). When AIDS incidence increased by 1 per 100,000 people, there was an increase after the day of the first imported mpox case in Chongqing of 36.22% (95% CI 3.29%-69.15%; P=.04) after controlling for other covariates. Online search activity (concentration index=0.18; P<.001) was more concentrated among populations with a higher economic status. Unlike the central area, the eastern (concentration index=0.234; P<.001) and western areas (concentration index=0.047; P=.04) had significant economic-related disparities (P for difference <.001) in online search activity. The overall concentration index of changes in online search activity became lower over time. Conclusions: Regions with a higher economic level showed more interest in mpox, especially Beijing and Shanghai. After the day of the first imported mpox case in Chongqing, changes in online search activity were affected by AIDS incidence rate. Economic-related disparities in changes in online search activity became lower over time. It would be desirable to construct a reliable information source in regions with a higher economic level and higher AIDS incidence rate and promote public knowledge in regions with a lower economic level in China, especially after important public events. UR - https://formative.jmir.org/2023/1/e44031 UR - http://dx.doi.org/10.2196/44031 UR - http://www.ncbi.nlm.nih.gov/pubmed/37610816 ID - info:doi/10.2196/44031 ER - TY - JOUR AU - Yang, Fan Ellie AU - Kornfield, Rachel AU - Liu, Yan AU - Chih, Ming-Yuan AU - Sarma, Prathusha AU - Gustafson, David AU - Curtin, John AU - Shah, Dhavan PY - 2023/8/22 TI - Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum JO - J Med Internet Res SP - e45589 VL - 25 KW - supervised machine learning KW - online peer support forum KW - expression effects KW - content analysis KW - substance use disorder KW - mobile phone N2 - Background: Smartphone-based apps are increasingly used to prevent relapse among those with substance use disorders (SUDs). These systems collect a wealth of data from participants, including the content of messages exchanged in peer-to-peer support forums. How individuals self-disclose and exchange social support in these forums may provide insight into their recovery course, but a manual review of a large corpus of text by human coders is inefficient. Objective: The study sought to evaluate the feasibility of applying supervised machine learning (ML) to perform large-scale content analysis of an online peer-to-peer discussion forum. Machine-coded data were also used to understand how communication styles relate to writers? substance use and well-being outcomes. Methods: Data were collected from a smartphone app that connects patients with SUDs to online peer support via a discussion forum. Overall, 268 adult patients with SUD diagnoses were recruited from 3 federally qualified health centers in the United States beginning in 2014. Two waves of survey data were collected to measure demographic characteristics and study outcomes: at baseline (before accessing the app) and after 6 months of using the app. Messages were downloaded from the peer-to-peer forum and subjected to manual content analysis. These data were used to train supervised ML algorithms using features extracted from the Linguistic Inquiry and Word Count (LIWC) system to automatically identify the types of expression relevant to peer-to-peer support. Regression analyses examined how each expression type was associated with recovery outcomes. Results: Our manual content analysis identified 7 expression types relevant to the recovery process (emotional support, informational support, negative affect, change talk, insightful disclosure, gratitude, and universality disclosure). Over 6 months of app use, 86.2% (231/268) of participants posted on the app?s support forum. Of these participants, 93.5% (216/231) posted at least 1 message in the content categories of interest, generating 10,503 messages. Supervised ML algorithms were trained on the hand-coded data, achieving F1-scores ranging from 0.57 to 0.85. Regression analyses revealed that a greater proportion of the messages giving emotional support to peers was related to reduced substance use. For self-disclosure, a greater proportion of the messages expressing universality was related to improved quality of life, whereas a greater proportion of the negative affect expressions was negatively related to quality of life and mood. Conclusions: This study highlights a method of natural language processing with potential to provide real-time insights into peer-to-peer communication dynamics. First, we found that our ML approach allowed for large-scale content coding while retaining moderate-to-high levels of accuracy. Second, individuals? expression styles were associated with recovery outcomes. The expression types of emotional support, universality disclosure, and negative affect were significantly related to recovery outcomes, and attending to these dynamics may be important for appropriate intervention. UR - https://www.jmir.org/2023/1/e45589 UR - http://dx.doi.org/10.2196/45589 UR - http://www.ncbi.nlm.nih.gov/pubmed/37606984 ID - info:doi/10.2196/45589 ER - TY - JOUR AU - Alvarez-Mon, Angel Miguel AU - Pereira-Sanchez, Victor AU - Hooker, R. Elizabeth AU - Sanchez, Facundo AU - Alvarez-Mon, Melchor AU - Teo, R. Alan PY - 2023/8/22 TI - Content and User Engagement of Health-Related Behavior Tweets Posted by Mass Media Outlets From Spain and the United States Early in the COVID-19 Pandemic: Observational Infodemiology Study JO - JMIR Infodemiology SP - e43685 VL - 3 KW - COVID-19 KW - health communication KW - social media KW - Twitter KW - health promotion KW - public health KW - mass media N2 - Background: During the early pandemic, there was substantial variation in public and government responses to COVID-19 in Europe and the United States. Mass media are a vital source of health information and news, frequently disseminating this information through social media, and may influence public and policy responses to the pandemic. Objective: This study aims to describe the extent to which major media outlets in the United States and Spain tweeted about health-related behaviors (HRBs) relevant to COVID-19, compare the tweeting patterns between media outlets of both countries, and determine user engagement in response to these tweets. Methods: We investigated tweets posted by 30 major media outlets (n=17, 57% from Spain and n=13, 43% from the United States) between December 1, 2019 and May 31, 2020, which included keywords related to HRBs relevant to COVID-19. We classified tweets into 6 categories: mask-wearing, physical distancing, handwashing, quarantine or confinement, disinfecting objects, or multiple HRBs (any combination of the prior HRB categories). Additionally, we assessed the likes and retweets generated by each tweet. Poisson regression analyses compared the average predicted number of likes and retweets between the different HRB categories and between countries. Results: Of 50,415 tweets initially collected, 8552 contained content associated with an HRB relevant to COVID-19. Of these, 600 were randomly chosen for training, and 2351 tweets were randomly selected for manual content analysis. Of the 2351 COVID-19?related tweets included in the content analysis, 62.91% (1479/2351) mentioned at least one HRB. The proportion of COVID-19 tweets mentioning at least one HRB differed significantly between countries (P=.006). Quarantine or confinement was mentioned in nearly half of all the HRB tweets in both countries. In contrast, the least frequently mentioned HRBs were disinfecting objects in Spain 6.9% (56/809) and handwashing in the United States 9.1% (61/670). For tweets from the United States mentioning at least one HRB, disinfecting objects had the highest median likes and retweets, whereas mask-wearing? and handwashing-related tweets achieved the highest median number of likes in Spain. Tweets from Spain that mentioned social distancing or disinfecting objects had a significantly lower predicted count of likes compared with tweets mentioning a different HRB (P=.02 and P=.01, respectively). Tweets from the United States that mentioned quarantine or confinement or disinfecting objects had a significantly lower predicted number of likes compared with tweets mentioning a different HRB (P<.001), whereas mask- and handwashing-related tweets had a significantly greater predicted number of likes (P=.04 and P=.02, respectively). Conclusions: The type of HRB content and engagement with media outlet tweets varied between Spain and the United States early in the pandemic. However, content related to quarantine or confinement and engagement with handwashing was relatively high in both countries. UR - https://infodemiology.jmir.org/2023/1/e43685 UR - http://dx.doi.org/10.2196/43685 UR - http://www.ncbi.nlm.nih.gov/pubmed/37347948 ID - info:doi/10.2196/43685 ER - TY - JOUR AU - White, K. Becky AU - Gombert, Arnault AU - Nguyen, Tim AU - Yau, Brian AU - Ishizumi, Atsuyoshi AU - Kirchner, Laura AU - León, Alicia AU - Wilson, Harry AU - Jaramillo-Gutierrez, Giovanna AU - Cerquides, Jesus AU - D?Agostino, Marcelo AU - Salvi, Cristiana AU - Sreenath, Shankar Ravi AU - Rambaud, Kimberly AU - Samhouri, Dalia AU - Briand, Sylvie AU - Purnat, D. Tina PY - 2023/8/21 TI - Using Machine Learning Technology (Early Artificial Intelligence?Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study JO - JMIR Infodemiology SP - e47317 VL - 3 KW - infodemic KW - sentiment KW - narrative analysis KW - social listening KW - natural language processing KW - social media KW - public health KW - pandemic preparedness KW - pandemic response KW - artificial intelligence KW - AI text analytics KW - COVID-19 KW - information voids KW - machine learning N2 - Background: Amid the COVID-19 pandemic, there has been a need for rapid social understanding to inform infodemic management and response. Although social media analysis platforms have traditionally been designed for commercial brands for marketing and sales purposes, they have been underused and adapted for a comprehensive understanding of social dynamics in areas such as public health. Traditional systems have challenges for public health use, and new tools and innovative methods are required. The World Health Organization Early Artificial Intelligence?Supported Response with Social Listening (EARS) platform was developed to overcome some of these challenges. Objective: This paper describes the development of the EARS platform, including data sourcing, development, and validation of a machine learning categorization approach, as well as the results from the pilot study. Methods: Data for EARS are collected daily from web-based conversations in publicly available sources in 9 languages. Public health and social media experts developed a taxonomy to categorize COVID-19 narratives into 5 relevant main categories and 41 subcategories. We developed a semisupervised machine learning algorithm to categorize social media posts into categories and various filters. To validate the results obtained by the machine learning?based approach, we compared it to a search-filter approach, applying Boolean queries with the same amount of information and measured the recall and precision. Hotelling T2 was used to determine the effect of the classification method on the combined variables. Results: The EARS platform was developed, validated, and applied to characterize conversations regarding COVID-19 since December 2020. A total of 215,469,045 social posts were collected for processing from December 2020 to February 2022. The machine learning algorithm outperformed the Boolean search filters method for precision and recall in both English and Spanish languages (P<.001). Demographic and other filters provided useful insights on data, and the gender split of users in the platform was largely consistent with population-level data on social media use. Conclusions: The EARS platform was developed to address the changing needs of public health analysts during the COVID-19 pandemic. The application of public health taxonomy and artificial intelligence technology to a user-friendly social listening platform, accessible directly by analysts, is a significant step in better enabling understanding of global narratives. The platform was designed for scalability; iterations and new countries and languages have been added. This research has shown that a machine learning approach is more accurate than using only keywords and has the benefit of categorizing and understanding large amounts of digital social data during an infodemic. Further technical developments are needed and planned for continuous improvements, to meet the challenges in the generation of infodemic insights from social media for infodemic managers and public health professionals. UR - https://infodemiology.jmir.org/2023/1/e47317 UR - http://dx.doi.org/10.2196/47317 UR - http://www.ncbi.nlm.nih.gov/pubmed/37422854 ID - info:doi/10.2196/47317 ER - TY - JOUR AU - Sigalo, Nekabari AU - Awasthi, Naman AU - Abrar, Mohammad Saad AU - Frias-Martinez, Vanessa PY - 2023/8/21 TI - Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets JO - JMIR Infodemiology SP - e43703 VL - 3 KW - social media KW - Twitter KW - COVID-19 KW - vaccine KW - surveys KW - SARS-CoV-2 KW - vaccinations KW - hesitancy KW - vaccine hesitancy KW - forecast model KW - vaccine uptake KW - health promotion KW - infodemiology KW - health information KW - misinformation N2 - Background: Since the onset of the COVID-19 pandemic, there has been a global effort to develop vaccines that protect against COVID-19. Individuals who are fully vaccinated are far less likely to contract and therefore transmit the virus to others. Researchers have found that the internet and social media both play a role in shaping personal choices about vaccinations. Objective: This study aims to determine whether supplementing COVID-19 vaccine uptake forecast models with the attitudes found in tweets improves over baseline models that only use historical vaccination data. Methods: Daily COVID-19 vaccination data at the county level was collected for the January 2021 to May 2021 study period. Twitter?s streaming application programming interface was used to collect COVID-19 vaccine tweets during this same period. Several autoregressive integrated moving average models were executed to predict the vaccine uptake rate using only historical data (baseline autoregressive integrated moving average) and individual Twitter-derived features (autoregressive integrated moving average exogenous variable model). Results: In this study, we found that supplementing baseline forecast models with both historical vaccination data and COVID-19 vaccine attitudes found in tweets reduced root mean square error by as much as 83%. Conclusions: Developing a predictive tool for vaccination uptake in the United States will empower public health researchers and decisionmakers to design targeted vaccination campaigns in hopes of achieving the vaccination threshold required for the United States to reach widespread population protection. UR - https://infodemiology.jmir.org/2023/1/e43703 UR - http://dx.doi.org/10.2196/43703 UR - http://www.ncbi.nlm.nih.gov/pubmed/37390402 ID - info:doi/10.2196/43703 ER - TY - JOUR AU - Quijote, Llew Kirk AU - Castańeda, Therese Arielle Marie AU - Guevara, Edgar Bryan AU - Tangtatco, Aileen Jennifer PY - 2023/8/21 TI - A Descriptive Analysis of Dermatology Content and Creators on Social Media in the Philippines JO - JMIR Dermatol SP - e47530 VL - 6 KW - social media KW - dermatology KW - dermatologist KW - creator KW - content KW - impact KW - Philippines KW - Facebook KW - Instagram KW - Twitter KW - TikTok KW - YouTube UR - https://derma.jmir.org/2023/1/e47530 UR - http://dx.doi.org/10.2196/47530 UR - http://www.ncbi.nlm.nih.gov/pubmed/37603392 ID - info:doi/10.2196/47530 ER - TY - JOUR AU - Dasgupta, Pritam AU - Amin, Janaki AU - Paris, Cecile AU - MacIntyre, Raina C. PY - 2023/8/16 TI - News Coverage of Face Masks in Australia During the Early COVID-19 Pandemic: Topic Modeling Study JO - JMIR Infodemiology SP - e43011 VL - 3 KW - face masks KW - mask KW - COVID-19 KW - web-based news KW - community sentiment KW - topic modeling KW - latent Dirichlet allocation N2 - Background: During the COVID-19 pandemic, web-based media coverage of preventative strategies proliferated substantially. News media was constantly informing people about changes in public health policy and practices such as mask-wearing. Hence, exploring news media content on face mask use is useful to analyze dominant topics and their trends. Objective: The aim of the study was to examine news related to face masks as well as to identify related topics and temporal trends in Australian web-based news media during the early COVID-19 pandemic period. Methods: Following data collection from the Google News platform, a trend analysis on the mask-related news titles from Australian news publishers was conducted. Then, a latent Dirichlet allocation topic modeling algorithm was applied along with evaluation matrices (quantitative and qualitative measures). Afterward, topic trends were developed and analyzed in the context of mask use during the pandemic. Results: A total of 2345 face mask?related eligible news titles were collected from January 25, 2020, to January 25, 2021. Mask-related news showed an increasing trend corresponding to increasing COVID-19 cases in Australia. The best-fitted latent Dirichlet allocation model discovered 8 different topics with a coherence score of 0.66 and a perplexity measure of ?11.29. The major topics were T1 (mask-related international affairs), T2 (introducing mask mandate in places such as Melbourne and Sydney), and T4 (antimask sentiment). Topic trends revealed that T2 was the most frequent topic in January 2021 (77 news titles), corresponding to the mandatory mask-wearing policy in Sydney. Conclusions: This study demonstrated that Australian news media reflected a wide range of community concerns about face masks, peaking as COVID-19 incidence increased. Harnessing the news media platforms for understanding the media agenda and community concerns may assist in effective health communication during a pandemic response. UR - https://infodemiology.jmir.org/2023/1/e43011 UR - http://dx.doi.org/10.2196/43011 UR - http://www.ncbi.nlm.nih.gov/pubmed/37379362 ID - info:doi/10.2196/43011 ER - TY - JOUR AU - Goel, Rahul AU - Modhukur, Vijayachitra AU - Täär, Katrin AU - Salumets, Andres AU - Sharma, Rajesh AU - Peters, Maire PY - 2023/8/15 TI - Users? Concerns About Endometriosis on Social Media: Sentiment Analysis and Topic Modeling Study JO - J Med Internet Res SP - e45381 VL - 25 KW - endometriosis KW - latent Dirichlet allocation KW - pain KW - Reddit KW - sentiment analysis KW - social media KW - surgery KW - topic modeling KW - user engagement N2 - Background: Endometriosis is a debilitating and difficult-to-diagnose gynecological disease. Owing to limited information and awareness, women often rely on social media platforms as a support system to engage in discussions regarding their disease-related concerns. Objective: This study aimed to apply computational techniques to social media posts to identify discussion topics about endometriosis and to identify themes that require more attention from health care professionals and researchers. We also aimed to explore whether, amid the challenging nature of the disease, there are themes within the endometriosis community that gather posts with positive sentiments. Methods: We retrospectively extracted posts from the subreddits r/Endo and r/endometriosis from January 2011 to April 2022. We analyzed 45,693 Reddit posts using sentiment analysis and topic modeling?based methods in machine learning. Results: Since 2011, the number of posts and comments has increased steadily. The posts were categorized into 11 categories, and the highest number of posts were related to either asking for information (Question); sharing the experiences (Rant/Vent); or diagnosing and treating endometriosis, especially surgery (Surgery related). Sentiment analysis revealed that 92.09% (42,077/45,693) of posts were associated with negative sentiments, only 2.3% (1053/45,693) expressed positive feelings, and there were no categories with more positive than negative posts. Topic modeling revealed 27 major topics, and the most popular topics were Surgery, Questions/Advice, Diagnosis, and Pain. The Survey/Research topic, which brought together most research-related posts, was the last in terms of posts. Conclusions: Our study shows that posts on social media platforms can provide insights into the concerns of women with endometriosis symptoms. The analysis of the posts confirmed that women with endometriosis have to face negative emotions and pain daily. The large number of posts related to asking questions shows that women do not receive sufficient information from physicians and need community support to cope with the disease. Health care professionals should pay more attention to the symptoms and diagnosis of endometriosis, discuss these topics with patients to reduce their dissatisfaction with doctors, and contribute more to the overall well-being of women with endometriosis. Researchers should also become more involved in social media and share new science-based knowledge regarding endometriosis. UR - https://www.jmir.org/2023/1/e45381 UR - http://dx.doi.org/10.2196/45381 UR - http://www.ncbi.nlm.nih.gov/pubmed/37581905 ID - info:doi/10.2196/45381 ER - TY - JOUR AU - Long, Memphis AU - Forbes, E. Laura AU - Papagerakis, Petros AU - Lieffers, L. Jessica R. PY - 2023/8/10 TI - YouTube Videos on Nutrition and Dental Caries: Content Analysis JO - JMIR Infodemiology SP - e40003 VL - 3 KW - dental caries KW - diet KW - nutrition KW - YouTube KW - internet KW - consumer health information N2 - Background: Dental caries is the most common health condition worldwide, and nutrition and dental caries have a strong interconnected relationship. Foods and eating behaviors can be both harmful (eg, sugar) and healthful (eg, meal spacing) for dental caries. YouTube is a popular source for the public to access information. To date, there is no information available on the nutrition and dental caries content of easily accessible YouTube videos. Objective: This study aimed to analyze the content of YouTube videos on nutrition and dental caries. Methods: In total, 6 YouTube searches were conducted using keywords related to nutrition and dental caries. The first 20 videos were selected from each search. Video content was scored (17 possible points; higher scores were associated with more topics covered) by 2 individuals based on the inclusion of information regarding various foods and eating behaviors that impact dental caries risk. For each video, information on video characteristics (ie, view count, length, number of likes, number of dislikes, and video age) was captured. Videos were divided into 2 groups by view rate (views/day); differences in scores and types of nutrition messages between groups were determined using nonparametric statistics. Results: In total, 42 videos were included. Most videos were posted by or featured oral health professionals (24/42, 57%). The mean score was 4.9 (SD 3.4) out of 17 points. Videos with >30 views/day (high view rate; 20/42, 48% videos) had a trend toward a lower score (mean 4.0, SD 3.7) than videos with ?30 views/day (low view rate; 22/42, 52%; mean 5.8, SD 3.0; P=.06), but this result was not statistically significant. Sugar was the most consistently mentioned topic in the videos (31/42, 74%). No other topics were mentioned in more than 50% of videos. Low?view rate videos were more likely to mention messaging on acidic foods and beverages (P=.04), water (P=.09), and frequency of sugar intake (P=.047) than high?view rate videos. Conclusions: Overall, the analyzed videos had low scores for nutritional and dental caries content. This study provides insights into the messaging available on nutrition and dental caries for the public and guidance on how to make improvements in this area. UR - https://infodemiology.jmir.org/2023/1/e40003 UR - http://dx.doi.org/10.2196/40003 UR - http://www.ncbi.nlm.nih.gov/pubmed/37561564 ID - info:doi/10.2196/40003 ER - TY - JOUR AU - Mitsuhashi, Toshiharu PY - 2023/8/10 TI - Assessing Vulnerability to Surges in Suicide-Related Tweets Using Japan Census Data: Case-Only Study JO - JMIR Form Res SP - e47798 VL - 7 KW - case-only approach KW - mass media KW - public health KW - social media KW - suicidal risk KW - suicide prevention KW - suicide KW - suicide-related tweets KW - Twitter N2 - Background: As the use of social media becomes more widespread, its impact on health cannot be ignored. However, limited research has been conducted on the relationship between social media and suicide. Little is known about individuals? vulnerable to suicide, especially when social media suicide information is extremely prevalent. Objective: This study aims to identify the characteristics underlying individuals? vulnerability to suicide brought about by an increase in suicide-related tweets, thereby contributing to public health. Methods: A case-only design was used to investigate vulnerability to suicide using individual data of people who died by suicide and tweet data from January 1, 2011, through December 31, 2014. Mortality data were obtained from Japanese government statistics, and tweet data were provided by a commercial service. Tweet data identified the days when suicide-related tweets surged, and the date-keyed merging was performed by considering 3 and 7 lag days. For the merged data set for analysis, the logistic regression model was fitted with one of the personal characteristics of interest as a dependent variable and the dichotomous exposure variable. This analysis was performed to estimate the interaction between the surges in suicide-related tweets and personal characteristics of the suicide victims as case-only odds ratios (ORs) with 95% CIs. For the sensitivity analysis, unexpected deaths other than suicide were considered. Results: During the study period, there were 159,490 suicides and 115,072 unexpected deaths, and the number of suicide-related tweets was 2,804,999. Following the 3-day lag of a highly tweeted day, there were significant interactions for those who were aged 40 years or younger (OR 1.09, 95% CI 1.03-1.15), male (OR 1.12, 95% CI 1.07-1.18), divorced (OR 1.11, 95% CI 1.03 1.19), unemployed (OR 1.12, 95% CI 1.02-1.22), and living in urban areas (OR 1.26, 95% CI 1.17 1.35). By contrast, widowed individuals had significantly lower interactions (OR 0.83, 95% CI 0.77-0.89). Except for unemployment, significant relationships were also observed for the 7-day lag. For the sensitivity analysis, no significant interactions were observed for other unexpected deaths in the 3-day lag, and only the widowed had a significantly larger interaction than those who were married (OR 1.08, 95% CI 1.02-1.15) in the 7-day lag. Conclusions: This study revealed the interactions of personal characteristics associated with susceptibility to suicide-related tweets. In addition, a few significant relationships were observed in the sensitivity analysis, suggesting that such an interaction is specific to suicide deaths. In other words, individuals with these characteristics, such as being young, male, unemployed, and divorced, may be vulnerable to surges in suicide-related tweets. Thus, minimizing public health strain by identifying people who are vulnerable and susceptible to a surge in suicide-related information on the internet is necessary. UR - https://formative.jmir.org/2023/1/e47798 UR - http://dx.doi.org/10.2196/47798 UR - http://www.ncbi.nlm.nih.gov/pubmed/37561553 ID - info:doi/10.2196/47798 ER - TY - JOUR AU - Zaidi, Zainab AU - Ye, Mengbin AU - Samon, Fergus AU - Jama, Abdisalan AU - Gopalakrishnan, Binduja AU - Gu, Chenhao AU - Karunasekera, Shanika AU - Evans, Jamie AU - Kashima, Yoshihisa PY - 2023/8/8 TI - Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets JO - J Med Internet Res SP - e45069 VL - 25 KW - COVID-19 vaccine KW - vaccine hesitancy KW - antivax KW - stance detection KW - topic modeling KW - misinformation KW - disinformation N2 - Background: Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic but also for future pathogen outbreaks. There are various research efforts in this domain, although, a need still exists for a comprehensive topic-wise analysis of tweets in favor of and against COVID-19 vaccines. Objective: This study characterizes the discussion points in favor of and against COVID-19 vaccines posted on Twitter during the first year of the pandemic. The aim of this study was primarily to contrast the views expressed by both camps, their respective activity patterns, and their correlation with vaccine-related events. A further aim was to gauge the genuineness of the concerns expressed in antivax tweets. Methods: We examined a Twitter data set containing 75 million English tweets discussing the COVID-19 vaccination from March 2020 to March 2021. We trained a stance detection algorithm using natural language processing techniques to classify tweets as antivax or provax and examined the main topics of discourse using topic modeling techniques. Results: Provax tweets (37 million) far outnumbered antivax tweets (10 million) and focused mostly on vaccine development, whereas antivax tweets covered a wide range of topics, including opposition to vaccine mandate and concerns about safety. Although some antivax tweets included genuine concerns, there was a large amount of falsehood. Both stances discussed many of the same topics from opposite viewpoints. Memes and jokes were among the most retweeted messages. Most tweets from both stances (9,007,481/10,566,679, 85.24% antivax and 24,463,708/37,044,507, 66.03% provax tweets) came from dual-stance users who posted both provax and antivax tweets during the observation period. Conclusions: This study is a comprehensive account of COVID-19 vaccine discourse in the English language on Twitter from March 2020 to March 2021. The broad range of discussion points covered almost the entire conversation, and their temporal dynamics revealed a significant correlation with COVID-19 vaccine?related events. We did not find any evidence of polarization and prevalence of antivax discourse over Twitter. However, targeted countering of falsehoods is important because only a small fraction of antivax discourse touched on a genuine issue. Future research should examine the role of memes and humor in driving web-based social media activity. UR - https://www.jmir.org/2023/1/e45069 UR - http://dx.doi.org/10.2196/45069 UR - http://www.ncbi.nlm.nih.gov/pubmed/37552535 ID - info:doi/10.2196/45069 ER - TY - JOUR AU - Wang, Yijun AU - Chukwusa, Emeka AU - Koffman, Jonathan AU - Curcin, Vasa PY - 2023/8/7 TI - Public Opinions About Palliative and End-of-Life Care During the COVID-19 Pandemic: Twitter-Based Content Analysis JO - JMIR Form Res SP - e44774 VL - 7 KW - palliative care KW - end-of-life care KW - COVID-19 KW - Twitter KW - public opinions N2 - Background: Palliative and end-of-life care (PEoLC) played a critical role in relieving distress and providing grief support in response to the heavy toll caused by the COVID-19 pandemic. However, little is known about public opinions concerning PEoLC during the pandemic. Given that social media have the potential to collect real-time public opinions, an analysis of this evidence is vital to guide future policy-making. Objective: This study aimed to use social media data to investigate real-time public opinions regarding PEoLC during the COVID-19 crisis and explore the impact of vaccination programs on public opinions about PEoLC. Methods: This Twitter-based study explored tweets across 3 English-speaking countries: the United States, the United Kingdom, and Canada. From October 2020 to March 2021, a total of 7951 PEoLC-related tweets with geographic tags were retrieved and identified from a large-scale COVID-19 Twitter data set through the Twitter application programming interface. Topic modeling realized through a pointwise mutual information?based co-occurrence network and Louvain modularity was used to examine latent topics across the 3 countries and across 2 time periods (pre- and postvaccination program periods). Results: Commonalities and regional differences among PEoLC topics in the United States, the United Kingdom, and Canada were identified specifically: cancer care and care facilities were of common interest to the public across the 3 countries during the pandemic; the public expressed positive attitudes toward the COVID-19 vaccine and highlighted the protection it affords to PEoLC professionals; and although Twitter users shared their personal experiences about PEoLC in the web-based community during the pandemic, this was more prominent in the United States and Canada. The implementation of the vaccination programs raised the profile of the vaccine discussion; however, this did not influence public opinions about PEoLC. Conclusions: Public opinions on Twitter reflected a need for enhanced PEoLC services during the COVID-19 pandemic. The insignificant impact of the vaccination program on public discussion on social media indicated that public concerns regarding PEoLC continued to persist even after the vaccination efforts. Insights gleaned from public opinions regarding PEoLC could provide some clues for policy makers on how to ensure high-quality PEoLC during public health emergencies. In this post?COVID-19 era, PEoLC professionals may wish to continue to examine social media and learn from web-based public discussion how to ease the long-lasting trauma caused by this crisis and prepare for public health emergencies in the future. Besides, our results showed social media?s potential in acting as an effective tool to reflect public opinions in the context of PEoLC. UR - https://formative.jmir.org/2023/1/e44774 UR - http://dx.doi.org/10.2196/44774 UR - http://www.ncbi.nlm.nih.gov/pubmed/37368840 ID - info:doi/10.2196/44774 ER - TY - JOUR AU - Kami?ski, Miko?aj AU - Czarny, Jakub AU - Skrzypczak, Piotr AU - Sienicki, Krzysztof AU - Roszak, Magdalena PY - 2023/8/4 TI - The Characteristics, Uses, and Biases of Studies Related to Malignancies Using Google Trends: Systematic Review JO - J Med Internet Res SP - e47582 VL - 25 KW - Google Trends KW - oncology KW - malignancies KW - prophylaxis KW - celebrity KW - infodemiology KW - infoveillance KW - cancer KW - carcinoma KW - lymphoma KW - leukemia KW - multiple myeloma KW - sarcoma KW - internet KW - tumor KW - bias KW - quality N2 - Background: The internet is a primary source of health information for patients, supplementing physician care. Google Trends (GT), a popular tool, allows the exploration of public interest in health-related phenomena. Despite the growing volume of GT studies, none have focused explicitly on oncology, creating a need for a systematic review to bridge this gap. Objective: We aimed to systematically characterize studies related to oncology using GT to describe its utilities and biases. Methods: We included all studies that used GT to analyze Google searches related to malignancies. We excluded studies written in languages other than English. The search was performed using the PubMed engine on August 1, 2022. We used the following search input: ?Google trends? AND (?oncology? OR ?cancer? or ?malignancy? OR ?tumor? OR ?lymphoma? OR ?multiple myeloma? OR ?leukemia?). We analyzed sources of bias that included using search terms instead of topics, lack of confrontation of GT statistics with real-world data, and absence of sensitivity analysis. We performed descriptive statistics. Results: A total of 85 articles were included. The first study using GT for oncology research was published in 2013, and since then, the number of publications has increased annually. The studies were categorized as follows: 22% (19/85) were related to prophylaxis, 20% (17/85) pertained to awareness events, 11% (9/85) were celebrity-related, 13% (11/85) were related to COVID-19, and 47% (40/85) fell into other categories. The most frequently analyzed cancers were breast (n=28), prostate (n=26), lung (n=18), and colorectal cancers (n=18). We discovered that of the 85 studies, 17 (20%) acknowledged using GT topics instead of search terms, 79 (93%) disclosed all search input details necessary for replicating their results, and 34 (40%) compared GT statistics with real-world data. The most prevalent methods for analyzing the GT data were correlation analysis (55/85, 65%) and peak analysis (43/85, 51%). The authors of only 11% (9/85) of the studies performed a sensitivity analysis. Conclusions: The number of studies related to oncology using GT data has increased annually. The studies included in this systematic review demonstrate a variety of concerning topics, search strategies, and statistical methodologies. The most frequently analyzed cancers were breast, prostate, lung, colorectal, skin, and cervical cancers, potentially reflecting their prevalence in the population or public interest. Although most researchers provided reproducible search inputs, only one-fifth used GT topics instead of search terms, and many studies lacked a sensitivity analysis. Scientists using GT for medical research should ensure the quality of studies by providing a transparent search strategy to reproduce results, preferring to use topics over search terms, and performing robust statistical calculations coupled with sensitivity analysis. UR - https://www.jmir.org/2023/1/e47582 UR - http://dx.doi.org/10.2196/47582 UR - http://www.ncbi.nlm.nih.gov/pubmed/37540544 ID - info:doi/10.2196/47582 ER - TY - JOUR AU - van Gastel, Daniëlle AU - Antheunis, L. Marjolijn AU - Tenfelde, Kim AU - van de Graaf, L. Daniëlle AU - Geerts, Marieke AU - Nieboer, E. Theodoor AU - Bongers, Y. Marlies PY - 2023/8/3 TI - Social Support Among Women With Potential Essure-Related Complaints: Analysis of Facebook Group Content JO - JMIR Form Res SP - e32592 VL - 7 KW - Essure KW - social support KW - Facebook KW - sterilization KW - patient online communities KW - social media KW - social networks N2 - Background: Social support groups are an important resource for people to cope with problems. Previous studies have reported the different types of support in these groups, but little is known about the type of reactions that sharing of personal experiences induce among members. It is important to know how and to what extent members of support groups influence each other regarding the consumption of medical care. We researched this in a web-based Facebook group of women sterilized with Essure. Essure was a device intended for permanent contraception. From 2015 onward, women treated with Essure for tubal occlusion raised safety concerns and numerous complaints. Objective: This study aimed to evaluate the use of social support in a Facebook community named ?Essure problemen Nederland? (EPN; in English, ?Essure problems in the Netherlands?). Methods: All posts in the closed Facebook group EPN between March 8 and May 8, 2018, were included. In total, 3491 Facebook posts were analyzed using a modified version of the Social Support Behavior Codes framework created by Cutrona and Suhr in 1992. Posts were abstracted and aggregated into a database. Two investigators evaluated the posts, developed a modified version of the Social Support Behavior Codes framework, and applied the codes to the collected data. Results: We found that 92% of messages contained a form of social support. In 68.8% of posts, social support was provided, and in 31.2% of posts, social support was received. Informational and emotional support was the most frequently used form of provided social support (40.6% and 55.5%, respectively). The same distribution was seen with received social support: informational support in 81.5% and emotional support in 17.4% of cases. Our analysis showed a strong correlation between providing or receiving social support and the main form of social support (P<.001). In a total of only 74 (2.2%) cases, women advised each other to seek medical care. Conclusions: The main purpose of women in the EPN Facebook group was to provide and receive informational or emotional support or both. UR - https://formative.jmir.org/2023/1/e32592 UR - http://dx.doi.org/10.2196/32592 UR - http://www.ncbi.nlm.nih.gov/pubmed/37535412 ID - info:doi/10.2196/32592 ER - TY - JOUR AU - Meksawasdichai, Sununtha AU - Lerksuthirat, Tassanee AU - Ongphiphadhanakul, Boonsong AU - Sriphrapradang, Chutintorn PY - 2023/8/2 TI - Perspectives and Experiences of Patients With Thyroid Cancer at a Global Level: Retrospective Descriptive Study of Twitter Data JO - JMIR Cancer SP - e48786 VL - 9 KW - data mining KW - internet KW - natural language processing KW - sentiment analysis KW - social media KW - thyroid neoplasms KW - twitter KW - tweet KW - tweets KW - neoplasm KW - neoplasms KW - cancer KW - oncology KW - thyroid KW - NLP KW - perspective KW - perspectives KW - sentiment KW - sentiments KW - experience KW - experiences N2 - Background: Twitter has become a popular platform for individuals to broadcast their daily experiences and opinions on a wide range of topics and emotions. Tweets from patients with cancer could offer insights into their needs. However, limited research has been conducted using Twitter data to understand the needs of patients with cancer despite the substantial amount of health-related data posted on the platform daily. Objective: This study aimed to uncover the potential of using Twitter data to understand the perspectives and experiences of patients with thyroid cancer at a global level. Methods:  This retrospective descriptive study collected tweets relevant to thyroid cancer in 2020 using the Twitter scraping tool. Only English-language tweets were included, and data preprocessing was performed to remove irrelevant tweets, duplicates, and retweets. Both tweets and Twitter users were manually classified into various groups based on the content. Each tweet underwent sentiment analysis and was classified as either positive, neutral, or negative. Results: A total of 13,135 tweets related to thyroid cancer were analyzed. The authors of the tweets included patients with thyroid cancer (3225 tweets, 24.6%), patient?s families and friends (2449 tweets, 18.6%), medical journals and media (1733 tweets, 13.2%), health care professionals (1093 tweets, 8.3%), and medical health organizations (940 tweets, 7.2%), respectively. The most discussed topics related to living with cancer (3650 tweets, 27.8%), treatment (2891 tweets, 22%), diagnosis (1613 tweets, 12.3%), risk factors and prevention (1137 tweets, 8.7%), and research (953 tweets, 7.3%). An average of 36 tweets pertaining to thyroid cancer were posted daily. Notably, the release of a film addressing thyroid cancer and the public disclosure of a news reporter?s personal diagnosis of thyroid cancer resulted in a significant escalation in the volume of tweets. From the sentiment analysis, 53.5% (7025/13,135) of tweets were classified as neutral statements and 32.7% (4299/13,135) of tweets expressed negative emotions. Tweets from patients with thyroid cancer had the highest proportion of negative emotion (1385/3225 tweets, 42.9%), particularly when discussing symptoms. Conclusions:  This study provides new insights on using Twitter data as a valuable data source to understand the experiences of patients with thyroid cancer. Twitter may provide an opportunity to improve patient and physician engagement or apply as a potential research data source. UR - https://cancer.jmir.org/2023/1/e48786 UR - http://dx.doi.org/10.2196/48786 UR - http://www.ncbi.nlm.nih.gov/pubmed/37531163 ID - info:doi/10.2196/48786 ER - TY - JOUR AU - Golder, Su AU - O'Connor, Karen AU - Wang, Yunwen AU - Gonzalez Hernandez, Graciela PY - 2023/8/2 TI - The Role of Social Media for Identifying Adverse Drug Events Data in Pharmacovigilance: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e47068 VL - 12 KW - adverse event KW - pharmacovigilance KW - social media KW - real-world data KW - scoping review KW - protocol KW - review method KW - pharmacology KW - pharmaceutics KW - pharmacy KW - adverse drug event KW - adverse drug reaction N2 - Background: Adverse drug events (ADEs) are a considerable public health burden resulting in disability, hospitalization, and death. Even those ADEs deemed nonserious can severely impact a patient?s quality of life and adherence to intervention. Monitoring medication safety, however, is challenging. Social media may be a useful adjunct for obtaining real-world data on ADEs. While many studies have been undertaken to detect adverse events on social media, a consensus has not yet been reached as to the value of social media in pharmacovigilance or its role in pharmacovigilance in relation to more traditional data sources. Objective: The aim of the study is to evaluate and characterize the use of social media in ADE detection and pharmacovigilance as compared to other data sources. Methods: A scoping review will be undertaken. We will search 11 bibliographical databases as well as Google Scholar, hand-searching, and forward and backward citation searching. Records will be screened in Covidence by 2 independent reviewers at both title and abstract stage as well as full text. Studies will be included if they used any type of social media (such as Twitter or patient forums) to detect any type of adverse event associated with any type of medication and then compared the results from social media to any other data source (such as spontaneous reporting systems or clinical literature). Data will be extracted using a data extraction sheet piloted by the authors. Important data on the types of methods used (such as machine learning), any limitations of the methods used, types of adverse events and drugs searched for and included, availability of data and code, details of the comparison data source, and the results and conclusions will be extracted. Results: We will present descriptive summary statistics as well as identify any patterns in the types and timing of ADEs detected, including but not limited to the similarities and differences in what is reported, gaps in the evidence, and the methods used to extract ADEs from social media data. We will also summarize how the data from social media compares to conventional data sources. The literature will be organized by the data source for comparison. Where possible, we will analyze the impact of the types of adverse events, the social media platform used, and the methods used. Conclusions: This scoping review will provide a valuable summary of a large body of research and important information for pharmacovigilance as well as suggest future directions of further research in this area. Through the comparisons with other data sources, we will be able to conclude the added value of social media in monitoring adverse events of medications, in terms of type of adverse events and timing. International Registered Report Identifier (IRRID): PRR1-10.2196/47068 UR - https://www.researchprotocols.org/2023/1/e47068 UR - http://dx.doi.org/10.2196/47068 UR - http://www.ncbi.nlm.nih.gov/pubmed/37531158 ID - info:doi/10.2196/47068 ER - TY - JOUR AU - Parker, A. Maria AU - Valdez, Danny AU - Rao, K. Varun AU - Eddens, S. Katherine AU - Agley, Jon PY - 2023/7/28 TI - Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses JO - J Med Internet Res SP - e48405 VL - 25 KW - Twitter KW - LDA KW - drug use KW - digital epidemiology KW - unsupervised analysis KW - tweet KW - tweets KW - social media KW - epidemiology KW - epidemiological KW - machine learning KW - text mining KW - data mining KW - pharmacy KW - pharmaceutic KW - pharmaceutical KW - pharmaceuticals KW - drug KW - prescription KW - NLP KW - natural language processing N2 - Background: Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use. Objective: This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names. Methods: This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug?related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets. Results: We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity. Conclusions: Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter. UR - https://www.jmir.org/2023/1/e48405 UR - http://dx.doi.org/10.2196/48405 UR - http://www.ncbi.nlm.nih.gov/pubmed/37505795 ID - info:doi/10.2196/48405 ER - TY - JOUR AU - Lazard, J. Allison AU - Nicolla, Sydney AU - Vereen, N. Rhyan AU - Pendleton, Shanetta AU - Charlot, Marjory AU - Tan, Hung-Jui AU - DiFranzo, Dominic AU - Pulido, Marlyn AU - Dasgupta, Nabarun PY - 2023/7/28 TI - Exposure and Reactions to Cancer Treatment Misinformation and Advice: Survey Study JO - JMIR Cancer SP - e43749 VL - 9 KW - cancer KW - misinformation KW - social media KW - prosocial intervening KW - treatment KW - false information KW - alternative medicine KW - information spread KW - dissemination KW - infodemiology KW - mobile phone N2 - Background: Cancer treatment misinformation, or false claims about alternative cures, often spreads faster and farther than true information on social media. Cancer treatment misinformation can harm the psychosocial and physical health of individuals with cancer and their cancer care networks by causing distress and encouraging people to abandon support, potentially leading to deviations from evidence-based care. There is a pressing need to understand how cancer treatment misinformation is shared and uncover ways to reduce misinformation. Objective: We aimed to better understand exposure and reactions to cancer treatment misinformation, including the willingness of study participants to prosocially intervene and their intentions to share Instagram posts with cancer treatment misinformation. Methods: We conducted a survey on cancer treatment misinformation among US adults in December 2021. Participants reported their exposure and reactions to cancer treatment misinformation generally (saw or heard, source, type of advice, and curiosity) and specifically on social media (platform, believability). Participants were then randomly assigned to view 1 of 3 cancer treatment misinformation posts or an information post and asked to report their willingness to prosocially intervene and their intentions to share. Results: Among US adult participants (N=603; mean age 46, SD 18.83 years), including those with cancer and cancer caregivers, almost 1 in 4 (142/603, 23.5%) received advice about alternative ways to treat or cure cancer. Advice was primarily shared through family (39.4%) and friends (37.3%) for digestive (30.3%) and natural (14.1%) alternative cancer treatments, which generated curiosity among most recipients (106/142, 74.6%). More than half of participants (337/603, 55.9%) saw any cancer treatment misinformation on social media, with significantly higher exposure for those with cancer (53/109, 70.6%) than for those without cancer (89/494, 52.6%; P<.001). Participants saw cancer misinformation on Facebook (39.8%), YouTube (27%), Instagram (22.1%), and TikTok (14.1%), among other platforms. Participants (429/603, 71.1%) thought cancer treatment misinformation was true, at least sometimes, on social media. More than half (357/603, 59.2%) were likely to share any cancer misinformation posts shown. Many participants (412/603, 68.3%) were willing to prosocially intervene for any cancer misinformation posts, including flagging the cancer treatment misinformation posts as false (49.7%-51.4%) or reporting them to the platform (48.1%-51.4%). Among the participants, individuals with cancer and those who identified as Black or Hispanic reported greater willingness to intervene to reduce cancer misinformation but also higher intentions to share misinformation. Conclusions: Cancer treatment misinformation reaches US adults through social media, including on widely used platforms for support. Many believe that social media posts about alternative cancer treatment are true at least some of the time. The willingness of US adults, including those with cancer and members of susceptible populations, to prosocially intervene could initiate the necessary community action to reduce cancer treatment misinformation if coupled with strategies to help individuals discern false claims. UR - https://cancer.jmir.org/2023/1/e43749 UR - http://dx.doi.org/10.2196/43749 UR - http://www.ncbi.nlm.nih.gov/pubmed/37505790 ID - info:doi/10.2196/43749 ER - TY - JOUR AU - Jin, Qiang AU - Raza, Hassan Syed AU - Yousaf, Muhammad AU - Zaman, Umer AU - Ogadimma, C. Emenyeonu AU - Shah, Ali Amjad AU - Core, Rachel AU - Malik, Aqdas PY - 2023/7/26 TI - Assessing How Risk Communication Surveillance Prompts COVID-19 Vaccine Acceptance Among Internet Users by Applying the Situational Theory of Problem Solving: Cross-Sectional Study JO - JMIR Form Res SP - e43628 VL - 7 KW - COVID-19 KW - vaccine safety KW - risk communication KW - digital interventions KW - health communication KW - Situational Theory of Problem Solving N2 - Background: The World Health Organization has recently raised concerns regarding the low number of people fully vaccinated against COVID-19. The low ratio of fully vaccinated people and the emergence of renewed infectious variants correspond to worsening public health. Global health managers have highlighted COVID-19 vaccine?related infodemics as a significant risk perception factor hindering mass vaccination campaigns. Objective: Given the ambiguous digital communication environment that has fostered infodemics, resource-limited nations struggle to boost public willingness to encourage people to fully vaccinate. Authorities have launched some risk communication?laden digital interventions in response to infodemics. However, the value of the risk communication strategies used to tackle infodemics needs to be evaluated. The current research using the tenets of the Situational Theory of Problem Solving is novel, as it explores the impending effects of risk communication strategies. The relationship between infodemic-induced risk perception of COVID-19 vaccine safety and risk communication actions to intensify willingness to be fully vaccinated was examined. Methods: This study used a cross-sectional research design vis-ŕ-vis a nationally representative web-based survey. We collected data from 1946 internet users across Pakistan. Participants voluntarily participated in this research after completing the consent form and reading ethical permissions. Responses were received over 3 months, from May 2022 to July 2022. Results: The results delineated that infodemics positively affected risk perception. This realization pushed the public to engage in risky communicative actions through reliance on and searches for accurate information. Therefore, the prospect of managing infodemics through risk information exposure (eg, digital interventions) using the situational context could predict robust willingness to be fully vaccinated against COVID-19. Conclusions: These pioneering results offer strategic considerations for health authorities to effectively manage the descending spiral of optimal protection against COVID-19. This research concludes that the likelihood of managing infodemics using situational context through exposure to relevant information could improve one?s knowledge of forfending and selection, which can lead to robust protection against COVID-19. Hence, more situation-specific information about the underlying problem (ie, the selection of an appropriate vaccine) can be made accessible through several official digital sources to achieve a more active public health response. UR - https://formative.jmir.org/2023/1/e43628 UR - http://dx.doi.org/10.2196/43628 UR - http://www.ncbi.nlm.nih.gov/pubmed/37315198 ID - info:doi/10.2196/43628 ER - TY - JOUR AU - Kopsco, L. Heather AU - Krell, K. Rayda AU - Mather, N. Thomas AU - Connally, P. Neeta PY - 2023/7/26 TI - Identifying Trusted Sources of Lyme Disease Prevention Information Among Internet Users Connected to Academic Public Health Resources: Internet-Based Survey Study JO - JMIR Form Res SP - e43516 VL - 7 KW - communication KW - consumer health information KW - disease KW - internet KW - Lyme disease KW - online KW - pathogen KW - prevention KW - public health KW - resources KW - social media KW - survey KW - tickborne disease KW - ticks N2 - Background: Misinformation about Lyme disease and other tick-transmitted pathogens circulates frequently on the internet and can compete with, or even overshadow, science-based guidance on tick-borne disease (TBD) prevention. Objective: We surveyed internet users connected to academic tick-related resources to identify trusted sources of Lyme disease prevention information, explore confidence in tick bite prevention information, and examine associations of these responses with answers to commonly disputed issues. Methods: The survey was conducted through social media and website pages for Western Connecticut State University Tickborne Disease Prevention Laboratory and the University of Rhode Island TickEncounter Resource Center. Results: Respondents (N=1190) were predominantly female (903/1190, 76.3%), middle-aged (574/1182, 48.6%), and resided in New England states (663/1190, 55.7%). In total 984 of 1186 (83%) respondents identified conventional experts (eg, the Centers for Disease Control [CDC] or other government health agencies, physicians who follow Infectious Diseases Society of America guidelines for Lyme disease treatment guidelines, and academics) as trustworthy TBD prevention resources. However, nearly one-fourth of respondents would first consult personal contacts and web-based communities regarding prevention information before consulting conventional expert sources. The opinions of public health experts and physicians were rated among the top motivators underlying personal prevention decisions; yet, more than 50% of participants revealed distrustful attitudes toward, or were uncertain about, CDC-supported statements related to time to transmission of Lyme disease (708/1190, 59.5%), the safety of diethyltoluamide-based repellents for children (604/1183, 51.1%), and recommended use of antibiotic prophylaxis (773/1181, 65.4%). Multimodal regression models revealed that participants from high-Lyme-disease-incidence states were more likely to first seek TBD prevention information from personal networks and nontraditional sources before approaching conventional sources of TBD prevention information. We found that those reporting high rates of social media usage were more than twice as likely to first seek traditional expert sources of prevention information but were overall more likely to reject CDC-promoted Lyme disease information, in particular the established time to transmission of Lyme disease bacteria. Models also predicted that those participants who disagreed with the conventional scientific view on the antibiotic prophylaxis prevention statement were less likely to be confident in their ability to protect themselves from a tick bite. Overall, uncertainty in one?s ability to protect oneself against tick bites was strongly associated with uncertainty about beliefs in CDC-promoted TBD prevention information. Self-reported trust in experts and frequency of social media use suggest that these platforms may provide opportunities to engage directly with the public about TBD prevention practices. Conclusions: Using strategies to improve public trust and provide information where the public engages on social media may improve prevention communication and adoption of best practices. UR - https://formative.jmir.org/2023/1/e43516 UR - http://dx.doi.org/10.2196/43516 UR - http://www.ncbi.nlm.nih.gov/pubmed/37494089 ID - info:doi/10.2196/43516 ER - TY - JOUR AU - Xia, Xinming AU - Zhang, Yi AU - Jiang, Wenting AU - Wu, Yuhao Connor PY - 2023/7/24 TI - Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders JO - J Med Internet Res SP - e45757 VL - 25 KW - COVID-19 KW - Twitter KW - stay-at-home orders KW - dynamics of public opinion KW - multiperiod difference-in-differences model N2 - Background: Stay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies. Objective: This study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups. Methods: We constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups. Results: We combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect. Conclusions: This study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks. UR - https://www.jmir.org/2023/1/e45757 UR - http://dx.doi.org/10.2196/45757 UR - http://www.ncbi.nlm.nih.gov/pubmed/37486758 ID - info:doi/10.2196/45757 ER - TY - JOUR AU - Marani, Husayn AU - Song, Yunju Melodie AU - Jamieson, Margaret AU - Roerig, Monika AU - Allin, Sara PY - 2023/7/20 TI - Public Officials? Engagement on Social Media During the Rollout of the COVID-19 Vaccine: Content Analysis of Tweets JO - JMIR Infodemiology SP - e41582 VL - 3 KW - Twitter KW - COVID-19 KW - vaccines KW - sentiment analysis KW - public officials N2 - Background: Social media is an important way for governments to communicate with the public. This is particularly true in times of crisis, such as the COVID-19 pandemic, during which government officials played a strong role in promoting public health measures such as vaccines. Objective: In Canada, provincial COVID-19 vaccine rollout was delivered in 3 phases aligned with federal government COVID-19 vaccine guidance for priority populations. In this study, we examined how Canadian public officials used Twitter to engage with the public about vaccine rollout and how this engagement has shaped public response to vaccines across jurisdictions. Methods: We conducted a content analysis of tweets posted between December 28, 2020, and August 31, 2021. Leveraging the social media artificial intelligence tool Brandwatch Analytics, we constructed a list of public officials in 3 jurisdictions (Ontario, Alberta, and British Columbia) organized across 6 public official types and then conducted an English and French keyword search for tweets about vaccine rollout and delivery that mentioned, retweeted, or replied to the public officials. We identified the top 30 tweets with the highest impressions in each jurisdiction in each of the 3 phases (approximately a 26-day window) of the vaccine rollout. The metrics of engagement (impressions, retweets, likes, and replies) from the top 30 tweets per phase in each jurisdiction were extracted for additional annotation. We specifically annotated sentiment toward public officials? vaccine responses (ie, positive, negative, and neutral) in each tweet and annotated the type of social media engagement. A thematic analysis of tweets was then conducted to add nuance to extracted data characterizing sentiment and interaction type. Results: Among the 6 categories of public officials, 142 prominent accounts were included from Ontario, Alberta, and British Columbia. In total, 270 tweets were included in the content analysis and 212 tweets were direct tweets by public officials. Public officials mostly used Twitter for information provision (139/212, 65.6%), followed by horizontal engagement (37/212, 17.5%), citizen engagement (24/212, 11.3%), and public service announcements (12/212, 5.7%). Information provision by government bodies (eg, provincial government and public health authorities) or municipal leaders is more prominent than tweets by other public official groups. Neutral sentiment accounted for 51.5% (139/270) of all the tweets, whereas positive sentiment was the second most common sentiment (117/270, 43.3%). In Ontario, 60% (54/90) of the tweets were positive. Negative sentiment (eg, public officials criticizing vaccine rollout) accounted for 12% (11/90) of all the tweets. Conclusions: As governments continue to promote the uptake of the COVID-19 booster doses, findings from this study are useful in informing how governments can best use social media to engage with the public to achieve democratic goals. UR - https://infodemiology.jmir.org/2023/1/e41582 UR - http://dx.doi.org/10.2196/41582 UR - http://www.ncbi.nlm.nih.gov/pubmed/37315194 ID - info:doi/10.2196/41582 ER - TY - JOUR AU - Yang, Genevieve AU - King, G. Sarah AU - Lin, Hung-Mo AU - Goldstein, Z. Rita PY - 2023/7/19 TI - Emotional Expression on Social Media Support Forums for Substance Cessation: Observational Study of Text-Based Reddit Posts JO - J Med Internet Res SP - e45267 VL - 25 KW - sentiment analysis KW - text mining KW - addiction phenotype KW - subjective experience phenotype KW - naturalistic big data KW - natural language processing KW - phenomenology KW - experience sampling N2 - Background: Substance use disorder is characterized by distinct cognitive processes involved in emotion regulation as well as unique emotional experiences related to the relapsing cycle of drug use and recovery. Web-based communities and the posts they generate represent an unprecedented resource for studying subjective emotional experiences, capturing population types and sizes not typically available in the laboratory. Here, we mined text data from Reddit, a social media website that hosts discussions from pseudonymous users on specific topic forums, including forums for individuals who are trying to abstain from using drugs, to explore the putative specificity of the emotional experience of substance cessation. Objective: An important motivation for this study was to investigate transdiagnostic clues that could ultimately be used for mental health outreach. Specifically, we aimed to characterize the emotions associated with cessation of 3 major substances and compare them to emotional experiences reported in nonsubstance cessation posts, including on forums related to psychiatric conditions of high comorbidity with addiction. Methods: Raw text from 2 million posts made, respectively, in the fall of 2020 (discovery data set) and fall of 2019 (replication data set) were obtained from 394 forums hosted by Reddit through the application programming interface. We quantified emotion word frequencies in 3 substance cessation forums for alcohol, nicotine, and cannabis topic categories and performed comparisons with general forums. Emotion word frequencies were classified into distinct categories and represented as a multidimensional emotion vector for each forum. We further quantified the degree of emotional resemblance between different forums by computing cosine similarity on these vectorized representations. For substance cessation posts with self-reported time since last use, we explored changes in the use of emotion words as a function of abstinence duration. Results: Compared to posts from general forums, substance cessation posts showed more expressions of anxiety, disgust, pride, and gratitude words. ?Anxiety? emotion words were attenuated for abstinence durations >100 days compared to shorter durations (t12=3.08, 2-tailed; P=.001). The cosine similarity analysis identified an emotion profile preferentially expressed in the cessation posts across substances, with lesser but still prominent similarities to posts about social anxiety and attention-deficit/hyperactivity disorder. These results were replicated in the 2019 (pre?COVID-19) data and were distinct from control analyses using nonemotion words. Conclusions: We identified a unique subjective experience phenotype of emotions associated with the cessation of 3 major substances, replicable across 2 time periods, with changes as a function of abstinence duration. Although to a lesser extent, this phenotype also quantifiably resembled the emotion phenomenology of other relevant subjective experiences (social anxiety and attention-deficit/hyperactivity disorder). Taken together, these transdiagnostic results suggest a novel approach for the future identification of at-risk populations, allowing for the development and deployment of specific and timely interventions. UR - https://www.jmir.org/2023/1/e45267 UR - http://dx.doi.org/10.2196/45267 UR - http://www.ncbi.nlm.nih.gov/pubmed/37467010 ID - info:doi/10.2196/45267 ER - TY - JOUR AU - Azzolina, Danila AU - Bressan, Silvia AU - Lorenzoni, Giulia AU - Baldan, Andrea Giulia AU - Bartolotta, Patrizia AU - Scognamiglio, Federico AU - Francavilla, Andrea AU - Lanera, Corrado AU - Da Dalt, Liviana AU - Gregori, Dario PY - 2023/7/12 TI - Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning?Based Text-Mining Approach JO - JMIR Public Health Surveill SP - e44467 VL - 9 KW - machine learning KW - pediatrics KW - child and adolescent health KW - text mining KW - injury KW - death KW - surveillance KW - pediatric admission KW - hospitalization KW - patient record KW - unintentional injury KW - emergency department KW - emergency KW - epidemiological surveillance N2 - Background: Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. Objective: This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. Methods: The study includes 283,468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40,000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. Results: The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. Conclusions: This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals? efforts in manually classifying diagnoses for research purposes. UR - https://publichealth.jmir.org/2023/1/e44467 UR - http://dx.doi.org/10.2196/44467 UR - http://www.ncbi.nlm.nih.gov/pubmed/37436799 ID - info:doi/10.2196/44467 ER - TY - JOUR AU - Shankar, Kavitha AU - Chandrasekaran, Ranganathan AU - Jeripity Venkata, Pruthvinath AU - Miketinas, Derek PY - 2023/7/10 TI - Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis JO - J Med Internet Res SP - e47328 VL - 25 KW - social media KW - nutrition discourse KW - text mining KW - immunity building KW - food groups KW - Twitter KW - nutrition KW - food KW - immunity KW - COVID-19 KW - diet KW - immune system KW - assessment KW - tweets KW - dieticians KW - nutritionists N2 - Background: The COVID-19 pandemic has brought to the spotlight the critical role played by a balanced and healthy diet in bolstering the human immune system. There is burgeoning interest in nutrition-related information on social media platforms like Twitter. There is a critical need to assess and understand public opinion, attitudes, and sentiments toward nutrition-related information shared on Twitter. Objective: This study uses text mining to analyze nutrition-related messages on Twitter to identify and analyze how the general public perceives various food groups and diets for improving immunity to the SARS-CoV-2 virus. Methods: We gathered 71,178 nutrition-related tweets that were posted between January 01, 2020, and September 30, 2020. The Correlated Explanation text mining algorithm was used to identify frequently discussed topics that users mentioned as contributing to immunity building against SARS-CoV-2. We assessed the relative importance of these topics and performed a sentiment analysis. We also qualitatively examined the tweets to gain a closer understanding of nutrition-related topics and food groups. Results: Text-mining yielded 10 topics that users discussed frequently on Twitter, viz proteins, whole grains, fruits, vegetables, dairy-related, spices and herbs, fluids, supplements, avoidable foods, and specialty diets. Supplements were the most frequently discussed topic (23,913/71,178, 33.6%) with a higher proportion (20,935/23,913, 87.75%) exhibiting a positive sentiment with a score of 0.41. Consuming fluids (17,685/71,178, 24.85%) and fruits (14,807/71,178, 20.80%) were the second and third most frequent topics with favorable, positive sentiments. Spices and herbs (8719/71,178, 12.25%) and avoidable foods (8619/71,178, 12.11%) were also frequently discussed. Negative sentiments were observed for a higher proportion of avoidable foods (7627/8619, 84.31%) with a sentiment score of ?0.39. Conclusions: This study identified 10 important food groups and associated sentiments that users discussed as a means to improve immunity. Our findings can help dieticians and nutritionists to frame appropriate interventions and diet programs. UR - https://www.jmir.org/2023/1/e47328 UR - http://dx.doi.org/10.2196/47328 UR - http://www.ncbi.nlm.nih.gov/pubmed/37428522 ID - info:doi/10.2196/47328 ER - TY - JOUR AU - Cummins, A. Jack AU - Lipworth, D. Adam PY - 2023/7/6 TI - Reddit and Google Activity Related to Non-COVID Epidemic Diseases Surged at Start of COVID-19 Pandemic: Retrospective Study JO - JMIR Form Res SP - e44603 VL - 7 KW - COVID-19 KW - Reddit KW - Google Trends KW - chikungunya KW - Ebola KW - H1N1 KW - Middle Eastern respiratory syndrome KW - MERS KW - severe acute respiratory syndrome KW - SARS KW - Zika KW - infectious disease KW - social media KW - search data KW - search query KW - web-based search KW - information behavior KW - information seeking KW - public interest N2 - Background: Resources such as Google Trends and Reddit provide opportunities to gauge real-time popular interest in public health issues. Despite the potential for these publicly available and free resources to help optimize public health campaigns, use for this purpose has been limited. Objective: The purpose of this study is to determine whether early public awareness of COVID-19 correlated with elevated public interest in other infectious diseases of public health importance. Methods: Google Trends search data and Reddit comment data were analyzed from 2018 through 2020 for the frequency of keywords ?chikungunya,? ?Ebola,? ?H1N1,? ?MERS,? ?SARS,? and ?Zika,? 6 highly publicized epidemic diseases in recent decades. After collecting Google Trends relative popularity scores for each of these 6 terms, unpaired 2-tailed t tests were used to compare the 2020 weekly scores for each term to their average level over the 3-year study period. The number of Reddit comments per month with each of these 6 terms was collected and then adjusted for the total estimated Reddit monthly comment volume to derive a measure of relative use, analogous to the Google Trends popularity score. The relative monthly incidence of comments with each search term was then compared to the corresponding search term?s pre-COVID monthly comment data, again using unpaired 2-tailed t tests. P value cutoffs for statistical significance were determined a priori with a Bonferroni correction. Results: Google Trends and Reddit data both demonstrate large and statistically significant increases in the usage of each evaluated disease term through at least the initial months of the pandemic. Google searches and Reddit comments that included any of the evaluated infectious disease search terms rose significantly in the first months of 2020 above their baseline usage, peaking in March 2020. Google searches for ?SARS? and ?MERS? remained elevated for the entirety of the 2020 calendar year, as did Reddit comments with the words ?Ebola,? ?H1N1,? ?MERS,? and ?SARS? (P<.001, for each weekly or monthly comparison, respectively). Conclusions: Google Trends and Reddit can readily be used to evaluate real-time general interest levels in public health?related topics, providing a tool to better time and direct public health initiatives that require a receptive target audience. The start of the COVID-19 pandemic correlated with increased public interest in other epidemic infectious diseases. We have demonstrated that for 6 distinct infectious causes of epidemics over the last 2 decades, public interest rose substantially and rapidly with the outbreak of COVID-19. Our data suggests that for at least several months after the initial outbreak, the public may have been particularly receptive to dialogue on these topics. Public health officials should consider using Google Trends and social media data to identify patterns of engagement with public health topics in real time and to optimize the timing of public health campaigns. UR - https://formative.jmir.org/2023/1/e44603 UR - http://dx.doi.org/10.2196/44603 UR - http://www.ncbi.nlm.nih.gov/pubmed/37256832 ID - info:doi/10.2196/44603 ER - TY - JOUR AU - Filipponi, Chiara AU - Chichua, Mariam AU - Masiero, Marianna AU - Mazzoni, Davide AU - Pravettoni, Gabriella PY - 2023/7/3 TI - Cancer Pain Experience Through the Lens of Patients and Caregivers: Mixed Methods Social Media Study JO - JMIR Cancer SP - e41594 VL - 9 KW - pain KW - cancer KW - quality of life KW - social support KW - emotion KW - personality KW - decision-making N2 - Background: Cancer pain represents a challenge for cancer patients and their family members. Despite progression in pain management, pain is still underreported and undertreated, and there is limited information on the related needs that patients and caregivers may have. Online platforms represent a fundamental tool for research to reveal the unmet needs of these users and their emotions outside the medical setting. Objective: This study aimed to (1) reveal the unmet needs of both patients and caregivers and (2) detect the emotional activation associated with cancer pain by analyzing the textual patterns of both users. Methods: A descriptive and quantitative analysis of qualitative data was performed in RStudio v.2022.02.3 (RStudio Team). We analyzed 679 posts (161 from caregivers and 518 from patients) published over 10 years on the ?cancer? subreddit of Reddit to identify unmet needs and emotions related to cancer pain. Hierarchical clustering, and emotion and sentiment analysis were conducted. Results: The language used for describing experiences related to cancer pain and expressed needs differed between patients and caregivers. For patients (agglomerative coefficient=0.72), the large cluster labeled unmet needs included the following clusters: (1A) reported experiences, with the subclusters (a) relationship with doctors/spouse and (b) reflections on physical features; and (1B) changes observed over time, with the subclusters (a) regret and (b) progress. For caregivers (agglomerative coefficient=0.80), the main clusters were as follows: (1A) social support and (1B) reported experiences, with the subclusters (a) psychosocial challenges and (b) grief. Moreover, comparison between the 2 groups (entanglement coefficient=0.28) showed that they shared a common cluster labeled uncertainty. Regarding emotion and sentiment analysis, patients expressed a significantly higher negative sentiment than caregivers (z=?2.14; P<.001). On the contrary, caregivers expressed a higher positive sentiment compared with patients (z=?2.26; P<.001), with trust (z=?4.12; P<.001) and joy (z=?2.03; P<.001) being the most prevalent positive emotions. Conclusions: Our study emphasized different perceptions of cancer pain in patients and caregivers. We revealed different needs and emotional activations in the 2 groups. Moreover, our study findings highlight the importance of considering caregivers in medical care. Overall, this study increases knowledge about the unmet needs and emotions of patients and caregivers, which may have important clinical implications in pain management. UR - https://cancer.jmir.org/2023/1/e41594 UR - http://dx.doi.org/10.2196/41594 UR - http://www.ncbi.nlm.nih.gov/pubmed/37399067 ID - info:doi/10.2196/41594 ER - TY - JOUR AU - Qin, Lang AU - Zheng, Ming AU - Schwebel, C. David AU - Li, Li AU - Cheng, Peixia AU - Rao, Zhenzhen AU - Peng, Ruisha AU - Ning, Peishan AU - Hu, Guoqing PY - 2023/6/30 TI - Content Quality of Web-Based Short-Form Videos for Fire and Burn Prevention in China: Content Analysis JO - J Med Internet Res SP - e47343 VL - 25 KW - fire KW - burn KW - prevention KW - first aid KW - short video KW - content quality KW - public impact KW - China N2 - Background: Web-based short-form videos are increasingly popular for disseminating fire and burn prevention information, but their content quality is unknown. Objective: We aimed to systematically assess the characteristics, content quality, and public impact of web-based short-form videos offering primary and secondary (first aid) prevention recommendations for fires and burns in China between 2018 and 2021. Methods: We retrieved short-form videos offering both primary and secondary (first aid) information to prevent fire and burn injuries published on the 3 most popular web-based short-form video platforms in China: TikTok, Kwai, and Bilibili. To assess video content quality, we calculated the proportion of short-form videos that included information on each of the 15 recommendations for burn prevention education from the World Health Organization (WHO; P1) and that correctly disseminated each recommendation (P2). High P1 and P2 indicated better content quality. To assess their public impact, we calculated the median (IQR) of 3 indicators: the number of comments, likes, and saves as a favorite by viewers. Chi-square test, trend chi-square test, and Kruskal-Wallis H test examined differences in indicators across the 3 platforms, years, content, and time duration of videos and between videos disseminating correct versus incorrect information. Results: Overall, 1459 eligible short-form videos were included. The number of short-form videos increased by 16 times between 2018 and 2021. Of them, 93.97% (n=1371) were about secondary prevention (first aid) and 86.02% (n=1255) lasted <2 minutes. The proportion of short-form videos including each of the 15 WHO recommendations ranged from 0% to 77.86% (n=1136). Recommendations 8, 13, and 11 had the highest proportions (n=1136, 77.86%; n=827, 56.68%; and n=801, 54.9%, respectively), whereas recommendations 3 and 5 were never mentioned. Among the short-form videos that included the WHO recommendations, recommendations 1, 2, 4, 6, 9, and 12 were always disseminated correctly, but the other 9 recommendations were correctly disseminated in 59.11% (120/203) to 98.68% (1121/1136) of videos. The proportion of short-form videos including and correctly disseminating the WHO recommendations varied across platforms and years. The public impact of short videos varied greatly across videos, with a median (IQR) of 5 (0-34) comments, 62 (7-841) likes, and 4 (0-27) saves as a favorite. Short-form videos disseminating correct recommendations had larger public impact than those disseminating either partially correct or incorrect knowledge (median 5 vs 4 comments, 68 vs 51 likes, and 5 vs 3 saves as a favorite, respectively; all P<.05). Conclusions: Despite the rapid increase in the number of web-based short-form videos about fire and burn prevention available in China, their content quality and public impact were generally low. Systematic efforts are recommended to improve the content quality and public impact of short-form videos on injury prevention topics such as fire and burn prevention. UR - https://www.jmir.org/2023/1/e47343 UR - http://dx.doi.org/10.2196/47343 UR - http://www.ncbi.nlm.nih.gov/pubmed/37389906 ID - info:doi/10.2196/47343 ER - TY - JOUR AU - Yang, Kunhao AU - Tanaka, Mikihito PY - 2023/6/29 TI - Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis JO - J Med Internet Res SP - e45024 VL - 25 KW - scientific uncertainty KW - COVID-19 KW - Wikipedia KW - crowdsourcing information production N2 - Background: A worldwide overabundance of information comprising misinformation, rumors, and propaganda concerning COVID-19 has been observed in addition to the pandemic. By addressing this data confusion, Wikipedia has become an important source of information. Objective: This study aimed to investigate how the editors of Wikipedia have handled COVID-19?related information. Specifically, it focused on 2 questions: What were the knowledge preferences of the editors who participated in producing COVID-19?related information? and How did editors with different knowledge preferences collaborate? Methods: This study used a large-scale data set, including >2 million edits in the histories of 1857 editors who edited 133 articles related to COVID-19 on Japanese Wikipedia. Machine learning methods, including graph neural network methods, Bayesian inference, and Granger causality analysis, were used to establish the editors? topic proclivity and collaboration patterns. Results: Overall, 3 trends were observed. Two groups of editors were involved in the production of information on COVID-19. One group had a strong preference for sociopolitical topics (social-political group), and the other group strongly preferred scientific and medical topics (scientific-medical group). The social-political group played a central role (contributing 16,544,495/23,485,683, 70.04% of bits of content and 57,969/76,673, 75.61% of the references) in the information production part of the COVID-19 articles on Wikipedia, whereas the scientific-medical group played only a secondary role. The severity of the pandemic in Japan activated the editing behaviors of the social-political group, leading them to contribute more to COVID-19 information production on Wikipedia while simultaneously deactivating the editing behaviors of the scientific-medical group, resulting in their less contribution to COVID-19 information production on Wikipedia (Pearson correlation coefficient=0.231; P<.001). Conclusions: The results of this study showed that lay experts (ie, Wikipedia editors) in the fields of science and medicine tended to remain silent when facing high scientific uncertainty related to the pandemic. Considering the high quality of the COVID-19?related articles on Japanese Wikipedia, this research also suggested that the sidelining of the science and medicine editors in discussions is not necessarily a problem. Instead, the social and political context of the issues with high scientific uncertainty is more important than the scientific discussions that support accuracy. UR - https://www.jmir.org/2023/1/e45024 UR - http://dx.doi.org/10.2196/45024 UR - http://www.ncbi.nlm.nih.gov/pubmed/37384371 ID - info:doi/10.2196/45024 ER - TY - JOUR AU - Yim, Dobin AU - Khuntia, Jiban AU - King, Elliot AU - Treskon, Matthew AU - Galiatsatos, Panagis PY - 2023/6/27 TI - Expert Credibility and Sentiment in Infodemiology of Hydroxychloroquine?s Efficacy on Cable News Programs: Empirical Analysis JO - JMIR Infodemiology SP - e45392 VL - 3 KW - source credibility KW - infodemic KW - infoveillance KW - broadcasting KW - cable television KW - COVID-19 N2 - Background: Infodemic exacerbates public health concerns by disseminating unreliable and false scientific facts to a population. During the COVID-19 pandemic, the efficacy of hydroxychloroquine as a therapeutic solution emerged as a challenge to public health communication. Internet and social media spread information about hydroxychloroquine, whereas cable television was a vital source. To exemplify, experts discussed in cable television broadcasts about hydroxychloroquine for treating COVID-19. However, how the experts? comments influenced airtime allocation on cable television to help in public health communication, either during COVID-10 or at other times, is not understood. Objective: This study aimed to examine how 3 factors, that is, the credibility of experts as doctors (DOCTOREXPERT), the credibility of government representatives (GOVTEXPERT), and the sentiments (SENTIMENT) expressed in discussions and comments, influence the allocation of airtime (AIRTIME) in cable television broadcasts. SENTIMENT pertains to the information credibility conveyed through the tone and language of experts? comments during cable television broadcasts, in contrast to the individual credibility of the doctor or government representatives because of the degree or affiliations. Methods: We collected transcriptions of relevant hydroxychloroquine-related broadcasts on cable television between March 2020 and October 2020. We coded the experts as DOCTOREXPERT or GOVTEXPERT using publicly available data. To determine the sentiments expressed in the broadcasts, we used a machine learning algorithm to code them as POSITIVE, NEGATIVE, NEUTRAL, or MIXED sentiments. Results: The analysis revealed a counterintuitive association between the expertise of doctors (DOCTOREXPERT) and the allocation of airtime, with doctor experts receiving less airtime (P<.001) than the nonexperts in a base model. A more nuanced interaction model suggested that government experts with a doctorate degree received even less airtime (P=.03) compared with nonexperts. Sentiments expressed during the broadcasts played a significant role in airtime allocation, particularly for their direct effects on airtime allocation, more so for NEGATIVE (P<.001), NEUTRAL (P<.001), and MIXED (P=.03) sentiments. Only government experts expressing POSITIVE sentiments during the broadcast received a more extended airtime (P<.001) than nonexperts. Furthermore, NEGATIVE sentiments in the broadcasts were associated with less airtime both for DOCTOREXPERT (P<.001) and GOVTEXPERT (P<.001). Conclusions: Source credibility plays a crucial role in infodemics by ensuring the accuracy and trustworthiness of the information communicated to audiences. However, cable television media may prioritize likeability over credibility, potentially hindering this goal. Surprisingly, the findings of our study suggest that doctors did not get good airtime on hydroxychloroquine-related discussions on cable television. In contrast, government experts as sources received more airtime on hydroxychloroquine-related discussions. Doctors presenting facts with negative sentiments may not help them gain airtime. Conversely, government experts expressing positive sentiments during broadcasts may have better airtime than nonexperts. These findings have implications on the role of source credibility in public health communications. UR - https://infodemiology.jmir.org/2023/1/e45392 UR - http://dx.doi.org/10.2196/45392 UR - http://www.ncbi.nlm.nih.gov/pubmed/37204334 ID - info:doi/10.2196/45392 ER - TY - JOUR AU - Fu, Jiaqi AU - Li, Chaixiu AU - Zhou, Chunlan AU - Li, Wenji AU - Lai, Jie AU - Deng, Shisi AU - Zhang, Yujie AU - Guo, Zihan AU - Wu, Yanni PY - 2023/6/26 TI - Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review JO - J Med Internet Res SP - e43349 VL - 25 KW - social media KW - health care KW - internet information KW - content analysis KW - big data mining KW - review method KW - scoping KW - online information KW - methodology N2 - Background: Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care. Objective: This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care? Methods: A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted. Results: Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education). Conclusions: Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field. UR - https://www.jmir.org/2023/1/e43349 UR - http://dx.doi.org/10.2196/43349 UR - http://www.ncbi.nlm.nih.gov/pubmed/37358900 ID - info:doi/10.2196/43349 ER - TY - JOUR AU - Lotto, Matheus AU - Zakir Hussain, Irfhana AU - Kaur, Jasleen AU - Butt, Ahmad Zahid AU - Cruvinel, Thiago AU - Morita, P. Plinio PY - 2023/6/20 TI - Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study JO - J Med Internet Res SP - e44586 VL - 25 KW - fluoride KW - health information KW - infodemiology KW - infoveillance KW - misinformation KW - social media KW - Twitter KW - oral care KW - healthy lifestyle KW - hygiene N2 - Background: Although social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis?driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests. Objective: This study aimed to analyze ?fluoride-free? tweets regarding their topics and frequency of publication over time. Methods: A total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword ?fluoride-free? were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software. Results: We identified 3 issues by applying the LDA topic modeling: ?healthy lifestyle? (topic 1), ?consumption of natural/organic oral care products? (topic 2), and ?recommendations for using fluoride-free products/measures? (topic 3). Topic 1 was related to users? concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users? personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users? recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward. Conclusions: Public concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of ?fluoride-free? tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population. UR - https://www.jmir.org/2023/1/e44586 UR - http://dx.doi.org/10.2196/44586 UR - http://www.ncbi.nlm.nih.gov/pubmed/37338975 ID - info:doi/10.2196/44586 ER - TY - JOUR AU - Mao, Lingchao AU - Chu, Emily AU - Gu, Jinghong AU - Hu, Tao AU - Weiner, J. Bryan AU - Su, Yanfang PY - 2023/6/13 TI - A 4D Theoretical Framework for Measuring Topic-Specific Influence on Twitter: Development and Usability Study on Dietary Sodium Tweets JO - J Med Internet Res SP - e45897 VL - 25 KW - social media KW - health education KW - health promotion KW - dissemination strategy KW - influence KW - Twitter KW - activity KW - priority KW - originality KW - popularity N2 - Background: Social media has emerged as a prominent approach for health education and promotion. However, it is challenging to understand how to best promote health-related information on social media platforms such as Twitter. Despite commercial tools and prior studies attempting to analyze influence, there is a gap to fill in developing a publicly accessible and consolidated framework to measure influence and analyze dissemination strategies. Objective: We aimed to develop a theoretical framework to measure topic-specific user influence on Twitter and to examine its usability by analyzing dietary sodium tweets to support public health agencies in improving their dissemination strategies. Methods: We designed a consolidated framework for measuring influence that can capture topic-specific tweeting behaviors. The core of the framework is a summary indicator of influence decomposable into 4 dimensions: activity, priority, originality, and popularity. These measures can be easily visualized and efficiently computed for any Twitter account without the need for private access. We demonstrated the proposed methods by using a case study on dietary sodium tweets with sampled stakeholders and then compared the framework with a traditional measure of influence. Results: More than half a million dietary sodium tweets from 2006 to 2022 were retrieved for 16 US domestic and international stakeholders in 4 categories, that is, public agencies, academic institutions, professional associations, and experts. We discovered that World Health Organization, American Heart Association, Food and Agriculture Organization of the United Nations (UN-FAO), and World Action on Salt (WASH) were the top 4 sodium influencers in the sample. Each had different strengths and weaknesses in their dissemination strategies, and 2 stakeholders with similar overall influence, that is, UN-FAO and WASH, could have significantly different tweeting patterns. In addition, we identified exemplars in each dimension of influence. Regarding tweeting activity, a dedicated expert published more sodium tweets than any organization in the sample in the past 16 years. In terms of priority, WASH had more than half of its tweets dedicated to sodium. UN-FAO had both the highest proportion of original sodium tweets and posted the most popular sodium tweets among all sampled stakeholders. Regardless of excellence in 1 dimension, the 4 most influential stakeholders excelled in at least 2 out of 4 dimensions of influence. Conclusions: Our findings demonstrate that our method not only aligned with a traditional measure of influence but also advanced influence analysis by analyzing the 4 dimensions that contribute to topic-specific influence. This consolidated framework provides quantifiable measures for public health entities to understand their bottleneck of influence and refine their social media campaign strategies. Our framework can be applied to improve the dissemination of other health topics as well as assist policy makers and public campaign experts to maximize population impact. UR - https://www.jmir.org/2023/1/e45897 UR - http://dx.doi.org/10.2196/45897 UR - http://www.ncbi.nlm.nih.gov/pubmed/37310774 ID - info:doi/10.2196/45897 ER - TY - JOUR AU - Kim, Hyunuk AU - Proctor, R. Chris AU - Walker, Dylan AU - McCarthy, R. Ronan PY - 2023/6/12 TI - Understanding the Consumption of Antimicrobial Resistance?Related Content on Social Media: Twitter Analysis JO - J Med Internet Res SP - e42363 VL - 25 KW - antimicrobial resistance KW - AMR KW - social media KW - Twitter KW - engagement KW - antimicrobial KW - effective KW - public health KW - awareness KW - disease KW - microbiology KW - pathogen KW - development N2 - Background: Antimicrobial resistance (AMR) is one of the most pressing concerns in our society. Today, social media can function as an important channel to disseminate information about AMR. The way in which this information is engaged with depends on a number of factors, including the target audience and the content of the social media post. Objective: The aim of this study is to better understand how AMR-related content is consumed on the social media platform Twitter and to understand some of the drivers of engagement. This is essential to designing effective public health strategies, raising awareness about antimicrobial stewardship, and enabling academics to effectively promote their research on social media. Methods: We took advantage of unrestricted access to the metrics associated with the Twitter bot @AntibioticResis, which has over 13,900 followers. This bot posts the latest AMR research in the format of a title and a URL link to the PubMed page for an article. The tweets do not contain other attributes such as author, affiliation, or journal. Therefore, engagement with the tweets is only affected by the words used in the titles. Using negative binomial regression models, we measured the impact of pathogen names in paper titles, academic attention inferred from publication counts, and general attention estimated from Twitter on URL clicks to AMR research papers. Results: Followers of @AntibioticResis consisted primarily of health care professionals and academic researchers whose interests comprised mainly AMR, infectious diseases, microbiology, and public health. Three World Health Organization (WHO) critical priority pathogens?Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacteriaceae?were positively associated with URL clicks. Papers with shorter titles tended to have more engagements. We also described some key linguistic characteristics that should be considered when a researcher is trying to maximize engagement with their publication. Conclusions: Our finding suggests that specific pathogens gain more attention on Twitter than others and that the levels of attention do not necessarily correspond to their status on the WHO priority pathogen list. This suggests that more targeted public health strategies may be needed to raise awareness about AMR among specific pathogens. Analysis of follower data suggests that in the busy schedules of health care professionals, social media offers a fast and accessible gateway to staying abreast of the latest developments in this field. UR - https://www.jmir.org/2023/1/e42363 UR - http://dx.doi.org/10.2196/42363 UR - http://www.ncbi.nlm.nih.gov/pubmed/37307042 ID - info:doi/10.2196/42363 ER - TY - JOUR AU - Lane, M. Jamil AU - Habib, Daniel AU - Curtis, Brenda PY - 2023/6/12 TI - Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data JO - J Med Internet Res SP - e39484 VL - 25 KW - Twitter KW - public health interventions KW - surveillance data KW - health communication KW - natural language processing N2 - Background: Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health?related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. Objective: The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users? tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. Methods: A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. Results: A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users? opinions and feelings. Conclusions: Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers? ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions. UR - https://www.jmir.org/2023/1/e39484 UR - http://dx.doi.org/10.2196/39484 UR - http://www.ncbi.nlm.nih.gov/pubmed/37307062 ID - info:doi/10.2196/39484 ER - TY - JOUR AU - Morita, Pelegrini Plinio AU - Zakir Hussain, Irfhana AU - Kaur, Jasleen AU - Lotto, Matheus AU - Butt, Ahmad Zahid PY - 2023/6/9 TI - Tweeting for Health Using Real-time Mining and Artificial Intelligence?Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter JO - J Med Internet Res SP - e44356 VL - 25 KW - big data KW - deep learning KW - infodemics KW - misinformation KW - social media KW - infoveillance N2 - Background: Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analyzing large volumes of social media data in real time. Objective: This study aimed to design and develop a big data pipeline and ecosystem (UbiLab Misinformation Analysis System [U-MAS]) to identify and analyze false or misleading information disseminated via social media on a certain topic or set of related topics. Methods: U-MAS is a platform-independent ecosystem developed in Python that leverages the Twitter V2 application programming interface and the Elastic Stack. The U-MAS expert system has 5 major components: data extraction framework, latent Dirichlet allocation (LDA) topic model, sentiment analyzer, misinformation classification model, and Elastic Cloud deployment (indexing of data and visualizations). The data extraction framework queries the data through the Twitter V2 application programming interface, with queries identified by public health experts. The LDA topic model, sentiment analyzer, and misinformation classification model are independently trained using a small, expert-validated subset of the extracted data. These models are then incorporated into U-MAS to analyze and classify the remaining data. Finally, the analyzed data are loaded into an index in the Elastic Cloud deployment and can then be presented on dashboards with advanced visualizations and analytics pertinent to infodemiology and infoveillance analysis. Results: U-MAS performed efficiently and accurately. Independent investigators have successfully used the system to extract significant insights into a fluoride-related health misinformation use case (2016 to 2021). The system is currently used for a vaccine hesitancy use case (2007 to 2022) and a heat wave?related illnesses use case (2011 to 2022). Each component in the system for the fluoride misinformation use case performed as expected. The data extraction framework handles large amounts of data within short periods. The LDA topic models achieved relatively high coherence values (0.54), and the predicted topics were accurate and befitting to the data. The sentiment analyzer performed at a correlation coefficient of 0.72 but could be improved in further iterations. The misinformation classifier attained a satisfactory correlation coefficient of 0.82 against expert-validated data. Moreover, the output dashboard and analytics hosted on the Elastic Cloud deployment are intuitive for researchers without a technical background and comprehensive in their visualization and analytics capabilities. In fact, the investigators of the fluoride misinformation use case have successfully used the system to extract interesting and important insights into public health, which have been published separately. Conclusions: The novel U-MAS pipeline has the potential to detect and analyze misleading information related to a particular topic or set of related topics. UR - https://www.jmir.org/2023/1/e44356 UR - http://dx.doi.org/10.2196/44356 UR - http://www.ncbi.nlm.nih.gov/pubmed/37294603 ID - info:doi/10.2196/44356 ER - TY - JOUR AU - Solans Noguero, David AU - Ramírez-Cifuentes, Diana AU - Ríssola, Andrés Esteban AU - Freire, Ana PY - 2023/6/8 TI - Gender Bias When Using Artificial Intelligence to Assess Anorexia Nervosa on Social Media: Data-Driven Study JO - J Med Internet Res SP - e45184 VL - 25 KW - anorexia nervosa KW - gender bias KW - artificial intelligence KW - social media N2 - Background: Social media sites are becoming an increasingly important source of information about mental health disorders. Among them, eating disorders are complex psychological problems that involve unhealthy eating habits. In particular, there is evidence showing that signs and symptoms of anorexia nervosa can be traced in social media platforms. Knowing that input data biases tend to be amplified by artificial intelligence algorithms and, in particular, machine learning, these methods should be revised to mitigate biased discrimination in such important domains. Objective: The main goal of this study was to detect and analyze the performance disparities across genders in algorithms trained for the detection of anorexia nervosa on social media posts. We used a collection of automated predictors trained on a data set in Spanish containing cases of 177 users that showed signs of anorexia (471,262 tweets) and 326 control cases (910,967 tweets). Methods: We first inspected the predictive performance differences between the algorithms for male and female users. Once biases were detected, we applied a feature-level bias characterization to evaluate the source of such biases and performed a comparative analysis of such features and those that are relevant for clinicians. Finally, we showcased different bias mitigation strategies to develop fairer automated classifiers, particularly for risk assessment in sensitive domains. Results: Our results revealed concerning predictive performance differences, with substantially higher false negative rates (FNRs) for female samples (FNR=0.082) compared with male samples (FNR=0.005). The findings show that biological processes and suicide risk factors were relevant for classifying positive male cases, whereas age, emotions, and personal concerns were more relevant for female cases. We also proposed techniques for bias mitigation, and we could see that, even though disparities can be mitigated, they cannot be eliminated. Conclusions: We concluded that more attention should be paid to the assessment of biases in automated methods dedicated to the detection of mental health issues. This is particularly relevant before the deployment of systems that are thought to assist clinicians, especially considering that the outputs of such systems can have an impact on the diagnosis of people at risk. UR - https://www.jmir.org/2023/1/e45184 UR - http://dx.doi.org/10.2196/45184 UR - http://www.ncbi.nlm.nih.gov/pubmed/37289496 ID - info:doi/10.2196/45184 ER - TY - JOUR AU - Edinger, Andy AU - Valdez, Danny AU - Walsh-Buhi, Eric AU - Trueblood, S. Jennifer AU - Lorenzo-Luaces, Lorenzo AU - Rutter, A. Lauren AU - Bollen, Johan PY - 2023/6/6 TI - Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning JO - J Med Internet Res SP - e43841 VL - 25 KW - COVID-19 KW - deep learning KW - misinformation KW - monkeypox KW - mpox KW - outbreak KW - public health KW - social media KW - Twitter N2 - Background: Shortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication. Objective: This study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning. Methods: We leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes. Results: Our deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials. Conclusions: Within a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies. UR - https://www.jmir.org/2023/1/e43841 UR - http://dx.doi.org/10.2196/43841 UR - http://www.ncbi.nlm.nih.gov/pubmed/37163694 ID - info:doi/10.2196/43841 ER - TY - JOUR AU - Tan, Ying Si AU - Tang, Sun Matilda Swee AU - Ong, Johnny Chin-Ann AU - Tan, Mien Veronique Kiak AU - Shannon, Brian Nicholas PY - 2023/6/6 TI - Impact of COVID-19 on Public Interest in Breast Cancer Screening and Related Symptoms: Google Trends Analysis JO - JMIR Cancer SP - e39105 VL - 9 KW - breast cancer screening KW - breast cancer symptoms KW - COVID-19 KW - public interest KW - Google Trends KW - screening KW - breast cancer KW - symptoms KW - cancer KW - trend KW - mammography KW - monitoring N2 - Background: The COVID-19 pandemic has led to a decrease in cancer screening due to the redeployment of health care resources and public avoidance of health care facilities. Breast cancer is the most common cancer diagnosed in female individuals, with improved survival rates from early detection. An avoidance of screening, resulting in late detection, greatly affects survival and increases health care resource burden and costs. Objective: This study aimed to evaluate if a sustained decrease in public interest in screening occurred and to evaluate other search terms, and hence interest, associated with that. Methods: This study used Google Trends to analyze public interest in breast cancer screening and symptoms. We queried search data for 4 keyword terms (?mammogram,? ?breast pain,? ?breast lump,? and ?nipple discharge?) from January 1, 2019, to January 1, 2022. The relative search frequency metric was used to assess interest in these terms, and related queries were retrieved for each keyword to evaluate trends in search patterns. Results: Despite an initial drastic drop in interest in mammography from March to April 2020, this quickly recovered by July 2020. After this period, alongside the recovery of interest in screening, there was a rapid increase in interest for arranging for mammography. Relative search frequencies of perceived breast cancer?related symptoms such as breast lump, nipple discharge, and breast pain remained stable. There was increase public interest in natural and alternative therapy of breast lumps despite the recovery of interest in mammography and breast biopsy. There was a significant correlation between search activity and Breast Cancer Awareness Month in October. Conclusions: Online search interest in breast cancer screening experienced a sharp decline at the beginning of the COVID-19 pandemic, with a subsequent return to baseline interest in arranging for mammography followed this short period of decreased interest. UR - https://cancer.jmir.org/2023/1/e39105 UR - http://dx.doi.org/10.2196/39105 UR - http://www.ncbi.nlm.nih.gov/pubmed/37163461 ID - info:doi/10.2196/39105 ER - TY - JOUR AU - Wang, Siqin AU - Ning, Huan AU - Huang, Xiao AU - Xiao, Yunyu AU - Zhang, Mengxi AU - Yang, Fan Ellie AU - Sadahiro, Yukio AU - Liu, Yan AU - Li, Zhenlong AU - Hu, Tao AU - Fu, Xiaokang AU - Li, Zi AU - Zeng, Ye PY - 2023/6/2 TI - Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022 JO - J Med Internet Res SP - e47225 VL - 25 KW - suicide KW - suicidal ideation KW - suicide-risk identification KW - natural language processing KW - social media KW - Japan N2 - Background: Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people?s expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. Objective: This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. Methods: This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. Results: Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. Conclusions: Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner. UR - https://www.jmir.org/2023/1/e47225 UR - http://dx.doi.org/10.2196/47225 UR - http://www.ncbi.nlm.nih.gov/pubmed/37267022 ID - info:doi/10.2196/47225 ER - TY - JOUR AU - Fahim, Christine AU - Cooper, Jeanette AU - Theivendrampillai, Suvabna AU - Pham, Ba' AU - Straus, Sharon PY - 2023/6/2 TI - Ontarians? Perceptions of Public Health Communications and Misinformation During the COVID-19 Pandemic: Survey Study JO - JMIR Form Res SP - e38323 VL - 7 KW - misinformation KW - information seeking KW - COVID-19 KW - trust KW - dissemination KW - health communication KW - risk KW - communication KW - policy maker KW - transmission KW - health emergency KW - age KW - gender KW - survey N2 - Background: Clear, accurate, and transparent risk communication is critical to providing policy makers and the public with directions to effectively implement public health strategies during a health emergency. Objective: We aimed to explore the public?s preferred sources of obtaining COVID-19 information, perceptions on the prevalence and drivers of misinformation during the pandemic, and suggestions to optimize health communications during future public health emergencies. Methods: We administered a web-based survey that included Likert scale, multiple choice and open-ended response questions to residents of Ontario, Canada. We aimed to recruit a sample that reflected population diversity with respect to age and gender. Data were collected between June 10, 2020, and December 31, 2020, and were analyzed using descriptive statistics; open-ended data were analyzed using content analysis. Subgroup analyses to explore perceptions by age and gender were conducted using ordinal regression. Results: A total of 1823 individuals participated in the survey (n=990, 54% women; n=703, 39% men; n=982, 54% aged 18-40 years; n=518, 28% aged 41-60 years; and n=215, 12% aged ?61 years). Participants most commonly obtained COVID-19 information from local television news (n=1118, 61%) followed by social media (n=938, 51%), national or international television news (n=888, 49%), and friends and family (n=835, 46%). Approximately 55% (n=1010) of the participants believed they had encountered COVID-19?related misinformation; 70% (n=1284) of the participants reported high levels of trust in health authority websites and health care providers; 66% (n=1211) reported high levels of trust in health ministers or public health organizations. Sources perceived to be less trustworthy included friends and family, talk radio, social media, as well as blogs and opinion websites. Men were more likely to report encountering misinformation and to trust friends or family (odds ratio [OR] 1.49, 95% CI 1.24-1.79) and blogs or opinion websites (OR 1.24, 95% CI 1.03-1.50), compared to women. Compared to those aged 18-40 years, participants aged ?41years were more likely to trust all assessed information sources, with the exception of web-based media sources, and less likely to report encountering misinformation. Of those surveyed, 58% (n=1053) had challenges identifying or appraising COVID-19 information. Conclusions: Over half of our participants perceived that they had encountered COVID-19 misinformation, and 58% had challenges identifying or appraising COVID-19 information. Gender and age differences in perceptions of misinformation and trust in information sources were observed. Future research to confirm the validity of these perceptions and to explore information-seeking patterns by population subgroups may provide useful insights on how to optimize health communication during public health emergencies. UR - https://formative.jmir.org/2023/1/e38323 UR - http://dx.doi.org/10.2196/38323 UR - http://www.ncbi.nlm.nih.gov/pubmed/37159394 ID - info:doi/10.2196/38323 ER - TY - JOUR AU - Zenone, Marco AU - Snyder, Jeremy AU - Bélisle-Pipon, Jean-Christophe AU - Caulfield, Timothy AU - van Schalkwyk, May AU - Maani, Nason PY - 2023/5/31 TI - Advertising Alternative Cancer Treatments and Approaches on Meta Social Media Platforms: Content Analysis JO - JMIR Infodemiology SP - e43548 VL - 3 KW - cancer KW - advertising KW - misinformation KW - false hope KW - Meta KW - Facebook KW - Instagram KW - Messenger KW - social media KW - exploitation KW - infodemiology KW - cancer treatment KW - online health information N2 - Background: Alternative cancer treatment is associated with a greater risk of death than cancer patients undergoing conventional treatments. Anecdotal evidence suggests cancer patients view paid advertisements promoting alternative cancer treatment on social media, but the extent and nature of this advertising remain unknown. This context suggests an urgent need to investigate alternative cancer treatment advertising on social media. Objective: This study aimed to systematically analyze the advertising activities of prominent alternative cancer treatment practitioners on Meta platforms, including Facebook, Instagram, Messenger, and Audience Network. We specifically sought to determine (1) whether paid advertising for alternative cancer treatment occurs on Meta social media platforms, (2) the strategies and messages of alternative cancer providers to reach and appeal to prospective patients, and (3) how the efficacy of alternative treatments is portrayed. Methods: Between December 6, 2021, and December 12, 2021, we collected active advertisements from alternative cancer clinics using the Meta Ad Library. The information collected included identification number, URL, active/inactive status, dates launched/ran, advertiser page name, and a screenshot (image) or recording (video) of the advertisement. We then conducted a content analysis to determine how alternative cancer providers communicate the claimed benefits of their services and evaluated how they portrayed alternative cancer treatment efficacy. Results: We identified 310 paid advertisements from 11 alternative cancer clinics on Meta (Facebook, Instagram, or Messenger) marketing alternative treatment approaches, care, and interventions. Alternative cancer providers appealed to prospective patients through eight strategies: (1) advertiser representation as a legitimate medical provider (n=289, 93.2%); (2) appealing to persons with limited treatments options (n=203, 65.5%); (3) client testimonials (n=168, 54.2%); (4) promoting holistic approaches (n=121, 39%); (5) promoting messages of care (n=81, 26.1%); (6) rhetoric related to science and research (n=72, 23.2%); (7) rhetoric pertaining to the latest technology (n=63, 20.3%); and (8) focusing treatment on cancer origins and cause (n=43, 13.9%). Overall, 25.8% (n=80) of advertisements included a direct statement claiming provider treatment can cure cancer or prolong life. Conclusions: Our results provide evidence alternative cancer providers are using Meta advertising products to market scientifically unsupported cancer treatments. Advertisements regularly referenced ?alternative? and ?natural? treatment approaches to cancer. Imagery and text content that emulated evidence-based medical providers created the impression that the offered treatments were effective medical options for cancer. Advertisements exploited the hope of patients with terminal and poor prognoses by sharing testimonials of past patients who allegedly were cured or had their lives prolonged. We recommend that Meta introduce a mandatory, human-led authorization process that is not reliant upon artificial intelligence for medical-related advertisers before giving advertising permissions. Further research should focus on the conflict of interest between social media platforms advertising products and public health. UR - https://infodemiology.jmir.org/2023/1/e43548 UR - http://dx.doi.org/10.2196/43548 UR - http://www.ncbi.nlm.nih.gov/pubmed/37256649 ID - info:doi/10.2196/43548 ER - TY - JOUR AU - Harter, Claire AU - Ness, Marina AU - Goldin, Aleah AU - Lee, Christine AU - Merenda, Christine AU - Riberdy, Anne AU - Saha, Anindita AU - Araojo, Richardae AU - Tarver, Michelle PY - 2023/5/30 TI - Exploring Chronic Pain and Pain Management Perspectives: Qualitative Pilot Analysis of Web-Based Health Community Posts JO - JMIR Infodemiology SP - e41672 VL - 3 KW - chronic pain KW - pain management KW - online health community N2 - Background: Patient?perspectives are central to the?US Food and Drug Administration?s benefit-risk decision-making process in the evaluation of medical products. Traditional channels of communication may not be feasible for all patients and consumers. Social media websites have increasingly been recognized by researchers as a means to gain insights into patients? views about treatment and diagnostic options, the health care system, and their experiences living with their conditions. Consideration of multiple patient perspective data sources offers the Food and Drug Administration the opportunity to capture diverse patient voices and experiences with chronic pain. Objective: This pilot study explores posts from a web-based patient platform to gain insights into the key challenges and barriers to treatment faced by patients with chronic pain and their caregivers. Methods: This research compiles and analyzes unstructured patient data to draw out the key themes. To extract relevant posts for this study, predefined keywords were identified. Harvested posts were published between January 1, 2017, and October 22, 2019, and had to include #ChronicPain and at least one other relevant disease tag, a relevant chronic pain management tag, or a chronic pain management tag for a treatment or activity specific to chronic pain. Results: The most common topics discussed among persons living with chronic pain were related to disease burden, the need for support, advocacy, and proper diagnosis. Patients? discussions focused on the negative impact chronic pain had on their emotions, playing sports, or exercising, work and school, sleep, social life, and other activities of daily life. The 2 most frequently discussed treatments were opioids or narcotics and devices such as transcutaneous electrical nerve stimulation machines and spinal cord stimulators. Conclusions: Social listening data may provide valuable insights into patients? and caregivers? perspectives, preferences, and unmet needs, especially when conditions may be highly stigmatized. UR - https://infodemiology.jmir.org/2023/1/e41672 UR - http://dx.doi.org/10.2196/41672 UR - http://www.ncbi.nlm.nih.gov/pubmed/37252767 ID - info:doi/10.2196/41672 ER - TY - JOUR AU - Lenti, Jacopo AU - Mejova, Yelena AU - Kalimeri, Kyriaki AU - Panisson, André AU - Paolotti, Daniela AU - Tizzani, Michele AU - Starnini, Michele PY - 2023/5/24 TI - Global Misinformation Spillovers in the Vaccination Debate Before and During the COVID-19 Pandemic: Multilingual Twitter Study JO - JMIR Infodemiology SP - e44714 VL - 3 KW - vaccination hesitancy KW - vaccine KW - misinformation KW - Twitter KW - social media KW - COVID-19 N2 - Background: Antivaccination views pervade online social media, fueling distrust in scientific expertise and increasing the number of vaccine-hesitant individuals. Although previous studies focused on specific countries, the COVID-19 pandemic has brought the vaccination discourse worldwide, underpinning the need to tackle low-credible information flows on a global scale to design effective countermeasures. Objective: This study aimed to quantify cross-border misinformation flows among users exposed to antivaccination (no-vax) content and the effects of content moderation on vaccine-related misinformation. Methods: We collected 316 million vaccine-related Twitter (Twitter, Inc) messages in 18 languages from October 2019 to March 2021. We geolocated users in 28 different countries and reconstructed a retweet network and cosharing network for each country. We identified communities of users exposed to no-vax content by detecting communities in the retweet network via hierarchical clustering and manual annotation. We collected a list of low-credibility domains and quantified the interactions and misinformation flows among no-vax communities of different countries. Results: The findings showed that during the pandemic, no-vax communities became more central in the country-specific debates and their cross-border connections strengthened, revealing a global Twitter antivaccination network. US users are central in this network, whereas Russian users also became net exporters of misinformation during vaccination rollout. Interestingly, we found that Twitter?s content moderation efforts, in particular the suspension of users following the January 6 US Capitol attack, had a worldwide impact in reducing the spread of misinformation about vaccines. Conclusions: These findings may help public health institutions and social media platforms mitigate the spread of health-related, low-credibility information by revealing vulnerable web-based communities. UR - https://infodemiology.jmir.org/2023/1/e44714 UR - http://dx.doi.org/10.2196/44714 UR - http://www.ncbi.nlm.nih.gov/pubmed/37223965 ID - info:doi/10.2196/44714 ER - TY - JOUR AU - Elkaim, M. Lior AU - Levett, J. Jordan AU - Niazi, Farbod AU - Alvi, A. Mohammed AU - Shlobin, A. Nathan AU - Linzey, R. Joseph AU - Robertson, Faith AU - Bokhari, Rakan AU - Alotaibi, M. Naif AU - Lasry, Oliver PY - 2023/5/22 TI - Cervical Myelopathy and Social Media: Mixed Methods Analysis JO - J Med Internet Res SP - e42097 VL - 25 KW - social media KW - twitter KW - cervical KW - myelopathy KW - spine KW - neurological KW - condition KW - degenerative KW - patient KW - caretaker KW - clinician KW - researcher KW - user KW - tweets KW - engagement KW - online KW - education KW - support N2 - Background: Degenerative cervical myelopathy (DCM) is a progressive neurologic condition caused by age-related degeneration of the cervical spine. Social media has become a crucial part of many patients? lives; however, little is known about social media use pertaining to DCM. Objective: This manuscript describes the landscape of social media use and DCM in patients, caretakers, clinicians, and researchers. Methods: A comprehensive search of the entire Twitter application programing interface database from inception to March 2022 was performed to identify all tweets about cervical myelopathy. Data on Twitter users included geographic location, number of followers, and number of tweets. The number of tweet likes, retweets, quotes, and total engagement were collected. Tweets were also categorized based on their underlying themes. Mentions pertaining to past or upcoming surgical procedures were recorded. A natural language processing algorithm was used to assign a polarity score, subjectivity score, and analysis label to each tweet for sentiment analysis. Results: Overall, 1859 unique tweets from 1769 accounts met the inclusion criteria. The highest frequency of tweets was seen in 2018 and 2019, and tweets decreased significantly in 2020 and 2021. Most (888/1769, 50.2%) of the tweets? authors were from the United States, United Kingdom, or Canada. Account categorization showed that 668 of 1769 (37.8%) users discussing DCM on Twitter were medical doctors or researchers, 415 of 1769 (23.5%) were patients or caregivers, and 201 of 1769 (11.4%) were news media outlets. The 1859 tweets most often discussed research (n=761, 40.9%), followed by spreading awareness or informing the public on DCM (n=559, 30.1%). Tweets describing personal patient perspectives on living with DCM were seen in 296 (15.9%) posts, with 65 (24%) of these discussing upcoming or past surgical experiences. Few tweets were related to advertising (n=31, 1.7%) or fundraising (n=7, 0.4%). A total of 930 (50%) tweets included a link, 260 (14%) included media (ie, photos or videos), and 595 (32%) included a hashtag. Overall, 847 of the 1859 tweets (45.6%) were classified as neutral, 717 (38.6%) as positive, and 295 (15.9%) as negative. Conclusions: When categorized thematically, most tweets were related to research, followed by spreading awareness or informing the public on DCM. Almost 25% (65/296) of tweets describing patients? personal experiences with DCM discussed past or upcoming surgical interventions. Few posts pertained to advertising or fundraising. These data can help identify areas for improvement of public awareness online, particularly regarding education, support, and fundraising. UR - https://www.jmir.org/2023/1/e42097 UR - http://dx.doi.org/10.2196/42097 UR - http://www.ncbi.nlm.nih.gov/pubmed/37213188 ID - info:doi/10.2196/42097 ER - TY - JOUR AU - Smail, J. Emily AU - Livingston, Torie AU - Wolach, Adam AU - Cenko, Erta AU - Kaufmann, N. Christopher AU - Manini, M. Todd PY - 2023/5/22 TI - Media Consumption and COVID-19?Related Precautionary Behaviors During the Early Pandemic: Survey Study of Older Adults JO - JMIR Form Res SP - e46230 VL - 7 KW - health communication KW - COVID-19 KW - older adult KW - precautionary behavior change KW - behavior change KW - behavior KW - precaution KW - awareness KW - behavior modification KW - media KW - news KW - association N2 - Background: During the COVID-19 pandemic, media sources dedicated significant time and resources to improve knowledge of COVID-19 precautionary behaviors (eg, wearing a mask). Many older adults report using the television, radio, print newspapers, or web-based sources to get information on political news, yet little is known about whether consuming news in the early phase of the pandemic led to behavior change, particularly in older adults. Objective: The goals of this study were to determine (1) whether dosage of news consumption on the COVID-19 pandemic was associated with COVID-19 precautionary behaviors; (2) whether being an ever-user of social media was associated with engagement in COVID-19 precautionary behaviors; and (3) among social media users, whether change in social media use during the early stages of the pandemic was associated with engagement in COVID-19 precautionary behaviors. Methods: Data were obtained from a University of Florida?administered study conducted in May and June of 2020. Linear regression models were used to assess the association between traditional news and social media use on COVID-19 precautionary behaviors (eg, mask wearing, hand washing, and social distancing behaviors). Analyses were adjusted for demographic characteristics, including age, sex, marital status, and education level. Results: In a sample of 1082 older adults (mean age 73, IQR 68-78 years; 615/1082, 56.8% female), reporting 0 and <1 hour per day of media consumption, relative to >3 hours per day, was associated with lower engagement in COVID-19 precautionary behaviors in models adjusted for demographic characteristics (?=?2.00; P<.001 and ?=?.41; P=.01, respectively). In addition, increasing social media use (relative to unchanged use) was associated with engagement in more COVID-19 precautionary behaviors (?=.70, P<.001). No associations were found between being an ever-user of social media and engaging in COVID-19 precautionary behaviors. Conclusions: The results demonstrated an association between higher media consumption and greater engagement in COVID-19 precautionary behaviors in older adults. These findings suggest that media can be effectively used as a public health tool for communication of prevention strategies and best practices during future health threats, even among populations who are historically less engaged in certain types of media. UR - https://formative.jmir.org/2023/1/e46230 UR - http://dx.doi.org/10.2196/46230 UR - http://www.ncbi.nlm.nih.gov/pubmed/37213166 ID - info:doi/10.2196/46230 ER - TY - JOUR AU - Quon, M. Cameron AU - Walker, Macey AU - Graves, Lisa PY - 2023/5/17 TI - The Influence of Mass Media on the COVID-19 Vaccination Decision-making Process: Prospective Survey-Based Study JO - J Med Internet Res SP - e45417 VL - 25 KW - accuracy KW - attitudes KW - behavior KW - communication KW - COVID-19 KW - decision-making KW - dissemination KW - efficacy KW - employment KW - mass media KW - reliability KW - safety KW - survey study KW - usage KW - vaccination KW - vaccine hesitancy N2 - Background: Vaccine hesitancy during the COVID-19 pandemic was exacerbated by an infodemic of conflating accurate and inaccurate information with divergent political messages, leading to varying adherence to health-related behaviors. In addition to the media, people received information about COVID-19 and the vaccine from their physicians and closest networks of family and friends. Objective: This study explored individuals? decision-making processes in receiving the COVID-19 vaccine, focusing on the influence of specific media outlets, political orientation, personal networks, and the physician-patient relationship. We also evaluated the effect of other demographic data like age and employment status. Methods: An internet survey was disseminated through the Western Michigan University Homer Stryker MD School of Medicine Facebook account. The survey included questions on media sources for COVID-19 information, political affiliation, presidential candidate choice, and multiple Likert-type agreement scale questions on conceptions of the vaccine. Each respondent was assigned a media source score, which represented the political leaning of their media consumption. This was calculated using a model based on data from the Pew Research Center that assigned an ideological profile to various news outlets. Results: The sample consisted of 1757 respondents, with 89.58% (1574/1757) of them choosing to take the COVID-19 vaccine. Those employed part-time and the unemployed were at 1.94 (95% CI 1.15-3.27) and 2.48 (95% CI 1.43-4.39) greater odds of choosing the vaccine than those employed full-time. For every 1-year increase in age, there was a 1.04 (95% CI 1.02-1.06) multiplicative increase in odds of choosing to receive the vaccine. For every 1-point increase in media source score toward more Liberal or Democrat, there was a 1.06 (95% CI 1.04-1.07) multiplicative increase in odds of choosing to take the COVID-19 vaccine. The Likert-type agreement scale showed statistically significant differences (P<.001) between respondents; those who chose the vaccine agreed more strongly on their belief in the safety and efficacy of vaccines, the influence of their personal beliefs, and the encouragement and positive experiences of family and friends. Most respondents rated their personal relationship with their physician to be good, but this factor did not correlate with differences in vaccine decision. Conclusions: Although multiple factors are involved, the role of mass media in shaping attitudes toward vaccines cannot be ignored, especially its ability to spread misinformation and foster division. Surprisingly, the effect of one?s personal physician may not weigh as heavily in one?s decision-making process, potentially indicating the need for physicians to alter their communication style, including involvement in social media. In the era of information overload, effective communication is critical in ensuring the dissemination of accurate and reliable information to optimize the vaccination decision-making process. UR - https://www.jmir.org/2023/1/e45417 UR - http://dx.doi.org/10.2196/45417 UR - http://www.ncbi.nlm.nih.gov/pubmed/37195740 ID - info:doi/10.2196/45417 ER - TY - JOUR AU - Chen, Liuliu AU - Jeong, Jiwon AU - Simpkins, Bridgette AU - Ferrara, Emilio PY - 2023/5/17 TI - Exploring the Behavior of Users With Attention-Deficit/Hyperactivity Disorder on Twitter: Comparative Analysis of Tweet Content and User Interactions JO - J Med Internet Res SP - e43439 VL - 25 KW - social media KW - mental health KW - attention-deficit/hyperactivity disorder KW - ADHD KW - Twitter KW - behaviors KW - interactions N2 - Background: With the widespread use of social media, people share their real-time thoughts and feelings via interactions on these platforms, including those revolving around mental health problems. This can provide a new opportunity for researchers to collect health-related data to study and analyze mental disorders. However, as one of the most common mental disorders, there are few studies regarding the manifestations of attention-deficit/hyperactivity disorder (ADHD) on social media. Objective: This study aims to examine and identify the different behavioral patterns and interactions of users with ADHD on Twitter through the text content and metadata of their posted tweets. Methods: First, we built 2 data sets: an ADHD user data set containing 3135 users who explicitly reported having ADHD on Twitter and a control data set made up of 3223 randomly selected Twitter users without ADHD. All historical tweets of users in both data sets were collected. We applied mixed methods in this study. We performed Top2Vec topic modeling to extract topics frequently mentioned by users with ADHD and those without ADHD and used thematic analysis to further compare the differences in contents that were discussed by the 2 groups under these topics. We used a distillBERT sentiment analysis model to calculate the sentiment scores for the emotion categories and compared the sentiment intensity and frequency. Finally, we extracted users? posting time, tweet categories, and the number of followers and followings from the metadata of tweets and compared the statistical distribution of these features between ADHD and non-ADHD groups. Results: In contrast to the control group of the non-ADHD data set, users with ADHD tweeted about the inability to concentrate and manage time, sleep disturbance, and drug abuse. Users with ADHD felt confusion and annoyance more frequently, while they felt less excitement, caring, and curiosity (all P<.001). Users with ADHD were more sensitive to emotions and felt more intense feelings of nervousness, sadness, confusion, anger, and amusement (all P<.001). As for the posting characteristics, compared with controls, users with ADHD were more active in posting tweets (P=.04), especially at night between midnight and 6 AM (P<.001); posting more tweets with original content (P<.001); and following fewer people on Twitter (P<.001). Conclusions: This study revealed how users with ADHD behave and interact differently on Twitter compared with those without ADHD. On the basis of these differences, researchers, psychiatrists, and clinicians can use Twitter as a potentially powerful platform to monitor and study people with ADHD, provide additional health care support to them, improve the diagnostic criteria of ADHD, and design complementary tools for automatic ADHD detection. UR - https://www.jmir.org/2023/1/e43439 UR - http://dx.doi.org/10.2196/43439 UR - http://www.ncbi.nlm.nih.gov/pubmed/37195757 ID - info:doi/10.2196/43439 ER - TY - JOUR AU - Wang, Zhaohan AU - He, Jun AU - Jin, Bolin AU - Zhang, Lizhi AU - Han, Chenyu AU - Wang, Meiqi AU - Wang, Hao AU - An, Shuqi AU - Zhao, Meifang AU - Zhen, Qing AU - Tiejun, Shui AU - Zhang, Xinyao PY - 2023/5/16 TI - Using Baidu Index Data to Improve Chickenpox Surveillance in Yunnan, China: Infodemiology Study JO - J Med Internet Res SP - e44186 VL - 25 KW - Baidu index KW - chickenpox KW - support vector machine regression model KW - disease surveillance KW - disease KW - infectious KW - vaccine KW - surveillance system KW - model KW - prevention KW - control KW - monitoring KW - epidemic N2 - Background: Chickenpox is an old but easily neglected infectious disease. Although chickenpox is preventable by vaccines, vaccine breakthroughs often occur, and the chickenpox epidemic is on the rise. Chickenpox is not included in the list of regulated communicable diseases that must be reported and controlled by public and health departments; therefore, it is crucial to rapidly identify and report varicella outbreaks during the early stages. The Baidu index (BDI) can supplement the traditional surveillance system for infectious diseases, such as brucellosis and dengue, in China. The number of reported chickenpox cases and internet search data also showed a similar trend. BDI can be a useful tool to display the outbreak of infectious diseases. Objective: This study aimed to develop an efficient disease surveillance method that uses BDI to assist in traditional surveillance. Methods: Chickenpox incidence data (weekly from January 2017 to June 2021) reported by the Yunnan Province Center for Disease Control and Prevention were obtained to evaluate the relationship between the incidence of chickenpox and BDI. We applied a support vector machine regression (SVR) model and a multiple regression prediction model with BDI to predict the incidence of chickenpox. In addition, we used the SVR model to predict the number of chickenpox cases from June 2021 to the first week of April 2022. Results: The analysis showed that there was a close correlation between the weekly number of newly diagnosed cases and the BDI. In the search terms we collected, the highest Spearman correlation coefficient was 0.747. Most BDI search terms, such as ?chickenpox,? ?chickenpox treatment,? ?treatment of chickenpox,? ?chickenpox symptoms,? and ?chickenpox virus,? trend consistently. Some BDI search terms, such as ?chickenpox pictures,? ?symptoms of chickenpox,? ?chickenpox vaccine,? and ?is chickenpox vaccine necessary,? appeared earlier than the trend of ?chickenpox virus.? The 2 models were compared, the SVR model performed better in all the applied measurements: fitting effect, R2=0.9108, root mean square error (RMSE)=96.2995, and mean absolute error (MAE)=73.3988; and prediction effect, R2=0.548, RMSE=189.1807, and MAE=147.5412. In addition, we applied the SVR model to predict the number of reported cases weekly in Yunnan from June 2021 to April 2022 using the same period of the BDI. The results showed that the fluctuation of the time series from July 2021 to April 2022 was similar to that of the last year and a half with no change in the level of prevention and control. Conclusions: These findings indicated that the BDI in Yunnan Province can predict the incidence of chickenpox in the same period. Thus, the BDI is a useful tool for monitoring the chickenpox epidemic and for complementing traditional monitoring systems. UR - https://www.jmir.org/2023/1/e44186 UR - http://dx.doi.org/10.2196/44186 UR - http://www.ncbi.nlm.nih.gov/pubmed/37191983 ID - info:doi/10.2196/44186 ER - TY - JOUR AU - Pollack, Catherine AU - Gilbert-Diamond, Diane AU - Onega, Tracy AU - Vosoughi, Soroush AU - O'Malley, James A. AU - Emond, A. Jennifer PY - 2023/5/16 TI - Obesity-Related Discourse on Facebook and Instagram Throughout the COVID-19 Pandemic: Comparative Longitudinal Evaluation JO - JMIR Infodemiology SP - e40005 VL - 3 KW - obesity KW - Facebook KW - Instagram KW - COVID-19 KW - social media KW - news KW - infodemiology KW - public health KW - online health information N2 - Background: COVID-19 severity is amplified among individuals with obesity, which may have influenced mainstream media coverage of the disease by both improving understanding of the condition and increasing weight-related stigma. Objective: We aimed to measure obesity-related conversations on Facebook and Instagram around key dates during the first year of the COVID-19 pandemic. Methods: Public Facebook and Instagram posts were extracted for 29-day windows in 2020 around January 28 (the first US COVID-19 case), March 11 (when COVID-19 was declared a global pandemic), May 19 (when obesity and COVID-19 were linked in mainstream media), and October 2 (when former US president Trump contracted COVID-19 and obesity was mentioned most frequently in the mainstream media). Trends in daily posts and corresponding interactions were evaluated using interrupted time series. The 10 most frequent obesity-related topics on each platform were also examined. Results: On Facebook, there was a temporary increase in 2020 in obesity-related posts and interactions on May 19 (posts +405, 95% CI 166 to 645; interactions +294,930, 95% CI 125,986 to 463,874) and October 2 (posts +639, 95% CI 359 to 883; interactions +182,814, 95% CI 160,524 to 205,105). On Instagram, there were temporary increases in 2020 only in interactions on May 19 (+226,017, 95% CI 107,323 to 344,708) and October 2 (+156,974, 95% CI 89,757 to 224,192). Similar trends were not observed in controls. Five of the most frequent topics overlapped (COVID-19, bariatric surgery, weight loss stories, pediatric obesity, and sleep); additional topics specific to each platform included diet fads, food groups, and clickbait. Conclusions: Social media conversations surged in response to obesity-related public health news. Conversations contained both clinical and commercial content of possibly dubious accuracy. Our findings support the idea that major public health announcements may coincide with the spread of health-related content (truthful or otherwise) on social media. UR - https://infodemiology.jmir.org/2023/1/e40005 UR - http://dx.doi.org/10.2196/40005 UR - http://www.ncbi.nlm.nih.gov/pubmed/37191990 ID - info:doi/10.2196/40005 ER - TY - JOUR AU - Xue, Jia AU - Zhang, Bolun AU - Zhang, Qiaoru AU - Hu, Ran AU - Jiang, Jielin AU - Liu, Nian AU - Peng, Yingdong AU - Li, Ziqian AU - Logan, Judith PY - 2023/5/15 TI - Using Twitter-Based Data for Sexual Violence Research: Scoping Review JO - J Med Internet Res SP - e46084 VL - 25 KW - Twitter data KW - sexual violence KW - sexual assault KW - scoping review KW - review method KW - data analysis KW - data collection KW - Twitter KW - social media KW - women?s health KW - violence KW - abuse KW - public health KW - domestic violence N2 - Background: Scholars have used data from in-person interviews, administrative systems, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and underexplored area of study. Objective: We aimed to perform a scoping review of the current literature on using Twitter data for researching sexual violence, elaborate on the validity of the methods, and discuss the implications and limitations of existing studies. Methods: We performed a literature search in the following 6 databases: APA PsycInfo (Ovid), Scopus, PubMed, International Bibliography of Social Sciences (ProQuest), Criminal Justice Abstracts (EBSCO), and Communications Abstracts (EBSCO), in April 2022. The initial search identified 3759 articles that were imported into Covidence. Seven independent reviewers screened these articles following 2 steps: (1) title and abstract screening, and (2) full-text screening. The inclusion criteria were as follows: (1) empirical research, (2) focus on sexual violence, (3) analysis of Twitter data (ie, tweets or Twitter metadata), and (4) text in English. Finally, we selected 121 articles that met the inclusion criteria and coded these articles. Results: We coded and presented the 121 articles using Twitter-based data for sexual violence research. About 70% (89/121, 73.6%) of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million tweets. The primary approaches to using Twitter as a data source were content text analysis (112/121, 92.5%) and sentiment analysis (31/121, 25.6%). Hashtags (103/121, 85.1%) were the most prominent metadata feature, followed by tweet time and date, retweets, replies, URLs, and geotags. More than a third of the articles (51/121, 42.1%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (eg, sentiment analysis, supervised machine learning, unsupervised machine learning, and social network analysis), and quantitative analysis. Only 10.7% (13/121) of the studies discussed ethical considerations. Conclusions: We described the current state of using Twitter data for sexual violence research, developed a new taxonomy describing Twitter as a data source, and evaluated the methodologies. Research recommendations include the following: development of methods for data collection and analysis, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, examination of tweets in multiple languages, and decontextualization of Twitter data. This review demonstrates the potential of using Twitter data in sexual violence research. UR - https://www.jmir.org/2023/1/e46084 UR - http://dx.doi.org/10.2196/46084 UR - http://www.ncbi.nlm.nih.gov/pubmed/37184899 ID - info:doi/10.2196/46084 ER - TY - JOUR AU - Li, Xuan AU - Tang, Kun PY - 2023/5/12 TI - The Effects of Online Health Information?Seeking Behavior on Sexually Transmitted Disease in China: Infodemiology Study of the Internet Search Queries JO - J Med Internet Res SP - e43046 VL - 25 KW - sexually transmitted infections KW - Baidu search index KW - Baidu search rate KW - online health information-seeking behavior KW - long-term effect KW - effect KW - disease KW - internet KW - prevention KW - data KW - treatment KW - surveillance N2 - Background: Sexually transmitted diseases (STDs) are a serious issue worldwide. With the popularity of the internet, online health information-seeking behavior (OHISB) has been widely adopted to improve health and prevent disease. Objective: This study aimed to investigate the short-term and long-term effects of different types of OHISBs on STDs, including syphilis, gonorrhea, and AIDS due to HIV, based on the Baidu index. Methods: Multisource big data were collected, including case numbers of STDs, search queries based on the Baidu index, provincial total population, male-female ratio, the proportion of the population older than 65 years, gross regional domestic product (GRDP), and health institution number data in 2011-2018 in mainland China. We categorized OHISBs into 4 types: concept, symptoms, treatment, and prevention. Before and after controlling for socioeconomic and medical conditions, we applied multiple linear regression to analyze associations between the Baidu search index (BSI) and Baidu search rate (BSR) and STD case numbers. In addition, we compared the effects of 4 types of OHISBs and performed time lag cross-correlation analyses to investigate the long-term effect of OHISB. Results: The distributions of both STD case numbers and OHISBs presented variability. For case number, syphilis, and gonorrhea, cases were mainly distributed in southeastern and northwestern areas of China, while HIV/AIDS cases were mostly distributed in southwestern areas. For the search query, the eastern region had the highest BSI and BSR, while the western region had the lowest ones. For 4 types of OHISB for 3 diseases, the BSI was positively related to the case number, while the BSR was significantly negatively related to the case number (P<.05). Different categories of OHISB have different effects on STD case numbers. Searches for prevention tended to have a larger impact, while searches for treatment tended to have a smaller impact. Besides, due to the time lag effect, those impacts would increase over time. Conclusions: Our study validated the significant associations between 4 types of OHISBs and STD case numbers, and the impact of OHISBs on STDs became stronger over time. It may provide insights into how to use internet big data to better achieve disease surveillance and prevention goals. UR - https://www.jmir.org/2023/1/e43046 UR - http://dx.doi.org/10.2196/43046 UR - http://www.ncbi.nlm.nih.gov/pubmed/37171864 ID - info:doi/10.2196/43046 ER - TY - JOUR AU - Roberts-Lewis, F. Sarah AU - Baxter, A. Helen AU - Mein, Gill AU - Quirke-McFarlane, Sophia AU - Leggat, J. Fiona AU - Garner, M. Hannah AU - Powell, Martha AU - White, Sarah AU - Bearne, Lindsay PY - 2023/5/12 TI - The Use of Social Media for Dissemination of Research Evidence to Health and Social Care Practitioners: Protocol for a Systematic Review JO - JMIR Res Protoc SP - e45684 VL - 12 KW - dissemination KW - health care KW - podcast KW - practitioners KW - research evidence KW - social care KW - social media KW - social networking KW - Twitter KW - videos N2 - Background: Effective dissemination of research to health and social care practitioners enhances clinical practice and evidence-based care. Social media use has potential to facilitate dissemination to busy practitioners. Objective: This is a protocol for a systematic review that will quantitatively synthesize evidence of the effectiveness of social media, compared with no social media, for dissemination of research evidence to health and social care practitioners. Social media platforms, formats, and sharing mechanisms used for effective dissemination of research evidence will also be identified and compared. Methods: Electronic database searches (MEDLINE, PsycINFO, CINAHL, ERIC, LISTA, and OpenGrey) will be conducted from January 1, 2010, to January 10, 2023, for studies published in English. Randomized, nonrandomized, pre-post study designs or case studies evaluating the effect of social media on dissemination of research evidence to postregistration health and social care practitioners will be included. Studies that do not involve social media or dissemination or those that evaluate dissemination of nonresearch information (eg, multisource educational materials) to students or members of the public only, or without quantitative data on outcomes of interest, will be excluded. Screening will be carried out by 2 independent reviewers. Data extraction and quality assessment, using either the Cochrane tool for assessing risk of bias or the Newcastle-Ottawa Scale, will be completed by 2 independent reviewers. Outcomes of interest will be reported in 4 domains (reach, engagement, dissemination, and impact). Data synthesis will include quantitative comparisons using narrative text, tables, and figures. A meta-analysis of standardized pooled effects will be undertaken, and subgroup analyses will be applied, if appropriate. Results: Searches and screening will be completed by the end of May 2023. Data extraction and analyses will be completed by the end of July 2023, after which findings will be synthesized and reported by the end of October 2023. Conclusions: This systematic review will summarize the evidence for the effectiveness of social media for the dissemination of research evidence to health and social care practitioners. The limitations of the evidence may include multiple outcomes or methodological heterogeneity that limit meta-analyses, potential risk of bias in included studies, and potential publication bias. The limitations of the study design may include potential insensitivity of the electronic database search strategy. The findings from this review will inform the dissemination practice of health and care research. Trial Registration: PROSPERO CRD42022378793; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=378793 International Registered Report Identifier (IRRID): DERR1-10.2196/45684 UR - https://www.researchprotocols.org/2023/1/e45684 UR - http://dx.doi.org/10.2196/45684 UR - http://www.ncbi.nlm.nih.gov/pubmed/37171840 ID - info:doi/10.2196/45684 ER - TY - JOUR AU - Maleki, Negar AU - Padmanabhan, Balaji AU - Dutta, Kaushik PY - 2023/5/11 TI - The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis JO - J Med Internet Res SP - e44307 VL - 25 KW - health care analytics KW - social media KW - incentive mechanisms KW - content analysis KW - contrastive topic modeling N2 - Background: While there is high-quality online health information, a lot of recent work has unfortunately highlighted significant issues with the health content on social media platforms (eg, fake news and misinformation), the consequences of which are severe in health care. One solution is to investigate methods that encourage users to post high-quality content. Objective: Incentives have been shown to work in many domains, but until recently, there was no method to provide financial incentives easily on social media for users to generate high-quality content. This study investigates the following question: What effect does the provision of incentives have on the creation of social media health care content? Methods: We analyzed 8328 health-related posts from an incentive-based platform (Steemit) and 1682 health-related posts from a traditional platform (Reddit). Using topic modeling and sentiment analysis?based methods in machine learning, we analyzed these posts across the following 3 dimensions: (1) emotion and language style using the IBM Watson Tone Analyzer service, (2) topic similarity and difference from contrastive topic modeling, and (3) the extent to which posts resemble clickbait. We also conducted a survey using 276 Amazon Mechanical Turk (MTurk) users and asked them to score the quality of Steemit and Reddit posts. Results: Using the Watson Tone Analyzer in a sample of 2000 posts from Steemit and Reddit, we found that more than double the number of Steemit posts had a confident language style compared with Reddit posts (77 vs 30). Moreover, 50% more Steemit posts had analytical content and 33% less Steemit posts had a tentative language style compared with Reddit posts (619 vs 430 and 416 vs 627, respectively). Furthermore, more than double the number of Steemit posts were considered joyful compared with Reddit posts (435 vs 200), whereas negative posts (eg, sadness, fear, and anger) were 33% less on Steemit than on Reddit (384 vs 569). Contrastive topic discovery showed that only 20% (2/10) of topics were common, and Steemit had more unique topics than Reddit (5 vs 3). Qualitatively, Steemit topics were more informational, while Reddit topics involved discussions, which may explain some of the quantitative differences. Manual labeling marked more Steemit headlines as clickbait than Reddit headlines (66 vs 26), and machine learning model labeling consistently identified a higher percentage of Steemit headlines as clickbait than Reddit headlines. In the survey, MTurk users said that at least 57% of Steemit posts had better quality than Reddit posts, and they were at least 52% more likely to like and comment on Steemit posts than Reddit posts. Conclusions: It is becoming increasingly important to ensure high-quality health content on social media; therefore, incentive-based social media could be important in the design of next-generation social platforms for health information. UR - https://www.jmir.org/2023/1/e44307 UR - http://dx.doi.org/10.2196/44307 UR - http://www.ncbi.nlm.nih.gov/pubmed/37166952 ID - info:doi/10.2196/44307 ER - TY - JOUR AU - Bekalu, Awoke Mesfin AU - Sato, Taisuke AU - Viswanath, K. PY - 2023/5/10 TI - Conceptualizing and Measuring Social Media Use in Health and Well-being Studies: Systematic Review JO - J Med Internet Res SP - e43191 VL - 25 KW - social media KW - health KW - well-being KW - conceptualization KW - measurement KW - technology use KW - screen time KW - computer use KW - usage KW - addict N2 - Background: Despite an increasing number of studies revealing both the benefits and harms of social media use on well-being, there is heterogeneity and a lack of consensus on how social media use is conceptualized, defined, and measured. Additionally, little is known whether existing literature focuses on ill-being or well-being outcomes and whether studies use theories. Objective: The main objective of this review was to examine (1) how social media use has been conceptualized and measured, (2) what health and well-being outcomes have been focused on, and (3) whether studies used theories. Methods: Studies were located through a comprehensive search strategy involving 4 steps. First, keyword searches were conducted on 6 major databases: PubMed, Web of Science, PsycINFO, Embase, ProQuest, and Annual Reviews. Second, a search was conducted on Google Scholar using the same sets of search terms, and the first 100 results were examined. Third, the reference sections of reviews identified in the first 2 rounds of searches were examined, and finally, the reference lists of the final set of papers included in the review were searched. Through a multistage screening, papers that met our inclusion criteria were analyzed. Results: The review included a total of 233 papers published between 2007 and 2020 in 51 different countries. While 66 (28%) of the studies investigated the effects of the problematic use or addiction of social media on health and well-being, 167 (72%) studied the effects of social media use as a ?normal? behavior. Most of the studies used measures assessing the time users spend using social media. Most of the studies that examined the effects of problematic social media use or addiction used addiction scales. Most studies examined the association of social media use with mental illnesses such as depression, anxiety, self-esteem, and loneliness. While there are a considerable number of studies investigating physical health outcomes such as self-rated health, sleep, and sitting time or lack of physical activity, relatively a small number of studies examined social, psychological, and emotional well-being. Most of the studies 183 (79%) did not use any theory. Conclusions: Most studies conceptualized social media use as a ?normal? behavior and mostly used time-spent measures, whereas a considerable number of studies conceptualized social media use as an addiction and used various addiction measures. The studies disproportionately focused on investigating the associations of social media use with negative health and well-being outcomes. The findings suggest the need for going beyond time spent to more sophisticated measurement approaches that consider the multiplicity of activities that users perform on social media platforms and the need for more theory-based studies on the association of social media use with not only negative well-being or ?ill-being? but also with positive health and well-being outcomes. UR - https://www.jmir.org/2023/1/e43191 UR - http://dx.doi.org/10.2196/43191 UR - http://www.ncbi.nlm.nih.gov/pubmed/37163319 ID - info:doi/10.2196/43191 ER - TY - JOUR AU - Di Cara, H. Nina AU - Maggio, Valerio AU - Davis, P. Oliver S. AU - Haworth, A. Claire M. PY - 2023/5/8 TI - Methodologies for Monitoring Mental Health on Twitter: Systematic Review JO - J Med Internet Res SP - e42734 VL - 25 KW - social media KW - mental health KW - mental illness KW - machine learning N2 - Background: The use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. However, it is crucial that the methodologies used to create models for this purpose are of high quality from both a mental health and machine learning perspective. Twitter has been a popular choice of social media because of the accessibility of its data, but access to big data sets is not a guarantee of robust results. Objective: This study aims to review the current methodologies used in the literature for predicting mental health outcomes from Twitter data, with a focus on the quality of the underlying mental health data and the machine learning methods used. Methods: A systematic search was performed across 6 databases, using keywords related to mental health disorders, algorithms, and social media. In total, 2759 records were screened, of which 164 (5.94%) papers were analyzed. Information about methodologies for data acquisition, preprocessing, model creation, and validation was collected, as well as information about replicability and ethical considerations. Results: The 164 studies reviewed used 119 primary data sets. There were an additional 8 data sets identified that were not described in enough detail to include, and 6.1% (10/164) of the papers did not describe their data sets at all. Of these 119 data sets, only 16 (13.4%) had access to ground truth data (ie, known characteristics) about the mental health disorders of social media users. The other 86.6% (103/119) of data sets collected data by searching keywords or phrases, which may not be representative of patterns of Twitter use for those with mental health disorders. The annotation of mental health disorders for classification labels was variable, and 57.1% (68/119) of the data sets had no ground truth or clinical input on this annotation. Despite being a common mental health disorder, anxiety received little attention. Conclusions: The sharing of high-quality ground truth data sets is crucial for the development of trustworthy algorithms that have clinical and research utility. Further collaboration across disciplines and contexts is encouraged to better understand what types of predictions will be useful in supporting the management and identification of mental health disorders. A series of recommendations for researchers in this field and for the wider research community are made, with the aim of enhancing the quality and utility of future outputs. UR - https://www.jmir.org/2023/1/e42734 UR - http://dx.doi.org/10.2196/42734 UR - http://www.ncbi.nlm.nih.gov/pubmed/37155236 ID - info:doi/10.2196/42734 ER - TY - JOUR AU - Ondrikova, Nikola AU - Harris, P. John AU - Douglas, Amy AU - Hughes, E. Helen AU - Iturriza-Gomara, Miren AU - Vivancos, Roberto AU - Elliot, J. Alex AU - Cunliffe, A. Nigel AU - Clough, E. Helen PY - 2023/5/8 TI - Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study JO - J Med Internet Res SP - e37540 VL - 25 KW - syndromic data KW - syndromic surveillance KW - surveillance KW - infodemiology KW - norovirus KW - Google Trends KW - Wikipedia KW - prediction KW - variable importance KW - mental model KW - infoveillance KW - trend KW - gastroenteritis KW - gastroenterology KW - gastroenterologist KW - internal medicine KW - viral disease KW - viral KW - virus KW - communicable disease KW - infection prevention KW - infection control KW - infectious disease KW - viral infection KW - disease spread KW - big data KW - Granger causality framework KW - predict KW - model KW - web-based data KW - internet data KW - transmission N2 - Background: Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. Objective: This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. Methods: We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. Results: Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ?65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including ?flu symptoms,? ?norovirus in pregnancy,? and norovirus activity in specific years, such as ?norovirus 2016.? Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. Conclusions: Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information?seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual?s conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies. UR - https://www.jmir.org/2023/1/e37540 UR - http://dx.doi.org/10.2196/37540 UR - http://www.ncbi.nlm.nih.gov/pubmed/37155231 ID - info:doi/10.2196/37540 ER - TY - JOUR AU - Simhadri, Suguna AU - Yalamanchi, Sriha AU - Stone, Sean AU - Srinivasan, Mythily PY - 2023/5/8 TI - Perceptions on Oral Ulcers From Facebook Page Categories: Observational Study JO - JMIR Form Res SP - e45281 VL - 7 KW - oral ulcer KW - internet KW - Facebook KW - information KW - apthous stomatitis KW - cold sore N2 - Background: Oral ulcers are a common condition affecting a considerable proportion of the population, and they are often associated with trauma and stress. They are very painful, and interfere with eating. As they are usually considered an annoyance, people may turn to social media for potential management options. Facebook is one of the most commonly accessed social media platforms and is the primary source of news information, including health information, for a significant percentage of American adults. Given the increasing importance of social media as a source of health information, potential remedies, and prevention strategies, it is essential to understand the type and quality of information available on Facebook regarding oral ulcers. Objective: The goal of our study was to evaluate information on recurrent oral ulcers that can be accessed via the most popular social media network?Facebook. Methods: We performed a keyword search of Facebook pages on 2 consecutive days in March 2022, using duplicate, newly created accounts, and then anonymized all posts. The collected pages were filtered, using predefined criteria to include only English-language pages wherein oral ulcer information was posted by the general public and to exclude pages created by professional dentists, associated professionals, organizations, and academic researchers. The selected pages were then screened for page origin and Facebook categories. Results: Our initial keyword search yielded 517 pages; interestingly however, only 112 (22%) of pages had information relevant to oral ulcers, and 405 (78%) had irrelevant information, with ulcers being mentioned in relation to other parts of the human body. Excluding professional pages and pages without relevant posts resulted in 30 pages, of which 9 (30%) were categorized as ?health/beauty? pages or as ?product/service? pages, 3 (10%) were categorized as ?medical & health? pages, and 5 (17%) were categorized as ?community? pages. Majority of the pages (22/30, 73%) originated from 6 countries; most originated from the United States (7 pages), followed by India (6 pages). There was little information on oral ulcer prevention, long-term treatment, and complications. Conclusions: Facebook, in oral ulcer information dissemination, appears to be primarily used as an adjunct to business enterprises for marketing or for enhancing access to a product. Consequently, it was unsurprising that there was little information on oral ulcer prevention, long-term treatment, and complications. Although we made efforts to identify and select Facebook pages related to oral ulcers, we did not manually verify the authenticity or accuracy of the pages included in our analysis, potentially limiting the reliability of our findings or resulting in bias toward specific products or services. Although this work forms something of a pilot project, we plan to expand the project to encompass text mining for content analysis and include multiple social media platforms in the future. UR - https://formative.jmir.org/2023/1/e45281 UR - http://dx.doi.org/10.2196/45281 UR - http://www.ncbi.nlm.nih.gov/pubmed/37155234 ID - info:doi/10.2196/45281 ER - TY - JOUR AU - Goadsby, Peter AU - Ruiz de la Torre, Elena AU - Constantin, Luminita AU - Amand, Caroline PY - 2023/5/5 TI - Social Media Listening and Digital Profiling Study of People With Headache and Migraine: Retrospective Infodemiology Study JO - J Med Internet Res SP - e40461 VL - 25 KW - brand, headache KW - internet KW - migraine KW - social media KW - social support KW - self-management KW - management KW - digital KW - technology KW - symptoms KW - medicinal treatment KW - treatment KW - Twitter KW - blog KW - Youtube KW - drugs KW - ibuprofen KW - hydration KW - relaxation N2 - Background: There is an unmet need for a better understanding and management of headache, particularly migraine, beyond specialist centers, which may be facilitated using digital technology. Objective: The objective of this study was to identify where, when, and how people with headache and migraine describe their symptoms and the nonpharmaceutical and medicinal treatments used as indicated on social media. Methods: Social media sources, including Twitter, web-based forums, blogs, YouTube, and review sites, were searched using a predefined search string related to headache and migraine. The real-time data from social media posts were collected retrospectively for a 1-year period from January 1, 2018, to December 31, 2018 (Japan), or a 2-year period from January 1, 2017, to December 31, 2018 (Germany and France). The data were analyzed after collection, using content analysis and audience profiling. Results: A total of 3,509,828 social media posts related to headache and migraine were obtained from Japan in 1 year and 146,257 and 306,787 posts from Germany and France, respectively, in 2 years. Among social media sites, Twitter was the most used platform across these countries. Japanese sufferers used specific terminology, such as ?tension headaches? or ?cluster headaches? (36%), whereas French sufferers even mentioned specific migraine types, such as ocular (7%) and aura (2%). The most detailed posts on headache or migraine were from Germany. The French sufferers explicitly mentioned ?headache or migraine attacks? in the ?evening (41%) or morning (38%),? whereas Japanese mentioned ?morning (48%) or night (27%)? and German sufferers mentioned ?evening (22%) or night (41%).? The use of ?generic terms? such as medicine, tablet, and pill were prevalent. The most discussed drugs were ibuprofen and naproxen combination (43%) in Japan; ibuprofen (29%) in Germany; and acetylsalicylic acid, paracetamol, and caffeine combination (75%) in France. The top 3 nonpharmaceutical treatments are hydration, caffeinated beverages, and relaxation methods. Of the sufferers, 44% were between 18 and 24 years of age. Conclusions: In this digital era, social media listening studies present an opportunity to provide unguided, self-reported, sufferers? perceptions in the real world. The generation of social media evidence requires appropriate methodology to translate data into scientific information and relevant medical insights. This social media listening study showed country-specific differences in headache and migraine symptoms experienced and in the times of the day and treatments used. Furthermore, this study highlighted the prevalence of social media usage by younger sufferers compared to that by older sufferers. UR - https://www.jmir.org/2023/1/e40461 UR - http://dx.doi.org/10.2196/40461 UR - http://www.ncbi.nlm.nih.gov/pubmed/37145844 ID - info:doi/10.2196/40461 ER - TY - JOUR AU - Eaton, C. Melissa AU - Probst, C. Yasmine AU - Smith, A. Marc PY - 2023/5/5 TI - Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis JO - JMIR Infodemiology SP - e38245 VL - 3 KW - social media KW - popular diets KW - nutrition KW - public health KW - social network analysis N2 - Background: Social media has transformed the way health messages are communicated. This has created new challenges and ethical considerations while providing a platform to share nutrition information for communities to connect and for information to spread. However, research exploring the web-based diet communities of popular diets is limited. Objective: This study aims to characterize the web-based discourse of popular diets, describe information dissemination, identify influential voices, and explore interactions between community networks and themes of mental health. Methods: This exploratory study used Twitter social media posts for an online social network analysis. Popular diet keywords were systematically developed, and data were collected and analyzed using the NodeXL metrics tool (Social Media Research Foundation) to determine the key network metrics (vertices, edges, cluster algorithms, graph visualization, centrality measures, text analysis, and time-series analytics). Results: The vegan and ketogenic diets had the largest networks, whereas the zone diet had the smallest network. In total, 31.2% (54/173) of the top users endorsed the corresponding diet, and 11% (19/173) claimed a health or science education, which included 1.2% (2/173) of dietitians. Complete fragmentation and hub and spoke messaging were the dominant network structures. In total, 69% (11/16) of the networks interacted, where the ketogenic diet was mentioned most, with depression and anxiety and eating disorder words most prominent in the ?zone diet? network and the least prominent in the ?soy-free,? ?vegan,? ?dairy-free,? and ?gluten-free? diet networks. Conclusions: Social media activity reflects diet trends and provides a platform for nutrition information to spread through resharing. A longitudinal exploration of popular diet networks is needed to further understand the impact social media can have on dietary choices. Social media training is vital, and nutrition professionals must work together as a community to actively reshare evidence-based posts on the web. UR - https://infodemiology.jmir.org/2023/1/e38245 UR - http://dx.doi.org/10.2196/38245 UR - http://www.ncbi.nlm.nih.gov/pubmed/37159259 ID - info:doi/10.2196/38245 ER - TY - JOUR AU - Chopra, Harshita AU - Vashishtha, Aniket AU - Pal, Ridam AU - AU - Tyagi, Ananya AU - Sethi, Tavpritesh PY - 2023/5/2 TI - Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study JO - JMIR Infodemiology SP - e34315 VL - 3 KW - COVID-19 KW - COVID-19 vaccination KW - vaccine hesitancy KW - public health KW - unsupervised word embeddings KW - natural language preprocessing KW - social media KW - Twitter N2 - Background: Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online. Objective: This study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia. Methods: We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories?emotions and influencing factors. Using cosine distance from selected seed words? embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks. Results: Our findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41% to 39% in India. We also observed a significant change (P<.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42% of tweets coming from India and 45% of tweets from the United States represented the ?vaccine_rollout? category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases. Conclusions: By extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions. UR - https://infodemiology.jmir.org/2023/1/e34315 UR - http://dx.doi.org/10.2196/34315 UR - http://www.ncbi.nlm.nih.gov/pubmed/37192952 ID - info:doi/10.2196/34315 ER - TY - JOUR AU - Josey, Maria AU - Gaid, Dina AU - Bishop, D. Lisa AU - Blackwood, Michael AU - Najafizada, Maisam AU - Donnan, R. Jennifer PY - 2023/5/2 TI - The Quality, Readability, and Accuracy of the Information on Google About Cannabis and Driving: Quantitative Content Analysis JO - JMIR Infodemiology SP - e43001 VL - 3 KW - cannabis KW - driving KW - quality KW - readability KW - accuracy KW - public education KW - internet KW - Google search KW - analysis KW - accessibility KW - information KW - evaluation KW - tool KW - data KW - misinterpretation N2 - Background: The public perception of driving under the influence of cannabis (DUIC) is not consistent with current evidence. The internet is an influential source of information available for people to find information about cannabis. Objective: The purpose of this study was to assess the quality, readability, and accuracy of the information about DUIC found on the internet using the Google Canada search engine. Methods: A quantitative content analysis of the top Google search web pages was conducted to analyze the information available to the public about DUIC. Google searches were performed using keywords, and the first 20 pages were selected. Web pages or web-based resources were eligible if they had text on cannabis and driving in English. We assessed (1) the quality of information using the Quality Evaluation Scoring Tool (QUEST) and the presence of the Health on the Net (HON) code; (2) the readability of information using the Gunning Fox Index (GFI), Flesch Reading Ease Scale (FRES), Flesch-Kincaid Grade Level (FKGL), and Simple Measure of Gobbledygook (SMOG) scores; and (3) the accuracy of information pertaining to the effects of cannabis consumption, prevalence of DUIC, DUIC effects on driving ability, risk of collision, and detection by law enforcement using an adapted version of the 5Cs website evaluation tool. Results: A total of 82 web pages were included in the data analysis. The average QUEST score was 17.4 (SD 5.6) out of 28. The average readability scores were 9.7 (SD 2.3) for FKGL, 11.4 (SD 2.9) for GFI, 12.2 (SD 1.9) for SMOG index, and 49.9 (SD 12.3) for FRES. The readability scores demonstrated that 8 (9.8%) to 16 (19.5%) web pages were considered readable by the public. The accuracy results showed that of the web pages that presented information on each key topic, 96% (22/23) of them were accurate about the effects of cannabis consumption; 97% (30/31) were accurate about the prevalence of DUIC; 92% (49/53) were accurate about the DUIC effects on driving ability; 80% (41/51) were accurate about the risk of collision; and 71% (35/49) were accurate about detection by law enforcement. Conclusions: Health organizations should consider health literacy of the public when creating content to help prevent misinterpretation and perpetuate prevailing misperceptions surrounding DUIC. Delivering high quality, readable, and accurate information in a way that is comprehensible to the public is needed to support informed decision-making. UR - https://infodemiology.jmir.org/2023/1/e43001 UR - http://dx.doi.org/10.2196/43001 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/43001 ER - TY - JOUR AU - Movahedi Nia, Zahra AU - Bragazzi, Nicola AU - Asgary, Ali AU - Orbinski, James AU - Wu, Jianhong AU - Kong, Jude PY - 2023/5/1 TI - Mpox Panic, Infodemic, and Stigmatization of the Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual Community: Geospatial Analysis, Topic Modeling, and Sentiment Analysis of a Large, Multilingual Social Media Database JO - J Med Internet Res SP - e45108 VL - 25 KW - monkeypox KW - infectious outbreak KW - infodemic KW - stigma KW - natural language processing KW - sentiment analysis KW - Twitter KW - community KW - discrimination KW - social media KW - virus N2 - Background: The global Mpox (formerly, Monkeypox) outbreak is disproportionately affecting the gay and bisexual men having sex with men community. Objective: The aim of this study is to use social media to study country-level variations in topics and sentiments toward Mpox and Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual (2SLGBTQIAP+)?related topics. Previous infectious outbreaks have shown that stigma intensifies an outbreak. This work helps health officials control fear and stop discrimination. Methods: In total, 125,424 Twitter and Facebook posts related to Mpox and the 2SLGBTQIAP+ community were extracted from May 1 to December 25, 2022, using Twitter application programming interface academic accounts and Facebook-scraper tools. The tweets? main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. The tweets? and Facebook posts? languages were understood using the Twitter application programming interface platform and pycld3 library, respectively. Using ArcGis Online, the hot spots of the geotagged tweets were identified. Mann-Whitney U, ANOVA, and Dunn tests were used to compare the sentiment polarity of different topics and countries. Results: The number of Mpox posts and the number of posts with Mpox and 2SLGBTQIAP+ keywords were 85% correlated (P<.001). Interestingly, the number of posts with Mpox and 2SLGBTQIAP+ keywords had a higher correlation with the number of Mpox cases (correlation=0.36, P<.001) than the number of posts on Mpox (correlation=0.24, P<.001). Of the 10 topics, 8 were aimed at stigmatizing the 2SLGBTQIAP+ community, 3 of which had a significantly lower sentiment score than other topics (ANOVA P<.001). The Mann-Whitney U test shows that negative sentiments have a lower intensity than neutral and positive sentiments (P<.001) and neutral sentiments have a lower intensity than positive sentiments (P<.001). In addition, English sentiments have a higher negative and lower neutral and positive intensities than Spanish and French sentiments (P<.001), and Spanish sentiments have a higher negative and lower positive intensities than French sentiments (P<.001). The hot spots of the tweets with Mpox and 2SLGBTQIAP+ keywords were recognized as the United States, the United Kingdom, Canada, Spain, Portugal, India, Ireland, and Italy. Canada was identified as having more tweets with negative polarity and a lower sentiment score (P<.04). Conclusions: The 2SLGBTQIAP+ community is being widely stigmatized for spreading the Mpox virus on social media. This turns the community into a highly vulnerable population, widens the disparities, increases discrimination, and accelerates the spread of the virus. By identifying the hot spots and key topics of the related tweets, this work helps decision makers and health officials inform more targeted policies. UR - https://www.jmir.org/2023/1/e45108 UR - http://dx.doi.org/10.2196/45108 UR - http://www.ncbi.nlm.nih.gov/pubmed/37126377 ID - info:doi/10.2196/45108 ER - TY - JOUR AU - Li, Jiayu AU - He, Zhiyu AU - Zhang, Min AU - Ma, Weizhi AU - Jin, Ye AU - Zhang, Lei AU - Zhang, Shuyang AU - Liu, Yiqun AU - Ma, Shaoping PY - 2023/4/28 TI - Estimating Rare Disease Incidences With Large-scale Internet Search Data: Development and Evaluation of a Two-step Machine Learning Method JO - JMIR Infodemiology SP - e42721 VL - 3 KW - disease incidence estimation KW - rare disease KW - internet search engine KW - infoveillance KW - deep learning KW - public health N2 - Background: As rare diseases (RDs) receive increasing attention, obtaining accurate RD incidence estimates has become an essential concern in public health. Since RDs are difficult to diagnose, include diverse types, and have scarce cases, traditional epidemiological methods are costly in RD registries. With the development of the internet, users have become accustomed to searching for disease-related information through search engines before seeking medical treatment. Therefore, online search data provide a new source for estimating RD incidences. Objective: The aim of this study was to estimate the incidences of multiple RDs in distinct regions of China with online search data. Methods: Our research scale included 15 RDs in China from 2016 to 2019. The online search data were obtained from Sogou, one of the top 3 commercial search engines in China. By matching to multilevel keywords related to 15 RDs during the 4 years, we retrieved keyword-matched RD-related queries. The queries used before and after the keyword-matched queries formed the basis of the RD-related search sessions. A two-step method was developed to estimate RD incidences with users? intents conveyed by the sessions. In the first step, a combination of long short-term memory and multilayer perceptron algorithms was used to predict whether the intents of search sessions were RD-concerned, news-concerned, or others. The second step utilized a linear regression (LR) model to estimate the incidences of multiple RDs in distinct regions based on the RD- and news-concerned session numbers. For evaluation, the estimated incidences were compared with RD incidences collected from China?s national multicenter clinical database of RDs. The root mean square error (RMSE) and relative error rate (RER) were used as the evaluation metrics. Results: The RD-related online data included 2,749,257 queries and 1,769,986 sessions from 1,380,186 users from 2016 to 2019. The best LR model with sessions as the input estimated the RD incidences with an RMSE of 0.017 (95% CI 0.016-0.017) and an RER of 0.365 (95% CI 0.341-0.388). The best LR model with queries as input had an RMSE of 0.023 (95% CI 0.017-0.029) and an RER of 0.511 (95% CI 0.377-0.645). Compared with queries, using session intents achieved an error decrease of 28.57% in terms of the RER (P=.01). Analysis of different RDs and regions showed that session input was more suitable for estimating the incidences of most diseases (14 of 15 RDs). Moreover, examples focusing on two RDs showed that news-concerned session intents reflected news of an outbreak and helped correct the overestimation of incidences. Experiments on RD types further indicated that type had no significant influence on the RD estimation task. Conclusions: This work sheds light on a novel method for rapid estimation of RD incidences in the internet era, and demonstrates that search session intents were especially helpful for the estimation. The proposed two-step estimation method could be a valuable supplement to the traditional registry for understanding RDs, planning policies, and allocating medical resources. The utilization of search sessions in disease detection and estimation could be transferred to infoveillance of large-scale epidemics or chronic diseases. UR - https://infodemiology.jmir.org/2023/1/e42721 UR - http://dx.doi.org/10.2196/42721 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/42721 ER - TY - JOUR AU - Dupuy-Zini, Alexandre AU - Audeh, Bissan AU - Gérardin, Christel AU - Duclos, Catherine AU - Gagneux-Brunon, Amandine AU - Bousquet, Cedric PY - 2023/4/24 TI - Users? Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts JO - J Med Internet Res SP - e37237 VL - 25 KW - COVID-19 Vaccines KW - Social Media KW - Deep Learning KW - France KW - Sentiment Analysis N2 - Background: Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France. Objective: This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis. Methods: This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets. Results: A set of 69 relevant keywords were identified as the semantic concept of the word ?vaccin? (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users? tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies. Conclusions: Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust. UR - https://www.jmir.org/2023/1/e37237 UR - http://dx.doi.org/10.2196/37237 UR - http://www.ncbi.nlm.nih.gov/pubmed/36596215 ID - info:doi/10.2196/37237 ER - TY - JOUR AU - Lam, Sing Chun AU - Zhou, Keary AU - Loong, Ho-Fung Herbert AU - Chung, Chi-Ho Vincent AU - Ngan, Chun-Kit AU - Cheung, Ting Yin PY - 2023/4/21 TI - The Use of Traditional, Complementary, and Integrative Medicine in Cancer: Data-Mining Study of 1 Million Web-Based Posts From Health Forums and Social Media Platforms JO - J Med Internet Res SP - e45408 VL - 25 KW - traditional KW - complementary KW - integrative KW - social media KW - cancer KW - forums, digital health KW - traditional, complementary, and integrative medicine KW - TCIM KW - perceptions KW - machine learning KW - cancer care N2 - Background: Patients with cancer are increasingly using forums and social media platforms to access health information and share their experiences, particularly in the use of traditional, complementary, and integrative medicine (TCIM). Despite the popularity of TCIM among patients with cancer, few related studies have used data from these web-based sources to explore the use of TCIM among patients with cancer. Objective: This study leveraged multiple forums and social media platforms to explore patients? use, interest, and perception of TCIM for cancer care. Methods: Posts (in English) related to TCIM were collected from Facebook, Twitter, Reddit, and 16 health forums from inception until February 2022. Both manual assessments and natural language processing were performed. Descriptive analyses were performed to explore the most commonly discussed TCIM modalities for each symptom and cancer type. Sentiment analyses were performed to measure the polarity of each post or comment, and themes were identified from posts with positive and negative sentiments. TCIM modalities that are emerging or recommended in the guidelines were identified a priori. Exploratory topic-modeling analyses with latent Dirichlet allocation were conducted to investigate the patients? perceptions of these modalities. Results: Among the 1,620,755 posts available, cancer-related symptoms, such as pain (10/10, 100% cancer types), anxiety and depression (9/10, 90%), and poor sleep (9/10, 90%), were commonly discussed. Cannabis was among the most frequently discussed TCIM modalities for pain in 7 (70%) out of 10 cancer types, as well as nausea and vomiting, loss of appetite, anxiety and depression, and poor sleep. A total of 7 positive and 7 negative themes were also identified. The positive themes included TCIM, making symptoms manageable, and reducing the need for medication and their side effects. The belief that TCIM and conventional treatments were not mutually exclusive and intolerance to conventional treatment may facilitate TCIM use. Conversely, TCIM was viewed as leading to patients? refusal of conventional treatment or delays in diagnosis and treatment. Doctors? ignorance regarding TCIM and the lack of information provided about TCIM may be barriers to its use. Exploratory analyses showed that TCIM recommendations were well discussed among patients; however, these modalities were also used for many other indications. Other notable topics included concerns about the legalization of cannabis, acupressure techniques, and positive experiences of meditation. Conclusions: Using machine learning techniques, social media and health forums provide a valuable resource for patient-generated data regarding the pattern of use and patients? perceptions of TCIM. Such information will help clarify patients? needs and concerns and provide directions for research on integrating TCIM into cancer care. Our results also suggest that effective communication about TCIM should be achieved and that doctors should be more open-minded to actively discuss TCIM use with their patients. UR - https://www.jmir.org/2023/1/e45408 UR - http://dx.doi.org/10.2196/45408 UR - http://www.ncbi.nlm.nih.gov/pubmed/37083752 ID - info:doi/10.2196/45408 ER - TY - JOUR AU - Porras Fimbres, Cristina Denisse AU - Quinn, P. Alyssa AU - Cooper, R. Benjamin AU - Presley, L. Colby AU - Jacobs, Jennifer AU - Rundle, W. Chandler AU - Dellavalle, P. Robert PY - 2023/4/21 TI - Cross-sectional Analysis of Dermatologists and Sponsored Content on TikTok JO - JMIR Dermatol SP - e44413 VL - 6 KW - social media KW - TikTok KW - sponsorship KW - stewardship KW - ethics KW - dermatology KW - dermatologist KW - content analysis UR - https://derma.jmir.org/2023/1/e44413 UR - http://dx.doi.org/10.2196/44413 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632930 ID - info:doi/10.2196/44413 ER - TY - JOUR AU - Choi, Won-Seok AU - Han, Junhee AU - Hong, Ju Hyun PY - 2023/4/20 TI - Association Between Internet Searches Related to Suicide/Self-harm and Adolescent Suicide Death in South Korea in 2016-2020: Secondary Data Analysis JO - J Med Internet Res SP - e46254 VL - 25 KW - adolescent KW - suicide KW - self-mutilation KW - internet KW - search engine KW - Korea KW - suicide death KW - surveillance KW - monitoring KW - internet search N2 - Background: Previous studies have investigated the association between suicide and internet search volumes of terms related to suicide or self-harm. However, the results varied by people?s age, period, and country, and no study has exclusively investigated suicide or self-harm rates among adolescents. Objective: This study aims to determine the association between the internet search volumes of terms related to suicide/self-harm and the number of suicides among South Korean adolescents. We investigated gender differences in this association and the time lag between the internet search volumes of the terms and the connected suicide deaths. Methods: We selected 26 search terms related to suicide and self-harm among South Korean adolescents, and the search volumes of these terms for adolescents aged 13-18 years were obtained from the leading internet search engine in South Korea (Naver Datalab). A data set was constructed by combining data from Naver Datalab and the number of suicide deaths of adolescents on a daily basis from January 1, 2016, to December 31, 2020. Spearman rank correlation and multivariate Poisson regression analyses were performed to identify the association between the search volumes of the terms and the suicide deaths during that period. The time lag between suicide death and the increasing trend in the search volumes of the related terms was estimated from the cross-correlation coefficients. Results: Significant correlations were observed within the search volumes of the 26 terms related to suicide/self-harm. The internet search volumes of several terms were associated with the number of suicide deaths among South Korean adolescents, and this association differed by gender. The search volume for ?dropout? showed a statistically significant correlation with the number of suicides in all adolescent population groups. The correlation between the internet search volume for ?dropout? and the connected suicide deaths was the strongest for a time lag of 0 days. In females, self-harm and academic score showed significant associations with suicide deaths, but academic score showed a negative correlation, and the time lags with the strongest correlations were 0 and ?11 days, respectively. In the total population, self-harm and suicide method were associated with the number of suicides, and the time lags with the strongest correlations were +7 and 0 days, respectively. Conclusions: This study identifies a correlation between suicides and internet search volumes related to suicide/self-harm among South Korean adolescents, but the relatively weak correlation (incidence rate ratio 0.990-1.068) should be interpreted with caution. UR - https://www.jmir.org/2023/1/e46254 UR - http://dx.doi.org/10.2196/46254 UR - http://www.ncbi.nlm.nih.gov/pubmed/37079349 ID - info:doi/10.2196/46254 ER - TY - JOUR AU - Yao, Franzl Lean AU - Ferawati, Kiki AU - Liew, Kongmeng AU - Wakamiya, Shoko AU - Aramaki, Eiji PY - 2023/4/20 TI - Disruptions in the Cystic Fibrosis Community?s Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments JO - J Med Internet Res SP - e45249 VL - 25 KW - COVID-19 KW - Reddit KW - time series analysis KW - BERTopic KW - topic modeling KW - cystic fibrosis N2 - Background: The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods. Objective: This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community?s experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases. Methods: We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of ?1? was assigned to months in 2020 and ?0? otherwise and tested for its statistical significance. Results: A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community?s experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period. Conclusions: There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them. UR - https://www.jmir.org/2023/1/e45249 UR - http://dx.doi.org/10.2196/45249 UR - http://www.ncbi.nlm.nih.gov/pubmed/37079359 ID - info:doi/10.2196/45249 ER - TY - JOUR AU - Zheng, Zihe AU - Xie, Zidian AU - Goniewicz, Maciej AU - Rahman, Irfan AU - Li, Dongmei PY - 2023/4/20 TI - Potential Impact of the COVID-19 Pandemic on Public Perception of Water Pipes on Reddit: Observational Study JO - JMIR Infodemiology SP - e40913 VL - 3 KW - water pipes KW - Reddit KW - COVID-19 KW - COVID-19 pandemic KW - public perception N2 - Background: Socializing is one of the main motivations for water pipe smoking. Restrictions on social gatherings during the COVID-19 pandemic might have influenced water pipe smokers? behaviors. As one of the most popular social media platforms, Reddit has been used to study public opinions and user experiences. Objective: In this study, we aimed to examine the influence of the COVID-19 pandemic on public perception and discussion of water pipe tobacco smoking using Reddit data. Methods: We collected Reddit posts between December 1, 2018, and June 30, 2021, from a Reddit archive (PushShift) using keywords such as ?waterpipe,? ?hookah,? and ?shisha.? We examined the temporal trend in Reddit posts mentioning water pipes and different locations (such as homes and lounges or bars). The temporal trend was further tested using interrupted time series analysis. Sentiment analysis was performed to study the change in sentiment of water pipe?related posts before and during the pandemic. Topic modeling using latent Dirichlet allocation (LDA) was used to examine major topics discussed in water pipe?related posts before and during the pandemic. Results: A total of 45,765 nonpromotion water pipe?related Reddit posts were collected and used for data analysis. We found that the weekly number of Reddit posts mentioning water pipes significantly increased at the beginning of the COVID-19 pandemic (P<.001), and gradually decreased afterward (P<.001). In contrast, Reddit posts mentioning water pipes and lounges or bars showed an opposite trend. Compared to the period before the COVID-19 pandemic, the average number of Reddit posts mentioning lounges or bars was lower at the beginning of the pandemic but gradually increased afterward, while the average number of Reddit posts mentioning the word ?home? remained similar during the COVID-19 pandemic (P=.29). While water pipe?related posts with a positive sentiment were dominant (12,526/21,182, 59.14% before the pandemic; 14,686/24,583, 59.74% after the pandemic), there was no change in the proportion of water pipe?related posts with different sentiments before and during the pandemic (P=.19, P=.26, and P=.65 for positive, negative, and neutral posts, respectively). Most topics related to water pipes on Reddit were similar before and during the pandemic. There were more discussions about the opening and closing of hookah lounges or bars during the pandemic. Conclusions: This study provides a first evaluation of the possible impact of the COVID-19 pandemic on public perceptions of and discussions about water pipes on Reddit. UR - https://infodemiology.jmir.org/2023/1/e40913 UR - http://dx.doi.org/10.2196/40913 UR - http://www.ncbi.nlm.nih.gov/pubmed/37124245 ID - info:doi/10.2196/40913 ER - TY - JOUR AU - Bui, Tam Kim AU - Li, Zoe AU - Dhillon, M. Haryana AU - Kiely, E. Belinda AU - Blinman, Prunella PY - 2023/4/19 TI - Scanxiety Conversations on Twitter: Observational Study JO - JMIR Cancer SP - e43609 VL - 9 KW - anxiety KW - cancer KW - medical imaging KW - oncology KW - psycho-oncology KW - social media KW - twitter KW - tweet KW - scanxiety KW - mental health KW - sentiment analysis KW - thematic analysis KW - screen time KW - scan KW - hyperawareness KW - radiology N2 - Background: Scan-associated anxiety (or ?scanxiety?) is commonly experienced by people having cancer-related scans. Social media platforms such as Twitter provide a novel source of data for observational research. Objective: We aimed to identify posts on Twitter (or ?tweets?) related to scanxiety, describe the volume and content of these tweets, and describe the demographics of users posting about scanxiety. Methods: We manually searched for ?scanxiety? and associated keywords in cancer-related, publicly available, English-language tweets posted between January 2018 and December 2020. We defined ?conversations? as a primary tweet (the first tweet about scanxiety) and subsequent tweets (interactions stemming from the primary tweet). User demographics and the volume of primary tweets were assessed. Conversations underwent inductive thematic and content analysis. Results: A total of 2031 unique Twitter users initiated a conversation about scanxiety from cancer-related scans. Most were patients (n=1306, 64%), female (n=1343, 66%), from North America (n=1130, 56%), and had breast cancer (449/1306, 34%). There were 3623 Twitter conversations, with a mean of 101 per month (range 40-180). Five themes were identified. The first theme was experiences of scanxiety, identified in 60% (2184/3623) of primary tweets, which captured the personal account of scanxiety by patients or their support person. Scanxiety was often described with negative adjectives or similes, despite being experienced differently by users. Scanxiety had psychological, physical, and functional impacts. Contributing factors to scanxiety included the presence and duration of uncertainty, which was exacerbated during the COVID-19 pandemic. The second theme (643/3623, 18%) was the acknowledgment of scanxiety, where users summarized or labeled an experience as scanxiety without providing emotive clarification, and advocacy of scanxiety, where users raised awareness of scanxiety without describing personal experiences. The third theme was messages of support (427/3623, 12%), where users expressed well wishes and encouraged positivity for people experiencing scanxiety. The fourth theme was strategies to reduce scanxiety (319/3623, 9%), which included general and specific strategies for patients and strategies that required improvements in clinical practice by clinicians or health care systems. The final theme was research about scanxiety (50/3623, 1%), which included tweets about the epidemiology, impact, and contributing factors of scanxiety as well as novel strategies to reduce scanxiety. Conclusions: Scanxiety was often a negative experience described by patients having cancer-related scans. Social media platforms like Twitter enable individuals to share their experiences and offer support while providing researchers with unique data to improve their understanding of a problem. Acknowledging scanxiety as a term and increasing awareness of scanxiety is an important first step in reducing scanxiety. Research is needed to guide evidence-based approaches to reduce scanxiety, though some low-cost, low-resource practical strategies identified in this study could be rapidly introduced into clinical care. UR - https://cancer.jmir.org/2023/1/e43609 UR - http://dx.doi.org/10.2196/43609 UR - http://www.ncbi.nlm.nih.gov/pubmed/37074770 ID - info:doi/10.2196/43609 ER - TY - JOUR AU - Kjćrulff, Mřlholm Emilie AU - Andersen, Helms Tue AU - Kingod, Natasja AU - Nexř, Andersen Mette PY - 2023/4/17 TI - When People With Chronic Conditions Turn to Peers on Social Media to Obtain and Share Information: Systematic Review of the Implications for Relationships With Health Care Professionals JO - J Med Internet Res SP - e41156 VL - 25 KW - patient-physician relationship KW - social media KW - internet KW - health information KW - diabetes KW - chronic diseases KW - systematic review KW - information-seeking behavior KW - retrieval KW - sharing N2 - Background: People living with chronic conditions such as diabetes turn to peers on social media to obtain and share information. Although social media use has grown dramatically in the past decade, little is known about its implications for the relationships between people with chronic conditions and health care professionals (HCPs). Objective: We aimed to systematically review the content and quality of studies examining what the retrieval and sharing of information by people with chronic conditions on social media implies for their relationships with HCPs. Methods: We conducted a search of studies in MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), and CINAHL (EBSCO). Eligible studies were primary studies; examined social media use; included adults with any type of diabetes, cardiovascular diseases that are closely linked with diabetes, obesity, hypertension, or dyslipidemia; and reported on the implications for people with chronic conditions?HCP relationships when people with chronic conditions access and share information on social media. We used the Mixed Methods Appraisal Tool version 2018 to assess the quality of the studies, and the included studies were narratively synthesized. Results: Of the 3111 screened studies, 17 (0.55%) were included. Most studies (13/17, 76%) were of low quality. The narrative synthesis identified implications for people with chronic conditions?HCP relationships when people with chronic conditions access and share information on social media, divided into 3 main categories with 7 subcategories. These categories of implications address how the peer interactions of people with chronic conditions on social media can influence their communication with HCPs, how people with chronic conditions discuss advice and medical information from HCPs on social media, and how relationships with HCPs are discussed by people with chronic conditions on social media. The implications are illustrated collectively in a conceptual model. Conclusions: More evidence is needed to draw conclusions, but the findings indicate that the peer interactions of people with chronic conditions on social media are implicated in the ways in which people with chronic conditions equip themselves for clinical consultations, evaluate the information and advice provided by HCPs, and manage their relationships with HCPs. Future populations with chronic conditions will be raised in a digital world, and social media will likely remain a strategy for obtaining support and information. However, the generally low quality of the studies included in this review points to the relatively immature state of research exploring social media and its implications for people with chronic conditions?HCP relationships. Better study designs and methods for conducting research on social media are needed to generate robust evidence. UR - https://www.jmir.org/2023/1/e41156 UR - http://dx.doi.org/10.2196/41156 UR - http://www.ncbi.nlm.nih.gov/pubmed/37067874 ID - info:doi/10.2196/41156 ER - TY - JOUR AU - Wu, Xiaoqian AU - Li, Ziyu AU - Xu, Lin AU - Li, Pengfei AU - Liu, Ming AU - Huang, Cheng PY - 2023/4/14 TI - COVID-19 Vaccine?Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis JO - J Med Internet Res SP - e45051 VL - 25 KW - health belief model KW - COVID-19 vaccines KW - WeChat KW - content analysis KW - topic modeling KW - public health KW - COVID-19 N2 - Background: The COVID-19 vaccine is an effective tool in the fight against the COVID-19 outbreak. As the main channel of information dissemination in the context of the epidemic, social media influences public trust and acceptance of the vaccine. The rational application of health behavior theory is a guarantee of effective public health information dissemination. However, little is known about the application of health behavior theory in web-based COVID-19 vaccine messages, especially from Chinese social media posts. Objective: This study aimed to understand the main topics and communication characteristics of hot papers related to COVID-19 vaccine on the WeChat platform and assess the health behavior theory application with the aid of health belief model (HBM). Methods: A systematic search was conducted on the Chinese social media platform WeChat to identify COVID-19 vaccine?related papers. A coding scheme was established based on the HBM, and the sample was managed and coded using NVivo 12 (QSR International) to assess the application of health behavior theory. The main topics of the papers were extracted through the Latent Dirichlet Allocation algorithm. Finally, temporal analysis was used to explore trends in the evolution of themes and health belief structures in the papers. Results: A total of 757 papers were analyzed. Almost all (671/757, 89%) of the papers did not have an original logo. By topic modeling, 5 topics were identified, which were vaccine development and effectiveness (267/757, 35%), disease infection and protection (197/757, 26%), vaccine safety and adverse reactions (52/757, 7%), vaccine access (136/757, 18%), and vaccination science popularization (105/757, 14%). All papers identified at least one structure in the extended HBM, but only 29 papers included all of the structures. Descriptions of solutions to obstacles (585/757, 77%) and benefit (468/757, 62%) were the most emphasized components in all samples. Relatively few elements of susceptibility (208/757, 27%) and the least were descriptions of severity (135/757, 18%). Heat map visualization revealed the change in health belief structure before and after vaccine entry into the market. Conclusions: To the best of our knowledge, this is the first study to assess the structural expression of health beliefs in information related to the COVID-19 vaccine on the WeChat public platform based on an HBM. The study also identified topics and communication characteristics before and after the market entry of vaccines. Our findings can inform customized education and communication strategies to promote vaccination not only in this pandemic but also in future pandemics. UR - https://www.jmir.org/2023/1/e45051 UR - http://dx.doi.org/10.2196/45051 UR - http://www.ncbi.nlm.nih.gov/pubmed/37058349 ID - info:doi/10.2196/45051 ER - TY - JOUR AU - Handayani, Wuri Putu AU - Zagatti, Augusto Guilherme AU - Kefi, Hajer AU - Bressan, Stéphane PY - 2023/4/13 TI - Impact of Social Media Usage on Users? COVID-19 Protective Behavior: Survey Study in Indonesia JO - JMIR Form Res SP - e46661 VL - 7 KW - COVID-19 KW - pandemic KW - infectious diseases KW - social media KW - trust KW - behavior KW - Indonesia N2 - Background: Social media have become the source of choice for many users to search for health information on COVID-19 despite possible detrimental consequences. Several studies have analyzed the association between health information?searching behavior and mental health. Some of these studies examined users? intentions in searching health information on social media and the impact of social media use on mental health in Indonesia. Objective: This study investigates both active and passive participation in social media, shedding light on cofounding effects from these different forms of engagement. In addition, this study analyses the role of trust in social media platforms and its effect on public health outcomes. Thus, the purpose of this study is to analyze the impact of social media usage on COVID-19 protective behavior in Indonesia. The most commonly used social media platforms are Instagram, Facebook, YouTube, TikTok, and Twitter. Methods: We used primary data from an online survey. We processed 414 answers to a structured questionnaire to evaluate the relationship between these users? active and passive participation in social media, trust in social media, anxiety, self-efficacy, and protective behavior to COVID-19. We modeled the data using partial least square structural equation modeling. Results: This study reveals that social media trust is a crucial antecedent, where trust in social media is positively associated with active contribution and passive consumption of COVID-19 content in social media, users? anxiety, self-efficacy, and protective behavior. This study found that active contribution of content related to COVID-19 on social media is positively correlated with anxiety, while passive participation increases self-efficacy and, in turn, protective behavior. This study also found that active participation is associated with negative health outcomes, while passive participation has the opposite effects. The results of this study can potentially be used for other infectious diseases, for example, dengue fever and diseases that can be transmitted through the air and have handling protocols similar to that of COVID-19. Conclusions: Public health campaigns can use social media for health promotion. Public health campaigns should post positive messages and distil the received information parsimoniously to avoid unnecessary and possibly counterproductive increased anxiety of the users. UR - https://formative.jmir.org/2023/1/e46661 UR - http://dx.doi.org/10.2196/46661 UR - http://www.ncbi.nlm.nih.gov/pubmed/37052987 ID - info:doi/10.2196/46661 ER - TY - JOUR AU - Shepherd, Thomas AU - Robinson, Michelle AU - Mallen, Christian PY - 2023/4/13 TI - Online Health Information Seeking for Mpox in Endemic and Nonendemic Countries: Google Trends Study JO - JMIR Form Res SP - e42710 VL - 7 KW - monkeypox KW - mpox KW - infodemiology: surveillance KW - public health KW - health information seeking KW - Google Trends KW - joinpoint regression KW - epidemic KW - outbreak KW - infectious disease KW - disease KW - online N2 - Background: The recent global outbreak of mpox (monkeypox) has already been declared a public health emergency of international concern by the World Health Organization. Given the health, social, and economic impacts of the COVID-19 pandemic, there is understandable concern and anxiety around the emergence of another infectious disease?especially one about which little is known. Objective: We used Google Trends to explore online health information seeking patterns for mpox in endemic and nonendemic countries and investigated the impact of the publication of the first in-country case on internet search volume. Methods: Google Trends is a publicly accessible and free data source that aggregates worldwide Google search data. Google search data were used as a surrogate measure of online health information seeking for 178 days between February 18 and August 18, 2022. Searching data were downloaded across this time period for nonendemic countries with the highest case count (United States, Spain, Germany, United Kingdom, and France) and 5 endemic countries (Democratic Republic of Congo, Nigeria, Ghana, Central African Republic, and Cameroon). Joinpoint regression analysis was used to measure changes in searching trends for mpox preceding and following the announcement of the first human case. Results: Online health information seeking significantly increased after the publication of the first case in all the nonendemic countries?United States, Spain, Germany, United Kingdom, and France, as illustrated by significant joinpoint regression models. Joinpoint analysis revealed that models with 3 significant joinpoints were the most appropriate fit for these data, where the first joinpoint represents the initial rise in mpox searching trend, the second joinpoint reflects the start of the decrease in the mpox searching trend, and the third joinpoint represents searching trends? return to searching levels prior to the first case announcement. Although this model was also found in 2 endemic countries (ie, Ghana and Nigeria), it was not found in Central African Republic, Democratic Republic of Congo, or Cameroon. Conclusions: Findings demonstrate a surge in online heath information seeking relating to mpox after the first in-country case was publicized in all the nonendemic countries and in Ghana and Nigeria among the endemic counties. The observed increases in mpox searching levels are characterized by sharp but short-lived periods of searching before steep declines back to levels observed prior to the publication of the first case. These findings emphasize the importance of the provision of accurate, relevant online public health information during disease outbreaks. However, online health information seeking behaviors only occur for a short time period, and the provision of accurate information needs to be timely in relation to the publication of new case-related information. UR - https://formative.jmir.org/2023/1/e42710 UR - http://dx.doi.org/10.2196/42710 UR - http://www.ncbi.nlm.nih.gov/pubmed/37052999 ID - info:doi/10.2196/42710 ER - TY - JOUR AU - Lindelöf, Gabriel AU - Aledavood, Talayeh AU - Keller, Barbara PY - 2023/4/12 TI - Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts JO - J Med Internet Res SP - e41319 VL - 25 KW - COVID-19 vaccines KW - SARS-CoV-2 KW - vaccine hesitancy KW - social media KW - Twitter KW - natural language processing KW - machine learning KW - stance detection KW - topic modeling N2 - Background: Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized, as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. A substantial portion of these discussions occurs openly on social media platforms. This allows us to closely monitor the opinions of different groups and their changes over time. Objective: This study investigated posts related to COVID-19 vaccines on Twitter (Twitter Inc) and focused on those that had a negative stance toward vaccines. It examined the evolution of the percentage of negative tweets over time. It also examined the different topics discussed in these tweets to understand the concerns and discussion points of those holding a negative stance toward the vaccines. Methods: A data set of 16,713,238 English tweets related to COVID-19 vaccines was collected, covering the period from March 1, 2020, to July 31, 2021. We used the scikit-learn Python library to apply a support vector machine classifier to identify the tweets with a negative stance toward COVID-19 vaccines. A total of 5163 tweets were used to train the classifier, of which a subset of 2484 tweets was manually annotated by us and made publicly available along with this paper. We used the BERTopic model to extract the topics discussed within the negative tweets and investigate them, including how they changed over time. Results: We showed that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine rollouts. We identified 37 topics of discussion and presented their respective importance over time. We showed that popular topics not only consisted of conspiratorial discussions, such as 5G towers and microchips, but also contained legitimate concerns around vaccination safety and side effects as well as concerns about policies. The most prevalent topic among vaccine-hesitant tweets was related to the use of messenger RNA and fears about its speculated negative effects on our DNA. Conclusions: Hesitancy toward vaccines existed before the COVID-19 pandemic. However, given the dimension of and circumstances surrounding the COVID-19 pandemic, some new areas of hesitancy and negativity toward COVID-19 vaccines have arisen, for example, whether there has been enough time for them to be properly tested. There is also an unprecedented number of conspiracy theories associated with them. Our study shows that even unpopular opinions or conspiracy theories can become widespread when paired with a widely popular discussion topic such as COVID-19 vaccines. Understanding the concerns, the discussed topics, and how they change over time is essential for policy makers and public health authorities to provide better in-time information and policies to facilitate the vaccination of the population in future similar crises. UR - https://www.jmir.org/2023/1/e41319 UR - http://dx.doi.org/10.2196/41319 UR - http://www.ncbi.nlm.nih.gov/pubmed/36877804 ID - info:doi/10.2196/41319 ER - TY - JOUR AU - Murthy, Dhiraj AU - Lee, Juhan AU - Dashtian, Hassan AU - Kong, Grace PY - 2023/4/12 TI - Influence of User Profile Attributes on e-Cigarette?Related Searches on YouTube: Machine Learning Clustering and Classification JO - JMIR Infodemiology SP - e42218 VL - 3 KW - electronic cigarettes KW - electronic nicotine delivery systems KW - ENDS KW - tobacco products KW - YouTube KW - social media KW - minority groups KW - exposure KW - youth KW - behavior KW - user KW - machine learning KW - policy N2 - Background: The proliferation of e-cigarette content on YouTube is concerning because of its possible effect on youth use behaviors. YouTube has a personalized search and recommendation algorithm that derives attributes from a user?s profile, such as age and sex. However, little is known about whether e-cigarette content is shown differently based on user characteristics. Objective: The aim of this study was to understand the influence of age and sex attributes of user profiles on e-cigarette?related YouTube search results. Methods: We created 16 fictitious YouTube profiles with ages of 16 and 24 years, sex (female and male), and ethnicity/race to search for 18 e-cigarette?related search terms. We used unsupervised (k-means clustering and classification) and supervised (graph convolutional network) machine learning and network analysis to characterize the variation in the search results of each profile. We further examined whether user attributes may play a role in e-cigarette?related content exposure by using networks and degree centrality. Results: We analyzed 4201 nonduplicate videos. Our k-means clustering suggested that the videos could be clustered into 3 categories. The graph convolutional network achieved high accuracy (0.72). Videos were classified based on content into 4 categories: product review (49.3%), health information (15.1%), instruction (26.9%), and other (8.5%). Underage users were exposed mostly to instructional videos (37.5%), with some indication that more female 16-year-old profiles were exposed to this content, while young adult age groups (24 years) were exposed mostly to product review videos (39.2%). Conclusions: Our results indicate that demographic attributes factor into YouTube?s algorithmic systems in the context of e-cigarette?related queries on YouTube. Specifically, differences in the age and sex attributes of user profiles do result in variance in both the videos presented in YouTube search results as well as in the types of these videos. We find that underage profiles were exposed to e-cigarette content despite YouTube?s age-restriction policy that ostensibly prohibits certain e-cigarette content. Greater enforcement of policies to restrict youth access to e-cigarette content is needed. UR - https://infodemiology.jmir.org/2023/1/e42218 UR - http://dx.doi.org/10.2196/42218 UR - http://www.ncbi.nlm.nih.gov/pubmed/37124246 ID - info:doi/10.2196/42218 ER - TY - JOUR AU - Shan, Yi AU - Ji, Meng AU - Xing, Zhaoquan AU - Dong, Zhaogang AU - Xu, Xiaofei PY - 2023/4/5 TI - Susceptibility to Breast Cancer Misinformation Among Chinese Patients: Cross-sectional Study JO - JMIR Form Res SP - e42782 VL - 7 KW - susceptibility KW - breast cancer misinformation KW - Chinese patients KW - logistic regression KW - predicting factors KW - cancer KW - misinformation KW - China KW - breast cancer KW - policy KW - age KW - gender KW - education KW - literacy KW - clinical N2 - Background: Currently, breast cancer is the most commonly diagnosed cancer and the sixth-leading cause of cancer-related deaths among Chinese women. Worse still, misinformation contributes to the aggravation of the breast cancer burden in China. There is a pressing need to investigate the susceptibility to breast cancer misinformation among Chinese patients. However, no study has been performed in this respect. Objective: This study aims to ascertain whether some demographics (age, gender, and education), some health literacy skills, and the internal locus of control are significantly associated with the susceptibility to misinformation about all types of breast cancers among randomly sampled Chinese patients of both genders in order to provide insightful implications for clinical practice, health education, medical research, and health policy making. Methods: We first designed a questionnaire comprising 4 sections of information: age, gender, and education (section 1); self-assessed disease knowledge (section 2); the All Aspects of Health Literacy Scale (AAHLS), the eHealth Literacy Scale (eHEALS), the 6-item General Health Numeracy Test (GHNT-6), and the ?Internal? subscale of the Multidimensional Health Locus of Control (MHLC) scales (section 3); and 10 breast cancer myths collected from some officially registered and authenticated websites (section 4). Subsequently, we recruited patients from Qilu Hospital of Shandong University, China, using randomized sampling. The questionnaire was administered via wenjuanxing, the most popular online survey platform in China. The collected data were manipulated in a Microsoft Excel file. We manually checked the validity of each questionnaire using the predefined validity criterion. After that, we coded all valid questionnaires according to the predefined coding scheme, based on Likert scales of different point (score) ranges for different sections of the questionnaire. In the subsequent step, we calculated the sums of the subsections of the AAHLS and the sums of the 2 health literacy scales (the eHEALS and GHNT-6) and the 10 breast cancer myths. Finally, we applied logistic regression modeling to relate the scores in section 4 to the scores in sections 1-3 of the questionnaire to identify what significantly contributes to the susceptibility to breast cancer misinformation among Chinese patients. Results: All 447 questionnaires collected were valid according to the validity criterion. The participants were aged 38.29 (SD 11.52) years on average. The mean score for their education was 3.68 (SD 1.46), implying that their average educational attainment was between year 12 and a diploma (junior college). Of the 447 participants, 348 (77.85%) were women. The mean score for their self-assessed disease knowledge was 2.50 (SD 0.92), indicating that their self-assessed disease knowledge status was between ?knowing a lot? and ?knowing some.? The mean scores of the subconstructs in the AAHLS were 6.22 (SD 1.34) for functional health literacy, 5.22 (SD 1.54) for communicative health literacy, and 11.19 (SD 1.99) for critical health literacy. The mean score for eHealth literacy was 24.21 (SD 5.49). The mean score for the 6 questions in the GHNT-6 was 1.57 (SD 0.49), 1.21 (SD 0.41), 1.24 (SD 0.43), 1.90 (SD 0.30), 1.82 (SD 0.39), and 1.73 (SD 0.44), respectively. The mean score for the patients? health beliefs and self-confidence was 21.19 (SD 5.63). The mean score for their response to each myth ranged from 1.24 (SD 0.43) to 1.67 (SD 0.47), and the mean score for responses to the 10 myths was 14.03 (SD 1.78). Through interpreting these descriptive statistics, we found that Chinese female patients? limited ability to rebut breast cancer misinformation is mainly attributed to 5 factors: (1) lower communicative health literacy, (2) certainty about self-assessed eHealth literacy skills, (3) lower general health numeracy, (4) positive self-assessment of general disease knowledge, and (5) more negative health beliefs and lower levels of self-confidence. Conclusions: Drawing on logistic regression modeling, we studied the susceptibility to breast cancer misinformation among Chinese patients. The predicting factors of the susceptibility to breast cancer misinformation identified in this study can provide insightful implications for clinical practice, health education, medical research, and health policy making. UR - https://formative.jmir.org/2023/1/e42782 UR - http://dx.doi.org/10.2196/42782 UR - http://www.ncbi.nlm.nih.gov/pubmed/37018020 ID - info:doi/10.2196/42782 ER - TY - JOUR AU - Zhu, Jianghong AU - Li, Zepeng AU - Zhang, Xiu AU - Zhang, Zhenwen AU - Hu, Bin PY - 2023/4/4 TI - Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis JO - J Med Internet Res SP - e45777 VL - 25 KW - anxiety disorder KW - linguistic feature KW - topic model KW - public attitude KW - social media N2 - Background: Anxiety disorder has become a major clinical and public health problem, causing a significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, and social activities of people with anxiety disorder. Objective: The purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts. Methods: From April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword ?anxiety disorder? were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users? attitudes toward anxiety. Results: The changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R2=0.6512; P<.001 to R2=0.8133; P<.001, respectively) and were greatly impacted by the beginning of a new semester (spring/fall). The analysis of linguistic features showed that the frequency of the cognitive process (R2=0.1782; P=.003), perceptual process (R2=0.1435; P=.008), biological process (R2=0.3225; P<.001), and assent words (R2=0.4412; P<.001) increased significantly over time, while the frequency of the social process words (R2=0.2889; P<.001) decreased significantly, and public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with those of other psychological words. Semantic content analysis identified 5 common topical areas: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. Our results showed that the occurrence probability of the topical area ?discrimination and stigma? reached the highest value and averagely accounted for 26.66% in the 4-year period. The occurrence probability of the topical area ?family and life? (R2=0.1888; P=.09) decreased over time, while that of the other 4 topical areas increased. Conclusions: The findings of our study indicate that public discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma. UR - https://www.jmir.org/2023/1/e45777 UR - http://dx.doi.org/10.2196/45777 UR - http://www.ncbi.nlm.nih.gov/pubmed/37014691 ID - info:doi/10.2196/45777 ER - TY - JOUR AU - Ahmed, Wasim AU - Das, Ronnie AU - Vidal-Alaball, Josep AU - Hardey, Mariann AU - Fuster-Casanovas, Aďna PY - 2023/3/31 TI - Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets JO - J Med Internet Res SP - e43497 VL - 25 KW - COVID-19 KW - coronavirus KW - Twitter KW - social network analysis KW - misinformation KW - online social capital N2 - Background: The popularity of the magnetic vaccine conspiracy theory and other conspiracy theories of a similar nature creates challenges to promoting vaccines and disseminating accurate health information. Objective: Health conspiracy theories are gaining in popularity. This study's objective was to evaluate the Twitter social media network related to the magnetic vaccine conspiracy theory and apply social capital theory to analyze the unique social structures of influential users. As a strategy for web-based public health surveillance, we conducted a social network analysis to identify the important opinion leaders sharing the conspiracy, the key websites, and the narratives. Methods: A total of 18,706 tweets were retrieved and analyzed by using social network analysis. Data were retrieved from June 1 to June 13, 2021, using the keyword vaccine magnetic. Tweets were retrieved via a dedicated Twitter application programming interface. More specifically, the Academic Track application programming interface was used, and the data were analyzed by using NodeXL Pro (Social Media Research Foundation) and Gephi. Results: There were a total of 22,762 connections between Twitter users within the data set. This study found that the most influential user within the network consisted of a news account that was reporting on the magnetic vaccine conspiracy. There were also several other users that became influential, such as an epidemiologist, a health economist, and a retired sports athlete who exerted their social capital within the network. Conclusions: Our study found that influential users were effective broadcasters against the conspiracy, and their reach extended beyond their own networks of Twitter followers. We emphasize the need for trust in influential users with regard to health information, particularly in the context of the widespread social uncertainty resulting from the COVID-19 pandemic, when public sentiment on social media may be unpredictable. This study highlights the potential of influential users to disrupt information flows of conspiracy theories via their unique social capital. UR - https://www.jmir.org/2023/1/e43497 UR - http://dx.doi.org/10.2196/43497 UR - http://www.ncbi.nlm.nih.gov/pubmed/36927550 ID - info:doi/10.2196/43497 ER - TY - JOUR AU - Agley, Jon AU - Xiao, Yunyu AU - Thompson, E. Esi AU - Golzarri-Arroyo, Lilian PY - 2023/3/30 TI - Using Normative Language When Describing Scientific Findings: Randomized Controlled Trial of Effects on Trust and Credibility JO - J Med Internet Res SP - e45482 VL - 25 KW - trust KW - trust in science KW - scientific communication KW - meta-science KW - RCT N2 - Background: Scientists often make cognitive claims (eg, the results of their work) and normative claims (eg, what should be done based on those results). Yet, these types of statements contain very different information and implications. This randomized controlled trial sought to characterize the granular effects of using normative language in science communication. Objective: Our study examined whether viewing a social media post containing scientific claims about face masks for COVID-19 using both normative and cognitive language (intervention arm) would reduce perceptions of trust and credibility in science and scientists compared with an identical post using only cognitive language (control arm). We also examined whether effects were mediated by political orientation. Methods: This was a 2-arm, parallel group, randomized controlled trial. We aimed to recruit 1500 US adults (age 18+) from the Prolific platform who were representative of the US population census by cross sections of age, race/ethnicity, and gender. Participants were randomly assigned to view 1 of 2 images of a social media post about face masks to prevent COVID-19. The control image described the results of a real study (cognitive language), and the intervention image was identical, but also included recommendations from the same study about what people should do based on the results (normative language). Primary outcomes were trust in science and scientists (21-item scale) and 4 individual items related to trust and credibility; 9 additional covariates (eg, sociodemographics, political orientation) were measured and included in analyses. Results: From September 4, 2022, to September 6, 2022, 1526 individuals completed the study. For the sample as a whole (eg, without interaction terms), there was no evidence that a single exposure to normative language affected perceptions of trust or credibility in science or scientists. When including the interaction term (study arm × political orientation), there was some evidence of differential effects, such that individuals with liberal political orientation were more likely to trust scientific information from the social media post?s author if the post included normative language, and political conservatives were more likely to trust scientific information from the post?s author if the post included only cognitive language (?=0.05, 95% CI 0.00 to 0.10; P=.04). Conclusions: This study does not support the authors? original hypotheses that single exposures to normative language can reduce perceptions of trust or credibility in science or scientists for all people. However, the secondary preregistered analyses indicate the possibility that political orientation may differentially mediate the effect of normative and cognitive language from scientists on people?s perceptions. We do not submit this paper as definitive evidence thereof but do believe that there is sufficient evidence to support additional research into this topic, which may have implications for effective scientific communication. Trial Registration: OSF Registries osf.io/kb3yh; https://osf.io/kb3yh International Registered Report Identifier (IRRID): RR2-10.2196/41747 UR - https://www.jmir.org/2023/1/e45482 UR - http://dx.doi.org/10.2196/45482 UR - http://www.ncbi.nlm.nih.gov/pubmed/36995753 ID - info:doi/10.2196/45482 ER - TY - JOUR AU - Jones, Ffion Leah AU - Bonfield, Stefanie AU - Farrell, Jade AU - Weston, Dale PY - 2023/3/29 TI - Understanding the Public?s Attitudes Toward COVID-19 Vaccines in Nottinghamshire, United Kingdom: Qualitative Social Media Analysis JO - J Med Internet Res SP - e38404 VL - 25 KW - COVID-19 KW - vaccine KW - social media KW - qualitative KW - vaccine hesitancy KW - infodemic KW - misinformation KW - infodemiology KW - online health information KW - content analysis KW - Facebook KW - Twitter KW - transmission N2 - Background: COVID-19 vaccines remain central to the UK government?s plan for tackling the COVID-19 pandemic. Average uptake of 3 doses in the United Kingdom stood at 66.7% as of March 2022; however, this rate varies across localities. Understanding the views of groups who have low vaccine uptake is crucial to guide efforts to improve vaccine uptake. Objective: This study aims to understand the public?s attitudes toward COVID-19 vaccines in Nottinghamshire, United Kingdom. Methods: A qualitative thematic analysis of social media posts from Nottinghamshire-based profiles and data sources was conducted. A manual search strategy was used to search the Nottingham Post website and local Facebook and Twitter accounts from September 2021 to October 2021. Only comments in the public domain and in English were included in the analysis. Results: A total of 3508 comments from 1238 users on COVID-19 vaccine posts by 10 different local organizations were analyzed, and 6 overarching themes were identified: trust in the vaccines, often characterized by a lack of trust in vaccine information, information sources including the media, and the government; beliefs about safety including doubts about the speed of development and approval process, the severity of side effects, and belief that the ingredients are harmful; belief that the vaccines are not effective as people can still become infected and spread the virus and that the vaccines may increase transmission through shedding; belief that the vaccines are not necessary due to low perceived risk of death and severe outcomes and use of other protective measures such as natural immunity, ventilation, testing, face coverings, and self-isolation; individual rights and freedoms to be able to choose to be vaccinated or not without judgement or discrimination; and barriers to physical access. Conclusions: The findings revealed a wide range of beliefs and attitudes toward COVID-19 vaccination. Implications for the vaccine program in Nottinghamshire include communication strategies delivered by trusted sources to address the gaps in knowledge identified while acknowledging some negatives such as side effects alongside emphasizing the benefits. These strategies should avoid perpetuating myths and avoid using scare tactics when addressing risk perceptions. Accessibility should also be considered with a review of current vaccination site locations, opening hours, and transport links. Additional research may benefit from using qualitative interviews or focus groups to further probe on the themes identified and explore the acceptability of the recommended interventions. UR - https://www.jmir.org/2023/1/e38404 UR - http://dx.doi.org/10.2196/38404 UR - http://www.ncbi.nlm.nih.gov/pubmed/36812390 ID - info:doi/10.2196/38404 ER - TY - JOUR AU - Beauchamp, M. Alaina AU - Lehmann, U. Christoph AU - Medford, J. Richard AU - Hughes, E. Amy PY - 2023/3/27 TI - The Association of a Geographically Wide Social Media Network on Depression: County-Level Ecological Analysis JO - J Med Internet Res SP - e43623 VL - 25 KW - Facebook KW - social connectedness KW - depression KW - county-level analysis KW - social media KW - mental health KW - research KW - ecological KW - geography KW - GIS N2 - Background: Social connectedness decreases human mortality, improves cancer survival, cardiovascular health, and body mass, results in better-controlled glucose levels, and strengthens mental health. However, few public health studies have leveraged large social media data sets to classify user network structure and geographic reach rather than the sole use of social media platforms. Objective: The objective of this study was to determine the association between population-level digital social connectedness and reach and depression in the population across geographies of the United States. Methods: Our study used an ecological assessment of aggregated, cross-sectional population measures of social connectedness, and self-reported depression across all counties in the United States. This study included all 3142 counties in the contiguous United States. We used measures obtained between 2018 and 2020 for adult residents in the study area. The study?s main exposure of interest is the Social Connectedness Index (SCI), a pair-wise composite index describing the ?strength of connectedness between 2 geographic areas as represented by Facebook friendship ties.? This measure describes the density and geographical reach of average county residents? social network using Facebook friendships and can differentiate between local and long-distance Facebook connections. The study?s outcome of interest is self-reported depressive disorder as published by the Centers for Disease Control and Prevention. Results: On average, 21% (21/100) of all adult residents in the United States reported a depressive disorder. Depression frequency was the lowest for counties in the Northeast (18.6%) and was highest for southern counties (22.4%). Social networks in northeastern counties involved moderately local connections (SCI 5-10 the 20th percentile for n=70, 36% of counties), whereas social networks in Midwest, southern, and western counties contained mostly local connections (SCI 1-2 the 20th percentile for n=598, 56.7%, n=401, 28.2%, and n=159, 38.4%, respectively). As the quantity and distance that social connections span (ie, SCI) increased, the prevalence of depressive disorders decreased by 0.3% (SE 0.1%) per rank. Conclusions: Social connectedness and depression showed, after adjusting for confounding factors such as income, education, cohabitation, natural resources, employment categories, accessibility, and urbanicity, that a greater social connectedness score is associated with a decreased prevalence of depression. UR - https://www.jmir.org/2023/1/e43623 UR - http://dx.doi.org/10.2196/43623 UR - http://www.ncbi.nlm.nih.gov/pubmed/36972109 ID - info:doi/10.2196/43623 ER - TY - JOUR AU - Renner, Simon AU - Loussikian, Paul AU - Foulquié, Pierre AU - Marrel, Alexia AU - Barbier, Valentin AU - Mebarki, Adel AU - Schück, Stéphane AU - Bharmal, Murtuza PY - 2023/3/27 TI - Patient and Caregiver Perceptions of Advanced Bladder Cancer Systemic Treatments: Infodemiology Study Based on Social Media Data JO - JMIR Cancer SP - e45011 VL - 9 KW - bladder cancer KW - social media KW - patient KW - caregiver KW - chemotherapy KW - immunotherapy KW - qualitative research KW - cancer treatment KW - first-line therapy KW - patient support KW - adverse event KW - peer support KW - cancer KW - oncology KW - perception KW - pharmacotherapy KW - opinion KW - attitude N2 - Background: In 2022, it was estimated that more than 80,000 new cases of bladder cancer (BC) were diagnosed in the United States, 12% of which were locally advanced or metastatic BC (advanced BC). These forms of cancer are aggressive and have a poor prognosis, with a 5-year survival rate of 7.7% for metastatic BC. Despite recent therapeutic advances for advanced BC, little is known about patient and caregiver perceptions of different systemic treatments. To further explore this topic, social media can be used to collect the perceptions of patients and caregivers when they discuss their experiences on forums and online communities. Objective: The aim of this study was to assess patient and caregiver perceptions of chemotherapy and immunotherapy for treating advanced BC from social media?posted data. Methods: Public posts on social media in the United States between January 2015 and April 2021 from patients with advanced BC and their caregivers were collected. The posts included in this analysis were geolocalized to the United States; collected from publicly available domains and sites, including social media sites such as Twitter and forums such as patient association forums; and were written in English. Posts mentioning any line of chemotherapy or immunotherapy were qualitatively analyzed by two researchers to classify perceptions of treatments (positive, negative, mixed, or without perception). Results: A total of 80 posts by 69 patients and 142 posts by 127 caregivers mentioning chemotherapy, and 42 posts by 31 patients and 35 posts by 32 caregivers mentioning immunotherapy were included for analysis. These posts were retrieved from 39 public social media sites. Among patients with advanced BC and their caregivers, treatment perceptions of chemotherapy were more negative (36%) than positive (7%). Most of the patients? posts (71%) mentioned chemotherapy factually without expressing a perception of the treatment. The caregivers? perceptions of treatment were negative in 44%, mixed in 8%, and positive in 7% of posts. In combined patient and caregiver posts, immunotherapy was perceived positively in 47% of posts and negatively in 22% of posts. Caregivers also posted more negative perceptions (37%) of immunotherapy than patients (9%). Negative perceptions of both chemotherapy and immunotherapy were mainly due to side effects and perceived lack of effectiveness. Conclusions: Despite chemotherapy being standard first-line therapy for advanced BC, negative perceptions were identified on social media, particularly among caregivers. Addressing these negative perceptions of treatment may improve treatment adoption. Strengthening support for patients receiving chemotherapy and their caregivers to help them manage side effects and understand the role of chemotherapy in the treatment of advanced BC would potentially enable a more positive experience. UR - https://cancer.jmir.org/2023/1/e45011 UR - http://dx.doi.org/10.2196/45011 UR - http://www.ncbi.nlm.nih.gov/pubmed/36972135 ID - info:doi/10.2196/45011 ER - TY - JOUR AU - Li, Yue AU - Gee, William AU - Jin, Kun AU - Bond, Robert PY - 2023/3/23 TI - Examining Homophily, Language Coordination, and Analytical Thinking in Web-Based Conversations About Vaccines on Reddit: Study Using Deep Neural Network Language Models and Computer-Assisted Conversational Analyses JO - J Med Internet Res SP - e41882 VL - 25 KW - vaccine hesitancy KW - social media KW - web-based conversations KW - neural network language models KW - computer-assisted conversational analyses N2 - Background: Vaccine hesitancy has been deemed one of the top 10 threats to global health. Antivaccine information on social media is a major barrier to addressing vaccine hesitancy. Understanding how vaccine proponents and opponents interact with each other on social media may help address vaccine hesitancy. Objective: We aimed to examine conversations between vaccine proponents and opponents on Reddit to understand whether homophily in web-based conversations impedes opinion exchange, whether people are able to accommodate their languages to each other in web-based conversations, and whether engaging with opposing viewpoints stimulates higher levels of analytical thinking. Methods: We analyzed large-scale conversational text data about human vaccines on Reddit from 2016 to 2018. Using deep neural network language models and computer-assisted conversational analyses, we obtained each Redditor?s stance on vaccines, each post?s stance on vaccines, each Redditor?s language coordination score, and each post or comment?s analytical thinking score. We then performed chi-square tests, 2-tailed t tests, and multilevel modeling to test 3 questions of interest. Results: The results show that both provaccine and antivaccine Redditors are more likely to selectively respond to Redditors who indicate similar views on vaccines (P<.001). When Redditors interact with others who hold opposing views on vaccines, both provaccine and antivaccine Redditors accommodate their language to out-group members (provaccine Redditors: P=.044; antivaccine Redditors: P=.047) and show no difference in analytical thinking compared with interacting with congruent views (P=.63), suggesting that Redditors do not engage in motivated reasoning. Antivaccine Redditors, on average, showed higher analytical thinking in their posts and comments than provaccine Redditors (P<.001). Conclusions: This study shows that although vaccine proponents and opponents selectively communicate with their in-group members on Reddit, they accommodate their language and do not engage in motivated reasoning when communicating with out-group members. These findings may have implications for the design of provaccine campaigns on social media. UR - https://www.jmir.org/2023/1/e41882 UR - http://dx.doi.org/10.2196/41882 UR - http://www.ncbi.nlm.nih.gov/pubmed/36951921 ID - info:doi/10.2196/41882 ER - TY - JOUR AU - Gilbert, James Barnabas AU - Lu, Chunling AU - Yom-Tov, Elad PY - 2023/3/22 TI - Tracking Population-Level Anxiety Using Search Engine Data: Ecological Study JO - JMIR Form Res SP - e44055 VL - 7 KW - anxiety disorders KW - anxiety themes KW - Bing search KW - country-level KW - epidemiology KW - Google trends KW - internet search data KW - mental disorder KW - search engine KW - socioeconomic N2 - Background: Anxiety disorders are the most prevalent mental disorders globally, with a substantial impact on quality of life. The prevalence of anxiety disorders has increased substantially following the COVID-19 pandemic, and it is likely to be further affected by a global economic recession. Understanding anxiety themes and how they change over time and across countries is crucial for preventive and treatment strategies. Objective: The aim of this study was to track the trends in anxiety themes between 2004 and 2020 in the 50 most populous countries with high volumes of internet search data. This study extends previous research by using a novel search-based methodology and including a longer time span and more countries at different income levels. Methods: We used a crowdsourced questionnaire, alongside Bing search query data and Google Trends search volume data, to identify themes associated with anxiety disorders across 50 countries from 2004 to 2020. We analyzed themes and their mutual interactions and investigated the associations between countries? socioeconomic attributes and anxiety themes using time-series linear models. This study was approved by the Microsoft Research Institutional Review Board. Results: Query volume for anxiety themes was highly stable in countries from 2004 to 2019 (Spearman r=0.89) and moderately correlated with geography (r=0.49 in 2019). Anxiety themes were predominantly long-term and personal, with ?having kids,? ?pregnancy,? and ?job? the most voluminous themes in most countries and years. In 2020, ?COVID-19? became a dominant theme in 27 countries. Countries with a constant volume of anxiety themes over time had lower fragile state indexes (P=.007) and higher individualism (P=.003). An increase in the volume of the most searched anxiety themes was associated with a reduction in the volume of the remaining themes in 13 countries and an increase in 17 countries, and these 30 countries had a lower prevalence of mental disorders (P<.001) than the countries where no correlations were found. Conclusions: Internet search data could be a potential source for predicting the country-level prevalence of anxiety disorders, especially in understudied populations or when an in-person survey is not viable. UR - https://formative.jmir.org/2023/1/e44055 UR - http://dx.doi.org/10.2196/44055 UR - http://www.ncbi.nlm.nih.gov/pubmed/36947130 ID - info:doi/10.2196/44055 ER - TY - JOUR AU - Almomani, Hamzeh AU - Patel, Nilesh AU - Donyai, Parastou PY - 2023/3/21 TI - News Media Coverage of the Problem of Purchasing Fake Prescription Medicines on the Internet: Thematic Analysis JO - JMIR Form Res SP - e45147 VL - 7 KW - prescription medicine KW - internet KW - online pharmacy KW - fake medicine KW - media KW - newspaper article KW - Theory of Planned Behavior KW - thematic analysis N2 - Background: More people are turning to internet pharmacies to purchase their prescription medicines. This kind of purchase is associated with serious risks, including the risk of buying fake medicines, which are widely available on the internet. This underresearched issue has been highlighted by many newspaper articles in the past few years. Newspapers can play an important role in shaping public perceptions of the risks associated with purchasing prescription medicines on the internet. Thus, it is important to understand how the news media present this issue. Objective: This study aimed to explore newspaper coverage of the problem of purchasing fake prescription medicines on the internet. Methods: Newspaper articles were retrieved from the ProQuest electronic database using search terms related to the topic of buying fake prescription medicines on the internet. The search was limited to articles published between April 2019 and March 2022 to retrieve relevant articles in this fast-developing field. Articles were included if they were published in English and focused on prescription medicines. Thematic analysis was employed to analyze the articles, and the Theory of Planned Behavior framework was used as a conceptual lens to develop the coding of themes. Results: A total of 106 articles were included and analyzed using thematic analysis. We identified 4 superordinate themes that represent newspaper coverage of the topic of buying prescription medicines on the internet. These themes are (1) the risks of purchasing medicines on the internet (eg, health risks and product quality concerns, financial risks, lack of accountability, risk of purchasing stolen medicines), (2) benefits that entice consumers to make the purchase (eg, convenience and quick purchase, lower cost, privacy of the purchase), (3) social influencing factors of the purchase (influencers, health care providers), and (4) facilitators of the purchase (eg, medicines shortages, pandemic disease such as COVID-19, social media, search engines, accessibility, low risk perception). Conclusions: This theory-based study explored the news media coverage of the problem of fake prescription medicines being purchased on the internet by highlighting the complexity of personal beliefs and the range of external circumstances that could influence people to make these purchases. Further research is needed in this area to identify the factors that lead people to buy prescription medicines on the internet. Identifying these factors could enable the development of interventions to dissuade people from purchasing medicines from unsafe sources on the internet, thus protecting consumers from unsafe or illegal medicines. UR - https://formative.jmir.org/2023/1/e45147 UR - http://dx.doi.org/10.2196/45147 UR - http://www.ncbi.nlm.nih.gov/pubmed/36943354 ID - info:doi/10.2196/45147 ER - TY - JOUR AU - Roe, L. Kyle AU - Giordano, R. Katherine AU - Ezzell, A. Gary AU - Lifshitz, Jonathan PY - 2023/3/17 TI - Public Awareness of the Fencing Response as an Indicator of Traumatic Brain Injury: Quantitative Study of Twitter and Wikipedia Data JO - JMIR Form Res SP - e39061 VL - 7 KW - athlete KW - brain KW - concussion KW - fencing response KW - health communication KW - health information KW - injury pattern KW - posture KW - public education KW - science communication KW - social media KW - sport KW - trauma KW - traumatic brain injury N2 - Background: Traumatic brain injury (TBI) is a disruption in normal brain function caused by an impact of external forces on the head. TBI affects millions of individuals per year, many potentially experiencing chronic symptoms and long-term disability, creating a public health crisis and an economic burden on society. The public discourse around sport-related TBIs has increased in recent decades; however, recognition of a possible TBI remains a challenge. The fencing response is an immediate posturing of the limbs, which can occur in individuals who sustain a TBI and can be used as an overt indicator of TBI. Typically, an individual demonstrating the fencing response exhibits extension in 1 arm and flexion in the contralateral arm immediately upon impact to the head; variations of forearm posturing among each limb have been observed. The tonic posturing is retained for several seconds, sufficient for observation and recognition of a TBI. Since the publication of the original peer-reviewed article on the fencing response, there have been efforts to raise awareness of the fencing response as a visible sign of TBI through publicly available web-based platforms, such as Twitter and Wikipedia. Objective: We aimed to quantify trends that demonstrate levels of public discussion and awareness of the fencing response over time using data from Twitter and Wikipedia. Methods: Raw Twitter data from January 1, 2010, to December 31, 2019, were accessed using the RStudio package academictwitteR and queried for the text ?fencing response.? Data for page views of the Fencing Response Wikipedia article from January 1, 2010, to December 31, 2019, were accessed using the RStudio packages wikipediatrend and pageviews. Data were clustered by weekday, month, half-year (to represent the American football season vs off-season), and year to identify trends over time. Seasonal regression analysis was used to analyze the relationship between the number of fencing response tweets and page views and month of the year. Results: Twitter mentions of the fencing response and Wikipedia page views increased overall from 2010 to 2019, with hundreds of tweets and hundreds of thousands of Wikipedia page views per year. Twitter mentions peaked during the American football season, especially on and following game days. Wikipedia page views did not demonstrate a clear weekday or seasonal pattern, but instead had multiple peaks across various months and years, with January having more page views than May. Conclusions: Here, we demonstrated increased awareness of the fencing response over time using public data from Twitter and Wikipedia. Effective scientific communication through free public platforms can help spread awareness of clinical indicators of TBI, such as the fencing response. Greater awareness of the fencing response as a ?red-flag? sign of TBI among coaches, athletic trainers, and sports organizations can help with medical care and return-to-play decisions. UR - https://formative.jmir.org/2023/1/e39061 UR - http://dx.doi.org/10.2196/39061 UR - http://www.ncbi.nlm.nih.gov/pubmed/36930198 ID - info:doi/10.2196/39061 ER - TY - JOUR AU - Ueda, Michiko AU - Watanabe, Kohei AU - Sueki, Hajime PY - 2023/3/16 TI - Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm JO - J Med Internet Res SP - e44965 VL - 25 KW - mental health KW - COVID-19 KW - Twitter KW - social media KW - depression KW - suicidal ideation KW - loneliness KW - public health crisis KW - psychological well-being KW - infodemiology KW - machine learning framework KW - digital surveillance KW - emotional distress KW - online survey N2 - Background: Monitoring the psychological conditions of social media users during rapidly developing public health crises, such as the COVID-19 pandemic, using their posts on social media has rapidly gained popularity as a relatively easy and cost-effective method. However, the characteristics of individuals who created these posts are largely unknown, making it difficult to identify groups of individuals most affected by such crises. In addition, large annotated data sets for mental health conditions are not easily available, and thus, supervised machine learning algorithms can be infeasible or too costly. Objective: This study proposes a machine learning framework for the real-time surveillance of mental health conditions that does not require extensive training data. Using survey-linked tweets, we tracked the level of emotional distress during the COVID-19 pandemic by the attributes and psychological conditions of social media users in Japan. Methods: We conducted online surveys of adults residing in Japan in May 2022 and collected their basic demographic information, socioeconomic status, and mental health conditions, along with their Twitter handles (N=2432). We computed emotional distress scores for all the tweets posted by the study participants between January 1, 2019, and May 30, 2022 (N=2,493,682) using a semisupervised algorithm called latent semantic scaling (LSS), with higher values indicating higher levels of emotional distress. After excluding users by age and other criteria, we examined 495,021 (19.85%) tweets generated by 560 (23.03%) individuals (age 18-49 years) in 2019 and 2020. We estimated fixed-effect regression models to examine their emotional distress levels in 2020 relative to the corresponding weeks in 2019 by the mental health conditions and characteristics of social media users. Results: The estimated level of emotional distress of our study participants increased in the week when school closure started (March 2020), and it peaked at the beginning of the state of emergency (estimated coefficient=0.219, 95% CI 0.162-0.276) in early April 2020. Their level of emotional distress was unrelated to the number of COVID-19 cases. We found that the government-induced restrictions disproportionately affected the psychological conditions of vulnerable individuals, including those with low income, precarious employment, depressive symptoms, and suicidal ideation. Conclusions: This study establishes a framework to implement near-real-time monitoring of the emotional distress level of social media users, highlighting a great potential to continuously monitor their well-being using survey-linked social media posts as a complement to administrative and large-scale survey data. Given its flexibility and adaptability, the proposed framework is easily extendable for other purposes, such as detecting suicidality among social media users, and can be used on streaming data for continuous measurement of the conditions and sentiment of any group of interest. UR - https://www.jmir.org/2023/1/e44965 UR - http://dx.doi.org/10.2196/44965 UR - http://www.ncbi.nlm.nih.gov/pubmed/36809798 ID - info:doi/10.2196/44965 ER - TY - JOUR AU - Chen, Jiarui AU - Xue, Siyu AU - Xie, Zidian AU - Li, Dongmei PY - 2023/3/15 TI - Characterizing Heated Tobacco Products Marketing on Instagram: Observational Study JO - JMIR Form Res SP - e43334 VL - 7 KW - IQOS KW - Instagram KW - heated tobacco products KW - web-based tobacco marketing N2 - Background: Heated tobacco products (HTPs), including I Quit Ordinary Smoking (IQOS), are new tobacco products that use an electronic device to heat compressed tobacco leaves to generate an aerosol for consumers to inhale. Marketing of HTPs is prevalent on Instagram, a popular social media platform. Objective: This study aims to characterize posts related to HTPs on Instagram and their associations with user engagement. Methods: Through the Instagram application programming interface, 979 Instagram posts were collected using keywords related to HTPs, such as ?IQOS? and ?heat-not-burn.? Among them, 596 posts were related to IQOS and other HTP marketing. The codebook was developed from a randomly selected 200 posts on the post content by hand coding, which was applied to the remaining 396 Instagram posts. Summary statistics were calculated, and statistical hypothesis testing was conducted to understand the popularity of Instagram posts on HTPs. Negative binomial regression models were applied to identify Instagram post characteristics associated with user engagement (eg, count). Results: Among Instagram posts related to HTP marketing (N=596), ?product display? was dominant (n=550, 92.28%), followed by ?brand promotion? (n=41, 6.88%), and ?others? (n=5, 0.84%). Among posts within ?product display,? ?device only? was the most popular (n=338, 61.45%), followed by ?heatstick only? (n=80, 14.55%), ?accessory? (n=66, 12%), ?device and heatstick? (n=56, 10.18%), and ?capsule? (n=10, 1.82%). A univariate negative binomial regression model with pairwise comparisons across ?product display? types showed that the number of likes for posts with HTP heatsticks was significantly lower compared to posts with HTP devices, accessories, and device-heatstick sets. Multivariate negative binomial regression models showed that HTP-related Instagram posts with a model or lifestyle elements (;=.60, 95% CI 0.36-0.84) or without obvious product advertising information (=.69, 95% CI 0.49-0.89) received more likes. Conclusions: It is shown that posts with product displays were dominant among HTP-related posts on Instagram. Posts with model or lifestyle elements are associated with high user engagement, which might be one of the web-based marketing strategies of HTPs. UR - https://formative.jmir.org/2023/1/e43334 UR - http://dx.doi.org/10.2196/43334 UR - http://www.ncbi.nlm.nih.gov/pubmed/36920463 ID - info:doi/10.2196/43334 ER - TY - JOUR AU - Wu, Jiageng AU - Wang, Lumin AU - Hua, Yining AU - Li, Minghui AU - Zhou, Li AU - Bates, W. David AU - Yang, Jie PY - 2023/3/14 TI - Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study JO - J Med Internet Res SP - e45419 VL - 25 KW - social media KW - network analysis KW - public health KW - data mining KW - COVID-19 N2 - Background: For an emergent pandemic, such as COVID-19, the statistics of symptoms based on hospital data may be biased or delayed due to the high proportion of asymptomatic or mild-symptom infections that are not recorded in hospitals. Meanwhile, the difficulty in accessing large-scale clinical data also limits many researchers from conducting timely research. Objective: Given the wide coverage and promptness of social media, this study aimed to present an efficient workflow to track and visualize the dynamic characteristics and co-occurrence of symptoms for the COVID-19 pandemic from large-scale and long-term social media data. Methods: This retrospective study included 471,553,966 COVID-19?related tweets from February 1, 2020, to April 30, 2022. We curated a hierarchical symptom lexicon for social media containing 10 affected organs/systems, 257 symptoms, and 1808 synonyms. The dynamic characteristics of COVID-19 symptoms over time were analyzed from the perspectives of weekly new cases, overall distribution, and temporal prevalence of reported symptoms. The symptom evolutions between virus strains (Delta and Omicron) were investigated by comparing the symptom prevalence during their dominant periods. A co-occurrence symptom network was developed and visualized to investigate inner relationships among symptoms and affected body systems. Results: This study identified 201 COVID-19 symptoms and grouped them into 10 affected body systems. There was a significant correlation between the weekly quantity of self-reported symptoms and new COVID-19 infections (Pearson correlation coefficient=0.8528; P<.001). We also observed a 1-week leading trend (Pearson correlation coefficient=0.8802; P<.001) between them. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms in the later stages. We identified the difference in symptoms between the Delta and Omicron periods. There were fewer severe symptoms (coma and dyspnea), more flu-like symptoms (throat pain and nasal congestion), and fewer typical COVID symptoms (anosmia and taste altered) in the Omicron period than in the Delta period (all P<.001). Network analysis revealed co-occurrences among symptoms and systems corresponding to specific disease progressions, including palpitations (cardiovascular) and dyspnea (respiratory), and alopecia (musculoskeletal) and impotence (reproductive). Conclusions: This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies. UR - https://www.jmir.org/2023/1/e45419 UR - http://dx.doi.org/10.2196/45419 UR - http://www.ncbi.nlm.nih.gov/pubmed/36812402 ID - info:doi/10.2196/45419 ER - TY - JOUR AU - Sarker, Abeed AU - Lakamana, Sahithi AU - Liao, Ruqi AU - Abbas, Aamir AU - Yang, Yuan-Chi AU - Al-Garadi, Mohammed PY - 2023/3/14 TI - The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection JO - JMIR Infodemiology SP - e43694 VL - 3 KW - coronavirus KW - COVID-19 drug treatment KW - social media KW - infodemiology KW - public health surveillance KW - COVID-19 KW - misinformation KW - natural language processing KW - neural network KW - data mining N2 - Background: Social media has served as a lucrative platform for spreading misinformation and for promoting fraudulent products for the treatment, testing, and prevention of COVID-19. This has resulted in the issuance of many warning letters by the US Food and Drug Administration (FDA). While social media continues to serve as the primary platform for the promotion of such fraudulent products, it also presents the opportunity to identify these products early by using effective social media mining methods. Objective: Our objectives were to (1) create a data set of fraudulent COVID-19 products that can be used for future research and (2) propose a method using data from Twitter for automatically detecting heavily promoted COVID-19 products early. Methods: We created a data set from FDA-issued warnings during the early months of the COVID-19 pandemic. We used natural language processing and time-series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We compared the anomaly signal generation date for each product with the corresponding FDA letter issuance date. We also performed a brief manual analysis of chatter associated with 2 products to characterize their contents. Results: FDA warning issue dates ranged from March 6, 2020, to June 22, 2021, and 44 key phrases representing fraudulent products were included. From 577,872,350 posts made between February 19 and December 31, 2020, which are all publicly available, our unsupervised approach detected 34 out of 44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6 (13.6%) within a week following the corresponding FDA letters. Content analysis revealed misinformation, information, political, and conspiracy theories to be prominent topics. Conclusions: Our proposed method is simple, effective, easy to deploy, and does not require high-performance computing machinery unlike deep neural network?based methods. The method can be easily extended to other types of signal detection from social media data. The data set may be used for future research and the development of more advanced methods. UR - https://infodemiology.jmir.org/2023/1/e43694 UR - http://dx.doi.org/10.2196/43694 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113382 ID - info:doi/10.2196/43694 ER - TY - JOUR AU - Lorenzo-Luaces, Lorenzo AU - Dierckman, Clare AU - Adams, Sydney PY - 2023/3/13 TI - Attitudes and (Mis)information About Cognitive Behavioral Therapy on TikTok: An Analysis of Video Content JO - J Med Internet Res SP - e45571 VL - 25 KW - social media KW - cognitive behavioral therapy KW - misinformation KW - public health KW - mental health KW - TikTok KW - psychotherapy KW - content analysis KW - therapist KW - online health information UR - https://www.jmir.org/2023/1/e45571 UR - http://dx.doi.org/10.2196/45571 UR - http://www.ncbi.nlm.nih.gov/pubmed/36912883 ID - info:doi/10.2196/45571 ER - TY - JOUR AU - Honcharov, Vlad AU - Li, Jiawei AU - Sierra, Maribel AU - Rivadeneira, A. Natalie AU - Olazo, Kristan AU - Nguyen, T. Thu AU - Mackey, K. Tim AU - Sarkar, Urmimala PY - 2023/3/10 TI - Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis JO - JMIR Infodemiology SP - e40575 VL - 3 KW - Twitter KW - anti-vaccination KW - Biterm Topic modeling KW - inductive content analysis KW - COVID-19 KW - social media KW - health information KW - vaccination KW - vaccine hesitancy KW - infodemiology KW - misinformation N2 - Background: Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although anti-vaccine sentiment has pervaded social media throughout the COVID-19 pandemic, it is unclear to what extent interest in public figures is generating anti-vaccine discourse. Objective: We examined Twitter messages that included anti-vaccination hashtags and mentions of public figures to assess the connection between interest in these individuals and the possible spread of anti-vaccine messages. Methods: We used a data set of COVID-19?related Twitter posts collected from the public streaming application programming interface from March to October 2020 and filtered it for anti-vaccination hashtags ?antivaxxing,? ?antivaxx,? ?antivaxxers,? ?antivax,? ?anti-vaxxer,? ?discredit,? ?undermine,? ?confidence,? and ?immune.? Next, we applied the Biterm Topic model (BTM) to output topic clusters associated with the entire corpus. Topic clusters were manually screened by examining the top 10 posts most highly correlated in each of the 20 clusters, from which we identified 5 clusters most relevant to public figures and vaccination attitudes. We extracted all messages from these clusters and conducted inductive content analysis to characterize the discourse. Results: Our keyword search yielded 118,971 Twitter posts after duplicates were removed, and subsequently, we applied BTM to parse these data into 20 clusters. After removing retweets, we manually screened the top 10 tweets associated with each cluster (200 messages) to identify clusters associated with public figures. Extraction of these clusters yielded 768 posts for inductive analysis. Most messages were either pro-vaccination (n=329, 43%) or neutral about vaccination (n=425, 55%), with only 2% (14/768) including anti-vaccination messages. Three main themes emerged: (1) anti-vaccination accusation, in which the message accused the public figure of holding anti-vaccination beliefs; (2) using ?anti-vax? as an epithet; and (3) stating or implying the negative public health impact of anti-vaccination discourse. Conclusions: Most discussions surrounding public figures in common hashtags labelled as ?anti-vax? did not reflect anti-vaccination beliefs. We observed that public figures with known anti-vaccination beliefs face scorn and ridicule on Twitter. Accusing public figures of anti-vaccination attitudes is a means of insulting and discrediting the public figure rather than discrediting vaccines. The majority of posts in our sample condemned public figures expressing anti-vax beliefs by undermining their influence, insulting them, or expressing concerns over public health ramifications. This points to a complex information ecosystem, where anti-vax sentiment may not reside in common anti-vax?related keywords or hashtags, necessitating further assessment of the influence that public figures have on this discourse. UR - https://infodemiology.jmir.org/2023/1/e40575 UR - http://dx.doi.org/10.2196/40575 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113377 ID - info:doi/10.2196/40575 ER - TY - JOUR AU - Chan, Y. Isaac H. AU - Gofine, Miriam AU - Arora, Shitij AU - Shaikh, Ahmed AU - Balsari, Satchit PY - 2023/3/8 TI - Technology, Training, and Task Shifting at the World?s Largest Mass Gathering in 2025: An Opportunity for Antibiotic Stewardship in India JO - JMIR Public Health Surveill SP - e45121 VL - 9 KW - digital tools KW - mass gathering KW - Kumbh Mela KW - antibiotics KW - antimicrobial KW - stewardship KW - surveillance KW - public health KW - informatics KW - India UR - https://publichealth.jmir.org/2023/1/e45121 UR - http://dx.doi.org/10.2196/45121 UR - http://www.ncbi.nlm.nih.gov/pubmed/36805363 ID - info:doi/10.2196/45121 ER - TY - JOUR AU - Nguyen, Vincent AU - Liu, Yunzhe AU - Mumford, Richard AU - Flanagan, Benjamin AU - Patel, Parth AU - Braithwaite, Isobel AU - Shrotri, Madhumita AU - Byrne, Thomas AU - Beale, Sarah AU - Aryee, Anna AU - Fong, Erica Wing Lam AU - Fragaszy, Ellen AU - Geismar, Cyril AU - Navaratnam, D. Annalan M. AU - Hardelid, Pia AU - Kovar, Jana AU - Pope, Addy AU - Cheng, Tao AU - Hayward, Andrew AU - Aldridge, Robert AU - PY - 2023/3/8 TI - Tracking Changes in Mobility Before and After the First SARS-CoV-2 Vaccination Using Global Positioning System Data in England and Wales (Virus Watch): Prospective Observational Community Cohort Study JO - JMIR Public Health Surveill SP - e38072 VL - 9 KW - COVID-19 KW - SARS-CoV-2 KW - vaccination KW - global positioning system KW - GPS KW - movement tracking KW - geographical tracking KW - mobile app KW - health application KW - surveillance KW - public health KW - mHealth KW - mobile surveillance KW - tracking device KW - geolocation N2 - Background: Evidence suggests that individuals may change adherence to public health policies aimed at reducing the contact, transmission, and spread of the SARS-CoV-2 virus after they receive their first SARS-CoV-2 vaccination when they are not fully vaccinated. Objective: We aimed to estimate changes in median daily travel distance of our cohort from their registered addresses before and after receiving a SARS-CoV-2 vaccine. Methods: Participants were recruited into Virus Watch starting in June 2020. Weekly surveys were sent out to participants, and vaccination status was collected from January 2021 onward. Between September 2020 and February 2021, we invited 13,120 adult Virus Watch participants to contribute toward our tracker subcohort, which uses the GPS via a smartphone app to collect data on movement. We used segmented linear regression to estimate the median daily travel distance before and after the first self-reported SARS-CoV-2 vaccine dose. Results: We analyzed the daily travel distance of 249 vaccinated adults. From 157 days prior to vaccination until the day before vaccination, the median daily travel distance was 9.05 (IQR 8.06-10.09) km. From the day of vaccination to 105 days after vaccination, the median daily travel distance was 10.08 (IQR 8.60-12.42) km. From 157 days prior to vaccination until the vaccination date, there was a daily median decrease in mobility of 40.09 m (95% CI ?50.08 to ?31.10; P<.001). After vaccination, there was a median daily increase in movement of 60.60 m (95% CI 20.90-100; P<.001). Restricting the analysis to the third national lockdown (January 4, 2021, to April 5, 2021), we found a median daily movement increase of 18.30 m (95% CI ?19.20 to 55.80; P=.57) in the 30 days prior to vaccination and a median daily movement increase of 9.36 m (95% CI 38.6-149.00; P=.69) in the 30 days after vaccination. Conclusions: Our study demonstrates the feasibility of collecting high-volume geolocation data as part of research projects and the utility of these data for understanding public health issues. Our various analyses produced results that ranged from no change in movement after vaccination (during the third national lock down) to an increase in movement after vaccination (considering all periods, up to 105 days after vaccination), suggesting that, among Virus Watch participants, any changes in movement distances after vaccination are small. Our findings may be attributable to public health measures in place at the time such as movement restrictions and home working that applied to the Virus Watch cohort participants during the study period. UR - https://publichealth.jmir.org/2023/1/e38072 UR - http://dx.doi.org/10.2196/38072 UR - http://www.ncbi.nlm.nih.gov/pubmed/36884272 ID - info:doi/10.2196/38072 ER - TY - JOUR AU - Ahmed, Wasim AU - Vidal-Alaball, Josep AU - Vilaseca Llobet, Maria Josep PY - 2023/3/8 TI - Analyzing Discussions Around Rural Health on Twitter During the COVID-19 Pandemic: Social Network Analysis of Twitter Data JO - JMIR Infodemiology SP - e39209 VL - 3 KW - rural health KW - Twitter messaging KW - social media KW - COVID-19 KW - SARS-CoV-2 KW - coronavirus KW - social network analysis N2 - Background: Individuals from rural areas are increasingly using social media as a means of communication, receiving information, or actively complaining of inequalities and injustices. Objective: The aim of our study is to analyze conversations about rural health taking place on Twitter during a particular phase of the COVID-19 pandemic. Methods: This study captured 57 days? worth of Twitter data related to rural health from June to August 2021, using English-language keywords. The study used social network analysis and natural language processing to analyze the data. Results: It was found that Twitter served as a fruitful platform to raise awareness of problems faced by users living in rural areas. Overall, Twitter was used in rural areas to express complaints, debate, and share information. Conclusions: Twitter could be leveraged as a powerful social listening tool for individuals and organizations that want to gain insight into popular narratives around rural health. UR - https://infodemiology.jmir.org/2023/1/e39209 UR - http://dx.doi.org/10.2196/39209 UR - http://www.ncbi.nlm.nih.gov/pubmed/36936067 ID - info:doi/10.2196/39209 ER - TY - JOUR AU - Robinson, Eric AU - Jones, Andrew PY - 2023/3/3 TI - Hangover-Related Internet Searches Before and During the COVID-19 Pandemic in England: Observational Study JO - JMIR Form Res SP - e40518 VL - 7 KW - alcohol KW - COVID-19 KW - hangover KW - Google Trends KW - social media KW - public health KW - online information KW - alcohol use KW - internet search N2 - Background: It is unclear whether heavy alcohol use and associated hangover symptoms changed as a result of the COVID-19 pandemic. Due to a lack of available accurate and nonretrospective self-reported data, it is difficult to directly assess hangover symptoms during the COVID-19 pandemic. Objective: This study aimed to examine whether alcohol-induced hangover-related internet searches (eg, ?how to cure a hangover??) increased, decreased, or remained the same in England before versus during the COVID-19 pandemic (2020-2021) and during periods of national lockdown. Secondary aims were to examine if hangover-related internet searches in England differed compared to a country that did not impose similar COVID-19 lockdown restrictions. Methods: Using historical data from Google Trends for England, we compared the relative search volume (RSV) of hangover-related searches in the years before (2016-2019) versus during the COVID-19 pandemic (2020-2021), as well as in periods of national lockdown versus the same periods in 2016-2019. We also compared the RSV of hangover-related searches during the same time frames in a European country that did not introduce national COVID-19 lockdowns at the beginning of the pandemic (Sweden). Hangover-related search terms were identified through consultation with a panel of alcohol researchers and a sample from the general public. Statistical analyses were preregistered prior to data collection. Results: There was no overall significant difference in the RSV of hangover-related terms in England during 2016-2019 versus 2020-2021 (P=.10; robust d=0.02, 95% CI 0.00-0.03). However, during national lockdowns, searches for hangover-related terms were lower, particularly during the first national lockdown in England (P<.001; d=.19, 95% CI 0.16-0.24; a 44% relative decrease). In a comparison country that did not introduce a national lockdown in the early stages of the pandemic (Sweden), there was no significant decrease in hangover-related searches during the same time period (P=.06). However, across both England and Sweden, during later periods of COVID-19 restrictions in 2020 and 2021, the RSV of hangover-related terms was lower than that in the same periods during 2016-2019. Exploratory analyses revealed that national monthly variation in alcohol sales both before and during the COVID-19 pandemic were positively correlated with the frequency of hangover-related searches, suggesting that changes in hangover-related searches may act as a proxy for changes in alcohol consumption. Conclusions: Hangover-related internet searches did not differ before versus during the COVID-19 pandemic in England but did reduce during periods of national lockdown. Further research is required to confirm how changes in hangover-related search volume relate to heavy episodic alcohol use. Trial Registration: Open Science Framework 2Y86E; https://osf.io/2Y86E UR - https://formative.jmir.org/2023/1/e40518 UR - http://dx.doi.org/10.2196/40518 UR - http://www.ncbi.nlm.nih.gov/pubmed/36827489 ID - info:doi/10.2196/40518 ER - TY - JOUR AU - Mokhberi, Maryam AU - Biswas, Ahana AU - Masud, Zarif AU - Kteily-Hawa, Roula AU - Goldstein, Abby AU - Gillis, Roy Joseph AU - Rayana, Shebuti AU - Ahmed, Ishtiaque Syed PY - 2023/2/28 TI - Development of a COVID-19?Related Anti-Asian Tweet Data Set: Quantitative Study JO - JMIR Form Res SP - e40403 VL - 7 KW - COVID-19 KW - stigma KW - hate speech KW - classification KW - annotation KW - data set KW - Sinophobia KW - Twitter KW - BERT KW - pandemic KW - data KW - online KW - community KW - Asian KW - research KW - discrimination N2 - Background: Since the advent of the COVID-19 pandemic, individuals of Asian descent (colloquial usage prevalent in North America, where ?Asian? is used to refer to people from East Asia, particularly China) have been the subject of stigma and hate speech in both offline and online communities. One of the major venues for encountering such unfair attacks is social networks, such as Twitter. As the research community seeks to understand, analyze, and implement detection techniques, high-quality data sets are becoming immensely important. Objective: In this study, we introduce a manually labeled data set of tweets containing anti-Asian stigmatizing content. Methods: We sampled over 668 million tweets posted on Twitter from January to July 2020 and used an iterative data construction approach that included 3 different stages of algorithm-driven data selection. Finally, we found volunteers who manually annotated the tweets by hand to arrive at a high-quality data set of tweets and a second, more sampled data set with higher-quality labels from multiple annotators. We presented this final high-quality Twitter data set on stigma toward Chinese people during the COVID-19 pandemic. The data set and instructions for labeling can be viewed in the Github repository. Furthermore, we implemented some state-of-the-art models to detect stigmatizing tweets to set initial benchmarks for our data set. Results: Our primary contributions are labeled data sets. Data Set v3.0 contained 11,263 tweets with primary labels (unknown/irrelevant, not-stigmatizing, stigmatizing-low, stigmatizing-medium, stigmatizing-high) and tweet subtopics (eg, wet market and eating habits, COVID-19 cases, bioweapon). Data Set v3.1 contained 4998 (44.4%) tweets randomly sampled from Data Set v3.0, where a second annotator labeled them only on the primary labels and then a third annotator resolved conflicts between the first and second annotators. To demonstrate the usefulness of our data set, preliminary experiments on the data set showed that the Bidirectional Encoder Representations from Transformers (BERT) model achieved the highest accuracy of 79% when detecting stigma on unseen data with traditional models, such as a support vector machine (SVM) performing at 73% accuracy. Conclusions: Our data set can be used as a benchmark for further qualitative and quantitative research and analysis around the issue. It first reaffirms the existence and significance of widespread discrimination and stigma toward the Asian population worldwide. Moreover, our data set and subsequent arguments should assist other researchers from various domains, including psychologists, public policy authorities, and sociologists, to analyze the complex economic, political, historical, and cultural underlying roots of anti-Asian stigmatization and hateful behaviors. A manually annotated data set is of paramount importance for developing algorithms that can be used to detect stigma or problematic text, particularly on social media. We believe this contribution will help predict and subsequently design interventions that will significantly help reduce stigma, hate, and discrimination against marginalized populations during future crises like COVID-19. UR - https://formative.jmir.org/2023/1/e40403 UR - http://dx.doi.org/10.2196/40403 UR - http://www.ncbi.nlm.nih.gov/pubmed/36693148 ID - info:doi/10.2196/40403 ER - TY - JOUR AU - Ramjee, Divya AU - Pollack, C. Catherine AU - Charpignon, Marie-Laure AU - Gupta, Shagun AU - Rivera, Malaty Jessica AU - El Hayek, Ghinwa AU - Dunn, G. Adam AU - Desai, N. Angel AU - Majumder, S. Maimuna PY - 2023/2/27 TI - Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter JO - J Med Internet Res SP - e40706 VL - 25 KW - face masks KW - COVID-19 KW - Twitter KW - science communication KW - political communication KW - public policy KW - public health KW - sentiment analysis KW - emotion analysis KW - infodemiology KW - infoveillance N2 - Background: Throughout the COVID-19 pandemic, US Centers for Disease Control and Prevention policies on face mask use fluctuated. Understanding how public health communications evolve around key policy decisions may inform future decisions on preventative measures by aiding the design of communication strategies (eg, wording, timing, and channel) that ensure rapid dissemination and maximize both widespread adoption and sustained adherence. Objective: We aimed to assess how sentiment on masks evolved surrounding 2 changes to mask guidelines: (1) the recommendation for mask use on April 3, 2020, and (2) the relaxation of mask use on May 13, 2021. Methods: We applied an interrupted time series method to US Twitter data surrounding each guideline change. Outcomes were changes in the (1) proportion of positive, negative, and neutral tweets and (2) number of words within a tweet tagged with a given emotion (eg, trust). Results were compared to COVID-19 Twitter data without mask keywords for the same period. Results: There were fewer neutral mask-related tweets in 2020 (?=?3.94 percentage points, 95% CI ?4.68 to ?3.21; P<.001) and 2021 (?=?8.74, 95% CI ?9.31 to ?8.17; P<.001). Following the April 3 recommendation (?=.51, 95% CI .43-.59; P<.001) and May 13 relaxation (?=3.43, 95% CI 1.61-5.26; P<.001), the percent of negative mask-related tweets increased. The quantity of trust-related terms decreased following the policy change on April 3 (?=?.004, 95% CI ?.004 to ?.003; P<.001) and May 13 (?=?.001, 95% CI ?.002 to 0; P=.008). Conclusions: The US Twitter population responded negatively and with less trust following guideline shifts related to masking, regardless of whether the guidelines recommended or relaxed mask usage. Federal agencies should ensure that changes in public health recommendations are communicated concisely and rapidly. UR - https://www.jmir.org/2023/1/e40706 UR - http://dx.doi.org/10.2196/40706 UR - http://www.ncbi.nlm.nih.gov/pubmed/36763687 ID - info:doi/10.2196/40706 ER - TY - JOUR AU - Khademi, Sedigh AU - Hallinan, Mary Christine AU - Conway, Mike AU - Bonomo, Yvonne PY - 2023/2/27 TI - Using Social Media Data to Investigate Public Perceptions of Cannabis as a Medicine: Narrative Review JO - J Med Internet Res SP - e36667 VL - 25 KW - social media KW - medicinal cannabis KW - public health surveillance KW - internet KW - medical marijuana N2 - Background: The use and acceptance of medicinal cannabis is on the rise across the globe. To support the interests of public health, evidence relating to its use, effects, and safety is required to match this community demand. Web-based user-generated data are often used by researchers and public health organizations for the investigation of consumer perceptions, market forces, population behaviors, and for pharmacoepidemiology. Objective: In this review, we aimed to summarize the findings of studies that have used user-generated text as a data source to study medicinal cannabis or the use of cannabis as medicine. Our objectives were to categorize the insights provided by social media research on cannabis as medicine and describe the role of social media for consumers using medicinal cannabis. Methods: The inclusion criteria for this review were primary research studies and reviews that reported on the analysis of web-based user-generated content on cannabis as medicine. The MEDLINE, Scopus, Web of Science, and Embase databases were searched from January 1974 to April 2022. Results: We examined 42 studies published in English and found that consumers value their ability to exchange experiences on the web and tend to rely on web-based information sources. Cannabis discussions have portrayed the substance as a safe and natural medicine to help with many health conditions including cancer, sleep disorders, chronic pain, opioid use disorders, headaches, asthma, bowel disease, anxiety, depression, and posttraumatic stress disorder. These discussions provide a rich resource for researchers to investigate medicinal cannabis?related consumer sentiment and experiences, including the opportunity to monitor cannabis effects and adverse events, given the anecdotal and often biased nature of the information is properly accounted for. Conclusions: The extensive web-based presence of the cannabis industry coupled with the conversational nature of social media discourse results in rich but potentially biased information that is often not well-supported by scientific evidence. This review summarizes what social media is saying about the medicinal use of cannabis and discusses the challenges faced by health governance agencies and professionals to make use of web-based resources to both learn from medicinal cannabis users and provide factual, timely, and reliable evidence-based health information to consumers. UR - https://www.jmir.org/2023/1/e36667 UR - http://dx.doi.org/10.2196/36667 UR - http://www.ncbi.nlm.nih.gov/pubmed/36848191 ID - info:doi/10.2196/36667 ER - TY - JOUR AU - Pierri, Francesco AU - DeVerna, R. Matthew AU - Yang, Kai-Cheng AU - Axelrod, David AU - Bryden, John AU - Menczer, Filippo PY - 2023/2/24 TI - One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study JO - J Med Internet Res SP - e42227 VL - 25 KW - content analysis KW - COVID-19 KW - infodemiology KW - misinformation KW - online health information KW - social media KW - trend analysis KW - Twitter KW - vaccines KW - vaccine hesitancy N2 - Background: Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. Past research has linked misinformation to increased hesitancy and lower vaccination rates. Gaps remain in our knowledge about the main drivers of vaccine misinformation on social media and effective ways to intervene. Objective: Our longitudinal study had two primary objectives: (1) to investigate the patterns of prevalence and contagion of COVID-19 vaccine misinformation on Twitter in 2021, and (2) to identify the main spreaders of vaccine misinformation. Given our initial results, we further considered the likely drivers of misinformation and its spread, providing insights for potential interventions. Methods: We collected almost 300 million English-language tweets related to COVID-19 vaccines using a list of over 80 relevant keywords over a period of 12 months. We then extracted and labeled news articles at the source level based on third-party lists of low-credibility and mainstream news sources, and measured the prevalence of different kinds of information. We also considered suspicious YouTube videos shared on Twitter. We focused our analysis of vaccine misinformation spreaders on verified and automated Twitter accounts. Results: Our findings showed a relatively low prevalence of low-credibility information compared to the entirety of mainstream news. However, the most popular low-credibility sources had reshare volumes comparable to those of many mainstream sources, and had larger volumes than those of authoritative sources such as the US Centers for Disease Control and Prevention and the World Health Organization. Throughout the year, we observed an increasing trend in the prevalence of low-credibility news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. Tweets by a small group of approximately 800 ?superspreaders? verified by Twitter accounted for approximately 35% of all reshares of misinformation on an average day, with the top superspreader (@RobertKennedyJr) responsible for over 13% of retweets. Finally, low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts. Conclusions: The wide spread of misinformation around COVID-19 vaccines on Twitter during 2021 shows that there was an audience for this type of content. Our findings are also consistent with the hypothesis that superspreaders are driven by financial incentives that allow them to profit from health misinformation. Despite high-profile cases of deplatformed misinformation superspreaders, our results show that in 2021, a few individuals still played an outsized role in the spread of low-credibility vaccine content. As a result, social media moderation efforts would be better served by focusing on reducing the online visibility of repeat spreaders of harmful content, especially during public health crises. UR - https://www.jmir.org/2023/1/e42227 UR - http://dx.doi.org/10.2196/42227 UR - http://www.ncbi.nlm.nih.gov/pubmed/36735835 ID - info:doi/10.2196/42227 ER - TY - JOUR AU - Abrams, P. Matthew AU - Pelullo, P. Arthur AU - Meisel, F. Zachary AU - Merchant, M. Raina AU - Purtle, Jonathan AU - Agarwal, K. Anish PY - 2023/2/24 TI - State and Federal Legislators? Responses on Social Media to the Mental Health and Burnout of Health Care Workers Throughout the COVID-19 Pandemic: Natural Language Processing and Sentiment Analysis JO - JMIR Infodemiology SP - e38676 VL - 3 KW - burnout KW - wellness KW - mental health KW - social media KW - policy KW - health care workforce KW - COVID-19 KW - infodemiology KW - healthcare worker KW - mental well-being KW - psychological distress KW - Twitter KW - content analysis KW - thematic analysis KW - policy maker KW - healthcare workforce KW - legislator N2 - Background: Burnout and the mental health burden of the COVID-19 pandemic have disproportionately impacted health care workers. The links between state policies, federal regulations, COVID-19 case counts, strains on health care systems, and the mental health of health care workers continue to evolve. The language used by state and federal legislators in public-facing venues such as social media is important, as it impacts public opinion and behavior, and it also reflects current policy-leader opinions and planned legislation. Objective: The objective of this study was to examine legislators? social media content on Twitter and Facebook throughout the COVID-19 pandemic to thematically characterize policy makers? attitudes and perspectives related to mental health and burnout in the health care workforce. Methods: Legislators? social media posts about mental health and burnout in the health care workforce were collected from January 2020 to November 2021 using Quorum, a digital database of policy-related documents. The total number of relevant social media posts per state legislator per calendar month was calculated and compared with COVID-19 case volume. Differences between themes expressed in Democratic and Republican posts were estimated using the Pearson chi-square test. Words within social media posts most associated with each political party were determined. Machine-learning was used to evaluate naturally occurring themes in the burnout- and mental health?related social media posts. Results: A total of 4165 social media posts (1400 tweets and 2765 Facebook posts) were generated by 2047 unique state and federal legislators and 38 government entities. The majority of posts (n=2319, 55.68%) were generated by Democrats, followed by Republicans (n=1600, 40.34%). Among both parties, the volume of burnout-related posts was greatest during the initial COVID-19 surge. However, there was significant variation in the themes expressed by the 2 major political parties. Themes most correlated with Democratic posts were (1) frontline care and burnout, (2) vaccines, (3) COVID-19 outbreaks, and (4) mental health services. Themes most correlated with Republican social media posts were (1) legislation, (2) call for local action, (3) government support, and (4) health care worker testing and mental health. Conclusions: State and federal legislators use social media to share opinions and thoughts on key topics, including burnout and mental health strain among health care workers. Variations in the volume of posts indicated that a focus on burnout and the mental health of the health care workforce existed early in the pandemic but has waned. Significant differences emerged in the content posted by the 2 major US political parties, underscoring how each prioritized different aspects of the crisis. UR - https://infodemiology.jmir.org/2023/1/e38676 UR - http://dx.doi.org/10.2196/38676 UR - http://www.ncbi.nlm.nih.gov/pubmed/37013000 ID - info:doi/10.2196/38676 ER - TY - JOUR AU - Beirakdar, Safwat AU - Klingborg, Leon AU - Herzig van Wees, Sibylle PY - 2023/2/22 TI - Attitudes of Swedish Language Twitter Users Toward COVID-19 Vaccination: Exploratory Qualitative Study JO - JMIR Infodemiology SP - e42357 VL - 3 KW - COVID-19 KW - vaccine hesitancy KW - COVID-19 vaccines KW - social media KW - Twitter KW - qualitative analysis KW - World Health Organization KW - WHO?s 3C model N2 - Background: Social media have played an important role in shaping COVID-19 vaccine choices during the pandemic. Understanding people?s attitudes toward the vaccine as expressed on social media can help address the concerns of vaccine-hesitant individuals. Objective: The aim of this study was to understand the attitudes of Swedish-speaking Twitter users toward COVID-19 vaccines. Methods: This was an exploratory qualitative study that used a social media?listening approach. Between January and March 2022, a total of 2877 publicly available tweets in Swedish were systematically extracted from Twitter. A deductive thematic analysis was conducted using the World Health Organization?s 3C model (confidence, complacency, and convenience). Results: Confidence in the safety and effectiveness of the COVID-19 vaccine appeared to be a major concern expressed on Twitter. Unclear governmental strategies in managing the pandemic in Sweden and the belief in conspiracy theories have further influenced negative attitudes toward vaccines. Complacency?the perceived risk of COVID-19 was low and booster vaccination was unnecessary; many expressed trust in natural immunity. Convenience?in terms of accessing the right information and the vaccine?highlighted a knowledge gap about the benefits and necessity of the vaccine, as well as complaints about the quality of vaccination services. Conclusions: Swedish-speaking Twitter users in this study had negative attitudes toward COVID-19 vaccines, particularly booster vaccines. We identified attitudes toward vaccines and misinformation, indicating that social media monitoring can help policy makers respond by developing proactive health communication interventions. UR - https://infodemiology.jmir.org/2023/1/e42357 UR - http://dx.doi.org/10.2196/42357 UR - http://www.ncbi.nlm.nih.gov/pubmed/37012999 ID - info:doi/10.2196/42357 ER - TY - JOUR AU - Hong, Yimin AU - Xie, Fang AU - An, Xinyu AU - Lan, Xue AU - Liu, Chunhe AU - Yan, Lei AU - Zhang, Han PY - 2023/2/16 TI - Evolution of Public Attitudes and Opinions Regarding COVID-19 Vaccination During the Vaccine Campaign in China: Year-Long Infodemiology Study of Weibo Posts JO - J Med Internet Res SP - e42671 VL - 25 KW - COVID-19 vaccines KW - social media KW - infodemiology KW - sentiment analysis KW - opinion analysis KW - monitoring public attitude KW - gender differences KW - LDA KW - COVID-19 N2 - Background: Monitoring people?s perspectives on the COVID-19 vaccine is crucial for understanding public vaccination hesitancy and developing effective, targeted vaccine promotion strategies. Although this is widely recognized, studies on the evolution of public opinion over the course of an actual vaccination campaign are rare. Objective: We aimed to track the evolution of public opinion and sentiment toward COVID-19 vaccines in online discussions over an entire vaccination campaign. Moreover, we aimed to reveal the pattern of gender differences in attitudes and perceptions toward vaccination. Methods: We collected COVID-19 vaccine?related posts by the general public that appeared on Sina Weibo from January 1, 2021, to December 31, 2021; this period covered the entire vaccination process in China. We identified popular discussion topics using latent Dirichlet allocation. We further examined changes in public sentiment and topics during the 3 stages of the vaccination timeline. Gender differences in perceptions toward vaccination were also investigated. Results: Of 495,229 crawled posts, 96,145 original posts from individual accounts were included. Most posts presented positive sentiments (positive: 65,981/96,145, 68.63%; negative: 23,184/96,145, 24.11%; neutral: 6980/96,145, 7.26%). The average sentiment scores were 0.75 (SD 0.35) for men and 0.67 (SD 0.37) for women. The overall trends in sentiment scores showed a mixed response to the number of new cases and significant events related to vaccine development and important holidays. The sentiment scores showed a weak correlation with new case numbers (R=0.296; P=.03). Significant sentiment score differences were observed between men and women (P<.001). Common and distinguishing characteristics were found among frequently discussed topics during the different stages, with significant differences in topic distribution between men and women (January 1, 2021, to March 31, 2021: ?23=3030.9; April 1, 2021, to September 30, 2021: ?24=8893.8; October 1, 2021, to December 31, 2021: ?25=3019.5; P<.001). Women were more concerned with side effects and vaccine effectiveness. In contrast, men reported broader concerns around the global pandemic, the progress of vaccine development, and economics affected by the pandemic. Conclusions: Understanding public concerns regarding vaccination is essential for reaching vaccine-induced herd immunity. This study tracked the year-long evolution of attitudes and opinions on COVID-19 vaccines according to the different stages of vaccination in China. These findings provide timely information that will enable the government to understand the reasons for low vaccine uptake and promote COVID-19 vaccination nationwide. UR - https://www.jmir.org/2023/1/e42671 UR - http://dx.doi.org/10.2196/42671 UR - http://www.ncbi.nlm.nih.gov/pubmed/36795467 ID - info:doi/10.2196/42671 ER - TY - JOUR AU - Diamond, Carrie AU - Quinn, P. Alyssa AU - Presley, L. Colby AU - Jacobs, Jennifer AU - Laughter, R. Melissa AU - Anderson, Jaclyn AU - Rundle, Chandler PY - 2023/2/16 TI - Telangiectasia-Related Social Media Posts: Cross-sectional Analysis of TikTok and Instagram JO - JMIR Dermatol SP - e41716 VL - 6 KW - social media KW - telangiectasias KW - varicose veins KW - health information KW - misinformation KW - dermatology KW - health education KW - dermatologic information KW - health content KW - accuracy KW - educational content UR - https://derma.jmir.org/2023/1/e41716 UR - http://dx.doi.org/10.2196/41716 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632919 ID - info:doi/10.2196/41716 ER - TY - JOUR AU - Liu, Yongtai AU - Yin, Zhijun AU - Ni, Congning AU - Yan, Chao AU - Wan, Zhiyu AU - Malin, Bradley PY - 2023/2/15 TI - Examining Rural and Urban Sentiment Difference in COVID-19?Related Topics on Twitter: Word Embedding?Based Retrospective Study JO - J Med Internet Res SP - e42985 VL - 25 KW - COVID-19 KW - social media KW - word embedding KW - topic analysis KW - sentiment analysis KW - Twitter KW - data KW - vaccination KW - prevention KW - urban KW - rural KW - epidemic KW - management KW - model KW - training KW - machine learning N2 - Background: By the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19?related topics. Objective: This study aimed to (1) identify the primary COVID-19?related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics. Methods: We collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets? geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models. Results: We created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the ?covidiots? and ?China virus? topics, while rural users exhibited stronger negative sentiments about the ?Dr. Fauci? and ?plandemic? topics. Finally, we observed that urban users? sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery. Conclusions: This study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19?related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts. UR - https://www.jmir.org/2023/1/e42985 UR - http://dx.doi.org/10.2196/42985 UR - http://www.ncbi.nlm.nih.gov/pubmed/36790847 ID - info:doi/10.2196/42985 ER - TY - JOUR AU - Bouchacourt, Lindsay AU - Henson-Garci?a, Mike AU - Sussman, Leah Kristen AU - Mandell, Dorothy AU - Wilcox, Gary AU - Mackert, Michael PY - 2023/2/15 TI - Web-Based Conversations Regarding Fathers Before and During the COVID-19 Pandemic: Qualitative Content Analysis JO - JMIR Pediatr Parent SP - e40371 VL - 6 KW - social media KW - expecting fathers KW - new fathers KW - Twitter KW - Reddit KW - content analysis KW - topic model KW - topic analysis KW - parent KW - father N2 - Background: Studies of new and expecting parents largely focus on the mother, leaving a gap in knowledge about fathers. Objective: This study aimed to understand web-based conversations regarding new and expecting fathers on social media and to explore whether the COVID-19 pandemic has changed the web-based conversation. Methods: A social media analysis was conducted. Brandwatch (Cision) captured social posts related to new and expecting fathers between February 1, 2019, and February 12, 2021. Overall, 2 periods were studied: 1 year before and 1 year during the pandemic. SAS Text Miner analyzed the data and produced 47% (9/19) of the topics in the first period and 53% (10/19) of the topics in the second period. The 19 topics were organized into 6 broad themes. Results: Overall, 26% (5/19) of the topics obtained during each period were the same, showing consistency in conversation. In total, 6 broad themes were created: fatherhood thoughts, fatherhood celebrations, advice seeking, fatherhood announcements, external parties targeting fathers, and miscellaneous. Conclusions: Fathers use social media to make announcements, celebrate fatherhood, seek advice, and interact with other fathers. Others used social media to advertise baby products and promote baby-related resources for fathers. Overall, the arrival of the COVID-19 pandemic appeared to have little impact on the excitement and resiliency of new fathers as they transition to parenthood. Altogether, these findings provide insight and guidance on the ways in which public health professionals can rapidly gather information about special populations?such as new and expecting fathers via the web?to monitor their beliefs, attitudes, emotional reactions, and unique lived experiences in context (ie, throughout a global pandemic). UR - https://pediatrics.jmir.org/2023/1/e40371 UR - http://dx.doi.org/10.2196/40371 UR - http://www.ncbi.nlm.nih.gov/pubmed/36790850 ID - info:doi/10.2196/40371 ER - TY - JOUR AU - Lin, Shuo-Yu AU - Cheng, Xiaolu AU - Zhang, Jun AU - Yannam, Sindhu Jaya AU - Barnes, J. Andrew AU - Koch, Randy J. AU - Hayes, Rashelle AU - Gimm, Gilbert AU - Zhao, Xiaoquan AU - Purohit, Hemant AU - Xue, Hong PY - 2023/2/13 TI - Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts JO - J Med Internet Res SP - e42863 VL - 25 KW - tobacco control KW - social media campaign KW - content analysis KW - natural language processing KW - topic modeling KW - social media KW - public health KW - tobacco KW - youth KW - Facebook KW - engagement KW - use KW - smoking N2 - Background: Social media platforms provide a valuable source of public health information, as one-third of US adults seek specific health information online. Many antitobacco campaigns have recognized such trends among youth and have shifted their advertising time and effort toward digital platforms. Timely evidence is needed to inform the adaptation of antitobacco campaigns to changing social media platforms. Objective: In this study, we conducted a content analysis of major antitobacco campaigns on Facebook using machine learning and natural language processing (NLP) methods, as well as a traditional approach, to investigate the factors that may influence effective antismoking information dissemination and user engagement. Methods: We collected 3515 posts and 28,125 associated comments from 7 large national and local antitobacco campaigns on Facebook between 2018 and 2021, including the Real Cost, Truth, CDC Tobacco Free (formally known as Tips from Former Smokers, where ?CDC? refers to the Centers for Disease Control and Prevention), the Tobacco Prevention Toolkit, Behind the Haze VA, the Campaign for Tobacco-Free Kids, and Smoke Free US campaigns. NLP methods were used for content analysis, including parsimonious rule?based models for sentiment analysis and topic modeling. Logistic regression models were fitted to examine the relationship of antismoking message-framing strategies and viewer responses and engagement. Results: We found that large campaigns from government and nonprofit organizations had more user engagements compared to local and smaller campaigns. Facebook users were more likely to engage in negatively framed campaign posts. Negative posts tended to receive more negative comments (odds ratio [OR] 1.40, 95% CI 1.20-1.65). Positively framed posts generated more negative comments (OR 1.41, 95% CI 1.19-1.66) as well as positive comments (OR 1.29, 95% CI 1.13-1.48). Our content analysis and topic modeling uncovered that the most popular campaign posts tended to be informational (ie, providing new information), where the key phrases included talking about harmful chemicals (n=43, 43%) as well as the risk to pets (n=17, 17%). Conclusions: Facebook users tend to engage more in antitobacco educational campaigns that are framed negatively. The most popular campaign posts are those providing new information, with key phrases and topics discussing harmful chemicals and risks of secondhand smoke for pets. Educational campaign designers can use such insights to increase the reach of antismoking campaigns and promote behavioral changes. UR - https://www.jmir.org/2023/1/e42863 UR - http://dx.doi.org/10.2196/42863 UR - http://www.ncbi.nlm.nih.gov/pubmed/36780224 ID - info:doi/10.2196/42863 ER - TY - JOUR AU - Zhou, Runtao AU - Tang, Qihang AU - Xie, Zidian AU - Li, Dongmei PY - 2023/2/10 TI - Public Perceptions of the Food and Drug Administration?s Proposed Rules Prohibiting Menthol Cigarettes on Twitter: Observational Study JO - JMIR Form Res SP - e42706 VL - 7 KW - menthol cigarettes KW - Food and Drug Administration KW - FDA KW - FDA's proposed rules KW - Twitter KW - perception N2 - Background: On April 28, 2022, the Food and Drug Administration (FDA) proposed rules that prohibited all menthol-flavored cigarettes and other flavored cigars to prevent the initiation of tobacco use in youth and reduce tobacco-related diseases and death. Objective: The objective of this study was to investigate public perceptions of the FDA?s proposed menthol cigarette rules on Twitter. Methods: Through the Twitter streaming application programming interface, tobacco-related tweets were collected between April 28, 2022, and May 27, 2022, using a set of keywords, such as smoking, cigarette, and nicotine. Furthermore, 1941 tweets related to the FDA?s proposed menthol cigarette rules were extracted. Based on 300 randomly selected example tweets, the codebook for the attitudes toward the FDA?s proposed rules and related topics was developed by 2 researchers and was used to label the rest of the tweets. Results: Among tweets related to the FDA?s proposed menthol cigarette rules, 536 (27.61%) showed a positive attitude, 443 (22.82%) had a negative attitude, and 962 (49.56%) had a neutral attitude toward the proposed rules. Social justice (210/536, 39%) and health issues (117/536, 22%) were two major topics in tweets with a positive attitude. For tweets with a negative attitude, alternative tobacco or nicotine products (127/443, 29%) and racial discrimination (84/536, 16%) were two of the most popular topics. Conclusions: In general, the public had a positive attitude toward the FDA?s proposed menthol cigarette rules. Our study provides important information to the FDA on the public perceptions of the proposed menthol cigarette rules, which will be helpful for future FDA regulations on menthol cigarettes. UR - https://formative.jmir.org/2023/1/e42706 UR - http://dx.doi.org/10.2196/42706 UR - http://www.ncbi.nlm.nih.gov/pubmed/36763414 ID - info:doi/10.2196/42706 ER - TY - JOUR AU - Klein, Z. Ari AU - Kunatharaju, Shriya AU - O'Connor, Karen AU - Gonzalez-Hernandez, Graciela PY - 2023/2/9 TI - Pregex: Rule-Based Detection and Extraction of Twitter Data in Pregnancy JO - J Med Internet Res SP - e40569 VL - 25 KW - natural language processing KW - data mining KW - social media KW - pregnancy UR - https://www.jmir.org/2023/1/e40569 UR - http://dx.doi.org/10.2196/40569 UR - http://www.ncbi.nlm.nih.gov/pubmed/36757756 ID - info:doi/10.2196/40569 ER - TY - JOUR AU - Sun, Fei AU - Zheng, Shusen AU - Wu, Jian PY - 2023/2/8 TI - Quality of Information in Gallstone Disease Videos on TikTok: Cross-sectional Study JO - J Med Internet Res SP - e39162 VL - 25 KW - hepatobiliary KW - gallstone KW - gallbladder KW - TikTok KW - social media KW - video quality KW - DISCERN KW - Journal of American Medical Association KW - JAMA KW - Global Quality Score KW - GQS KW - content analysis KW - health information KW - online health information KW - digital health KW - disease knowledge KW - medical information KW - misinformation KW - infodemiology KW - patient education KW - dissemination KW - accuracy KW - credibility KW - credible KW - reliability KW - reliable KW - information quality N2 - Background: TikTok was an important channel for consumers to access and adopt health information. But the quality of health content in TikTok remains underinvestigated. Objective: Our study aimed to identify upload sources, contents, and feature information of gallstone disease videos on TikTok and further evaluated the factors related to video quality. Methods: We investigated the first 100 gallstone-related videos on TikTok and analyzed these videos? upload sources, content, and characteristics. The quality of videos was evaluated using quantitative scoring tools such as DISCERN instrument, the Journal of American Medical Association (JAMA) benchmark criteria, and Global Quality Scores (GQS). Moreover, the correlation between video quality and video characteristics, including duration, likes, comments, and shares, was further investigated. Results: According to video sources, 81% of the videos were posted by doctors. Furthermore, disease knowledge was the most dominant video content, accounting for 56% of all the videos. The mean DISCERN, JAMA, and GQS scores of all 100 videos are 39.61 (SD 11.36), 2.00 (SD 0.40), and 2.76 (SD 0.95), respectively. According to DISCERN and GQS, gallstone-related videos? quality score on TikTok is not high, mainly at fair (43/100, 43%,) and moderate (46/100, 46%). The total DISCERN scores of doctors were significantly higher than that of individuals and news agencies, surgery techniques were significantly higher than lifestyle and news, and disease knowledge was significantly higher than news, respectively. DISCERN scores and video duration were positively correlated. Negative correlations were found between DISCERN scores and likes and shares of videos. In GQS analysis, no significant differences were found between groups based on different sources or different contents. JAMA was excluded in the video quality and correlation analysis due to a lack of discrimination and inability to evaluate the video quality accurately. Conclusions: Although the videos of gallstones on TikTok are mainly provided by doctors and contain disease knowledge, they are of low quality. We found a positive correlation between video duration and video quality. High-quality videos received low attention, and popular videos were of low quality. Medical information on TikTok is currently not rigorous enough to guide patients to make accurate judgments. TikTok was not an appropriate source of knowledge to educate patients due to the low quality and reliability of the information. UR - https://www.jmir.org/2023/1/e39162 UR - http://dx.doi.org/10.2196/39162 UR - http://www.ncbi.nlm.nih.gov/pubmed/36753307 ID - info:doi/10.2196/39162 ER - TY - JOUR AU - Athanasiou, Maria AU - Fragkozidis, Georgios AU - Zarkogianni, Konstantia AU - Nikita, S. Konstantina PY - 2023/2/6 TI - Long Short-term Memory?Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation JO - J Med Internet Res SP - e42519 VL - 25 KW - influenza-like illness KW - epidemiological surveillance KW - machine learning KW - deep learning KW - social media KW - Twitter KW - meteorological parameters N2 - Background: The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. Objective: The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. Methods: The model?s input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models? LSTM layers for the latter to feed a dense layer. Results: The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). Conclusions: The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics. UR - https://www.jmir.org/2023/1/e42519 UR - http://dx.doi.org/10.2196/42519 UR - http://www.ncbi.nlm.nih.gov/pubmed/36745490 ID - info:doi/10.2196/42519 ER - TY - JOUR AU - Humer, Elke AU - Keil, Thomas AU - Stupp, Carolin AU - Schlee, Winfried AU - Wildner, Manfred AU - Heuschmann, Peter AU - Winter, Michael AU - Probst, Thomas AU - Pryss, Rüdiger PY - 2023/2/3 TI - Associations of Country-Specific and Sociodemographic Factors With Self-Reported COVID-19?Related Symptoms: Multivariable Analysis of Data From the CoronaCheck Mobile Health Platform JO - JMIR Public Health Surveill SP - e40958 VL - 9 KW - COVID-19 KW - COVID-19 symptoms KW - gender KW - India KW - South Africa KW - Germany KW - symptoms KW - app KW - information KW - English KW - sociodemographic KW - weakness KW - muscle pain KW - pain KW - age KW - education N2 - Background: The COVID-19 symptom-monitoring apps provide direct feedback to users about the suspected risk of infection with SARS-CoV-2 and advice on how to proceed to prevent the spread of the virus. We have developed the CoronaCheck mobile health (mHealth) platform, the first free app that provides easy access to valid information about the risk of infection with SARS-CoV-2 in English and German. Previous studies have suggested that the clinical characteristics of individuals infected with SARS-CoV-2 vary by age, gender, and viral variant; however, potential differences between countries have not been adequately studied. Objective: The aim of this study is to describe the characteristics of the users of the CoronaCheck mHealth platform and to determine country-specific and sociodemographic associations of COVID-19?related symptoms and previous contacts with individuals infected with COVID-19. Methods: Between April 8, 2020, and February 3, 2022, data on sociodemographic characteristics, symptoms, and reports of previous close contacts with individuals infected with COVID-19 were collected from CoronaCheck users in different countries. Multivariable logistic regression analyses were performed to examine whether self-reports of COVID-19?related symptoms and recent contact with a person infected with COVID-19 differed between countries (Germany, India, South Africa), gender identities, age groups, education, and calendar year. Results: Most app users (N=23,179) were from Germany (n=8116, 35.0%), India (n=6622, 28.6%), and South Africa (n=3705, 16.0%). Most data were collected in 2020 (n=19,723, 85.1%). In addition, 64% (n=14,842) of the users were male, 52.1% (n=12,077) were ?30 years old, and 38.6% (n=8953) had an education level of more than 11 years of schooling. Headache, muscle pain, fever, loss of smell, loss of taste, and previous contacts with individuals infected with COVID-19 were reported more frequently by users in India (adjusted odds ratios [aORs] 1.3-8.3, 95% CI 1.2-9.2) and South Africa (aORs 1.1-2.6, 95% CI 1.0-3.0) than those in Germany. Cough, general weakness, sore throat, and shortness of breath were more frequently reported in India (aORs 1.3-2.6, 95% CI 1.2-2.9) compared to Germany. Gender-diverse users reported symptoms and contacts with confirmed COVID-19 cases more often compared to male users. Conclusions: Patterns of self-reported COVID-19?related symptoms and awareness of a previous contact with individuals infected with COVID-19 seemed to differ between India, South Africa, and Germany, as well as by gender identity in these countries. Viral symptom?collecting apps, such as the CoronaCheck mHealth platform, may be promising tools for pandemics to support appropriate assessments. Future mHealth research on country-specific differences during a pandemic should aim to recruit representative samples. UR - https://publichealth.jmir.org/2023/1/e40958 UR - http://dx.doi.org/10.2196/40958 UR - http://www.ncbi.nlm.nih.gov/pubmed/36515987 ID - info:doi/10.2196/40958 ER - TY - JOUR AU - Myneni, Sahiti AU - Cuccaro, Paula AU - Montgomery, Sarah AU - Pakanati, Vivek AU - Tang, Jinni AU - Singh, Tavleen AU - Dominguez, Olivia AU - Cohen, Trevor AU - Reininger, Belinda AU - Savas, S. Lara AU - Fernandez, E. Maria PY - 2023/2/3 TI - Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science JO - JMIR Infodemiology SP - e40156 VL - 3 KW - COVID-19 KW - misinformation KW - social media KW - health belief model KW - deep learning KW - community engagement N2 - Background: Despite increasing awareness about and advances in addressing social media misinformation, the free flow of false COVID-19 information has continued, affecting individuals? preventive behaviors, including masking, testing, and vaccine uptake. Objective: In this paper, we describe our multidisciplinary efforts with a specific focus on methods to (1) gather community needs, (2) develop interventions, and (3) conduct large-scale agile and rapid community assessments to examine and combat COVID-19 misinformation. Methods: We used the Intervention Mapping framework to perform community needs assessment and develop theory-informed interventions. To supplement these rapid and responsive efforts through large-scale online social listening, we developed a novel methodological framework, comprising qualitative inquiry, computational methods, and quantitative network models to analyze publicly available social media data sets to model content-specific misinformation dynamics and guide content tailoring efforts. As part of community needs assessment, we conducted 11 semistructured interviews, 4 listening sessions, and 3 focus groups with community scientists. Further, we used our data repository with 416,927 COVID-19 social media posts to gather information diffusion patterns through digital channels. Results: Our results from community needs assessment revealed the complex intertwining of personal, cultural, and social influences of misinformation on individual behaviors and engagement. Our social media interventions resulted in limited community engagement and indicated the need for consumer advocacy and influencer recruitment. The linking of theoretical constructs underlying health behaviors to COVID-19?related social media interactions through semantic and syntactic features using our computational models has revealed frequent interaction typologies in factual and misleading COVID-19 posts and indicated significant differences in network metrics such as degree. The performance of our deep learning classifiers was reasonable, with an F-measure of 0.80 for speech acts and 0.81 for behavior constructs. Conclusions: Our study highlights the strengths of community-based field studies and emphasizes the utility of large-scale social media data sets in enabling rapid intervention tailoring to adapt grassroots community interventions to thwart misinformation seeding and spread among minority communities. Implications for consumer advocacy, data governance, and industry incentives are discussed for the sustainable role of social media solutions in public health. UR - https://infodemiology.jmir.org/2023/1/e40156 UR - http://dx.doi.org/10.2196/40156 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113378 ID - info:doi/10.2196/40156 ER - TY - JOUR AU - Park, Susan AU - Suh, Young-Kyoon PY - 2023/1/31 TI - A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis JO - J Med Internet Res SP - e42623 VL - 25 KW - COVID-19 KW - vaccine KW - vaccination KW - Pfizer KW - Moderna KW - AstraZeneca KW - Janssen KW - Novavax N2 - Background:  The unprecedented speed of COVID-19 vaccine development and approval has raised public concern about its safety. However, studies on public discourses and opinions on social media focusing on adverse events (AEs) related to COVID-19 vaccine are rare. Objective:  This study aimed to analyze Korean tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, Janssen, and Novavax) after the vaccine rollout, explore the topics and sentiments of tweets regarding COVID-19 vaccines, and examine their changes over time. We also analyzed topics and sentiments focused on AEs related to vaccination using only tweets with terms about AEs. Methods:  We devised a sophisticated methodology consisting of 5 steps: keyword search on Twitter, data collection, data preprocessing, data analysis, and result visualization. We used the Twitter Representational State Transfer application programming interface for data collection. A total of 1,659,158 tweets were collected from February 1, 2021, to March 31, 2022. Finally, 165,984 data points were analyzed after excluding retweets, news, official announcements, advertisements, duplicates, and tweets with <2 words. We applied a variety of preprocessing techniques that are suitable for the Korean language. We ran a suite of analyses using various Python packages, such as latent Dirichlet allocation, hierarchical latent Dirichlet allocation, and sentiment analysis. Results:  The topics related to COVID-19 vaccines have a very large spectrum, including vaccine-related AEs, emotional reactions to vaccination, vaccine development and supply, and government vaccination policies. Among them, the top major topic was AEs related to COVID-19 vaccination. The AEs ranged from the adverse reactions listed in the safety profile (eg, myalgia, fever, fatigue, injection site pain, myocarditis or pericarditis, and thrombosis) to unlisted reactions (eg, irregular menstruation, changes in appetite and sleep, leukemia, and deaths). Our results showed a notable difference in the topics for each vaccine brand. The topics pertaining to the Pfizer vaccine mainly mentioned AEs. Negative public opinion has prevailed since the early stages of vaccination. In the sentiment analysis based on vaccine brand, the topics related to the Pfizer vaccine expressed the strongest negative sentiment. Conclusions:  Considering the discrepancy between academic evidence and public opinions related to COVID-19 vaccination, the government should provide accurate information and education. Furthermore, our study suggests the need for management to correct the misinformation related to vaccine-related AEs, especially those affecting negative sentiments. This study provides valuable insights into the public discourses and opinions regarding COVID-19 vaccination. UR - https://www.jmir.org/2023/1/e42623 UR - http://dx.doi.org/10.2196/42623 UR - http://www.ncbi.nlm.nih.gov/pubmed/36603153 ID - info:doi/10.2196/42623 ER - TY - JOUR AU - Han, Nuo AU - Li, Sijia AU - Huang, Feng AU - Wen, Yeye AU - Wang, Xiaoyang AU - Liu, Xiaoqian AU - Li, Linyan AU - Zhu, Tingshao PY - 2023/1/31 TI - Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study JO - J Med Internet Res SP - e41823 VL - 25 KW - mental health KW - psychological well-being KW - social media KW - machine learning KW - domain knowledge KW - mental well being KW - mental wellbeing KW - linguistic KW - predict KW - model KW - ground truth KW - lexicon N2 - Background: Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and monitor online users? PWB in a nonintrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB. Objective: This study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way. Methods: We recruited 1427 participants. Their well-being was evaluated using 6 dimensions of PWB. Their posts on social media were collected, and 6 psychological lexicons were used to extract linguistic features. A multiobjective prediction model was then built with the extracted linguistic features as input and PWB as the output. Further, the validity of the prediction model was confirmed by evaluating the model's discriminant validity, convergent validity, and criterion validity. The reliability of the model was also confirmed by evaluating the split-half reliability. Results: The correlation coefficients between the predicted PWB scores of social media users and the actual scores obtained using the linguistic prediction model of this study were between 0.49 and 0.54 (P<.001), which means that the model had good criterion validity. In terms of the model?s structural validity, it exhibited excellent convergent validity but less than satisfactory discriminant validity. The results also suggested that our model had good split-half reliability levels for every dimension (ranging from 0.65 to 0.85; P<.001). Conclusions: By confirming the availability and stability of the linguistic prediction model, this study verified the predictability of social media corresponding to ground truth well-being data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in nonprofessional settings such as self-testing or a large-scale user study. UR - https://www.jmir.org/2023/1/e41823 UR - http://dx.doi.org/10.2196/41823 UR - http://www.ncbi.nlm.nih.gov/pubmed/36719723 ID - info:doi/10.2196/41823 ER - TY - JOUR AU - Poirier, Canelle AU - Bouzillé, Guillaume AU - Bertaud, Valérie AU - Cuggia, Marc AU - Santillana, Mauricio AU - Lavenu, Audrey PY - 2023/1/31 TI - Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study JO - JMIR Public Health Surveill SP - e34982 VL - 9 KW - infectious disease KW - acute gastroenteritis KW - modeling KW - modeling disease outbreaks KW - machine learning KW - public health KW - machine learning in public health KW - forecasting KW - digital data N2 - Background: Disease surveillance systems capable of producing accurate real-time and short-term forecasts can help public health officials design timely public health interventions to mitigate the effects of disease outbreaks in affected populations. In France, existing clinic-based disease surveillance systems produce gastroenteritis activity information that lags real time by 1 to 3 weeks. This temporal data gap prevents public health officials from having a timely epidemiological characterization of this disease at any point in time and thus leads to the design of interventions that do not take into consideration the most recent changes in dynamics. Objective: The goal of this study was to evaluate the feasibility of using internet search query trends and electronic health records to predict acute gastroenteritis (AG) incidence rates in near real time, at the national and regional scales, and for long-term forecasts (up to 10 weeks). Methods: We present 2 different approaches (linear and nonlinear) that produce real-time estimates, short-term forecasts, and long-term forecasts of AG activity at 2 different spatial scales in France (national and regional). Both approaches leverage disparate data sources that include disease-related internet search activity, electronic health record data, and historical disease activity. Results: Our results suggest that all data sources contribute to improving gastroenteritis surveillance for long-term forecasts with the prominent predictive power of historical data owing to the strong seasonal dynamics of this disease. Conclusions: The methods we developed could help reduce the impact of the AG peak by making it possible to anticipate increased activity by up to 10 weeks. UR - https://publichealth.jmir.org/2023/1/e34982 UR - http://dx.doi.org/10.2196/34982 UR - http://www.ncbi.nlm.nih.gov/pubmed/36719726 ID - info:doi/10.2196/34982 ER - TY - JOUR AU - Rochford, Ben AU - Pendse, Sachin AU - Kumar, Neha AU - De Choudhury, Munmun PY - 2023/1/30 TI - Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data JO - JMIR Ment Health SP - e43253 VL - 10 KW - mental health KW - search engine algorithms KW - digital mental health KW - health equity N2 - Background: In the United States, 1 out of every 3 people lives in a mental health professional shortage area. Shortage areas tend to be rural, have higher levels of poverty, and have poor mental health outcomes. Previous work has demonstrated that these poor outcomes may arise from interactions between a lack of resources and lack of recognition of mental illness by medical professionals. Objective: We aimed to understand the differences in how people in shortage and nonshortage areas search for information about mental health on the web. Methods: We analyzed search engine log data related to health from 2017-2021 and examined the differences in mental health search behavior between shortage and nonshortage areas. We analyzed several axes of difference, including shortage versus nonshortage comparisons, urban versus rural comparisons, and temporal comparisons. Results: We found specific differences in search behavior between shortage and nonshortage areas. In shortage areas, broader and more general mental health symptom categories, namely anxiety (mean 2.03%, SD 0.44%), depression (mean 1.15%, SD 0.27%), fatigue (mean 1.21%, SD 0.28%), and headache (mean 1.03%, SD 0.23%), were searched significantly more often (Q<.0003). In contrast, specific symptom categories and mental health disorders such as binge eating (mean 0.02%, SD 0.02%), psychosis (mean 0.37%, SD 0.06%), and attention-deficit/hyperactivity disorder (mean 0.77%, SD 0.10%) were searched significantly more often (Q<.0009) in nonshortage areas. Although suicide rates are consistently known to be higher in shortage and rural areas, we see that the rates of suicide-related searching are lower in shortage areas (mean 0.05%, SD 0.04%) than in nonshortage areas (mean 0.10%, SD 0.03%; Q<.0003), more so when a shortage area is rural (mean 0.024%, SD 0.029%; Q<2 × 10?12). Conclusions: This study demonstrates differences in how people from geographically marginalized groups search on the web for mental health. One main implication of this work is the influence that search engine ranking algorithms and interface design might have on the kinds of resources that individuals use when in distress. Our results support the idea that search engine algorithm designers should be conscientious of the role that structural factors play in expressions of distress and they should attempt to design search engine algorithms and interfaces to close gaps in care. UR - https://mental.jmir.org/2023/1/e43253 UR - http://dx.doi.org/10.2196/43253 UR - http://www.ncbi.nlm.nih.gov/pubmed/36716082 ID - info:doi/10.2196/43253 ER - TY - JOUR AU - Chin, Hyojin AU - Lima, Gabriel AU - Shin, Mingi AU - Zhunis, Assem AU - Cha, Chiyoung AU - Choi, Junghoi AU - Cha, Meeyoung PY - 2023/1/27 TI - User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis JO - J Med Internet Res SP - e40922 VL - 25 KW - chatbot KW - COVID-19 KW - topic modeling KW - sentiment analysis KW - infodemiology KW - discourse KW - public perception KW - public health KW - infoveillance KW - conversational agent KW - global health KW - health information N2 - Background: Chatbots have become a promising tool to support public health initiatives. Despite their potential, little research has examined how individuals interacted with chatbots during the COVID-19 pandemic. Understanding user-chatbot interactions is crucial for developing services that can respond to people?s needs during a global health emergency. Objective: This study examined the COVID-19 pandemic?related topics online users discussed with a commercially available social chatbot and compared the sentiment expressed by users from 5 culturally different countries. Methods: We analyzed 19,782 conversation utterances related to COVID-19 covering 5 countries (the United States, the United Kingdom, Canada, Malaysia, and the Philippines) between 2020 and 2021, from SimSimi, one of the world?s largest open-domain social chatbots. We identified chat topics using natural language processing methods and analyzed their emotional sentiments. Additionally, we compared the topic and sentiment variations in the COVID-19?related chats across countries. Results: Our analysis identified 18 emerging topics, which could be categorized into the following 5 overarching themes: ?Questions on COVID-19 asked to the chatbot? (30.6%), ?Preventive behaviors? (25.3%), ?Outbreak of COVID-19? (16.4%), ?Physical and psychological impact of COVID-19? (16.0%), and ?People and life in the pandemic? (11.7%). Our data indicated that people considered chatbots as a source of information about the pandemic, for example, by asking health-related questions. Users turned to SimSimi for conversation and emotional messages when offline social interactions became limited during the lockdown period. Users were more likely to express negative sentiments when conversing about topics related to masks, lockdowns, case counts, and their worries about the pandemic. In contrast, small talk with the chatbot was largely accompanied by positive sentiment. We also found cultural differences, with negative words being used more often by users in the United States than by those in Asia when talking about COVID-19. Conclusions: Based on the analysis of user-chatbot interactions on a live platform, this work provides insights into people?s informational and emotional needs during a global health crisis. Users sought health-related information and shared emotional messages with the chatbot, indicating the potential use of chatbots to provide accurate health information and emotional support. Future research can look into different support strategies that align with the direction of public health policy. UR - https://www.jmir.org/2023/1/e40922 UR - http://dx.doi.org/10.2196/40922 UR - http://www.ncbi.nlm.nih.gov/pubmed/36596214 ID - info:doi/10.2196/40922 ER - TY - JOUR AU - Brewer, R. Hannah AU - Hirst, Yasemin AU - Chadeau-Hyam, Marc AU - Johnson, Eric AU - Sundar, Sudha AU - Flanagan, M. James PY - 2023/1/26 TI - Association Between Purchase of Over-the-Counter Medications and Ovarian Cancer Diagnosis in the Cancer Loyalty Card Study (CLOCS): Observational Case-Control Study JO - JMIR Public Health Surveill SP - e41762 VL - 9 KW - ovarian cancer KW - early diagnosis KW - transactional data KW - health informatics KW - cancer risk KW - medication KW - self-medication KW - self-care KW - over-the-counter medication KW - nonspecific symptoms KW - pain medication KW - indigestion medication N2 - Background: Over-the-counter (OTC) medications are frequently used to self-care for nonspecific ovarian cancer symptoms prior to diagnosis. Monitoring such purchases may provide an opportunity for earlier diagnosis. Objective: The aim of the Cancer Loyalty Card Study (CLOCS) was to investigate purchases of OTC pain and indigestion medications prior to ovarian cancer diagnosis in women with and without ovarian cancer in the United Kingdom using loyalty card data. Methods: An observational case-control study was performed comparing purchases of OTC pain and indigestion medications prior to diagnosis in women with (n=153) and without (n=120) ovarian cancer using loyalty card data from two UK-based high street retailers. Monthly purchases of pain and indigestion medications for cases and controls were compared using the Fisher exact test, conditional logistic regression, and receiver operating characteristic (ROC) curve analysis. Results: Pain and indigestion medication purchases were increased among cases 8 months before diagnosis, with maximum discrimination between cases and controls 8 months before diagnosis (Fisher exact odds ratio [OR] 2.9, 95% CI 2.1-4.1). An increase in indigestion medication purchases was detected up to 9 months before diagnosis (adjusted conditional logistic regression OR 1.38, 95% CI 1.04-1.83). The ROC analysis for indigestion medication purchases showed a maximum area under the curve (AUC) at 13 months before diagnosis (AUC=0.65, 95% CI 0.57-0.73), which further improved when stratified to late-stage ovarian cancer (AUC=0.68, 95% CI 0.59-0.78). Conclusions: There is a difference in purchases of pain and indigestion medications among women with and without ovarian cancer up to 8 months before diagnosis. Facilitating earlier presentation among those who self-care for symptoms using this novel data source could improve ovarian cancer patients? options for treatment and improve survival. Trial Registration: ClinicalTrials.gov NCT03994653; https://clinicaltrials.gov/ct2/show/NCT03994653 UR - https://publichealth.jmir.org/2023/1/e41762 UR - http://dx.doi.org/10.2196/41762 UR - http://www.ncbi.nlm.nih.gov/pubmed/36701184 ID - info:doi/10.2196/41762 ER - TY - JOUR AU - Cuomo, Raphael AU - Purushothaman, Vidya AU - Calac, J. Alec AU - McMann, Tiana AU - Li, Zhuoran AU - Mackey, Tim PY - 2023/1/25 TI - Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study JO - JMIR Form Res SP - e42162 VL - 7 KW - overdose KW - mortality KW - geospatial analysis KW - social media KW - drug overuse KW - substance use KW - social media data KW - mortality estimates KW - real-time data KW - public health data KW - demographic variables KW - county-level N2 - Background: There were an estimated 100,306 drug overdose deaths between April 2020 and April 2021, a three-quarter increase from the prior 12-month period. There is an approximate 6-month reporting lag for provisional counts of drug overdose deaths from the National Vital Statistics System, and the highest level of geospatial resolution is at the state level. By contrast, public social media data are available close to real-time and are often accessible with precise coordinates. Objective: The purpose of this study is to assess whether county-level overdose mortality burden could be estimated using opioid-related Twitter data. Methods: International Classification of Diseases (ICD) codes for poisoning or exposure to overdose at the county level were obtained from CDC WONDER. Demographics were collected from the American Community Survey. The Twitter Application Programming Interface was used to obtain tweets that contained any of the 36 terms with drug names. An unsupervised classification approach was used for clustering tweets. Population-normalized variables and polynomial population-normalized variables were produced. Furthermore, z scores of the Getis Ord Gi clustering statistic were produced, and both these scores and their polynomial counterparts were explored in regression modeling of county-level overdose mortality burden. A series of linear regression models were used for predictive modeling to explore the interpretability of the analytical output. Results: Modeling overdose mortality with normalized demographic variables alone explained only 7.4% of the variability in county-level overdose mortality, whereas this was approximately doubled by the use of specific demographic and Twitter data covariates based on a backward selection approach. The highest adjusted R2 and lowest AIC (Akaike Info Criterion) were obtained for the model with normalized demographic variables, normalized z scores from geospatial analyses, and normalized topic counts (adjusted R2=0.133, AIC=8546.8). The z scores of the Getis Ord Gi statistic appeared to have improved utility over population-normalization alone. In this model, median age, female population, and tweets about web-based drug sales were positively associated with opioid mortality. Asian race and Hispanic ethnicity were significantly negatively associated with county-level burdens of overdose mortality. Conclusions: Social media data, when transformed using certain statistical approaches, may add utility to the goal of producing closer to real-time county-level estimates of overdose mortality. Prediction of opioid-related outcomes can be advanced to inform prevention and treatment decisions. This interdisciplinary approach can facilitate evidence-based funding decisions for various substance use disorder prevention and treatment programs. UR - https://formative.jmir.org/2023/1/e42162 UR - http://dx.doi.org/10.2196/42162 UR - http://www.ncbi.nlm.nih.gov/pubmed/36548118 ID - info:doi/10.2196/42162 ER - TY - JOUR AU - Manne, Sharon AU - Pagoto, Sherry AU - Peterson, Susan AU - Heckman, Carolyn AU - Kashy, Deborah AU - Berger, Adam AU - Studts, Christina AU - Negrón, Rosalyn AU - Buller, David AU - Paddock, Lisa AU - Gallo, Joseph AU - Kulik, Alexandria AU - Frederick, Sara AU - Pesanelli, Morgan AU - Domider, Mara AU - Grosso, Marissa PY - 2023/1/24 TI - Facebook Intervention for Young-Onset Melanoma Survivors and Families: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e39640 VL - 12 KW - cancer survivors KW - melanoma survivors KW - skin self-examination KW - clinical skin examination KW - sun protection KW - behavioral intervention KW - social media N2 - Background: Individuals diagnosed with melanoma before the age of 40 years (young-onset melanoma survivors) and their first-degree relatives (FDRs) are a growing population at risk for developing recurrent melanoma or new melanomas. Regular surveillance using clinical skin examination (CSE) and skin self-examination (SSE) and engagement in preventive behaviors including sun protection are recommended. Given the growing population of survivors and their families who are at increased risk, it is surprising that no behavioral interventions have been developed and evaluated to improve risk-reduction behaviors. Objective: We describe the rationale and methodology for a randomized controlled trial evaluating the efficacy of a Facebook intervention providing information, goal setting, and peer support to increase CSE, SSE, and sun protection for young-onset melanoma survivors and their FDRs. Methods: Overall, 577 survivors and 577 FDRs will be randomly assigned to either the Young Melanoma Family Facebook Group or the Melanoma Family Healthy Lifestyle Facebook Group condition. Participants will complete measures of CSE, SSE, and sun protection, and mediator measures of attitudes and beliefs before and after the intervention. The primary aim is to evaluate the impact of the Young Melanoma Family Facebook intervention versus the Melanoma Family Healthy Lifestyle Facebook intervention on CSE, SSE frequency and comprehensiveness, and sun protection among FDRs. The secondary aims examine the efficacy of the Young Melanoma Family Facebook intervention on survivors? SSE frequency and comprehensiveness and sun protection behaviors and mechanisms of intervention efficacy for intervention impact on FDR and survivor outcomes. The exploratory aim is to evaluate the efficacy of the 2 interventions on perceived stress, physical activity, and healthy eating. Results: This project was funded by the National Institutes of Health (R01CA221854). The project began in May 2018, and recruitment started in January 2019. We anticipate completing enrollment by November 2023. Power calculations recommended a sample size of 577 survivors and 577 FDRs. Multilevel modeling treating family as the upper-level sampling unit and individual as the lower-level sampling unit will be the primary data analytic approach. Fixed effect predictors in these models will include condition, role, sex, all 2- and 3-way interactions, and covariates. Conclusions: The Young Melanoma Family Facebook intervention aims to improve primary and secondary skin cancer prevention for young-onset melanoma survivors and their family members. The intervention?s delivery via a popular, freely available social media platform increases its impact because of the potential for dissemination in many contexts. If efficacious, this program could be disseminated by dermatologist practices, public health or nonprofit organizations focused on melanoma, and existing melanoma and skin cancer Facebook groups, thereby expanding its reach. This project will produce a content library of posts and a moderation guide for others. Trial Registration: ClinicalTrials.gov NCT03677739; https://clinicaltrials.gov/ct2/show/NCT03677739 International Registered Report Identifier (IRRID): DERR1-10.2196/39640 UR - https://www.researchprotocols.org/2023/1/e39640 UR - http://dx.doi.org/10.2196/39640 UR - http://www.ncbi.nlm.nih.gov/pubmed/36692933 ID - info:doi/10.2196/39640 ER - TY - JOUR AU - Turner, Jason AU - Kantardzic, Mehmed AU - Vickers-Smith, Rachel AU - Brown, G. Andrew PY - 2023/1/23 TI - Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification JO - JMIR Infodemiology SP - e38390 VL - 3 KW - transformer KW - misinformation KW - deep learning KW - COVID-19 KW - infodemic KW - pandemic KW - language model KW - health information KW - social media KW - Twitter KW - content analysis KW - cannabidiol KW - sentence vector KW - infodemiology N2 - Background: COVID-19 has introduced yet another opportunity to web-based sellers of loosely regulated substances, such as cannabidiol (CBD), to promote sales under false pretenses of curing the disease. Therefore, it has become necessary to innovate ways to identify such instances of misinformation. Objective: We sought to identify COVID-19 misinformation as it relates to the sales or promotion of CBD and used transformer-based language models to identify tweets semantically similar to quotes taken from known instances of misinformation. In this case, the known misinformation was the publicly available Warning Letters from Food and Drug Administration (FDA). Methods: We collected tweets using CBD- and COVID-19?related terms. Using a previously trained model, we extracted the tweets indicating commercialization and sales of CBD and annotated those containing COVID-19 misinformation according to the FDA definitions. We encoded the collection of tweets and misinformation quotes into sentence vectors and then calculated the cosine similarity between each quote and each tweet. This allowed us to establish a threshold to identify tweets that were making false claims regarding CBD and COVID-19 while minimizing the instances of false positives. Results: We demonstrated that by using quotes taken from Warning Letters issued by FDA to perpetrators of similar misinformation, we can identify semantically similar tweets that also contain misinformation. This was accomplished by identifying a cosine distance threshold between the sentence vectors of the Warning Letters and tweets. Conclusions: This research shows that commercial CBD or COVID-19 misinformation can potentially be identified and curbed using transformer-based language models and known prior instances of misinformation. Our approach functions without the need for labeled data, potentially reducing the time at which misinformation can be identified. Our approach shows promise in that it is easily adapted to identify other forms of misinformation related to loosely regulated substances. UR - https://infodemiology.jmir.org/2023/1/e38390 UR - http://dx.doi.org/10.2196/38390 UR - http://www.ncbi.nlm.nih.gov/pubmed/36844029 ID - info:doi/10.2196/38390 ER - TY - JOUR AU - Omranian, Samaneh AU - Zolnoori, Maryam AU - Huang, Ming AU - Campos-Castillo, Celeste AU - McRoy, Susan PY - 2023/1/23 TI - Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media JO - JMIR Infodemiology SP - e37207 VL - 3 KW - machine learning KW - online forums KW - text classification KW - topic modeling KW - MetaMap KW - drug review KW - opioid treatment, opioid use disorder KW - patient-generated text N2 - Background: Medication-assisted treatment (MAT) is an effective method for treating opioid use disorder (OUD), which combines behavioral therapies with one of three Food and Drug Administration?approved medications: methadone, buprenorphine, and naloxone. While MAT has been shown to be effective initially, there is a need for more information from the patient perspective about the satisfaction with medications. Existing research focuses on patient satisfaction with the entirety of the treatment, making it difficult to determine the unique role of medication and overlooking the views of those who may lack access to treatment due to being uninsured or concerns over stigma. Studies focusing on patients? perspectives are also limited by the lack of scales that can efficiently collect self-reports across domains of concerns. Objective: A broad survey of patients? viewpoints can be obtained through social media and drug review forums, which are then assessed using automated methods to discover factors associated with medication satisfaction. Because the text is unstructured, it may contain a mix of formal and informal language. The primary aim of this study was to use natural language processing methods on text posted on health-related social media to detect patients? satisfaction with two well-studied OUD medications: methadone and buprenorphine/naloxone. Methods: We collected 4353 patient reviews of methadone and buprenorphine/naloxone from 2008 to 2021 posted on WebMD and Drugs.com. To build our predictive models for detecting patient satisfaction, we first employed different analyses to build four input feature sets using the vectorized text, topic models, duration of treatment, and biomedical concepts by applying MetaMap. We then developed six prediction models: logistic regression, Elastic Net, least absolute shrinkage and selection operator, random forest classifier, Ridge classifier, and extreme gradient boosting to predict patients? satisfaction. Lastly, we compared the prediction models? performance over different feature sets. Results: Topics discovered included oral sensation, side effects, insurance, and doctor visits. Biomedical concepts included symptoms, drugs, and illnesses. The F-score of the predictive models across all methods ranged from 89.9% to 90.8%. The Ridge classifier model, a regression-based method, outperformed the other models. Conclusions: Assessment of patients? satisfaction with opioid dependency treatment medication can be predicted using automated text analysis. Adding biomedical concepts such as symptoms, drug name, and illness, along with the duration of treatment and topic models, had the most benefits for improving the prediction performance of the Elastic Net model compared to other models. Some of the factors associated with patient satisfaction overlap with domains covered in medication satisfaction scales (eg, side effects) and qualitative patient reports (eg, doctors? visits), while others (insurance) are overlooked, thereby underscoring the value added from processing text on online health forums to better understand patient adherence. UR - https://infodemiology.jmir.org/2023/1/e37207 UR - http://dx.doi.org/10.2196/37207 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113381 ID - info:doi/10.2196/37207 ER - TY - JOUR AU - Kim, Donghun AU - Jung, Woojin AU - Jiang, Ting AU - Zhu, Yongjun PY - 2023/1/19 TI - An Exploratory Study of Medical Journal?s Twitter Use: Metadata, Networks, and Content Analyses JO - J Med Internet Res SP - e43521 VL - 25 KW - medical journals KW - social networks KW - Twitter N2 - Background: An increasing number of medical journals are using social media to promote themselves and communicate with their readers. However, little is known about how medical journals use Twitter and what their social media management strategies are. Objective: This study aimed to understand how medical journals use Twitter from a global standpoint. We conducted a broad, in-depth analysis of all the available Twitter accounts of medical journals indexed by major indexing services, with a particular focus on their social networks and content. Methods: The Twitter profiles and metadata of medical journals were analyzed along with the social networks on their Twitter accounts. Results: The results showed that overall, publishers used different strategies regarding Twitter adoption, Twitter use patterns, and their subsequent decisions. The following specific findings were noted: journals with Twitter accounts had a significantly higher number of publications and a greater impact than their counterparts; subscription journals had a slightly higher Twitter adoption rate (2%) than open access journals; journals with higher impact had more followers; and prestigious journals rarely followed other lesser-known journals on social media. In addition, an in-depth analysis of 2000 randomly selected tweets from 4 prestigious journals revealed that The Lancet had dedicated considerable effort to communicating with people about health information and fulfilling its social responsibility by organizing committees and activities to engage with a broad range of health-related issues; The New England Journal of Medicine and the Journal of the American Medical Association focused on promoting research articles and attempting to maximize the visibility of their research articles; and the British Medical Journal provided copious amounts of health information and discussed various health-related social problems to increase social awareness of the field of medicine. Conclusions: Our study used various perspectives to investigate how medical journals use Twitter and explored the Twitter management strategies of 4 of the most prestigious journals. Our study provides a detailed understanding of medical journals? use of Twitter from various perspectives and can help publishers, journals, and researchers to better use Twitter for their respective purposes. UR - https://www.jmir.org/2023/1/e43521 UR - http://dx.doi.org/10.2196/43521 UR - http://www.ncbi.nlm.nih.gov/pubmed/36656626 ID - info:doi/10.2196/43521 ER - TY - JOUR AU - Ji-Xu, Antonio AU - Htet, Zin Kyaw AU - Leslie, S. Kieron PY - 2023/1/17 TI - Monkeypox Content on TikTok: Cross-sectional Analysis JO - J Med Internet Res SP - e44697 VL - 25 KW - TikTok KW - social media KW - monkeypox KW - mpox KW - pandemic KW - epidemic KW - infectious disease KW - outbreak KW - quality assessment KW - content analysis UR - https://www.jmir.org/2023/1/e44697 UR - http://dx.doi.org/10.2196/44697 UR - http://www.ncbi.nlm.nih.gov/pubmed/36649057 ID - info:doi/10.2196/44697 ER - TY - JOUR AU - Meyerson, U. William AU - Fineberg, K. Sarah AU - Song, Kyung Ye AU - Faber, Adam AU - Ash, Garrett AU - Andrade, C. Fernanda AU - Corlett, Philip AU - Gerstein, B. Mark AU - Hoyle, H. Rick PY - 2023/1/17 TI - Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys JO - JMIR Form Res SP - e38112 VL - 7 KW - social media KW - sleep KW - parametric models KW - Reddit KW - observational model KW - research tool KW - sleep patterns KW - usage data KW - model KW - bedtime N2 - Background: Individuals with later bedtimes have an increased risk of difficulties with mood and substances. To investigate the causes and consequences of late bedtimes and other sleep patterns, researchers are exploring social media as a data source. Pioneering studies inferred sleep patterns directly from social media data. While innovative, these efforts are variously unscalable, context dependent, confined to specific sleep parameters, or rest on untested assumptions, and none of the reviewed studies apply to the popular Reddit platform or release software to the research community. Objective: This study builds on this prior work. We estimate the bedtimes of Reddit users from the times tamps of their posts, test inference validity against survey data, and release our model as an R package (The R Foundation). Methods: We included 159 sufficiently active Reddit users with known time zones and known, nonanomalous bedtimes, together with the time stamps of their 2.1 million posts. The model?s form was chosen by visualizing the aggregate distribution of the timing of users? posts relative to their reported bedtimes. The chosen model represents a user?s frequency of Reddit posting by time of day, with a flat portion before bedtime and a quadratic depletion that begins near the user?s bedtime, with parameters fitted to the data. This model estimates the bedtimes of individual Reddit users from the time stamps of their posts. Model performance is assessed through k-fold cross-validation. We then apply the model to estimate the bedtimes of 51,372 sufficiently active, nonbot Reddit users with known time zones from the time stamps of their 140 million posts. Results: The Pearson correlation between expected and observed Reddit posting frequencies in our model was 0.997 on aggregate data. On average, posting starts declining 45 minutes before bedtime, reaches a nadir 4.75 hours after bedtime that is 87% lower than the daytime rate, and returns to baseline 10.25 hours after bedtime. The Pearson correlation between inferred and reported bedtimes for individual users was 0.61 (P<.001). In 90 of 159 cases (56.6%), our estimate was within 1 hour of the reported bedtime; 128 cases (80.5%) were within 2 hours. There was equivalent accuracy in hold-out sets versus training sets of k-fold cross-validation, arguing against overfitting. The model was more accurate than a random forest approach. Conclusions: We uncovered a simple, reproducible relationship between Reddit users? reported bedtimes and the time of day when high daytime posting rates transition to low nighttime posting rates. We captured this relationship in a model that estimates users? bedtimes from the time stamps of their posts. Limitations include applicability only to users who post frequently, the requirement for time zone data, and limits on generalizability. Nonetheless, it is a step forward for inferring the sleep parameters of social media users passively at scale. Our model and precomputed estimated bedtimes of 50,000 Reddit users are freely available. UR - https://formative.jmir.org/2023/1/e38112 UR - http://dx.doi.org/10.2196/38112 UR - http://www.ncbi.nlm.nih.gov/pubmed/36649054 ID - info:doi/10.2196/38112 ER - TY - JOUR AU - van Kessel, Robin AU - Kyriopoulos, Ilias AU - Wong, Han Brian Li AU - Mossialos, Elias PY - 2023/1/16 TI - The Effect of the COVID-19 Pandemic on Digital Health?Seeking Behavior: Big Data Interrupted Time-Series Analysis of Google Trends JO - J Med Internet Res SP - e42401 VL - 25 KW - digital health KW - healthcare seeking behaviour KW - big data KW - real-world data KW - data KW - COVID-19 KW - pandemic KW - Google Trends KW - telehealth N2 - Background: Due to the emergency responses early in the COVID-19 pandemic, the use of digital health in health care increased abruptly. However, it remains unclear whether this introduction was sustained in the long term, especially with patients being able to decide between digital and traditional health services once the latter regained their functionality throughout the COVID-19 pandemic. Objective: We aim to understand how the public interest in digital health changed as proxy for digital health?seeking behavior and to what extent this change was sustainable over time. Methods: We used an interrupted time-series analysis of Google Trends data with break points on March 11, 2020 (declaration of COVID-19 as a pandemic by the World Health Organization), and December 20, 2020 (the announcement of the first COVID-19 vaccines). Nationally representative time-series data from February 2019 to August 2021 were extracted from Google Trends for 6 countries with English as their dominant language: Canada, the United States, the United Kingdom, New Zealand, Australia, and Ireland. We measured the changes in relative search volumes of the keywords online doctor, telehealth, online health, telemedicine, and health app. In doing so, we capture the prepandemic trend, the immediate change due to the announcement of COVID-19 being a pandemic, and the gradual change after the announcement. Results: Digital health search volumes immediately increased in all countries under study after the announcement of COVID-19 being a pandemic. There was some variation in what keywords were used per country. However, searches declined after this immediate spike, sometimes reverting to prepandemic levels. The announcement of COVID-19 vaccines did not consistently impact digital health search volumes in the countries under study. The exception is the search volume of health app, which was observed as either being stable or gradually increasing during the pandemic. Conclusions: Our findings suggest that the increased public interest in digital health associated with the pandemic did not sustain, alluding to remaining structural barriers. Further building of digital health capacity and developing robust digital health governance frameworks remain crucial to facilitating sustainable digital health transformation. UR - https://www.jmir.org/2023/1/e42401 UR - http://dx.doi.org/10.2196/42401 UR - http://www.ncbi.nlm.nih.gov/pubmed/36603152 ID - info:doi/10.2196/42401 ER - TY - JOUR AU - Li, Yongjie AU - Yan, Xiangyu AU - Wang, Zekun AU - Ma, Mingchang AU - Zhang, Bo AU - Jia, Zhongwei PY - 2023/1/11 TI - Comparison of the Users? Attitudes Toward Cannabidiol on Social Media Platforms: Topic Modeling Study JO - JMIR Public Health Surveill SP - e34132 VL - 9 KW - cannabidiol KW - drug policy KW - latent Dirichlet allocation KW - social media KW - sentiment analysis N2 - Background: As one of the major constituents of the cannabis sativa plant, cannabidiol (CBD) is approved for use in medical treatment and cosmetics because of its potential health benefits. With the rapid growth of the CBD market, customers purchase these products, and relevant discussions are becoming more active on social media. Objective: In this study, we aimed to understand the users? attitudes toward CBD products in various countries by conducting text mining on social media in countries with different substance management policies. Methods: We collected posts from Reddit and Xiaohongshu, conducted topic mining using the latent Dirichlet allocation model, and analyzed the characteristics of topics on different social media. Subsequently, a co-occurrence network of high-frequency keywords was constructed to explore potential relationships among topics. Moreover, we conducted sentiment analysis on the posts? comments and compared users? attitudes toward CBD products on Reddit and Xiaohongshu using chi-square test. Results: CBD-related posts on social media have been rapidly increasing, especially on Xiaohongshu since 2019. A total of 1790 posts from Reddit and 1951 posts from Xiaohongshu were included in the final analysis. The posts on the 2 social media platforms, Reddit and Xiaohongshu, were categorized into 7 and 8 topics, respectively, by the latent Dirichlet allocation model, and these topics on the 2 social media were grouped into 5 themes. Our study showed that the themes on Reddit were mainly related to the therapeutic effects of CBD, whereas the themes on Xiaohongshu concentrated on cosmetics, such as facial masks. Theme 2 (CBD market information) and theme 3 (attitudes toward CBD) on Reddit had more connections with other themes in the co-occurrence network, and theme 3 and theme 1 (CBD therapeutic effects) had a high co-occurrence frequency (22,803/73,865, 30.87%). Meanwhile, theme 1 (CBD cosmetics) on Xiaohongshu had various connections with others (169,961/384,575, 44.19%), and the co-occurrence frequency of theme 4 (CBD ingredients) and theme 1 was relatively prominent (27,128/49,312, 55.01%). Overall, users? comments tended to be positive for CBD-related information on both Reddit and Xiaohongshu, but the percentage was higher on Xiaohongshu (82.25% vs 86.18%; P<.001), especially in cosmetics and medical health care products. Conclusions: The CBD market has grown rapidly, and the topics related to CBD on social media have become active. There are apparent differences in users? attitudes toward CBD in countries with different substance management policies. Targeted CBD management measures should be formulated to suit the prevalence of CBD use of each country. UR - https://publichealth.jmir.org/2023/1/e34132 UR - http://dx.doi.org/10.2196/34132 UR - http://www.ncbi.nlm.nih.gov/pubmed/36630175 ID - info:doi/10.2196/34132 ER - TY - JOUR AU - Sharma, E. Anjana AU - Khosla, Kiran AU - Potharaju, Kameswari AU - Mukherjea, Arnab AU - Sarkar, Urmimala PY - 2023/1/5 TI - COVID-19?Associated Misinformation Across the South Asian Diaspora: Qualitative Study of WhatsApp Messages JO - JMIR Infodemiology SP - e38607 VL - 3 KW - misinformation KW - COVID-19 KW - South Asians KW - disparities KW - social media KW - infodemiology KW - WhatsApp KW - messages KW - apps KW - health information KW - reliability KW - communication KW - Asian KW - English KW - community KW - health KW - organization KW - public health KW - pandemic N2 - Background: South Asians, inclusive of individuals originating in India, Pakistan, Maldives, Bangladesh, Sri Lanka, Bhutan, and Nepal, comprise the largest diaspora in the world, with large South Asian communities residing in the Caribbean, Africa, Europe, and elsewhere. There is evidence that South Asian communities have disproportionately experienced COVID-19 infections and mortality. WhatsApp, a free messaging app, is widely used in transnational communication within the South Asian diaspora. Limited studies exist on COVID-19?related misinformation specific to the South Asian community on WhatsApp. Understanding communication on WhatsApp may improve public health messaging to address COVID-19 disparities among South Asian communities worldwide. Objective: We developed the COVID-19?Associated misinfoRmation On Messaging apps (CAROM) study to identify messages containing misinformation about COVID-19 shared via WhatsApp. Methods: We collected messages forwarded globally through WhatsApp from self-identified South Asian community members between March 23 and June 3, 2021. We excluded messages that were in languages other than English, did not contain misinformation, or were not relevant to COVID-19. We deidentified each message and coded them for one or more content categories, media types (eg, video, image, text, web link, or a combination of these elements), and tone (eg, fearful, well intentioned, or pleading). We then performed a qualitative content analysis to arrive at key themes of COVID-19 misinformation. Results: We received 108 messages; 55 messages met the inclusion criteria for the final analytic sample; 32 (58%) contained text, 15 (27%) contained images, and 13 (24%) contained video. Content analysis revealed the following themes: ?community transmission? relating to misinformation on how COVID-19 spreads in the community; ?prevention? and ?treatment,? including Ayurvedic and traditional remedies for how to prevent or treat COVID-19 infection; and messaging attempting to sell ?products or services? to prevent or cure COVID-19. Messages varied in audience from the general public to South Asians specifically; the latter included messages alluding to South Asian pride and solidarity. Scientific jargon and references to major organizations and leaders in health care were included to provide credibility. Messages with a pleading tone encouraged users to forward them to friends or family. Conclusions: Misinformation in the South Asian community on WhatsApp spreads erroneous ideas regarding disease transmission, prevention, and treatment. Content evoking solidarity, ?trustworthy? sources, and encouragement to forward messages may increase the spread of misinformation. Public health outlets and social media companies must actively combat misinformation to address health disparities among the South Asian diaspora during the COVID-19 pandemic and in future public health emergencies. UR - https://infodemiology.jmir.org/2023/1/e38607 UR - http://dx.doi.org/10.2196/38607 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113380 ID - info:doi/10.2196/38607 ER - TY - JOUR AU - Pollack, C. Catherine AU - Emond, A. Jennifer AU - O'Malley, James A. AU - Byrd, Anna AU - Green, Peter AU - Miller, E. Katherine AU - Vosoughi, Soroush AU - Gilbert-Diamond, Diane AU - Onega, Tracy PY - 2022/12/30 TI - Characterizing the Prevalence of Obesity Misinformation, Factual Content, Stigma, and Positivity on the Social Media Platform Reddit Between 2011 and 2019: Infodemiology Study JO - J Med Internet Res SP - e36729 VL - 24 IS - 12 KW - obesity KW - misinformation KW - social stigma KW - social media KW - Reddit KW - natural language processing N2 - Background: Reddit is a popular social media platform that has faced scrutiny for inflammatory language against those with obesity, yet there has been no comprehensive analysis of its obesity-related content. Objective: We aimed to quantify the presence of 4 types of obesity-related content on Reddit (misinformation, facts, stigma, and positivity) and identify psycholinguistic features that may be enriched within each one. Methods: All sentences (N=764,179) containing ?obese? or ?obesity? from top-level comments (n=689,447) made on non?age-restricted subreddits (ie, smaller communities within Reddit) between 2011 and 2019 that contained one of a series of keywords were evaluated. Four types of common natural language processing features were extracted: bigram term frequency?inverse document frequency, word embeddings derived from Bidirectional Encoder Representations from Transformers, sentiment from the Valence Aware Dictionary for Sentiment Reasoning, and psycholinguistic features from the Linguistic Inquiry and Word Count Program. These features were used to train an Extreme Gradient Boosting machine learning classifier to label each sentence as 1 of the 4 content categories or other. Two-part hurdle models for semicontinuous data (which use logistic regression to assess the odds of a 0 result and linear regression for continuous data) were used to evaluate whether select psycholinguistic features presented differently in misinformation (compared with facts) or stigma (compared with positivity). Results: After removing ambiguous sentences, 0.47% (3610/764,179) of the sentences were labeled as misinformation, 1.88% (14,366/764,179) were labeled as stigma, 1.94% (14,799/764,179) were labeled as positivity, and 8.93% (68,276/764,179) were labeled as facts. Each category had markers that distinguished it from other categories within the data as well as an external corpus. For example, misinformation had a higher average percent of negations (?=3.71, 95% CI 3.53-3.90; P<.001) but a lower average number of words >6 letters (?=?1.47, 95% CI ?1.85 to ?1.10; P<.001) relative to facts. Stigma had a higher proportion of swear words (?=1.83, 95% CI 1.62-2.04; P<.001) but a lower proportion of first-person singular pronouns (?=?5.30, 95% CI ?5.44 to ?5.16; P<.001) relative to positivity. Conclusions: There are distinct psycholinguistic properties between types of obesity-related content on Reddit that can be leveraged to rapidly identify deleterious content with minimal human intervention and provide insights into how the Reddit population perceives patients with obesity. Future work should assess whether these properties are shared across languages and other social media platforms. UR - https://www.jmir.org/2022/12/e36729 UR - http://dx.doi.org/10.2196/36729 UR - http://www.ncbi.nlm.nih.gov/pubmed/36583929 ID - info:doi/10.2196/36729 ER - TY - JOUR AU - Maghsoudi, Arash AU - Nowakowski, Sara AU - Agrawal, Ritwick AU - Sharafkhaneh, Amir AU - Kunik, E. Mark AU - Naik, D. Aanand AU - Xu, Hua AU - Razjouyan, Javad PY - 2022/12/27 TI - Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre? and Peri?COVID-19 Pandemic Retrospective Study JO - J Med Internet Res SP - e41517 VL - 24 IS - 12 KW - COVID-19 KW - coronavirus KW - sleep KW - Twitter KW - natural language processing KW - sentiment analysis KW - transformers KW - Dempster-Shafer theory KW - sleeping KW - social media KW - pandemic KW - effect KW - viral infection N2 - Background: The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. Objective: In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. Methods: We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users? insomnia experiences, using logistic regression. Results: We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval. Conclusions: The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep. UR - https://www.jmir.org/2022/12/e41517 UR - http://dx.doi.org/10.2196/41517 UR - http://www.ncbi.nlm.nih.gov/pubmed/36417585 ID - info:doi/10.2196/41517 ER - TY - JOUR AU - Turvy, Alex PY - 2022/12/23 TI - State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data JO - JMIR Form Res SP - e40825 VL - 6 IS - 12 KW - COVID-19 KW - search trends KW - prediction KW - case KW - political KW - symptom KW - pandemic KW - data KW - google KW - disease KW - prevention KW - model N2 - Background: Across each state, the emergence of the COVID-19 pandemic in the United States was marked by policies and rhetoric that often corresponded to the political party in power. These diverging responses have sparked broad ongoing discussion about how the political leadership of a state may affect not only the COVID-19 case numbers in a given state but also the subjective individual experience of the pandemic. Objective: This study leverages state-level data from Google Search Trends and Centers for Disease Control and Prevention (CDC) daily case data to investigate the temporal relationship between increases in relative search volume for COVID-19 symptoms and corresponding increases in case data. I aimed to identify whether there are state-level differences in patterns of lag time across each of the 4 spikes in the data (RQ1) and whether the political climate in a given state is associated with these differences (RQ2). Methods: Using publicly available data from Google Trends and the CDC, linear mixed modeling was utilized to account for random state-level intercepts. Lag time was operationalized as number of days between a peak (a sustained increase before a sustained decline) in symptom search data and a corresponding spike in case data and was calculated manually for each of the 4 spikes in individual states. Google offers a data set that tracks the relative search incidence of more than 400 potential COVID-19 symptoms, which is normalized on a 0-100 scale. I used the CDC?s definition of the 11 most common COVID-19 symptoms and created a single construct variable that operationalizes symptom searches. To measure political climate, I considered the proportion of 2020 Trump popular votes in a state as well as a dummy variable for the political party that controls the governorship and a continuous variable measuring proportional party control of federal Congressional representatives. Results: The strongest overall fit was for a linear mixed model that included proportion of 2020 Trump votes as the predictive variable of interest and included controls for mean daily cases and deaths as well as population. Additional political climate variables were discarded for lack of model fit. Findings indicated evidence that there are statistically significant differences in lag time by state but that no individual variable measuring political climate was a statistically significant predictor of these differences. Conclusions: Given that there will likely be future pandemics within this political climate, it is important to understand how political leadership affects perceptions of and corresponding responses to public health crises. Although this study did not fully model this relationship, I believe that future research can build on the state-level differences that I identified by approaching the analysis with a different theoretical model, method for calculating lag time, or level of geographic modeling. UR - https://formative.jmir.org/2022/12/e40825 UR - http://dx.doi.org/10.2196/40825 UR - http://www.ncbi.nlm.nih.gov/pubmed/36446048 ID - info:doi/10.2196/40825 ER - TY - JOUR AU - Kobayashi, Ryota AU - Takedomi, Yuka AU - Nakayama, Yuri AU - Suda, Towa AU - Uno, Takeaki AU - Hashimoto, Takako AU - Toyoda, Masashi AU - Yoshinaga, Naoki AU - Kitsuregawa, Masaru AU - Rocha, C. Luis E. PY - 2022/12/22 TI - Evolution of Public Opinion on COVID-19 Vaccination in Japan: Large-Scale Twitter Data Analysis JO - J Med Internet Res SP - e41928 VL - 24 IS - 12 KW - COVID-19 KW - vaccine KW - vaccination KW - Twitter KW - public opinion KW - topic modeling KW - longitudinal study KW - topic dynamics KW - social events KW - interrupted time series regression N2 - Background: Vaccines are promising tools to control the spread of COVID-19. An effective vaccination campaign requires government policies and community engagement, sharing experiences for social support, and voicing concerns about vaccine safety and efficiency. The increasing use of online social platforms allows us to trace large-scale communication and infer public opinion in real time. Objective: This study aimed to identify the main themes in COVID-19 vaccine-related discussions on Twitter in Japan and track how the popularity of the tweeted themes evolved during the vaccination campaign. Furthermore, we aimed to understand the impact of critical social events on the popularity of the themes. Methods: We collected more than 100 million vaccine-related tweets written in Japanese and posted by 8 million users (approximately 6.4% of the Japanese population) from January 1 to October 31, 2021. We used Latent Dirichlet Allocation to perform automated topic modeling of tweet text during the vaccination campaign. In addition, we performed an interrupted time series regression analysis to evaluate the impact of 4 critical social events on public opinion. Results: We identified 15 topics grouped into the following 4 themes: (1) personal issue, (2) breaking news, (3) politics, and (4) conspiracy and humor. The evolution of the popularity of themes revealed a shift in public opinion, with initial sharing of attention over personal issues (individual aspect), collecting information from news (knowledge acquisition), and government criticism to focusing on personal issues. Our analysis showed that the Tokyo Olympic Games affected public opinion more than other critical events but not the course of vaccination. Public opinion about politics was significantly affected by various social events, positively shifting attention in the early stages of the vaccination campaign and negatively shifting attention later. Conclusions: This study showed a striking shift in public interest in Japan, with users splitting their attention over various themes early in the vaccination campaign and then focusing only on personal issues, as trust in vaccines and policies increased. An interrupted time series regression analysis showed that the vaccination rollout to the general population (under 65 years) increased the popularity of tweets about practical advice and personal vaccination experience, and the Tokyo Olympic Games disrupted public opinion but not the course of the vaccination campaign. The methodology developed here allowed us to monitor the evolution of public opinion and evaluate the impact of social events on public opinion, using large-scale Twitter data. UR - https://www.jmir.org/2022/12/e41928 UR - http://dx.doi.org/10.2196/41928 UR - http://www.ncbi.nlm.nih.gov/pubmed/36343186 ID - info:doi/10.2196/41928 ER - TY - JOUR AU - Sylvestre, Emmanuelle AU - Cécilia-Joseph, Elsa AU - Bouzillé, Guillaume AU - Najioullah, Fatiha AU - Etienne, Manuel AU - Malouines, Fabrice AU - Rosine, Jacques AU - Julié, Sandrine AU - Cabié, André AU - Cuggia, Marc PY - 2022/12/22 TI - The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study JO - JMIR Public Health Surveill SP - e37122 VL - 8 IS - 12 KW - dengue KW - surveillance KW - real-word data KW - Big Data KW - Caribbean KW - dengue-endemic region N2 - Background: Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles. Objective: The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region. Methods: Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags. Results: The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of ?3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of ?2 weeks. The most correlated query from Google Trends was the ?Dengue? topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of ?3 weeks. Conclusions: Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories. UR - https://publichealth.jmir.org/2022/12/e37122 UR - http://dx.doi.org/10.2196/37122 UR - http://www.ncbi.nlm.nih.gov/pubmed/36548023 ID - info:doi/10.2196/37122 ER - TY - JOUR AU - Ravkin, D. Hersh AU - Yom-Tov, Elad AU - Nesher, Lior PY - 2022/12/21 TI - The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data JO - J Med Internet Res SP - e42781 VL - 24 IS - 12 KW - RSV KW - respiratory syncytial virus KW - search engine KW - Google Trends KW - Google KW - respiratory KW - children KW - pharmaceutical KW - intervention KW - COVID-19 KW - pandemic KW - virus KW - infection KW - health N2 - Background: Respiratory syncytial virus (RSV) is a major cause of respiratory infection in children. Despite usually following a consistent seasonal pattern, the 2020-2021 RSV season in many countries was delayed and changed in magnitude. Objective: This study aimed to test if these changes can be attributed to nonpharmaceutical interventions (NPIs) instituted around the world to combat SARS-CoV-2. Methods: We used the internet search volume for RSV, as obtained from Google Trends, as a proxy to investigate these abnormalities. Results: Our analysis shows a breakdown of the usual correlation between peak latency and magnitude during the year of the pandemic. Analyzing latency and magnitude separately, we found that the changes therein are associated with implemented NPIs. Among several important interventions, NPIs affecting population mobility are shown to be particularly relevant to RSV incidence. Conclusions: The 2020-2021 RSV season served as a natural experiment to test NPIs that are likely to restrict RSV spread, and our findings can be used to guide health authorities to possible interventions. UR - https://www.jmir.org/2022/12/e42781 UR - http://dx.doi.org/10.2196/42781 UR - http://www.ncbi.nlm.nih.gov/pubmed/36476385 ID - info:doi/10.2196/42781 ER - TY - JOUR AU - DePaula, Nic AU - Hagen, Loni AU - Roytman, Stiven AU - Alnahass, Dana PY - 2022/12/20 TI - Platform Effects on Public Health Communication: A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook JO - JMIR Infodemiology SP - e40198 VL - 2 IS - 2 KW - platform effects KW - COVID-19 KW - social media KW - health communication KW - message design KW - risk communication KW - Twitter KW - Facebook KW - user engagement KW - e-government N2 - Background: Public health agencies widely adopt social media for health and risk communication. Moreover, different platforms have different affordances, which may impact the quality and nature of the messaging and how the public engages with the content. However, these platform effects are not often compared in studies of health and risk communication and not previously for the COVID-19 pandemic. Objective: This study measures the potential media effects of Twitter and Facebook on public health message design and engagement by comparing message elements and audience engagement in COVID-19?related posts by local, state, and federal public health agencies in the United States during the pandemic, to advance theories of public health messaging on social media and provide recommendations for tailored social media communication strategies. Methods: We retrieved all COVID-19?related posts from major US federal agencies related to health and infectious disease, all major state public health agencies, and selected local public health departments on Twitter and Facebook. A total of 100,785 posts related to COVID-19, from 179 different accounts of 96 agencies, were retrieved for the entire year of 2020. We adopted a framework of social media message elements to analyze the posts across Facebook and Twitter. For manual content analysis, we subsampled 1677 posts. We calculated the prevalence of various message elements across the platforms and assessed the statistical significance of differences. We also calculated and assessed the association between message elements with normalized measures of shares and likes for both Facebook and Twitter. Results: Distributions of message elements were largely similar across both sites. However, political figures (P<.001), experts (P=.01), and nonpolitical personalities (P=.01) were significantly more present on Facebook posts compared to Twitter. Infographics (P<.001), surveillance information (P<.001), and certain multimedia elements (eg, hyperlinks, P<.001) were more prevalent on Twitter. In general, Facebook posts received more (normalized) likes (0.19%) and (normalized) shares (0.22%) compared to Twitter likes (0.08%) and shares (0.05%). Elements with greater engagement on Facebook included expressives and collectives, whereas posts related to policy were more engaged with on Twitter. Science information (eg, scientific explanations) comprised 8.5% (73/851) of Facebook and 9.4% (78/826) of Twitter posts. Correctives of misinformation only appeared in 1.2% (11/851) of Facebook and 1.4% (12/826) of Twitter posts. Conclusions: In general, we find a data and policy orientation for Twitter messages and users and a local and personal orientation for Facebook, although also many similarities across platforms. Message elements that impact engagement are similar across platforms but with some notable distinctions. This study provides novel evidence for differences in COVID-19 public health messaging across social media sites, advancing knowledge of public health communication on social media and recommendations for health and risk communication strategies on these online platforms. UR - https://infodemiology.jmir.org/2022/2/e40198 UR - http://dx.doi.org/10.2196/40198 UR - http://www.ncbi.nlm.nih.gov/pubmed/36575712 ID - info:doi/10.2196/40198 ER - TY - JOUR AU - Cai, Ruilie AU - Zhang, Jiajia AU - Li, Zhenlong AU - Zeng, Chengbo AU - Qiao, Shan AU - Li, Xiaoming PY - 2022/12/20 TI - Using Twitter Data to Estimate the Prevalence of Symptoms of Mental Disorders in the United States During the COVID-19 Pandemic: Ecological Cohort Study JO - JMIR Form Res SP - e37582 VL - 6 IS - 12 KW - mental health KW - anxiety disorder KW - depressive disorder KW - COVID-19 KW - national survey KW - social media KW - Twitter KW - mixed model KW - anxiety KW - National Household Pulse survey KW - geospatial N2 - Background: Existing research and national surveillance data suggest an increase of the prevalence of mental disorders during the COVID-19 pandemic. Social media platforms, such as Twitter, could be a source of data for estimation owing to its real-time nature, high availability, and large geographical coverage. However, there is a dearth of studies validating the accuracy of the prevalence of mental disorders on Twitter compared to that reported by the Centers for Disease Control and Prevention (CDC). Objective: This study aims to verify the feasibility of Twitter-based prevalence of mental disorders symptoms being an instrument for prevalence estimation, where feasibility is gauged via correlations between Twitter-based prevalence of mental disorder symptoms (ie, anxiety and depressive symptoms) and that based on national surveillance data. In addition, this study aims to identify how the correlations changed over time (ie, the temporal trend). Methods: State-level prevalence of anxiety and depressive symptoms was retrieved from the national Household Pulse Survey (HPS) of the CDC from April 2020 to July 2021. Tweets were retrieved from the Twitter streaming application programming interface during the same period and were used to estimate the prevalence of symptoms of mental disorders for each state using keyword analysis. Stratified linear mixed models were used to evaluate the correlations between the Twitter-based prevalence of symptoms of mental disorders and those reported by the CDC. The magnitude and significance of model parameters were considered to evaluate the correlations. Temporal trends of correlations were tested after adding the time variable to the model. Geospatial differences were compared on the basis of random effects. Results: Pearson correlation coefficients between the overall prevalence reported by the CDC and that on Twitter for anxiety and depressive symptoms were 0.587 (P<.001) and 0.368 (P<.001), respectively. Stratified by 4 phases (ie, April 2020, August 2020, October 2020, and April 2021) defined by the HPS, linear mixed models showed that Twitter-based prevalence for anxiety symptoms had a positive and significant correlation with CDC-reported prevalence in phases 2 and 3, while a significant correlation for depressive symptoms was identified in phases 1 and 3. Conclusions: Positive correlations were identified between Twitter-based and CDC-reported prevalence, and temporal trends of these correlations were found. Geospatial differences in the prevalence of symptoms of mental disorders were found between the northern and southern United States. Findings from this study could inform future investigation on leveraging social media platforms to estimate symptoms of mental disorders and the provision of immediate prevention measures to improve health outcomes. UR - https://formative.jmir.org/2022/12/e37582 UR - http://dx.doi.org/10.2196/37582 UR - http://www.ncbi.nlm.nih.gov/pubmed/36459569 ID - info:doi/10.2196/37582 ER - TY - JOUR AU - Lin, Chen AU - Yousefi, Safoora AU - Kahoro, Elvis AU - Karisani, Payam AU - Liang, Donghai AU - Sarnat, Jeremy AU - Agichtein, Eugene PY - 2022/12/19 TI - Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation JO - JMIR Form Res SP - e23422 VL - 6 IS - 12 KW - nowcasting of air pollution KW - web-based public health surveillance KW - neural network sequence modeling KW - search engine log analysis KW - air pollution exposure assessment KW - mobile phone N2 - Background: Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O3), oxides of nitrogen, and fine particulate matter (PM2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure. Objective: This study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines. Methods: We developed novel machine learning?based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O3, nitrogen dioxide, and PM2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction. Results: The top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F1-score 0.51) for O3, 0.74 (F1-score 0.41) for nitrogen dioxide, and 0.85 (F1-score 0.27) for PM2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data. Conclusions: The results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data. UR - https://formative.jmir.org/2022/12/e23422 UR - http://dx.doi.org/10.2196/23422 UR - http://www.ncbi.nlm.nih.gov/pubmed/36534457 ID - info:doi/10.2196/23422 ER - TY - JOUR AU - de Vere Hunt, Isabella AU - Linos, Eleni PY - 2022/12/14 TI - Social Media for Public Health: Framework for Social Media?Based Public Health Campaigns JO - J Med Internet Res SP - e42179 VL - 24 IS - 12 KW - social media KW - digital heath KW - health communication KW - campaign KW - public health KW - framework KW - health promotion KW - public awareness KW - misinformation KW - tailored message KW - tailored messaging KW - information sharing KW - information exchange KW - advertise KW - advertising UR - https://www.jmir.org/2022/12/e42179 UR - http://dx.doi.org/10.2196/42179 UR - http://www.ncbi.nlm.nih.gov/pubmed/36515995 ID - info:doi/10.2196/42179 ER - TY - JOUR AU - Taira, Kazuya AU - Itaya, Takahiro AU - Fujita, Sumio PY - 2022/12/14 TI - Predicting Smoking Prevalence in Japan Using Search Volumes in an Internet Search Engine: Infodemiology Study JO - J Med Internet Res SP - e42619 VL - 24 IS - 12 KW - health policy KW - internet use KW - quality indicators KW - search engine KW - smoking KW - tobacco use KW - public health KW - infodemiology KW - smoking trend KW - health indicator KW - health promotion N2 - Background: Tobacco smoking is an important public health issue and a core indicator of public health policy worldwide. However, global pandemics and natural disasters have prevented surveys from being conducted. Objective: The purpose of this study was to predict smoking prevalence by prefecture and sex in Japan using Internet search trends. Methods: This study used the infodemiology approach. The outcome variable was smoking prevalence by prefecture, obtained from national surveys. The predictor variables were the search volumes on Yahoo! Japan Search. We collected the search volumes for queries related to terms from the thesaurus of the Japanese medical article database Ichu-shi. Predictor variables were converted to per capita values and standardized as z scores. For smoking prevalence, the values for 2016 and 2019 were used, and for search volume, the values for the April 1 to March 31 fiscal year (FY) 1 year prior to the survey (ie, FY 2015 and FY 2018) were used. Partial correlation coefficients, adjusted for data year, were calculated between smoking prevalence and search volume, and a regression analysis using a generalized linear mixed model with random effects was conducted for each prefecture. Several models were tested, including a model that included all search queries, a variable reduction method, and one that excluded cigarette product names. The best model was selected with the Akaike information criterion corrected (AICC) for small sample size and the Bayesian information criterion (BIC). We compared the predicted and actual smoking prevalence in 2016 and 2019 based on the best model and predicted the smoking prevalence in 2022. Results: The partial correlation coefficients for men showed that 9 search queries had significant correlations with smoking prevalence, including cigarette (r=?0.417, P<.001), cigar in kanji (r=?0.412, P<.001), and cigar in katakana (r=-0.399, P<.001). For women, five search queries had significant correlations, including vape (r=0.335, P=.001), quitting smoking (r=0.288, P=.005), and cigar (r=0.286, P=.006). The models with all search queries were the best models for both AICC and BIC scores. Scatter plots of actual and estimated smoking prevalence in 2016 and 2019 confirmed a relatively high degree of agreement. The average estimated smoking prevalence in 2022 in the 47 prefectures for the total sample was 23.492% (95% CI 21.617%-25.367%), showing an increasing trend, with an average of 29.024% (95% CI 27.218%-30.830%) for men and 8.793% (95% CI 7.531%-10.054%) for women. Conclusions: This study suggests that the search volume of tobacco-related queries in internet search engines can predict smoking prevalence by prefecture and sex in Japan. These findings will enable the development of low-cost, timely, and crisis-resistant health indicators that will enable the evaluation of health measures and contribute to improved public health. UR - https://www.jmir.org/2022/12/e42619 UR - http://dx.doi.org/10.2196/42619 UR - http://www.ncbi.nlm.nih.gov/pubmed/36515993 ID - info:doi/10.2196/42619 ER - TY - JOUR AU - Wu, Dezhi AU - Kasson, Erin AU - Singh, Kumar Avineet AU - Ren, Yang AU - Kaiser, Nina AU - Huang, Ming AU - Cavazos-Rehg, A. Patricia PY - 2022/12/13 TI - Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use?Associated Lung Injury Outbreak: Comparative Study JO - J Med Internet Res SP - e39460 VL - 24 IS - 12 KW - vaping KW - e-cigarette KW - social media KW - Twitter KW - Reddit KW - e-cigarette and vaping use?associated lung injury KW - EVALI KW - sentiment analysis KW - topic analysis N2 - Background: Vaping or e-cigarette use has become dramatically more popular in the United States in recent years. e-Cigarette and vaping use?associated lung injury (EVALI) cases caused an increase in hospitalizations and deaths in 2019, and many instances were later linked to unregulated products. Previous literature has leveraged social media data for surveillance of health topics. Individuals are willing to share mental health experiences and other personal stories on social media platforms where they feel a sense of community, reduced stigma, and empowerment. Objective: This study aimed to compare vaping-related content on 2 popular social media platforms (ie, Twitter and Reddit) to explore the context surrounding vaping during the 2019 EVALI outbreak and to support the feasibility of using data from both social platforms to develop in-depth and intelligent vaping detection models on social media. Methods: Data were extracted from both Twitter (316,620 tweets) and Reddit (17,320 posts) from July 2019 to September 2019 at the peak of the EVALI crisis. High-throughput computational analyses (sentiment analysis and topic analysis) were conducted. In addition, in-depth manual content analyses were performed and compared with computational analyses of content on both platforms (577 tweets and 613 posts). Results: Vaping-related posts and unique users on Twitter and Reddit increased from July 2019 to September 2019, with the average post per user increasing from 1.68 to 1.81 on Twitter and 1.19 to 1.21 on Reddit. Computational analyses found the number of positive sentiment posts to be higher on Reddit (P<.001, 95% CI 0.4305-0.4475) and the number of negative posts to be higher on Twitter (P<.001, 95% CI ?0.4289 to ?0.4111). These results were consistent with the clinical content analyses results indicating that negative sentiment posts were higher on Twitter (273/577, 47.3%) than Reddit (184/613, 30%). Furthermore, topics prevalent on both platforms by keywords and based on manual post reviews included mentions of youth, marketing or regulation, marijuana, and interest in quitting. Conclusions: Post content and trending topics overlapped on both Twitter and Reddit during the EVALI period in 2019. However, crucial differences in user type and content keywords were also found, including more frequent mentions of health-related keywords on Twitter and more negative health outcomes from vaping mentioned on both Reddit and Twitter. Use of both computational and clinical content analyses is critical to not only identify signals of public health trends among vaping-related social media content but also to provide context for vaping risks and behaviors. By leveraging the strengths of both Twitter and Reddit as publicly available data sources, this research may provide technical and clinical insights to inform automatic detection of social media users who are vaping and may benefit from digital intervention and proactive outreach strategies on these platforms. UR - https://www.jmir.org/2022/12/e39460 UR - http://dx.doi.org/10.2196/39460 UR - http://www.ncbi.nlm.nih.gov/pubmed/36512403 ID - info:doi/10.2196/39460 ER - TY - JOUR AU - Li, Chuqin AU - Jordan, Alexis AU - Song, Jun AU - Ge, Yaorong AU - Park, Albert PY - 2022/12/13 TI - A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study JO - J Med Internet Res SP - e39340 VL - 24 IS - 12 KW - obesity KW - social media KW - machine learning KW - lifestyle KW - environment KW - food KW - correlation KW - modeling KW - predict KW - rates KW - outcome KW - category KW - dishes KW - popular KW - mobile phone N2 - Background: Community obesity outcomes can reflect the food environment to which the community belongs. Recent studies have suggested that the local food environment can be measured by the degree of food accessibility, and survey data are normally used to calculate food accessibility. However, compared with survey data, social media data are organic, continuously updated, and cheaper to collect. Objective: The objective of our study was to use publicly available social media data to learn the relationship between food environment and obesity rates at the state level. Methods: To characterize the caloric information of the local food environment, we used food categories from Yelp and collected caloric information from MyFitnessPal for each category based on their popular dishes. We then calculated the average calories for each category and created a weighted score for each state. We also calculated 2 other dimensions from the concept of access, acceptability and affordability, to build obesity prediction models. Results: The local food environment characterized using only publicly available social media data had a statistically significant correlation with the state obesity rate. We achieved a Pearson correlation of 0.796 between the predicted obesity rate and the reported obesity rate from the Behavioral Risk Factor Surveillance System across US states and the District of Columbia. The model with 3 generated feature sets achieved the best performance. Conclusions: Our study proposed a method for characterizing state-level food environments only using continuously updated social media data. State-level food environments were accurately described using social media data, and the model also showed a disparity in the available food between states with different obesity rates. The proposed method should elastically apply to local food environments of different sizes and predict obesity rates effectively. UR - https://www.jmir.org/2022/12/e39340 UR - http://dx.doi.org/10.2196/39340 UR - http://www.ncbi.nlm.nih.gov/pubmed/36512396 ID - info:doi/10.2196/39340 ER - TY - JOUR AU - Xu, Wayne Weiai AU - Tshimula, Marie Jean AU - Dubé, Čve AU - Graham, E. Janice AU - Greyson, Devon AU - MacDonald, E. Noni AU - Meyer, B. Samantha PY - 2022/12/9 TI - Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster?Based BERT Topic Modeling Approach JO - JMIR Infodemiology SP - e41198 VL - 2 IS - 2 KW - infoveillance KW - data analytics KW - Twitter KW - social media KW - user classification KW - COVID-19 N2 - Background: The COVID-19 pandemic has spotlighted the politicization of public health issues. A public health monitoring tool must be equipped to reveal a public health measure?s political context and guide better interventions. In its current form, infoveillance tends to neglect identity and interest-based users, hence being limited in exposing how public health discourse varies by different political groups. Adopting an algorithmic tool to classify users and their short social media texts might remedy that limitation. Objective: We aimed to implement a new computational framework to investigate discourses and temporal changes in topics unique to different user clusters. The framework was developed to contextualize how web-based public health discourse varies by identity and interest-based user clusters. We used masks and mask wearing during the early stage of the COVID-19 pandemic in the English-speaking world as a case study to illustrate the application of the framework. Methods: We first clustered Twitter users based on their identities and interests as expressed through Twitter bio pages. Exploratory text network analysis reveals salient political, social, and professional identities of various user clusters. It then uses BERT Topic modeling to identify topics by the user clusters. It reveals how web-based discourse has shifted over time and varied by 4 user clusters: conservative, progressive, general public, and public health professionals. Results: This study demonstrated the importance of a priori user classification and longitudinal topical trends in understanding the political context of web-based public health discourse. The framework reveals that the political groups and the general public focused on the science of mask wearing and the partisan politics of mask policies. A populist discourse that pits citizens against elites and institutions was identified in some tweets. Politicians (such as Donald Trump) and geopolitical tensions with China were found to drive the discourse. It also shows limited participation of public health professionals compared with other users. Conclusions: We conclude by discussing the importance of a priori user classification in analyzing web-based discourse and illustrating the fit of BERT Topic modeling in identifying contextualized topics in short social media texts. UR - https://infodemiology.jmir.org/2022/2/e41198 UR - http://dx.doi.org/10.2196/41198 UR - http://www.ncbi.nlm.nih.gov/pubmed/36536763 ID - info:doi/10.2196/41198 ER - TY - JOUR AU - Luo, Kai AU - Yang, Yang AU - Teo, Hai Hock PY - 2022/12/8 TI - The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study JO - JMIR Infodemiology SP - e37331 VL - 2 IS - 2 KW - COVID-19 KW - science communication KW - emotion KW - COVID-19 science KW - online social networks KW - computational social science KW - social media N2 - Background: Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-ŕ-vis preprints and what makes them viral. Objective: This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators? and policy makers? decisions on how to promote reliable sharing of crucial pandemic science on social media. Methods: We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate. Results: Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists? participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications. Conclusions: Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public. UR - https://infodemiology.jmir.org/2022/2/e37331 UR - http://dx.doi.org/10.2196/37331 UR - http://www.ncbi.nlm.nih.gov/pubmed/36536762 ID - info:doi/10.2196/37331 ER - TY - JOUR AU - Basch, H. Corey AU - Hillyer, C. Grace AU - Yalamanchili, Bhavya AU - Morris, Aldean PY - 2022/12/6 TI - How TikTok Is Being Used to Help Individuals Cope With Breast Cancer: Cross-sectional Content Analysis JO - JMIR Cancer SP - e42245 VL - 8 IS - 4 KW - TikTok KW - breast cancer KW - social media KW - short video apps KW - social support KW - content analysis KW - video KW - patient support KW - medical information KW - health information KW - peer support KW - online conversation KW - online health information N2 - Background: Acknowledging the popularity of TikTok, how quickly medical information can spread, and how users seek support on social media, there is a clear lack of research on breast cancer conversations on TikTok. There is a paucity of information on how these videos can advocate for those impacted by breast cancer as a means to provide support and information as well as raise awareness. Objective: The purpose of this cross-sectional content analysis was to describe the content of videos from the hashtag #breastcancer on TikTok. Content related to breast cancer support and coping, cancer education, and heightening the awareness of breast cancer early detection, prevention, and treatment was evaluated. Methods: This study included 100 of the most viewed TikTok videos related to breast cancer through June 30, 2022. Videos were excluded if they were not in the English language or relevant to the topic being studied. Content was deductively coded into categories related to video characteristics and content topics using a screener based on expert breast cancer information sheets. Univariable analyses were conducted to evaluate differences in video characteristics and content when stratified as advocating or not advocating for breast cancer (yes or no) support, education, and awareness. Results: The cumulative number of views of the videos included in this study was 369,504,590. The majority (n=81, 81%) of videos were created by patients and loved ones of individuals with breast cancer, and the most commonly discussed topic was breast cancer support (n=88, 88%), followed by coping with the myriad issues surrounding breast cancer (n=79, 79%). Overall, <50% of the videos addressed important issues such as body image (n=48, 48%), surgery (n=46, 46%), medication and therapy (n=41, 41%), or the stigma associated with a breast cancer diagnosis (n=44, 44%); however, in videos that were advocacy oriented, body image (40/62, 64% vs 8/38, 21%; P<.001), stigma associated with breast cancer (33/62, 53% vs 11/38, 29%; P=.02), and breast cancer surgery (36/62, 58% vs 10/38, 26%; P=.002) were discussed significantly more often than in videos that did not specifically advocate for breast cancer. Conclusions: The use of videos to display health journeys can facilitate engagement by patients, family members, and loved ones interested in information about challenging conditions. Collectively, these findings highlight the level of peer-to-peer involvement on TikTok and may provide insights for designing breast cancer educational campaigns. UR - https://cancer.jmir.org/2022/4/e42245 UR - http://dx.doi.org/10.2196/42245 UR - http://www.ncbi.nlm.nih.gov/pubmed/36472899 ID - info:doi/10.2196/42245 ER - TY - JOUR AU - Germone, Monique AU - Wright, D. Casey AU - Kimmons, Royce AU - Coburn, Skelley Shayna PY - 2022/12/5 TI - Twitter Trends for Celiac Disease and the Gluten-Free Diet: Cross-sectional Descriptive Analysis JO - JMIR Infodemiology SP - e37924 VL - 2 IS - 2 KW - celiac disease KW - social media KW - Twitter KW - gluten-free KW - social networking site KW - diet KW - infodemiology KW - education KW - online KW - content KW - accuracy KW - credibility N2 - Background: Few studies have systematically analyzed information regarding chronic medical conditions and available treatments on social media. Celiac disease (CD) is an exemplar of the need to investigate web-based educational sources. CD is an autoimmune condition wherein the ingestion of gluten causes intestinal damage and, if left untreated by a strict gluten-free diet (GFD), can result in significant nutritional deficiencies leading to cancer, bone disease, and death. Adherence to the GFD can be difficult owing to cost and negative stigma, including misinformation about what gluten is and who should avoid it. Given the significant impact that negative stigma and common misunderstandings have on the treatment of CD, this condition was chosen to systematically investigate the scope and nature of sources and information distributed through social media. Objective: To address concerns related to educational social media sources, this study explored trends on the social media platform Twitter about CD and the GFD to identify primary influencers and the type of information disseminated by these influencers. Methods: This cross-sectional study used data mining to collect tweets and users who used the hashtags #celiac and #glutenfree from an 8-month time frame. Tweets were then analyzed to describe who is disseminating information via this platform and the content, source, and frequency of such information. Results: More content was posted for #glutenfree (1501.8 tweets per day) than for #celiac (69 tweets per day). A substantial proportion of the content was produced by a small percentage of contributors (ie, ?Superuser?), who could be categorized as self-promotors (eg, bloggers, writers, authors; 13.9% of #glutenfree tweets and 22.7% of #celiac tweets), self-identified female family members (eg, mother; 4.3% of #glutenfree tweets and 8% of #celiac tweets), or commercial entities (eg, restaurants and bakeries). On the other hand, relatively few self-identified scientific, nonprofit, and medical provider users made substantial contributions on Twitter related to the GFD or CD (1% of #glutenfree tweets and 3.1% of #celiac tweets, respectively). Conclusions: Most material on Twitter was provided by self-promoters, commercial entities, or self-identified female family members, which may not have been supported by current medical and scientific practices. Researchers and medical providers could potentially benefit from contributing more to this space to enhance the web-based resources for patients and families. UR - https://infodemiology.jmir.org/2022/2/e37924 UR - http://dx.doi.org/10.2196/37924 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113453 ID - info:doi/10.2196/37924 ER - TY - JOUR AU - Tang, Qihang AU - Zhou, Runtao AU - Xie, Zidian AU - Li, Dongmei PY - 2022/12/5 TI - Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study JO - JMIR Form Res SP - e42241 VL - 6 IS - 12 KW - e-cigarettes KW - brand KW - flavor KW - Twitter N2 - Background: Flavored electronic cigarettes (e-cigarettes) have become very popular in recent years. e-Cigarette users like to share their e-cigarette products and e-cigarette use (vaping) experiences on social media. e-Cigarette marketing and promotions are also prevalent online. Objective: This study aims to develop a method to identify new e-cigarette brands and flavors mentioned on Twitter and to monitor e-cigarette brands and flavors mentioned on Twitter from May 2021 to December 2021. Methods: We collected 1.9 million tweets related to e-cigarettes between May 3, 2021, and December 31, 2021, by using the Twitter streaming application programming interface. Commercial and noncommercial tweets were characterized based on promotion-related keywords. We developed a depletion method to identify new e-cigarette brands by removing the keywords that already existed in the reference data set (Twitter data related to e-cigarettes from May 3, 2021, to August 31, 2021) or our previously identified brand list from the keywords in the target data set (e-cigarette?related Twitter data from September 1, 2021, to December 31, 2021), followed by a manual Google search to identify new e-cigarette brands. To identify new e-cigarette flavors, we constructed a flavor keyword list based on our previously collected e-cigarette flavor names, which were used to identify potential tweet segments that contain at least one of the e-cigarette flavor keywords. Tweets or tweet segments with flavor keywords but not any known flavor names were marked as potential new flavor candidates, which were further verified by a web-based search. The longitudinal trends in the number of tweets mentioning e-cigarette brands and flavors were examined in both commercial and noncommercial tweets. Results: Through our developed methods, we identified 34 new e-cigarette brands and 97 new e-cigarette flavors from commercial tweets as well as 56 new e-cigarette brands and 164 new e-cigarette flavors from noncommercial tweets. The longitudinal trend of the e-cigarette brands showed that JUUL was the most popular e-cigarette brand mentioned on Twitter; however, there was a decreasing trend in the mention of JUUL over time on Twitter. Menthol flavor was the most popular e-cigarette flavor mentioned in the commercial tweets, whereas mango flavor was the most popular e-cigarette flavor mentioned in the noncommercial tweets during our study period. Conclusions: Our proposed methods can successfully identify new e-cigarette brands and flavors mentioned on Twitter. Twitter data can be used for monitoring the dynamic changes in the popularity of e-cigarette brands and flavors. UR - https://formative.jmir.org/2022/12/e42241 UR - http://dx.doi.org/10.2196/42241 UR - http://www.ncbi.nlm.nih.gov/pubmed/36469415 ID - info:doi/10.2196/42241 ER - TY - JOUR AU - Patton, Thomas AU - Abramovitz, Daniela AU - Johnson, Derek AU - Leas, Eric AU - Nobles, Alicia AU - Caputi, Theodore AU - Ayers, John AU - Strathdee, Steffanie AU - Bórquez, Annick PY - 2022/12/1 TI - Characterizing Help-Seeking Searches for Substance Use Treatment From Google Trends and Assessing Their Use for Infoveillance: Longitudinal Descriptive and Validation Statistical Analysis JO - J Med Internet Res SP - e41527 VL - 24 IS - 12 KW - internet KW - search KW - help-seeking KW - substance use treatment KW - surveillance KW - infoveillance KW - google trends N2 - Background: There is no recognized gold standard method for estimating the number of individuals with substance use disorders (SUDs) seeking help within a given geographical area. This presents a challenge to policy makers in the effective deployment of resources for the treatment of SUDs. Internet search queries related to help seeking for SUDs using Google Trends may represent a low-cost, real-time, and data-driven infoveillance tool to address this shortfall in information. Objective: This paper assesses the feasibility of using search query data related to help seeking for SUDs as an indicator of unmet treatment needs, demand for treatment, and predictor of the health harms related to unmet treatment needs. We explore a continuum of hypotheses to account for different outcomes that might be expected to occur depending on the demand for treatment relative to the system capacity and the timing of help seeking in relation to trajectories of substance use and behavior change. Methods: We used negative binomial regression models to examine temporal trends in the annual SUD help-seeking internet search queries from Google Trends by US state for cocaine, methamphetamine, opioids, cannabis, and alcohol from 2010 to 2020. To validate the value of these data for surveillance purposes, we then used negative binomial regression models to investigate the relationship between SUD help-seeking searches and state-level outcomes across the continuum of care (including lack of care). We started by looking at associations with self-reported treatment need using data from the National Survey on Drug Use and Health, a national survey of the US general population. Next, we explored associations with treatment admission rates from the Treatment Episode Data Set, a national data system on SUD treatment facilities. Finally, we studied associations with state-level rates of people experiencing and dying from an opioid overdose, using data from the Agency for Healthcare Research and Quality and the CDC WONDER database. Results: Statistically significant differences in help-seeking searches were observed over time between 2010 and 2020 (based on P<.05 for the corresponding Wald tests). We were able to identify outlier states for each drug over time (eg, West Virginia for both opioids and methamphetamine), indicating significantly higher help-seeking behaviors compared to national trends. Results from our validation analyses across different outcomes showed positive, statistically significant associations for the models relating to treatment need for alcohol use, treatment admissions for opioid and methamphetamine use, emergency department visits related to opioid use, and opioid overdose mortality data (based on regression coefficients having P?.05). Conclusions: This study demonstrates the clear potential for using internet search queries from Google Trends as an infoveillance tool to predict the demand for substance use treatment spatially and temporally, especially for opioid use disorders. UR - https://www.jmir.org/2022/12/e41527 UR - http://dx.doi.org/10.2196/41527 UR - http://www.ncbi.nlm.nih.gov/pubmed/36454620 ID - info:doi/10.2196/41527 ER - TY - JOUR AU - Kiszla, Matthew Benjamin AU - Harris, Broughton Mia PY - 2022/12/1 TI - Trends in Tattoo-Related Google Search Data in the United States: Time-Series Analysis JO - JMIR Dermatol SP - e40540 VL - 5 IS - 4 KW - big data KW - dermatoepidemiology KW - infodemiology KW - tattoo KW - United States KW - web search KW - dermatology KW - tattoo care KW - skin care KW - guidance seeking KW - tattoo removal KW - tattoo application KW - information seeking KW - internet search KW - web searches KW - adverse reactions N2 - Background: Tattoos are becoming increasingly common in the United States. However, little information is available to help clinicians anticipate where, when, and on what topics patients will seek guidance regarding tattoo care, complications, and removal. Objective: The aim of this study was to model web searches concerning general interest in tattoo application, tattoo removal, and the geolocation of tattooing services. Methods: Relative search volumes (RSVs) were elicited from Google Trends, filtered to web searches made in the United States between January 15, 2008, and October 15, 2022. Longitudinal data were analyzed in GraphPad Prism and geospatial data were visualized with Datawrapper for general interest searches (tattoo and tattoo removal), aggregated geolocating searches (eg, tattoo shops near me), and symptomatic searches relating to adverse effects (eg, itchy tattoo). Results were compared to previous global literature and national surveys of tattoo prevalence. Results: In the United States, the search terms tattoo and tattoo removal have experienced stable RSVs over the past 14 years, with both showing peaks in the summer and troughs in the winter. RSVs for search terms that geolocate tattooing services have experienced a general increase in use since 2008. A compilation of results for all collated geolocating search terms localized these searches mainly to the American South, with lesser involvement in the eastern Midwest and inland West. Increased search interest in the Southeast at the expense of more populous coastal states and Great Plains or western Midwest states reflects the ongoing harmonization of tattoo prevalence across regions, as shown by national surveys. Searches for symptoms related to adverse reactions to tattooing experienced an increase over the period of interest, with the same distribution as previous global findings. Conclusions: Clinicians should be aware of an increase in search interest regarding tattoos and their removal, especially during the summer months in the Southeast and Midwest. This increase in interest is occurring together with increased tattoo prevalence and increased search interest for adverse reactions in a country lagging behind in tattoo ink regulation. UR - https://derma.jmir.org/2022/4/e40540 UR - http://dx.doi.org/10.2196/40540 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632878 ID - info:doi/10.2196/40540 ER - TY - JOUR AU - Galimov, Artur AU - Vassey, Julia AU - Galstyan, Ellen AU - Unger, B. Jennifer AU - Kirkpatrick, G. Matthew AU - Allem, Jon-Patrick PY - 2022/11/30 TI - Ice Flavor?Related Discussions on Twitter: Content Analysis JO - J Med Internet Res SP - e41785 VL - 24 IS - 11 KW - electronic cigarettes KW - Twitter KW - social media KW - ice flavors KW - tobacco policy KW - public health KW - infodemiology KW - FDA KW - tobacco KW - smoking KW - vaping KW - e-cigarette KW - public N2 - Background: The US Food and Drug Administration (FDA) recently restricted characterizing flavors in tobacco products. As a result, ice hybrid?flavored e-cigarettes, which combine a cooling flavor with fruit or other flavors (eg, banana ice), emerged on the market. Like menthol, ice-flavored e-cigarettes produce a cooling sensory experience. It is unclear if ice hybrid?flavored e-cigarettes should be considered characterizing flavors or menthol, limiting regulatory action. Monitoring the public?s conversations about ice-flavored e-cigarettes on Twitter may help inform the tobacco control community about these products and contribute to the US FDA policy targets in the future. Objective: This study documented the themes pertaining to vaping and ice flavor?related conversations on Twitter. Our goal was to identify key conversation trends and ascertain users? recent experiences with ice-flavored e-cigarette products. Methods: Posts containing vaping-related (eg, ?vape,? ?ecig,? ?e-juice,? or ?e-cigarette?) and ice-related (ie, ?Ice,? ?Cool,? ?Frost,? and ?Arctic?) terms were collected from Twitter?s streaming application programming interface from January 1 to July 21, 2021. After removing retweets, a random sample of posts (N=2001) was selected, with 590 posts included in the content analysis. Themes were developed through an inductive approach. Theme co-occurrence was also examined. Results: Many of the 590 posts were marked as (or consisted of) marketing material (n=306, 51.9%), contained positive personal testimonials (n=180, 30.5%), and mentioned disposable pods (n=117, 19.8%). Other themes had relatively low prevalence in the sample: neutral personal testimonials (n=45, 7.6%), cannabidiol products (n=41, 7%), negative personal testimonials (n=41, 7%), ?official? flavor description (n=37, 6.3%), ice-flavored JUUL (n=19, 3.2%), information seeking (n=14, 2.4%), and comparison to combustible tobacco (n=10, 1.7%). The most common co-occurring themes in a single tweet were related to marketing and disposable pods (n=73, 12.4%). Conclusions: Our findings offer insight into the public?s experience with and understanding of ice-flavored e-cigarette products. Ice-flavored e-cigarette products are actively marketed on Twitter, and the messages about them are positive. Public health education campaigns on the harms of flavored e-cigarettes may help to reduce positive social norms about ice-flavored products. Future studies should evaluate the relationship between exposure to personal testimonials of ice-flavored vaping products and curiosity, harm perceptions, and experimentation with these products among priority populations. UR - https://www.jmir.org/2022/11/e41785 UR - http://dx.doi.org/10.2196/41785 UR - http://www.ncbi.nlm.nih.gov/pubmed/36449326 ID - info:doi/10.2196/41785 ER - TY - JOUR AU - Ahmad, Areebah AU - Alhanshali, Lina AU - Jefferson, S. Itisha AU - Dellavalle, Robert PY - 2022/11/30 TI - Cochrane Skin Group?s Global Social Media Reach: Content Analysis of Facebook, Instagram, and Twitter Posts JO - JMIR Dermatol SP - e40905 VL - 5 IS - 4 KW - social media KW - Cochrane Skin KW - dermatology KW - content engagement KW - Facebook KW - Cochrane KW - Twitter KW - social media analysis KW - content analysis KW - skin disease KW - dermatologist N2 - Background: Researchers in all medical specialties increasingly use social media to educate the public, share new publications with peers, and diversify their audiences. Objective: Given Cochrane Skin Group?s expanded use of social media in the past years, we aimed to characterize Cochrane Skin Group's international social media audience and identify themes that result in increased content engagement. Methods: Cochrane Skin Group's Facebook, Instagram, and Twitter analytics data were extracted for follower demographics and the most viewed posts within a 3-year span (June 2019 to June 2022). Results: Overall, Cochrane Skin Group had the highest number of followers on Facebook (n=1037). The number of Instagram and Twitter followers reached 214 and 352, respectively. The greatest numbers of Facebook followers were from Brazil, Egypt, and India, with 271, 299, and 463 followers, respectively. Facebook?s most viewed post about Cochrane Skin Group?s annual meeting received 1041 views. The top post on Instagram, which introduced Cochrane Skin Group?s social media editors, received 2522 views. Conclusions: Each of the social media platforms used by Cochrane Skin Group reached varying audiences all over the world. Across social media platforms, posts regarding Cochrane Skin Group meetings, members, and professional opportunities received the most views. Overall, Cochrane Skin Group's multiplatform social media approach will continue to grow an international audience, connecting people interested in skin disease. UR - https://derma.jmir.org/2022/4/e40905 UR - http://dx.doi.org/10.2196/40905 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632904 ID - info:doi/10.2196/40905 ER - TY - JOUR AU - Takats, Courtney AU - Kwan, Amy AU - Wormer, Rachel AU - Goldman, Dari AU - Jones, E. Heidi AU - Romero, Diana PY - 2022/11/29 TI - Ethical and Methodological Considerations of Twitter Data for Public Health Research: Systematic Review JO - J Med Internet Res SP - e40380 VL - 24 IS - 11 KW - systematic review KW - Twitter KW - social media KW - public health ethics KW - public health KW - ethics KW - ethical considerations KW - public health research KW - research topics KW - Twitter data KW - ethical framework KW - research ethics N2 - Background: Much research is being carried out using publicly available Twitter data in the field of public health, but the types of research questions that these data are being used to answer and the extent to which these projects require ethical oversight are not clear. Objective: This review describes the current state of public health research using Twitter data in terms of methods and research questions, geographic focus, and ethical considerations including obtaining informed consent from Twitter handlers. Methods: We implemented a systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of articles published between January 2006 and October 31, 2019, using Twitter data in secondary analyses for public health research, which were found using standardized search criteria on SocINDEX, PsycINFO, and PubMed. Studies were excluded when using Twitter for primary data collection, such as for study recruitment or as part of a dissemination intervention. Results: We identified 367 articles that met eligibility criteria. Infectious disease (n=80, 22%) and substance use (n=66, 18%) were the most common topics for these studies, and sentiment mining (n=227, 62%), surveillance (n=224, 61%), and thematic exploration (n=217, 59%) were the most common methodologies employed. Approximately one-third of articles had a global or worldwide geographic focus; another one-third focused on the United States. The majority (n=222, 60%) of articles used a native Twitter application programming interface, and a significant amount of the remainder (n=102, 28%) used a third-party application programming interface. Only one-third (n=119, 32%) of studies sought ethical approval from an institutional review board, while 17% of them (n=62) included identifying information on Twitter users or tweets and 36% of them (n=131) attempted to anonymize identifiers. Most studies (n=272, 79%) included a discussion on the validity of the measures and reliability of coding (70% for interreliability of human coding and 70% for computer algorithm checks), but less attention was paid to the sampling frame, and what underlying population the sample represented. Conclusions: Twitter data may be useful in public health research, given its access to publicly available information. However, studies should exercise greater caution in considering the data sources, accession method, and external validity of the sampling frame. Further, an ethical framework is necessary to help guide future research in this area, especially when individual, identifiable Twitter users and tweets are shared and discussed. Trial Registration: PROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=148170 UR - https://www.jmir.org/2022/11/e40380 UR - http://dx.doi.org/10.2196/40380 UR - http://www.ncbi.nlm.nih.gov/pubmed/36445739 ID - info:doi/10.2196/40380 ER - TY - JOUR AU - Weeks, Rose AU - White, Sydney AU - Hartner, Anna-Maria AU - Littlepage, Shea AU - Wolf, Jennifer AU - Masten, Kristin AU - Tingey, Lauren PY - 2022/11/25 TI - COVID-19 Messaging on Social Media for American Indian and Alaska Native Communities: Thematic Analysis of Audience Reach and Web Behavior JO - JMIR Infodemiology SP - e38441 VL - 2 IS - 2 KW - COVID-19 KW - American Indian or Alaska Native KW - social media KW - communication KW - tribal organization KW - community health KW - infodemiology KW - Twitter KW - online behavior KW - content analysis KW - thematic analysis N2 - Background: During the COVID-19 pandemic, tribal and health organizations used social media to rapidly disseminate public health guidance highlighting protective behaviors such as masking and vaccination to mitigate the pandemic?s disproportionate burden on American Indian and Alaska Native (AI/AN) communities. Objective: Seeking to provide guidance for future communication campaigns prioritizing AI/AN audiences, this study aimed to identify Twitter post characteristics associated with higher performance, measured by audience reach (impressions) and web behavior (engagement rate). Methods: We analyzed Twitter posts published by a campaign by the Johns Hopkins Center for Indigenous Health from July 2020 to June 2021. Qualitative analysis was informed by in-depth interviews with members of a Tribal Advisory Board and thematically organized according to the Health Belief Model. A general linearized model was used to analyze associations between Twitter post themes, impressions, and engagement rates. Results: The campaign published 162 Twitter messages, which organically generated 425,834 impressions and 6016 engagements. Iterative analysis of these Twitter posts identified 10 unique themes under theory- and culture-related categories of framing knowledge, cultural messaging, normalizing mitigation strategies, and interactive opportunities, which were corroborated by interviews with Tribal Advisory Board members. Statistical analysis of Twitter impressions and engagement rate by theme demonstrated that posts featuring culturally resonant community role models (P=.02), promoting web-based events (P=.002), and with messaging as part of Twitter Chats (P<.001) were likely to generate higher impressions. In the adjusted analysis controlling for the date of posting, only the promotion of web-based events (P=.003) and Twitter Chat messaging (P=.01) remained significant. Visual, explanatory posts promoting self-efficacy (P=.01; P=.01) and humorous posts (P=.02; P=.01) were the most likely to generate high?engagement rates in both the adjusted and unadjusted analysis. Conclusions: Results from the 1-year Twitter campaign provide lessons to inform organizations designing social media messages to reach and engage AI/AN social media audiences. The use of interactive events, instructional graphics, and Indigenous humor are promising practices to engage community members, potentially opening audiences to receiving important and time-sensitive guidance. UR - https://infodemiology.jmir.org/2022/2/e38441 UR - http://dx.doi.org/10.2196/38441 UR - http://www.ncbi.nlm.nih.gov/pubmed/36471705 ID - info:doi/10.2196/38441 ER - TY - JOUR AU - Kim, Jung Sunny AU - Schiffelbein, E. Jenna AU - Imset, Inger AU - Olson, L. Ardis PY - 2022/11/24 TI - Countering Antivax Misinformation via Social Media: Message-Testing Randomized Experiment for Human Papillomavirus Vaccination Uptake JO - J Med Internet Res SP - e37559 VL - 24 IS - 11 KW - misinformation KW - vaccine hesitancy KW - vaccine communication KW - social media KW - human papillomavirus KW - HPV KW - HPV vaccine N2 - Background: Suboptimal adolescent human papillomavirus (HPV) vaccination rates have been attributed to parental perceptions of the HPV vaccine. The internet has been cited as a setting where misinformation and controversy about HPV vaccination have been amplified. Objective: We aimed to test message effectiveness in changing parents? attitudes and behavioral intentions toward HPV vaccination. Methods: We conducted a web-based message-testing experiment with 6 control messages and 25 experimental messages and 5 from each of the 5 salient themes about HPV vaccination (theme 1: safety, side effects, risk, and ingredient concerns and long-term or major adverse events; theme 2: distrust of the health care system; theme 3: HPV vaccine effectiveness concerns; theme 4: connection to sexual activity; and theme 5: misinformation about HPV or HPV vaccine). Themes were identified from previous web-based focus group research with parents, and specific messages were developed by the study team using content from credible scientific sources. Through an iterative process of message development, the messages were crafted to be appropriate for presentation on a social media platform. Among the 1713 participants recruited via social media and crowdsourcing sites, 1043 eligible parents completed a pretest survey questionnaire. Participants were then randomly assigned to 1 of the 31 messages and asked to complete a posttest survey questionnaire that assessed attitudes toward the vaccine and perceived effectiveness of the viewed message. A subgroup of participants (189/995, 19%) with unvaccinated children aged 9 to 14 years was also assessed for their behavioral intention to vaccinate their children against HPV. Results: Parents in the experimental group had increased positive attitudes toward HPV vaccination compared with those in the control group (t969=3.03, P=.003), which was associated with increased intention to vaccinate among parents of unvaccinated children aged 9 to 14 years (r=1.14, P=.05). At the thematic level, we identified 4 themes (themes 2-5) that were relatively effective in increasing behavioral intentions by positively influencing attitudes toward the HPV vaccine (?25=5.97, P=.31, root mean square error of approximation [RMSEA]=0.014, comparative fit index [CFI]=0.91, standardized root mean square residual [SRMR]=0.031). On the message level, messages that provided scientific evidence from government-related sources (eg, the Centers for Disease Control and Prevention) and corrected misinformation (eg, ?vaccines like the HPV vaccine are simply a way for pharmaceutical companies to make money. That isn?t true?) were effective in forming positive perceptions toward the HPV vaccination messages. Conclusions: Evidence-based messages directly countering misinformation and promoting HPV vaccination in social media environments can positively influence parents? attitudes and behavioral intentions to vaccinate their children against HPV. UR - https://www.jmir.org/2022/11/e37559 UR - http://dx.doi.org/10.2196/37559 UR - http://www.ncbi.nlm.nih.gov/pubmed/36422887 ID - info:doi/10.2196/37559 ER - TY - JOUR AU - Déguilhem, Amélia AU - Malaab, Joelle AU - Talmatkadi, Manissa AU - Renner, Simon AU - Foulquié, Pierre AU - Fagherazzi, Guy AU - Loussikian, Paul AU - Marty, Tom AU - Mebarki, Adel AU - Texier, Nathalie AU - Schuck, Stephane PY - 2022/11/22 TI - Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media JO - JMIR Infodemiology SP - e39849 VL - 2 IS - 2 KW - long COVID KW - social media KW - Long Haulers KW - difficulties encountered KW - symptoms KW - infodemiology study KW - infodemiology KW - COVID-19 KW - patient-reported outcomes KW - persistent KW - condition KW - topics KW - discussion KW - content N2 - Background: Long COVID?a condition with persistent symptoms post COVID-19 infection?is the first illness arising from social media. In France, the French hashtag #ApresJ20 described symptoms persisting longer than 20 days after contracting COVID-19. Faced with a lack of recognition from medical and official entities, patients formed communities on social media and described their symptoms as long-lasting, fluctuating, and multisystemic. While many studies on long COVID relied on traditional research methods with lengthy processes, social media offers a foundation for large-scale studies with a fast-flowing outburst of data. Objective: We aimed to identify and analyze Long Haulers? main reported symptoms, symptom co-occurrences, topics of discussion, difficulties encountered, and patient profiles. Methods: Data were extracted based on a list of pertinent keywords from public sites (eg, Twitter) and health-related forums (eg, Doctissimo). Reported symptoms were identified via the MedDRA dictionary, displayed per the volume of posts mentioning them, and aggregated at the user level. Associations were assessed by computing co-occurrences in users? messages, as pairs of preferred terms. Discussion topics were analyzed using the Biterm Topic Modeling; difficulties and unmet needs were explored manually. To identify patient profiles in relation to their symptoms, each preferred term?s total was used to create user-level hierarchal clusters. Results: Between January 1, 2020, and August 10, 2021, overall, 15,364 messages were identified as originating from 6494 patients of long COVID or their caregivers. Our analyses revealed 3 major symptom co-occurrences: asthenia-dyspnea (102/289, 35.3%), asthenia-anxiety (65/289, 22.5%), and asthenia-headaches (50/289, 17.3%). The main reported difficulties were symptom management (150/424, 35.4% of messages), psychological impact (64/424,15.1%), significant pain (51/424, 12.0%), deterioration in general well-being (52/424, 12.3%), and impact on daily and professional life (40/424, 9.4% and 34/424, 8.0% of messages, respectively). We identified 3 profiles of patients in relation to their symptoms: profile A (n=406 patients) reported exclusively an asthenia symptom; profile B (n=129) expressed anxiety (n=129, 100%), asthenia (n=28, 21.7%), dyspnea (n=15, 11.6%), and ageusia (n=3, 2.3%); and profile C (n=141) described dyspnea (n=141, 100%), and asthenia (n=45, 31.9%). Approximately 49.1% of users (79/161) continued expressing symptoms after more than 3 months post infection, and 20.5% (33/161) after 1 year. Conclusions: Long COVID is a lingering condition that affects people worldwide, physically and psychologically. It impacts Long Haulers? quality of life, everyday tasks, and professional activities. Social media played an undeniable role in raising and delivering Long Haulers? voices and can potentially rapidly provide large volumes of valuable patient-reported information. Since long COVID was a self-titled condition by patients themselves via social media, it is imperative to continuously include their perspectives in related research. Our results can help design patient-centric instruments to be further used in clinical practice to better capture meaningful dimensions of long COVID. UR - https://infodemiology.jmir.org/2022/2/e39849 UR - http://dx.doi.org/10.2196/39849 UR - http://www.ncbi.nlm.nih.gov/pubmed/36447795 ID - info:doi/10.2196/39849 ER - TY - JOUR AU - Erturk, Sinan AU - Hudson, Georgie AU - Jansli, M. Sonja AU - Morris, Daniel AU - Odoi, M. Clarissa AU - Wilson, Emma AU - Clayton-Turner, Angela AU - Bray, Vanessa AU - Yourston, Gill AU - Cornwall, Andrew AU - Cummins, Nicholas AU - Wykes, Til AU - Jilka, Sagar PY - 2022/11/22 TI - Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study JO - JMIR Infodemiology SP - e36871 VL - 2 IS - 2 KW - machine learning KW - patient and public involvement KW - codevelopment KW - misconceptions KW - stigma KW - Twitter KW - social media N2 - Background: Dementia misconceptions on Twitter can have detrimental or harmful effects. Machine learning (ML) models codeveloped with carers provide a method to identify these and help in evaluating awareness campaigns. Objective: This study aimed to develop an ML model to distinguish between misconceptions and neutral tweets and to develop, deploy, and evaluate an awareness campaign to tackle dementia misconceptions. Methods: Taking 1414 tweets rated by carers from our previous work, we built 4 ML models. Using a 5-fold cross-validation, we evaluated them and performed a further blind validation with carers for the best 2 ML models; from this blind validation, we selected the best model overall. We codeveloped an awareness campaign and collected pre-post campaign tweets (N=4880), classifying them with our model as misconceptions or not. We analyzed dementia tweets from the United Kingdom across the campaign period (N=7124) to investigate how current events influenced misconception prevalence during this time. Results: A random forest model best identified misconceptions with an accuracy of 82% from blind validation and found that 37% of the UK tweets (N=7124) about dementia across the campaign period were misconceptions. From this, we could track how the prevalence of misconceptions changed in response to top news stories in the United Kingdom. Misconceptions significantly rose around political topics and were highest (22/28, 79% of the dementia tweets) when there was controversy over the UK government allowing to continue hunting during the COVID-19 pandemic. After our campaign, there was no significant change in the prevalence of misconceptions. Conclusions: Through codevelopment with carers, we developed an accurate ML model to predict misconceptions in dementia tweets. Our awareness campaign was ineffective, but similar campaigns could be enhanced through ML to respond to current events that affect misconceptions in real time. UR - https://infodemiology.jmir.org/2022/2/e36871 UR - http://dx.doi.org/10.2196/36871 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113444 ID - info:doi/10.2196/36871 ER - TY - JOUR AU - Tan, Jin Rayner Kay AU - Lim, Mingjie Jane AU - Neo, Min Pearlyn Hui AU - Ong, Ee Suan PY - 2022/11/22 TI - Reinterpretation of Health Information in the Context of an Emerging Infectious Disease: A Digital Focus Group Study JO - JMIR Hum Factors SP - e39312 VL - 9 IS - 4 KW - health communication KW - infodemic KW - SARS-CoV-2 KW - coronavirus KW - Singapore KW - WhatsApp KW - COVID-19 KW - health information KW - misinformation KW - mobile health KW - smartphone KW - information quality KW - online health information N2 - Background: Misinformation related to the COVID-19 pandemic has accelerated global public concern and panic. The glut of information, or ?infodemic,? has caused concern for authorities due to its negative impacts on COVID-19 prevention and control, spurring calls for a greater scholarly focus on health literacy during the pandemic. Nevertheless, few studies have sought to qualitatively examine how individuals interpreted and assimilated health information at the initial wave of COVID-19 restrictions. Objective: We developed this qualitative study adopting chat-based focus group discussions to investigate how individuals interpreted COVID-19 health information during the first wave of COVID-19 restrictions. Methods: We conducted a qualitative study in Singapore to investigate how individuals perceive and interpret information that they receive on COVID-19. Data were generated through online focus group discussions conducted on the mobile messaging smartphone app WhatsApp. From March 28 to April 13, 2020, we held eight WhatsApp-based focus groups (N=60) with participants stratified by age groups, namely 21-30 years, 31-40 years, 41-50 years, and 51 years and above. Data were thematically analyzed. Results: A total of four types of COVID-19 health information were generated from the thematic analysis, labeled as formal health information, informal health information, suspicious health information, and fake health information, respectively. How participants interpreted these categories of information depended largely on the perceived trustworthiness of the information source as well as the perceived veracity of information. Both factors were instrumental in determining individuals? perceptions, and their subsequent treatment and assimilation of COVID-19?related information. Conclusions: Both perceived trustworthiness of the information source and perceived veracity of information were instrumental concepts in determining one?s perception, and thus subsequent treatment and assimilation of such information for one?s knowledge of COVID-19 or the onward propagation to their social networks. These findings have implications for how policymakers and health authorities communicate with the public and deal with fake health information in the context of COVID-19. UR - https://humanfactors.jmir.org/2022/4/e39312 UR - http://dx.doi.org/10.2196/39312 UR - http://www.ncbi.nlm.nih.gov/pubmed/36099011 ID - info:doi/10.2196/39312 ER - TY - JOUR AU - Xue, Haoning AU - Zhang, Jingwen AU - Sagae, Kenji AU - Nishimine, Brian AU - Fukuoka, Yoshimi PY - 2022/11/22 TI - Analyzing Public Conversations About Heart Disease and Heart Health on Facebook From 2016 to 2021: Retrospective Observational Study Applying Latent Dirichlet Allocation Topic Modeling JO - JMIR Cardio SP - e40764 VL - 6 IS - 2 KW - heart health KW - heart disease KW - topic modeling KW - sentiment analysis KW - social media KW - Facebook KW - COVID-19 KW - women?s heart health N2 - Background: Heart disease continues to be the leading cause of death in men and women in the United States. The COVID-19 pandemic has further led to increases in various long-term cardiovascular complications. Objective: This study analyzed public conversations related to heart disease and heart health on Facebook in terms of their thematic topics and sentiments. In addition, it provided in-depth analyses of 2 subtopics with important practical implications: heart health for women and heart health during the COVID-19 pandemic. Methods: We collected 34,885 posts and 51,835 comments spanning from June 2016 to June 2021 that were related to heart disease and health from public Facebook pages and groups. We used latent Dirichlet allocation topic modeling to extract discussion topics illuminating the public?s interests and concerns regarding heart disease and heart health. We also used Linguistic Inquiry and Word Count (Pennebaker Conglomerates, Inc) to identify public sentiments regarding heart health. Results: We observed an increase in discussions related to heart health on Facebook. Posts and comments increased from 3102 and 3632 in 2016 to 8550 (176% increase) and 14,617 (302% increase) in 2021, respectively. Overall, 35.37% (12,340/34,885) of the posts were created after January 2020, the start of the COVID-19 pandemic. In total, 39.21% (13,677/34,885) of the posts were by nonprofit health organizations. We identified 6 topics in the posts (heart health promotion, personal experiences, risk-reduction education, heart health promotion for women, educational information, and physicians? live discussion sessions). We identified 6 topics in the comments (personal experiences, survivor stories, risk reduction, religion, medical questions, and appreciation of physicians and information on heart health). During the pandemic (from January 2020 to June 2021), risk reduction was a major topic in both posts and comments. Unverified information on alternative treatments and promotional content was also prevalent. Among all posts, 14.91% (5200/34,885) were specifically about heart health for women centering on local event promotion and distinctive symptoms of heart diseases for women. Conclusions: Our results tracked the public?s ongoing discussions on heart disease and heart health on one prominent social media platform, Facebook. The public?s discussions and information sharing on heart health increased over time, especially since the start of the COVID-19 pandemic. Various levels of health organizations on Facebook actively promoted heart health information and engaged a large number of users. Facebook presents opportunities for more targeted heart health interventions that can reach and engage diverse populations. UR - https://cardio.jmir.org/2022/2/e40764 UR - http://dx.doi.org/10.2196/40764 UR - http://www.ncbi.nlm.nih.gov/pubmed/36318640 ID - info:doi/10.2196/40764 ER - TY - JOUR AU - Wang, Dandan AU - Zhou, Yadong AU - Ma, Feicheng PY - 2022/11/18 TI - Opinion Leaders and Structural Hole Spanners Influencing Echo Chambers in Discussions About COVID-19 Vaccines on Social Media in China: Network Analysis JO - J Med Internet Res SP - e40701 VL - 24 IS - 11 KW - COVID-19 KW - COVID-19 vaccine KW - echo chamber KW - opinion leader KW - structural hole spanner KW - topic KW - sentiment KW - social media KW - vaccine hesitancy KW - public health KW - vaccination KW - health promotion KW - online campaign KW - social network analysis N2 - Background: Social media provide an ideal medium for breeding and reinforcing vaccine hesitancy, especially during public health emergencies. Algorithmic recommendation?based technology along with users? selective exposure and group pressure lead to online echo chambers, causing inefficiency in vaccination promotion. Avoiding or breaking echo chambers largely relies on key users? behavior. Objective: With the ultimate goal of eliminating the impact of echo chambers related to vaccine hesitancy on social media during public health emergencies, the aim of this study was to develop a framework to quantify the echo chamber effect in users? topic selection and attitude contagion about COVID-19 vaccines or vaccinations; detect online opinion leaders and structural hole spanners based on network attributes; and explore the relationships of their behavior patterns and network locations, as well as the relationships of network locations and impact on topic-based and attitude-based echo chambers. Methods: We called the Sina Weibo application programming interface to crawl tweets related to the COVID-19 vaccine or vaccination and user information on Weibo, a Chinese social media platform. Adopting social network analysis, we examined the low echo chamber effect based on topics in representational networks of information, according to attitude in communication flow networks of users under different interactive mechanisms (retweeting, commenting). Statistical and visual analyses were used to characterize behavior patterns of key users (opinion leaders, structural hole spanners), and to explore their function in avoiding or breaking topic-based and attitude-based echo chambers. Results: Users showed a low echo chamber effect in vaccine-related topic selection and attitude interaction. For the former, the homophily was more obvious in retweeting than in commenting, whereas the opposite trend was found for the latter. Speakers, replicators, and monologists tended to be opinion leaders, whereas common users, retweeters, and networkers tended to be structural hole spanners. Both leaders and spanners tended to be ?bridgers? to disseminate diverse topics and communicate with users holding cross-cutting attitudes toward COVID-19 vaccines. Moreover, users who tended to echo a single topic could bridge multiple attitudes, while users who focused on diverse topics also tended to serve as bridgers for different attitudes. Conclusions: This study not only revealed a low echo chamber effect in vaccine hesitancy, but further elucidated the underlying reasons from the perspective of users, offering insights for research about the form, degree, and formation of echo chambers, along with depolarization, social capital, stakeholder theory, user portraits, dissemination pattern of topic, and sentiment. Therefore, this work can help to provide strategies for public health and public opinion managers to cooperate toward avoiding or correcting echo chamber chaos and effectively promoting online vaccine campaigns. UR - https://www.jmir.org/2022/11/e40701 UR - http://dx.doi.org/10.2196/40701 UR - http://www.ncbi.nlm.nih.gov/pubmed/36367965 ID - info:doi/10.2196/40701 ER - TY - JOUR AU - Russell, M. Alex AU - Valdez, Danny AU - Chiang, C. Shawn AU - Montemayor, N. Ben AU - Barry, E. Adam AU - Lin, Hsien-Chang AU - Massey, M. Philip PY - 2022/11/18 TI - Using Natural Language Processing to Explore ?Dry January? Posts on Twitter: Longitudinal Infodemiology Study JO - J Med Internet Res SP - e40160 VL - 24 IS - 11 KW - alcohol KW - drinking KW - social media KW - Twitter KW - Dry January KW - infodemiology KW - infoveillance KW - natural language processing N2 - Background: Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Though Dry January has become a global phenomenon, there has been limited investigation into Dry January participants? experiences. One means through which to gain insights into individuals? Dry January-related experiences is by leveraging large-scale social media data (eg, Twitter chatter) to explore and characterize public discourse concerning Dry January. Objective: We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multiple years of tweets (2020-2022)? (2) Do unique themes or patterns emerge in Dry January 2021 tweets after the onset of the COVID-19 pandemic? and (3) What is the association with tweet composition (ie, sentiment and human-authored vs bot-authored) and engagement with Dry January tweets? Methods: We applied natural language processing techniques to a large sample of tweets (n=222,917) containing the term ?dry january? or ?dryjanuary? posted from December 15 to February 15 across three separate years of participation (2020-2022). Term frequency inverse document frequency, k-means clustering, and principal component analysis were used for data visualization to identify the optimal number of clusters per year. Once data were visualized, we ran interpretation models to afford within-year (or within-cluster) comparisons. Latent Dirichlet allocation topic modeling was used to examine content within each cluster per given year. Valence Aware Dictionary and Sentiment Reasoner sentiment analysis was used to examine affect per cluster per year. The Botometer automated account check was used to determine average bot score per cluster per year. Last, to assess user engagement with Dry January content, we took the average number of likes and retweets per cluster and ran correlations with other outcome variables of interest. Results: We observed several similar topics per year (eg, Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content over time. Although there was overlap in themes across multiple years of tweets, unique themes related to individuals? experiences with alcohol during the midst of the COVID-19 global pandemic were detected in the corpus of tweets from 2021. Also, tweet composition was associated with engagement, including number of likes, retweets, and quote-tweets per post. Bot-dominant clusters had fewer likes, retweets, or quote tweets compared with human-authored clusters. Conclusions: The findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemplating, preparing for, or actively pursuing attempts to quit or cut down on their drinking. UR - https://www.jmir.org/2022/11/e40160 UR - http://dx.doi.org/10.2196/40160 UR - http://www.ncbi.nlm.nih.gov/pubmed/36343184 ID - info:doi/10.2196/40160 ER - TY - JOUR AU - Ljaji?, Adela AU - Prodanovi?, Nikola AU - Medvecki, Darija AU - Ba?aragin, Bojana AU - Mitrovi?, Jelena PY - 2022/11/17 TI - Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling JO - J Med Internet Res SP - e42261 VL - 24 IS - 11 KW - topic modeling KW - sentiment analysis KW - LDA KW - NMF KW - BERT KW - vaccine hesitancy KW - COVID-19 KW - Twitter KW - Serbian language processing KW - vaccine KW - public health KW - NLP KW - vaccination KW - Serbia N2 - Background: Since the first COVID-19 vaccine appeared, there has been a growing tendency to automatically determine public attitudes toward it. In particular, it was important to find the reasons for vaccine hesitancy, since it was directly correlated with pandemic protraction. Natural language processing (NLP) and public health researchers have turned to social media (eg, Twitter, Reddit, and Facebook) for user-created content from which they can gauge public opinion on vaccination. To automatically process such content, they use a number of NLP techniques, most notably topic modeling. Topic modeling enables the automatic uncovering and grouping of hidden topics in the text. When applied to content that expresses a negative sentiment toward vaccination, it can give direct insight into the reasons for vaccine hesitancy. Objective: This study applies NLP methods to classify vaccination-related tweets by sentiment polarity and uncover the reasons for vaccine hesitancy among the negative tweets in the Serbian language. Methods: To study the attitudes and beliefs behind vaccine hesitancy, we collected 2 batches of tweets that mention some aspects of COVID-19 vaccination. The first batch of 8817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant tweets were annotated as positive, negative, or neutral. We used the annotated tweets to train a sequential bidirectional encoder representations from transformers (BERT)-based classifier for 2 tweet classification tasks to augment this initial data set. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive, or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined data sets resulted in 3286 tweets with a negative sentiment: 1770 (53.9%) from the manually annotated data set and 1516 (46.1%) as a result of automatic classification. Topic modeling methods (latent Dirichlet allocation [LDA] and nonnegative matrix factorization [NMF]) were applied using the 3286 preprocessed tweets to detect the reasons for vaccine hesitancy. Results: The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85, and 0.85 for negative, neutral, and positive sentiments, respectively. By summarizing the topics obtained in both models, we extracted 5 main groups of reasons for vaccine hesitancy: concern over vaccine side effects, concern over vaccine effectiveness, concern over insufficiently tested vaccines, mistrust of authorities, and conspiracy theories. Conclusions: This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process. UR - https://www.jmir.org/2022/11/e42261 UR - http://dx.doi.org/10.2196/42261 UR - http://www.ncbi.nlm.nih.gov/pubmed/36301673 ID - info:doi/10.2196/42261 ER - TY - JOUR AU - Ackleh-Tingle, V. Jonathan AU - Jordan, M. Natalie AU - Onwubiko, N. Udodirim AU - Chandra, Christina AU - Harton, E. Paige AU - Rentmeester, T. Shelby AU - Chamberlain, T. Allison PY - 2022/11/17 TI - Prevalence and Correlates of COVID-19 Vaccine Information on Family Medicine Practices? Websites in the United States: Cross-sectional Website Content Analysis JO - JMIR Form Res SP - e38425 VL - 6 IS - 11 KW - primary care KW - vaccine hesitancy KW - COVID-19 KW - health communications KW - health information KW - health website KW - family practice KW - vaccine information KW - online health KW - health platform KW - online information N2 - Background: Primary care providers are regarded as trustworthy sources of information about COVID-19 vaccines. Although primary care practices often provide information about common medical and public health topics on their practice websites, little is known about whether they also provide information about COVID-19 vaccines on their practice websites. Objective: This study aimed to investigate the prevalence and correlates of COVID-19 vaccine information on family medicine practices? website home pages in the United States. Methods: We used the Centers for Medicare and Medicaid National Provider Identifier records to create a sampling frame of all family medicine providers based in the United States, from which we constructed a nationally representative random sample of 964 family medicine providers. Between September 20 and October 8, 2021, we manually examined the practice websites of these providers and extracted data on the availability of COVID-19 vaccine information, and we implemented a 10% cross-review quality control measure to resolve discordances in data abstraction. We estimated the prevalence of COVID-19 vaccine information on practice websites and website home pages and used Poisson regression with robust error variances to estimate crude and adjusted prevalence ratios for correlates of COVID-19 vaccine information, including practice size, practice region, university affiliation, and presence of information about seasonal influenza vaccines. Additionally, we performed sensitivity analyses to account for multiple comparisons. Results: Of the 964 included family medicine practices, most (n=509, 52.8%) had ?10 distinct locations, were unaffiliated with a university (n=838, 87.2%), and mentioned seasonal influenza vaccines on their websites (n=540, 56.1%). In total, 550 (57.1%) practices mentioned COVID-19 vaccines on their practices? website home page, specifically, and 726 (75.3%) mentioned COVID-19 vaccines anywhere on their practice website. As practice size increased, the likelihood of finding COVID-19 vaccine information on the home page increased (n=66, 27.7% among single-location practices, n=114, 52.5% among practices with 2-9 locations, n=66, 56.4% among practices with 10-19 locations, and n=304, 77.6% among practices with 20 or more locations, P<.001 for trend). Compared to clinics in the Northeast, those in the West and Midwest United States had a similar prevalence of COVID-19 vaccine information on website home pages, but clinics in the south had a lower prevalence (adjusted prevalence ratio 0.8, 95% CI 0.7 to 1.0; P=.02). Our results were largely unchanged in sensitivity analyses accounting for multiple comparisons. Conclusions: Given the ongoing COVID-19 pandemic, primary care practitioners who promote and provide vaccines should strongly consider utilizing their existing practice websites to share COVID-19 vaccine information. These existing platforms have the potential to serve as an extension of providers? influence on established and prospective patients who search the internet for information about COVID-19 vaccines. UR - https://formative.jmir.org/2022/11/e38425 UR - http://dx.doi.org/10.2196/38425 UR - http://www.ncbi.nlm.nih.gov/pubmed/36343211 ID - info:doi/10.2196/38425 ER - TY - JOUR AU - Vassey, Julia AU - Donaldson, I. Scott AU - Dormanesh, Allison AU - Allem, Jon-Patrick PY - 2022/11/16 TI - Themes in TikTok Videos Featuring Little Cigars and Cigarillos: Content Analysis JO - J Med Internet Res SP - e42441 VL - 24 IS - 11 KW - cigarillo KW - little cigar KW - social media KW - TikTok KW - video KW - cigar KW - cigarette KW - smoker KW - smoking KW - tobacco KW - content analysis KW - youth KW - young adult KW - adolescent KW - user generated content N2 - Background: Little cigars and cigarillos (LCCs) are popular tobacco products among youth (ie, adolescents and young adults). A variety of LCC-related promotional and user-generated content is present on social media. However, research on LCC-related posts on social media has been largely focused on platforms that are primarily text- or image-based, such as Twitter and Instagram. Objective: This study analyzed LCC-related content on TikTok, an audio and video?based platform popular among youth. Methods: Publicly available posts (N=811) that contained the LCC-related hashtags #swishersweets or #backwoods were collected on TikTok from January 2019 to May 2021. Metadata were also collected, including numbers of likes, comments, shares, and views per video. Using an inductive approach, a codebook consisting of 26 themes was developed to help summarize the underlying themes evident in the TikTok videos and corresponding captions. A pairwise co-occurrence analysis of themes was also conducted to evaluate connections among themes. Results: Among the 811 posts, the LCC presence theme (ie, a visible LCC) occurred in the most prominent number of posts (n=661, 81.5%), followed by music (n=559, 68.9%), youth (n=332, 40.9%), humor (n=263, 32.4%), LCC use (n=242, 29.8%), flavors (n=232, 28.6%), branding (n=182, 22.4%), paraphernalia (n=137, 16.9%), blunt rolling (n=94, 11.6%), and price (n=84, 10.4%). Product reviews had the highest engagement, with a median 44 (mean 2857, range 36,499) likes and median 491 (mean 15,711, range 193,590) views; followed by product comparisons, with a median 44 (mean 1920, range 36,500) likes and median 671 (mean 11,277, range 193,798) views. Promotions had the lowest engagement, with a median 4 (mean 8, range 34) likes and median 78 (mean 213, range 1131) views. The most prevalent themes co-occurring with LCC presence were (1) music (434/811, 53.5%), (2) youth (264/811, 32.6%), (3) humor (219/811, 27%), (4) flavors (214/811, 26.4%), and (5) LCC use (207/811, 25.5%). Conclusions: LCC-related marketing and user-generated content was present on TikTok, including videos showing youth discussing, displaying, or using LCCs. Such content may be in violation of TikTok?s community guidelines prohibiting the display, promotion, or posting of tobacco-related content on its platform, including the display of possession or consumption of tobacco by a minor. Further improvement in the enforcement of TikTok community guidelines and additional scrutiny from public health policy makers may be necessary for protecting youth from future exposure to tobacco-related posts. Observational and experimental studies are needed to understand the impact of exposure to LCC-related videos on attitudes and behaviors related to LCC use among youth. Finally, there may be a need for engaging antitobacco videos that appeal to youth on TikTok to counter the protobacco content on this platform. UR - https://www.jmir.org/2022/11/e42441 UR - http://dx.doi.org/10.2196/42441 UR - http://www.ncbi.nlm.nih.gov/pubmed/36383406 ID - info:doi/10.2196/42441 ER - TY - JOUR AU - Moyano, Luz Daniela AU - Lopez, Victoria María AU - Cavallo, Ana AU - Candia, Patricia Julia AU - Kaen, Aaron AU - Irazola, Vilma AU - Beratarrechea, Andrea PY - 2022/11/16 TI - The Use of 2 e-Learning Modalities for Diabetes Education Using Facebook in 2 Cities of Argentina During the COVID-19 Pandemic: Qualitative Study JO - JMIR Form Res SP - e38862 VL - 6 IS - 11 KW - COVID-19 KW - social media KW - diabetes mellitus KW - public health KW - qualitative research KW - COVID-19 pandemic KW - teaching and learning settings KW - online learning KW - eHealth literacy N2 - Background: The COVID-19 pandemic and the confinement that was implemented in Argentina generated a need to implement innovative tools for the strengthening of diabetes care. Diabetes self-management education (DSME) is a core element of diabetes care; however, because of COVID-19 restrictions, in-person diabetes educational activities were suspended. Social networks have played an instrumental role in this context to provide DSME in 2 cities of Argentina and help persons with diabetes in their daily self-management. Objective: The aim of this study is to evaluate 2 diabetes education modalities (synchronous and asynchronous) using the social media platform Facebook through the content of posts on diabetes educational sessions in 2 cities of Argentina during the COVID-19 pandemic. Methods: In this qualitative study, we explored 2 modalities of e-learning (synchronous and asynchronous) for diabetes education that used the Facebook pages of public health institutions in Chaco and La Rioja, Argentina, in the context of confinement. Social media metrics and the content of the messages posted by users were analyzed. Results: A total of 332 messages were analyzed. We found that in the asynchronous modality, there was a higher number of visualizations, while in the synchronous modality, there were more posts and interactions between educators and users. We also observed that the number of views increased when primary care clinics were incorporated as disseminators, sharing educational videos from the sessions via social media. Positive aspects were observed in the posts, consisting of messages of thanks and, to a lesser extent, reaffirmations, reflections or personal experiences, and consultations related to the subject treated. Another relevant finding was that the educator/moderator role had a greater presence in the synchronous modality, where posts were based on motivation for participation, help to resolve connectivity problems, and answers to specific user queries. Conclusions: Our findings show positive contributions of an educational intervention for diabetes care using the social media platform Facebook in the context of the COVID-19 pandemic. Although each modality (synchronous vs asynchronous) could have differential and particular advantages, we believe that these strategies have potential to be replicated and adapted to other contexts. However, more documented experiences are needed to explore their sustainability and long-term impact from the users' perspective. UR - https://formative.jmir.org/2022/11/e38862 UR - http://dx.doi.org/10.2196/38862 UR - http://www.ncbi.nlm.nih.gov/pubmed/36322794 ID - info:doi/10.2196/38862 ER - TY - JOUR AU - Teodorowski, Piotr AU - Rodgers, E. Sarah AU - Fleming, Kate AU - Frith, Lucy PY - 2022/11/15 TI - Use of the Hashtag #DataSavesLives on Twitter: Exploratory and Thematic Analysis JO - J Med Internet Res SP - e38232 VL - 24 IS - 11 KW - consumer involvement KW - patient participation KW - stakeholder participation KW - social media KW - public engagement KW - campaign KW - big data KW - research KW - trust KW - tweets KW - Twitter KW - perception KW - usage KW - users KW - data sharing KW - ethics KW - community KW - hashtag N2 - Background: ?Data Saves Lives? is a public engagement campaign that highlights the benefits of big data research and aims to establish public trust for this emerging research area. Objective: This study explores how the hashtag #DataSavesLives is used on Twitter. We focused on the period when the UK government and its agencies adopted #DataSavesLives in an attempt to support their plans to set up a new database holding National Health Service (NHS) users? medical data. Methods: Public tweets published between April 19 and July 15, 2021, using the hashtag #DataSavesLives were saved using NCapture for NVivo 12. All tweets were coded twice. First, each tweet was assigned a positive, neutral, or negative attitude toward the campaign. Second, inductive thematic analysis was conducted. The results of the thematic analysis were mapped under 3 models of public engagement: deficit, dialogue, and participatory. Results: Of 1026 unique tweets available for qualitative analysis, discussion around #DataSavesLives was largely positive (n=716, 69.8%) or neutral (n=276, 26.9%) toward the campaign with limited negative attitudes (n=34, 3.3%). Themes derived from the #DataSavesLives debate included ethical sharing, proactively engaging the public, coproducing knowledge with the public, harnessing potential, and gaining an understanding of big data research. The Twitter discourse was largely positive toward the campaign. The hashtag is predominantly used by similar-minded Twitter users to share information about big data projects and to spread positive messages about big data research when there are public controversies. The hashtag is generally used by organizations and people supportive of big data research. Tweet authors recognize that the public should be proactively engaged and involved in big data projects. The campaign remains UK centric. The results indicate that the communication around big data research is driven by the professional community and remains 1-way as members of the public rarely use the hashtag. Conclusions: The results demonstrate the potential of social media but draws attention to hashtag usage being generally confined to ?Twitter bubbles?: groups of similar-minded Twitter users. UR - https://www.jmir.org/2022/11/e38232 UR - http://dx.doi.org/10.2196/38232 UR - http://www.ncbi.nlm.nih.gov/pubmed/36378518 ID - info:doi/10.2196/38232 ER - TY - JOUR AU - Yoon, Young Ho AU - You, Han Kyung AU - Kwon, Hye Jung AU - Kim, Sun Jung AU - Rha, Young Sun AU - Chang, Jung Yoon AU - Lee, Sang-Cheol PY - 2022/11/14 TI - Understanding the Social Mechanism of Cancer Misinformation Spread on YouTube and Lessons Learned: Infodemiological Study JO - J Med Internet Res SP - e39571 VL - 24 IS - 11 KW - cancer misinformation KW - social media health misinformation KW - fenbendazole KW - self-administration KW - complex contagion KW - YouTube KW - social media factual information delivery strategy N2 - Background: A knowledge gap exists between the list of required actions and the action plan for countering cancer misinformation on social media. Little attention has been paid to a social media strategy for disseminating factual information while also disrupting misinformation on social media networks. Objective: The aim of this study was to, first, identify the spread structure of cancer misinformation on YouTube. We asked the question, ?How do YouTube videos play an important role in spreading information about the self-administration of anthelmintics for dogs as a cancer medicine for humans?? Second, the study aimed to suggest an action strategy for disrupting misinformation diffusion on YouTube by exploiting the network logic of YouTube information flow and the recommendation system. We asked the question, ?What would be a feasible and effective strategy to block cancer misinformation diffusion on YouTube?? Methods: The study used the YouTube case of the self-administration of anthelmintics for dogs as an alternative cancer medicine in South Korea. We gathered Korean YouTube videos about the self-administration of fenbendazole. Using the YouTube application programming interface for the query ?fenbendazole,? 702 videos from 227 channels were compiled. Then, videos with at least 50,000 views, uploaded between September 2019 and September 2020, were selected from the collection, resulting in 90 videos. Finally, 10 recommended videos for each of the 90 videos were compiled, totaling 573 videos. Social network visualization for the recommended videos was used to identify three intervention strategies for disrupting the YouTube misinformation network. Results: The study found evidence of complex contagion by human and machine recommendation systems. By exposing stakeholders to multiple information sources on fenbendazole self-administration and by linking them through a recommendation algorithm, YouTube has become the perfect infrastructure for reinforcing the belief that fenbendazole can cure cancer, despite government warnings about the risks and dangers of self-administration. Conclusions: Health authorities should upload pertinent information through multiple channels and should exploit the existing YouTube recommendation algorithm to disrupt the misinformation network. Considering the viewing habits of patients and caregivers, the direct use of YouTube hospital channels is more effective than the indirect use of YouTube news media channels or government channels that report public announcements and statements. Reinforcing through multiple channels is the key. UR - https://www.jmir.org/2022/11/e39571 UR - http://dx.doi.org/10.2196/39571 UR - http://www.ncbi.nlm.nih.gov/pubmed/36374534 ID - info:doi/10.2196/39571 ER - TY - JOUR AU - Chen, Xi AU - Yik, Michelle PY - 2022/11/14 TI - The Emotional Anatomy of the Wuhan Lockdown: Sentiment Analysis Using Weibo Data JO - JMIR Form Res SP - e37698 VL - 6 IS - 11 KW - Wuhan lockdown KW - COVID-19 KW - public health emergency KW - emotion KW - circumplex model of affect KW - Weibo KW - jiayou N2 - Background: On January 23, 2020, the city of Wuhan, China, was sealed off in response to the COVID-19 pandemic. Studies have found that the lockdown was associated with both positive and negative emotions, although their findings are not conclusive. In these studies, emotional responses to the Wuhan lockdown were identified using lexicons based on limited emotion types. Objective: This study aims to map Chinese people?s emotional responses to the Wuhan lockdown and compare Wuhan residents? emotions with those of people elsewhere in China by analyzing social media data from Weibo using a lexicon based on the circumplex model of affect. Methods: Social media posts on Weibo from 2 weeks before to 2 weeks after the Wuhan lockdown was imposed (January 9, 2020, to February 6, 2020) were collected. Each post was coded using a valence score and an arousal score. To map emotional trajectories during the study period, we used a data set of 359,190 posts. To compare the immediate emotional responses to the lockdown and its longer-term emotional impact on Wuhan residents (n=1236) and non-Hubei residents (n=12,714), we used a second data set of 57,685 posts for multilevel modeling analyses. Results: Most posts (248,757/359,190, 69.25%) made during the studied lockdown period indicated a pleasant mood with low arousal. A gradual increase in both valence and arousal before the lockdown was observed. The posts after the lockdown was imposed had higher valence and arousal than prelockdown posts. On the day of lockdown, the non-Hubei group had a temporarily boosted valence (?20=0.118; SE 0.021; P<.001) and arousal (?30=0.293; SE 0.022; P<.001). Compared with non-Hubei residents, the Wuhan group had smaller increases in valence (?21=?0.172; SE 0.052; P<.001) and arousal (?31=?0.262; SE 0.053; P<.001) on the day of lockdown. Weibo users? emotional valence (?40=0.000; SE 0.001; P=.71) and arousal (?40=0.001; SE 0.001; P=.56) remained stable over the 2 weeks after the lockdown was imposed regardless of geographical location (valence: ?41=?0.004, SE 0.003, and P=.16; arousal: ?41=0.003, SE 0.003, and P=.26). Conclusions: During the early stages of the pandemic, most Weibo posts indicated a pleasant mood with low arousal. The overall increase in the posts? valence and arousal after the lockdown announcement might indicate collective cohesion and mutual support in web-based communities during a public health crisis. Compared with the temporary increases in valence and arousal of non-Hubei users on the day of lockdown, Wuhan residents? emotions were less affected by the announcement. Overall, our data suggest that Weibo users were not influenced by the lockdown measures in the 2 weeks after the lockdown announcement. Our findings offer policy makers insights into the usefulness of social connections in maintaining the psychological well-being of people affected by a lockdown. UR - https://formative.jmir.org/2022/11/e37698 UR - http://dx.doi.org/10.2196/37698 UR - http://www.ncbi.nlm.nih.gov/pubmed/36166650 ID - info:doi/10.2196/37698 ER - TY - JOUR AU - Fittler, András AU - Paczolai, Péter AU - Ashraf, Reza Amir AU - Pourhashemi, Amir AU - Iványi, Péter PY - 2022/11/8 TI - Prevalence of Poisoned Google Search Results of Erectile Dysfunction Medications Redirecting to Illegal Internet Pharmacies: Data Analysis Study JO - J Med Internet Res SP - e38957 VL - 24 IS - 11 KW - internet pharmacies KW - search engine redirection KW - compromised websites KW - illegal medicines KW - patient safety KW - Europe KW - erectile dysfunction medications N2 - Background: Illegal online pharmacies function as affiliate networks, in which search engine results pages (SERPs) are poisoned by several links redirecting site visitors to unlicensed drug distribution pages upon clicking on the link of a legitimate, yet irrelevant domain. This unfair online marketing practice is commonly referred to as search redirection attack, a most frequently used technique in the online illegal pharmaceutical marketplace. Objective: This study is meant to describe the mechanism of search redirection attacks in Google search results in relation to erectile dysfunction medications in European countries and also to determine the local and global scales of this problem. Methods: The search engine query results regarding 4 erectile dysfunction medications were documented using Google. The search expressions were ?active ingredient? and ?buy? in the language of 12 European countries, including Hungary. The final destination website legitimacy was checked at LegitScript, and the estimated number of monthly unique visitors was obtained from SEMrush traffic analytics. Compromised links leading to international illegal medicinal product vendors via redirection were analyzed using Gephi graph visualization software. Results: Compromised links redirecting to active online pharmacies were present in search query results of all evaluated countries. The prevalence was highest in Spain (62/160, 38.8%), Hungary (52/160, 32.5%), Italy (46/160, 28.8%), and France (37/160, 23.1%), whereas the lowest was in Finland (12/160, 7.5%), Croatia (10/160, 6.3%), and Bulgaria (2/160, 1.3%), as per data recorded in November 2020. A decrease in the number of compromised sites linking visitors to illegitimate medicine sellers was observed in the Hungarian data set between 2019 and 2021, from 41% (33/80) to 5% (4/80), respectively. Out of 1920 search results in the international sample, 380 (19.79%) search query results were compromised, with the majority (n=342, 90%) of links redirecting individuals to 73 international illegal medicinal product vendors. Most of these illegal online pharmacies (41/73, 56%) received only 1 or 2 compromised links, whereas the top 3 domains with the highest in-degree link value received more than one-third of all incoming links. Traffic analysis of 35 pharmacy specific domains, accessible via compromised links in search engine queries, showed a total of 473,118 unique visitors in November 2020. Conclusions: Although the number of compromised links in SERPs has shown a decreasing tendency in Hungary, an analysis of the European search query data set points to the global significance of search engine poisoning. Our research illustrates that search engine poisoning is a constant threat, as illegitimate affiliate networks continue to flourish while uncoordinated interventions by authorities and individual stakeholders remain insufficient. Ultimately, without a dedicated and comprehensive effort on the part of search engine providers for effectively monitoring and moderating SERPs, they may never be entirely free of compromised links leading to illegal online pharmacy networks. UR - https://www.jmir.org/2022/11/e38957 UR - http://dx.doi.org/10.2196/38957 UR - http://www.ncbi.nlm.nih.gov/pubmed/36346655 ID - info:doi/10.2196/38957 ER - TY - JOUR AU - Ismail, Nashwa AU - Kbaier, Dhouha AU - Farrell, Tracie AU - Kane, Annemarie PY - 2022/11/2 TI - The Experience of Health Professionals With Misinformation and Its Impact on Their Job Practice: Qualitative Interview Study JO - JMIR Form Res SP - e38794 VL - 6 IS - 11 KW - health misinformation KW - social media KW - health professional KW - patients KW - trust KW - communication, COVID-19 KW - intervention KW - qualitative research KW - interpretive phenomenological analysis KW - thematic analysis KW - misinformation KW - health practitioner KW - infodemiology N2 - Background: Misinformation is often disseminated through social media, where information is spread rapidly and easily. Misinformation affects many patients' decisions to follow a treatment prescribed by health professionals (HPs). For example, chronic patients (eg, those with diabetes) may not follow their prescribed treatment plans. During the recent pandemic, misinformed people rejected COVID-19 vaccines and public health measures, such as masking and physical distancing, and used unproven treatments. Objective: This study investigated the impact of health-threatening misinformation on the practices of health care professionals in the United Kingdom, especially during the outbreaks of diseases where a great amount of health-threatening misinformation is produced and released. The study examined the misinformation surrounding the COVID-19 outbreak to determine how it may have impacted practitioners' perceptions of misinformation and how that may have influenced their practice. In particular, this study explored the answers to the following questions: How do HPs react when they learn that a patient has been misinformed? What misinformation do they believe has the greatest impact on medical practice? What aspects of change and intervention in HPs' practice are in response to misinformation? Methods: This research followed a qualitative approach to collect rich data from a smaller subset of health care practitioners working in the United Kingdom. Data were collected through 1-to-1 online interviews with 13 health practitioners, including junior and senior physicians and nurses in the United Kingdom. Results: Research findings indicated that HPs view misinformation in different ways according to the scenario in which it occurs. Some HPs consider it to be an acute incident exacerbated by the pandemic, while others see it as an ongoing phenomenon (always present) and address it as part of their daily work. HPs are developing pathways for dealing with misinformation. Two main pathways were identified: first, to educate the patient through coaching, advising, or patronizing and, second, to devote resources, such as time and effort, to facilitate 2-way communication between the patient and the health care provider through listening and talking to them. Conclusions: HPs do not receive the confidence they deserve from patients. The lack of trust in health care practitioners has been attributed to several factors, including (1) trusting alternative sources of information (eg, social media) (2) patients' doubts about HPs' experience (eg, a junior doctor with limited experience), and (3) limited time and availability for patients, especially during the pandemic. There are 2 dimensions of trust: patient-HP trust and patient-information trust. There are 2 necessary actions to address the issue of lack of trust in these dimensions: (1) building trust and (2) maintaining trust. The main recommendations of the HPs are to listen to patients, give them more time, and seek evidence-based resources. UR - https://formative.jmir.org/2022/11/e38794 UR - http://dx.doi.org/10.2196/38794 UR - http://www.ncbi.nlm.nih.gov/pubmed/36252133 ID - info:doi/10.2196/38794 ER - TY - JOUR AU - Johnson, K. Amy AU - Bhaumik, Runa AU - Nandi, Debarghya AU - Roy, Abhishikta AU - Mehta, D. Supriya PY - 2022/10/31 TI - Sexually Transmitted Disease?Related Reddit Posts During the COVID-19 Pandemic: Latent Dirichlet Allocation Analysis JO - J Med Internet Res SP - e37258 VL - 24 IS - 10 KW - infodemiology KW - Latent Dirichlet Allocation KW - natural language processing KW - Reddit KW - sexually transmitted infections KW - surveillance KW - social media KW - COVID-19 KW - social media content KW - content analysis KW - health outcome KW - infoveillance KW - health information KW - sexually transmitted disease KW - STD N2 - Background: Sexually transmitted diseases (STDs) are common and costly, impacting approximately 1 in 5 people annually. Reddit, the sixth most used internet site in the world, is a user-generated social media discussion platform that may be useful in monitoring discussion about STD symptoms and exposure. Objective: This study sought to define and identify patterns and insights into STD-related discussions on Reddit over the course of the COVID-19 pandemic. Methods: We extracted posts from Reddit from March 2019 through July 2021. We used a topic modeling method, Latent Dirichlet Allocation, to identify the most common topics discussed in the Reddit posts. We then used word clouds, qualitative topic labeling, and spline regression to characterize the content and distribution of the topics observed. Results: Our extraction resulted in 24,311 total posts. Latent Dirichlet Allocation topic modeling showed that with 8 topics for each time period, we achieved high coherence values (pre?COVID-19=0.41, prevaccination=0.42, and postvaccination=0.44). Although most topic categories remained the same over time, the relative proportion of topics changed and new topics emerged. Spline regression revealed that some key terms had variability in the percentage of posts that coincided with pre?COVID-19 and post?COVID-19 periods, whereas others were uniform across the study periods. Conclusions: Our study?s use of Reddit is a novel way to gain insights into STD symptoms experienced, potential exposures, testing decisions, common questions, and behavior patterns (eg, during lockdown periods). For example, reduction in STD screening may result in observed negative health outcomes due to missed cases, which also impacts onward transmission. As Reddit use is anonymous, users may discuss sensitive topics with greater detail and more freely than in clinical encounters. Data from anonymous Reddit posts may be leveraged to enhance the understanding of the distribution of disease and need for targeted outreach or screening programs. This study provides evidence in favor of establishing Reddit as having feasibility and utility to enhance the understanding of sexual behaviors, STD experiences, and needed health engagement with the public. UR - https://www.jmir.org/2022/10/e37258 UR - http://dx.doi.org/10.2196/37258 UR - http://www.ncbi.nlm.nih.gov/pubmed/36219757 ID - info:doi/10.2196/37258 ER - TY - JOUR AU - Ganser, Iris AU - Thiébaut, Rodolphe AU - Buckeridge, L. David PY - 2022/10/31 TI - Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis JO - JMIR Public Health Surveill SP - e36211 VL - 8 IS - 10 KW - event-based surveillance KW - digital disease detection KW - public health surveillance KW - influenza KW - infectious disease outbreak KW - surveillance KW - disease KW - outbreak KW - analysis KW - public health KW - data KW - detection KW - detect KW - epidemic N2 - Background: Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks by using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. Objective: The aim of this study was to assess the variation in the timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability by using the example of seasonal influenza epidemic in 24 countries. Methods: We obtained influenza-related reports between January 2013 and December 2019 from 2 EBS systems, that is, HealthMap and the World Health Organization Epidemic Intelligence from Open Sources (EIOS), and weekly virological influenza counts for the same period from FluNet as the gold standard. Influenza epidemic periods were detected based on report frequency by using Bayesian change point analysis. Timely sensitivity, that is, outbreak detection within the first 2 weeks before or after an outbreak onset was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. Results: Overall, while monitoring the frequency of EBS reports over 7 years in 24 countries, we detected 175 out of 238 outbreaks (73.5%) but only 22 out of 238 (9.2%) within 2 weeks before or after an outbreak onset; in the best case, while monitoring the frequency of health-related reports, we identified 2 out of 6 outbreaks (33%) within 2 weeks of onset. The positive predictive value varied between 9% and 100% for HealthMap and from 0 to 100% for EIOS, and timeliness of detection ranged from 13% to 94% for HealthMap and from 0% to 92% for EIOS, whereas system specificity was generally high (59%-100%). The number of EBS reports available within a country, the human development index, and the country?s geographical location partially explained the high variability in system performance across countries. Conclusions: We documented the global variation of EBS performance and demonstrated that monitoring the report frequency alone in EBS may be insufficient for the timely detection of outbreaks. In particular, in low- and middle-income countries, low data quality and report frequency impair the sensitivity and timeliness of disease surveillance through EBS. Therefore, advances in the development and evaluation and EBS are needed, particularly in low-resource settings. UR - https://publichealth.jmir.org/2022/10/e36211 UR - http://dx.doi.org/10.2196/36211 UR - http://www.ncbi.nlm.nih.gov/pubmed/36315218 ID - info:doi/10.2196/36211 ER - TY - JOUR AU - Ke, Yang Si AU - Neeley-Tass, Shannon E. AU - Barnes, Michael AU - Hanson, L. Carl AU - Giraud-Carrier, Christophe AU - Snell, Quinn PY - 2022/10/31 TI - COVID-19 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach JO - JMIR Infodemiology SP - e37861 VL - 2 IS - 2 KW - COVID-19 KW - Health Belief Model KW - deep learning KW - mask KW - vaccination KW - machine learning KW - vaccine data set KW - Twitter KW - content analysis KW - infodemic KW - infodemiology KW - misinformation KW - health belief N2 - Background: Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19?related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19. Objective: The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action. Methods: A total of 646,885,238 COVID-19?related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets. Results: In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action. Conclusions: During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals. UR - https://infodemiology.jmir.org/2022/2/e37861 UR - http://dx.doi.org/10.2196/37861 UR - http://www.ncbi.nlm.nih.gov/pubmed/36348979 ID - info:doi/10.2196/37861 ER - TY - JOUR AU - Kokoska, E. Ryan AU - Kim, S. Lori AU - Szeto, D. Mindy AU - Aukerman, L. Erica AU - Dellavalle, P. Robert PY - 2022/10/26 TI - Top Pediatric Dermatology Twitter Post Characteristics and Trends From 2020 to 2021: Content Analysis JO - JMIR Dermatol SP - e37029 VL - 5 IS - 4 KW - pediatric dermatology KW - pediatrics KW - dermatology KW - Twitter KW - social media KW - social media engagement KW - content analysis UR - https://derma.jmir.org/2022/4/e37029 UR - http://dx.doi.org/10.2196/37029 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632885 ID - info:doi/10.2196/37029 ER - TY - JOUR AU - Bacsu, Juanita-Dawne AU - Cammer, Allison AU - Ahmadi, Soheila AU - Azizi, Mehrnoosh AU - Grewal, S. Karl AU - Green, Shoshana AU - Gowda-Sookochoff, Rory AU - Berger, Corinne AU - Knight, Sheida AU - Spiteri, J. Raymond AU - O'Connell, E. Megan PY - 2022/10/26 TI - Examining the Twitter Discourse on Dementia During Alzheimer?s Awareness Month in Canada: Infodemiology Study JO - JMIR Form Res SP - e40049 VL - 6 IS - 10 KW - Twitter KW - social media KW - dementia KW - Alzheimer disease KW - awareness KW - public health campaigns N2 - Background: Twitter has become a primary platform for public health campaigns, ranging from mental health awareness week to diabetes awareness month. However, there is a paucity of knowledge about how Twitter is being used during health campaigns, especially for Alzheimer?s Awareness Month. Objective: The purpose of our study was to examine dementia discourse during Canada?s Alzheimer?s Awareness Month in January to inform future awareness campaigns. Methods: We collected 1289 relevant tweets using the Twint application in Python from January 1 to January 31, 2022. Thematic analysis was used to analyze the data. Results: Guided by our analysis, 4 primary themes were identified: dementia education and advocacy, fundraising and promotion, experiences of dementia, and opportunities for future actions. Conclusions: Although our study identified many educational, promotional, and fundraising tweets to support dementia awareness, we also found numerous tweets with cursory messaging (ie, simply referencing January as Alzheimer?s Awareness Month in Canada). While these tweets promoted general awareness, they also highlight an opportunity for targeted educational content to counter stigmatizing messages and misinformation about dementia. In addition, awareness strategies partnering with diverse stakeholders (such as celebrities, social media influencers, and people living with dementia and their care partners) may play a pivotal role in fostering dementia dialogue and education. Further research is needed to develop, implement, and evaluate dementia awareness strategies on Twitter. Increased knowledge, partnerships, and research are essential to enhancing dementia awareness during Canada?s Alzheimer?s Awareness Month and beyond. UR - https://formative.jmir.org/2022/10/e40049 UR - http://dx.doi.org/10.2196/40049 UR - http://www.ncbi.nlm.nih.gov/pubmed/36287605 ID - info:doi/10.2196/40049 ER - TY - JOUR AU - van Kampen, Katherine AU - Laski, Jeremi AU - Herman, Gabrielle AU - Chan, M. Teresa PY - 2022/10/25 TI - Investigating COVID-19 Vaccine Communication and Misinformation on TikTok: Cross-sectional Study JO - JMIR Infodemiology SP - e38316 VL - 2 IS - 2 KW - TikTok KW - COVID-19 vaccines KW - vaccinations KW - misinformation KW - COVID-19 KW - Infodemiology KW - social media KW - health information KW - content analysis KW - vaccine hesitancy KW - public health KW - web-based health information N2 - Background: The COVID-19 pandemic has highlighted the need for reliable information, especially around vaccines. Vaccine hesitancy is a growing concern and a great threat to broader public health. The prevalence of social media within our daily lives emphasizes the importance of accurately analyzing how health information is being disseminated to the public. TikTok is of particular interest, as it is an emerging social media platform that young adults may be increasingly using to access health information. Objective: The objective of this study was to examine and describe the content within the top 100 TikToks trending with the hashtag #covidvaccine. Methods: The top 250 most viewed TikToks with the hashtag #covidvaccine were batch downloaded on July 1, 2021, with their respective metadata. Each TikTok was subsequently viewed and encoded by 2 independent reviewers. Coding continued until 100 TikToks could be included based on language and content. Descriptive features were recorded including health care professional (HCP) status of creator, verification of HCP status, genre, and misinformation addressed. Primary inclusion criteria were any TikToks in English with discussion of a COVID-19 vaccine. Results: Of 102 videos included, the median number of plays was 1,700,000, with median shares of 9224 and 62,200 followers. Upon analysis, 14.7% (15/102) of TikToks included HCPs, of which 80% (12/102) could be verified via social media or regulatory body search; 100% (15/15) of HCP-created TikToks supported vaccine use, and overall, 81.3% (83/102) of all TikToks (created by either a layperson or an HCP) supported vaccine use. Conclusions: As the pandemic continues, vaccine hesitancy poses a threat to lifting restrictions, and discovering reasons for this hesitancy is important to public health measures. This study summarizes the discourse around vaccine use on TikTok. Importantly, it opens a frank discussion about the necessity to incorporate new social media platforms into medical education, so we might ensure our trainees are ready to engage with patients on novel platforms. UR - https://infodemiology.jmir.org/2022/2/e38316 UR - http://dx.doi.org/10.2196/38316 UR - http://www.ncbi.nlm.nih.gov/pubmed/36338548 ID - info:doi/10.2196/38316 ER - TY - JOUR AU - Lorenzo-Luaces, Lorenzo AU - Howard, Jacqueline AU - Edinger, Andy AU - Yan, Yaojun Harry AU - Rutter, A. Lauren AU - Valdez, Danny AU - Bollen, Johan PY - 2022/10/20 TI - Sociodemographics and Transdiagnostic Mental Health Symptoms in SOCIAL (Studies of Online Cohorts for Internalizing Symptoms and Language) I and II: Cross-sectional Survey and Botometer Analysis JO - JMIR Form Res SP - e39324 VL - 6 IS - 10 KW - depression KW - anxiety KW - pain KW - alcohol KW - social media N2 - Background: Internalizing, externalizing, and somatoform disorders are the most common and disabling forms of psychopathology. Our understanding of these clinical problems is limited by a reliance on self-report along with research using small samples. Social media has emerged as an exciting channel for collecting a large sample of longitudinal data from individuals to study psychopathology. Objective: This study reported the results of 2 large ongoing studies in which we collected data from Twitter and self-reported clinical screening scales, the Studies of Online Cohorts for Internalizing Symptoms and Language (SOCIAL) I and II. Methods: The participants were a sample of Twitter-using adults (SOCIAL I: N=1123) targeted to be nationally representative in terms of age, sex assigned at birth, race, and ethnicity, as well as a sample of college students in the Midwest (SOCIAL II: N=1988), of which 61.78% (1228/1988) were Twitter users. For all participants who were Twitter users, we asked for access to their Twitter handle, which we analyzed using Botometer, which rates the likelihood of an account belonging to a bot. We divided participants into 4 groups: Twitter users who did not give us their handle or gave us invalid handles (invalid), those who denied being Twitter users (no Twitter, only available for SOCIAL II), Twitter users who gave their handles but whose accounts had high bot scores (bot-like), and Twitter users who provided their handles and had low bot scores (valid). We explored whether there were significant differences among these groups in terms of their sociodemographic features, clinical symptoms, and aspects of social media use (ie, platforms used and time). Results: In SOCIAL I, most individuals were classified as valid (580/1123, 51.65%), and a few were deemed bot-like (190/1123, 16.91%). A total of 31.43% (353/1123) gave no handle or gave an invalid handle (eg, entered ?N/A?). In SOCIAL II, many individuals were not Twitter users (760/1988, 38.23%). Of the Twitter users in SOCIAL II (1228/1988, 61.78%), most were classified as either invalid (515/1228, 41.94%) or valid (484/1228, 39.41%), with a smaller fraction deemed bot-like (229/1228, 18.65%). Participants reported high rates of mental health diagnoses as well as high levels of symptoms, especially in SOCIAL II. In general, the differences between individuals who provided or did not provide their social media handles were small and not statistically significant. Conclusions: Triangulating passively acquired social media data and self-reported questionnaires offers new possibilities for large-scale assessment and evaluation of vulnerability to mental disorders. The propensity of participants to share social media handles is likely not a source of sample bias in subsequent social media analytics. UR - https://formative.jmir.org/2022/10/e39324 UR - http://dx.doi.org/10.2196/39324 UR - http://www.ncbi.nlm.nih.gov/pubmed/36264616 ID - info:doi/10.2196/39324 ER - TY - JOUR AU - Young, E. Lindsay AU - Tang, Lipei Jack AU - Nan, Yuanfeixue PY - 2022/10/20 TI - Social Media Communication and Network Correlates of HIV Infection and Transmission Risks Among Black Sexual Minority Men: Cross-sectional Digital Epidemiology Study JO - JMIR Form Res SP - e37982 VL - 6 IS - 10 KW - HIV surveillance KW - HIV prevention KW - digital epidemiology KW - social media KW - social networks KW - sexual minority men KW - men who have sex with men N2 - Background: In the United States, HIV disproportionately affects Black cisgender sexual minority men (BSMM). Although epidemiological and behavioral surveillance are integral to identifying BSMM at risk of HIV infection and transmission, overreliance on self-reported data, inability to observe social contexts, and neglect of populations with limited engagement in health care systems limits their effectiveness. Digital epidemiological approaches drawing on social media data offer an opportunity to overcome these limitations by passively observing in organic settings activities, beliefs, behaviors, and moods that indicate health risks but are otherwise challenging to capture. Objective: The primary aim of this study was to determine whether features of Facebook communication and networks were associated with biological, behavioral, and psychological indicators of HIV infection and transmission risk. Methods: Facebook and survey data were collected from BSMM aged 18 to 35 years living in Chicago (N=310). Participants? Facebook posts were characterized using 4 culturally tailored topic dictionaries related to aspects of HIV protection and risk among BSMM (sexual health; substance use; sex behavior; and ballroom culture, a salient subculture in lesbian, gay, bisexual, transgender, and queer communities of color). Social network methods were used to capture structural features of BSMM?s Facebook friendships (centrality, brokerage, and local clustering) and Facebook group affiliations. Multivariable regressions revealed relationships between these Facebook features and 5 ground truth indicators of HIV infection and transmission risk (sexually transmitted infection incidence, condomless sex, sex drug use, biomedical prevention, and depression). Results: Although analysis of participants? Facebook posts revealed that HIV-related topics occupied a small portion of the total messages posted by each participant, significant associations were found between the following HIV risk indicators and Facebook features: Condomless sex, including communication about sexual health (odds ratio [OR] 1.58, 95% CI 1.09-2.29), ballroom culture (OR 0.76, 95% CI 0.63-0.93), and friendship centrality (OR 0.69, 95% CI 0.52-0.92); Sex drug use, including communication about substance use (OR 1.81, 95% CI 1.17-2.79) and friendship centrality (OR 0.73, 95% CI 0.55-0.96) and brokerage (OR 0.71, 95% CI 0.51-0.99); Biomedical prevention, including communication about ballroom culture (OR 0.06, 95% CI 0.01-0.71); and Depression, including communication about sexual health (?=?0.72, 95% CI ?1.42 to ?0.02), ballroom culture (?=.80, 95% CI 0.27-1.34), friendship centrality (?=?0.90, 95% CI ?1.60 to ?0.21), and Facebook group affiliations (?=.84, 95% CI 0.25-1.43). Facebook features provided no significant explanatory value for sexually transmitted infection incidence. Conclusions: Finding innovative strategies to detect BSMM at risk of contracting or transmitting HIV is critical to eliminating HIV disparities in this community. The findings suggest that social media data enable passive observance of social and communicative contexts that would otherwise go undetected using traditional HIV surveillance methods. As such, social media data are promising complements to more traditional data sources. UR - https://formative.jmir.org/2022/10/e37982 UR - http://dx.doi.org/10.2196/37982 UR - http://www.ncbi.nlm.nih.gov/pubmed/36264617 ID - info:doi/10.2196/37982 ER - TY - JOUR AU - Blunck, Dominik AU - Kastner, Lena AU - Nissen, Michael AU - Winkler, Jacqueline PY - 2022/10/19 TI - The Effectiveness of Patient Training in Inflammatory Bowel Disease Knowledge via Instagram: Randomized Controlled Trial JO - J Med Internet Res SP - e36767 VL - 24 IS - 10 KW - social media KW - Instagram KW - patient training KW - patient education KW - disease-related knowledge KW - RCT KW - randomized controlled trial KW - Germany KW - inflammatory bowel disease KW - IBD-KNOW N2 - Background: Patients? knowledge was found to be a key contributor to the success of therapy. Many efforts have been made to educate patients in their disease. However, research found that many patients still lack knowledge regarding their disease. Integrating patient education into social media platforms can bring materials closer to recipients. Objective: The aim of this study is to test the effectiveness of patient education via Instagram. Methods: A randomized controlled trial was conducted to test the effectiveness of patient education via Instagram among patients with inflammatory bowel disease. Participants were recruited online from the open Instagram page of a patient organization. The intervention group was educated via Instagram for 5 weeks by the research team; the control group did not receive any educational intervention. The knowledge about their disease was measured pre- and postintervention using the Inflammatory Bowel Disease Knowledge questionnaire. Data were analyzed by comparing mean knowledge scores and by regression analysis. The trial was purely web based. Results: In total, 49 participants filled out both questionnaires. The intervention group included 25 participants, and the control group included 24 participants. The preintervention knowledge level of the intervention group was reflected as a score of 18.67 out of 24 points; this improved by 3 points to 21.67 postintervention. The postintervention difference between the control and intervention groups was 3.59 points and was statistically significant (t32.88=?4.56, 95% CI 1.98-5.19; P<.001). Results of the regression analysis, accounting for preintervention knowledge and group heterogeneity, indicated an increase of 3.33 points that was explained by the intervention (P<.001). Conclusions: Patient education via Instagram is an effective way to increase disease-related knowledge. Future studies are needed to assess the effects in other conditions and to compare different means of patient education. Trial Registration: German Clinical Trials Register DRKS00022935; https://tinyurl.com/bed4bzvh UR - https://www.jmir.org/2022/10/e36767 UR - http://dx.doi.org/10.2196/36767 UR - http://www.ncbi.nlm.nih.gov/pubmed/36260385 ID - info:doi/10.2196/36767 ER - TY - JOUR AU - Melton, A. Chad AU - White, M. Brianna AU - Davis, L. Robert AU - Bednarczyk, A. Robert AU - Shaban-Nejad, Arash PY - 2022/10/17 TI - Fine-tuned Sentiment Analysis of COVID-19 Vaccine?Related Social Media Data: Comparative Study JO - J Med Internet Res SP - e40408 VL - 24 IS - 10 KW - sentiment analysis KW - DistilRoBERTa KW - natural language processing KW - social media KW - Twitter KW - Reddit KW - COVID-19 KW - vaccination KW - vaccine KW - content analysis KW - public health KW - surveillance KW - misinformation KW - infodemiology KW - information quality N2 - Background: The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. Objective: Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms?Reddit and Twitter?harvested from January 1, 2020, to March 1, 2022. Methods: To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. Results: Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. Conclusions: Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population?s expressed sentiments that facilitate digital literacy, health information?seeking behavior, and precision health promotion could aid in clarifying such misinformation. UR - https://www.jmir.org/2022/10/e40408 UR - http://dx.doi.org/10.2196/40408 UR - http://www.ncbi.nlm.nih.gov/pubmed/36174192 ID - info:doi/10.2196/40408 ER - TY - JOUR AU - Jain, Shikha AU - Dhaon, R. Serena AU - Majmudar, Shivani AU - Zimmermann, J. Laura AU - Mordell, Lisa AU - Walker, Garth AU - Wallia, Amisha AU - Akbarnia, Halleh AU - Khan, Ali AU - Bloomgarden, Eve AU - Arora, M. Vineet PY - 2022/10/17 TI - Empowering Health Care Workers on Social Media to Bolster Trust in Science and Vaccination During the Pandemic: Making IMPACT Using a Place-Based Approach JO - J Med Internet Res SP - e38949 VL - 24 IS - 10 KW - misinformation KW - COVID-19 KW - place-based KW - infodemic KW - infographic KW - social media KW - advocacy KW - infodemiology KW - vaccination KW - health care worker KW - policy maker KW - health policy KW - community health N2 - Background: Given the widespread and concerted efforts to propagate health misinformation on social media, particularly centered around vaccination during the pandemic, many groups of clinicians and scientists were organized on social media to tackle misinformation and promote vaccination, using a national or international lens. Although documenting the impact of such social media efforts, particularly at the community level, can be challenging, a more hyperlocal or ?place-based approach? for social media campaigns could be effective in tackling misinformation and improving public health outcomes at a community level. Objective: We aimed to describe and document the effectiveness of a place-based strategy for a coordinated group of Chicago health care workers on social media to tackle misinformation and improve vaccination rates in the communities they serve. Methods: The Illinois Medical Professionals Action Collaborative Team (IMPACT) was founded in March 2020 in response to the COVID-19 pandemic, with representatives from major academic teaching hospitals in Chicago (eg, University of Chicago, Northwestern University, University of Illinois, and Rush University) and community-based organizations. Through crowdsourcing on multiple social media platforms (eg, Facebook, Twitter, and Instagram) with a place-based approach, IMPACT engaged grassroots networks of thousands of Illinois health care workers and the public to identify gaps, needs, and viewpoints to improve local health care delivery during the pandemic. Results: To address vaccine misinformation, IMPACT created 8 ?myth debunking? infographics and a ?vaccine information series? of 14 infographics that have generated >340,000 impressions and informed the development of vaccine education for the Chicago Public Libraries. IMPACT delivered 13 policy letters focusing on different topics, such as health care worker personal protective equipment, universal masking, and vaccination, with >4000 health care workers signatures collected through social media and delivered to policy makers; it published over 50 op-eds on COVID-19 topics in high-impact news outlets and contributed to >200 local and national news features.Using the crowdsourcing approach on IMPACT social media channels, IMPACT mobilized health care and lay volunteers to staff >400 vaccine events for >120,000 individuals, many in Chicago?s hardest-hit neighborhoods. The group?s recommendations have influenced public health awareness campaigns and initiatives, as well as research, advocacy, and policy recommendations, and they have been recognized with local and national awards. Conclusions: A coordinated group of health care workers on social media, using a hyperlocal place-based approach, can not only work together to address misinformation but also collaborate to boost vaccination rates in their surrounding communities. UR - https://www.jmir.org/2022/10/e38949 UR - http://dx.doi.org/10.2196/38949 UR - http://www.ncbi.nlm.nih.gov/pubmed/35917489 ID - info:doi/10.2196/38949 ER - TY - JOUR AU - Li, Minghui AU - Hua, Yining AU - Liao, Yanhui AU - Zhou, Li AU - Li, Xue AU - Wang, Ling AU - Yang, Jie PY - 2022/10/13 TI - Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study JO - J Med Internet Res SP - e39676 VL - 24 IS - 10 KW - COVID-19 KW - mental health KW - social media KW - Twitter KW - topic model KW - health care workers N2 - Background: The COVID-19 pandemic and its corresponding preventive and control measures have increased the mental burden on the public. Understanding and tracking changes in public mental status can facilitate optimizing public mental health intervention and control strategies. Objective: This study aimed to build a social media?based pipeline that tracks public mental changes and use it to understand public mental health status regarding the pandemic. Methods: This study used COVID-19?related tweets posted from February 2020 to April 2022. The tweets were downloaded using unique identifiers through the Twitter application programming interface. We created a lexicon of 4 mental health problems (depression, anxiety, insomnia, and addiction) to identify mental health?related tweets and developed a dictionary for identifying health care workers. We analyzed temporal and geographic distributions of public mental health status during the pandemic and further compared distributions among health care workers versus the general public, supplemented by topic modeling on their underlying foci. Finally, we used interrupted time series analysis to examine the statewide impact of a lockdown policy on public mental health in 12 states. Results: We extracted 4,213,005 tweets related to mental health and COVID-19 from 2,316,817 users. Of these tweets, 2,161,357 (51.3%) were related to ?depression,? whereas 1,923,635 (45.66%), 225,205 (5.35%), and 150,006 (3.56%) were related to ?anxiety,? ?insomnia,? and ?addiction,? respectively. Compared to the general public, health care workers had higher risks of all 4 types of problems (all P<.001), and they were more concerned about clinical topics than everyday issues (eg, ?students? pressure,? ?panic buying,? and ?fuel problems?) than the general public. Finally, the lockdown policy had significant associations with public mental health in 4 out of the 12 states we studied, among which Pennsylvania showed a positive association, whereas Michigan, North Carolina, and Ohio showed the opposite (all P<.05). Conclusions: The impact of COVID-19 and the corresponding control measures on the public?s mental status is dynamic and shows variability among different cohorts regarding disease types, occupations, and regional groups. Health agencies and policy makers should primarily focus on depression (reported by 51.3% of the tweets) and insomnia (which has had an ever-increasing trend since the beginning of the pandemic), especially among health care workers. Our pipeline timely tracks and analyzes public mental health changes, especially when primary studies and large-scale surveys are difficult to conduct. UR - https://www.jmir.org/2022/10/e39676 UR - http://dx.doi.org/10.2196/39676 UR - http://www.ncbi.nlm.nih.gov/pubmed/36191167 ID - info:doi/10.2196/39676 ER - TY - JOUR AU - Alhuzali, Hassan AU - Zhang, Tianlin AU - Ananiadou, Sophia PY - 2022/10/5 TI - Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis JO - J Med Internet Res SP - e40323 VL - 24 IS - 10 KW - Twitter KW - COVID-19 KW - geolocation KW - emotion detection KW - sentiment analysis KW - topic modeling KW - social media KW - natural language processing KW - deep learning N2 - Background: In recent years, the COVID-19 pandemic has brought great changes to public health, society, and the economy. Social media provide a platform for people to discuss health concerns, living conditions, and policies during the epidemic, allowing policymakers to use this content to analyze the public emotions and attitudes for decision-making. Objective: The aim of this study was to use deep learning?based methods to understand public emotions on topics related to the COVID-19 pandemic in the United Kingdom through a comparative geolocation and text mining analysis on Twitter. Methods: Over 500,000 tweets related to COVID-19 from 48 different cities in the United Kingdom were extracted, with the data covering the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning?based models for topic modeling to geospatially analyze the sentiment, emotion, and topics of tweets in the United Kingdom: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and combined topic modeling (CTM). Results: We observed a significant change in the number of tweets as the epidemiological situation and vaccination situation shifted over the 2 years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the United Kingdom. Then, the number of tweets gradually declined as of February 2020. Moreover, with identification of the COVID-19 Omicron variant in the United Kingdom in November 2021, the number of tweets grew again. Our findings reveal people?s attitudes and emotions toward topics related to COVID-19. For sentiment, approximately 60% of tweets were positive, 20% were neutral, and 20% were negative. For emotion, people tended to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions over time toward the end of 2021. The topics also changed during the pandemic. Conclusions: Through large-scale text mining of Twitter, our study found meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people?s thoughts and feelings, and to understand their behaviors. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak and epidemiological change, the government developed control measures and vaccination policies, and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions, and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area. UR - https://www.jmir.org/2022/10/e40323 UR - http://dx.doi.org/10.2196/40323 UR - http://www.ncbi.nlm.nih.gov/pubmed/36150046 ID - info:doi/10.2196/40323 ER - TY - JOUR AU - Fu, Chunye AU - Lyu, Xiaokang AU - Mi, Mingdi PY - 2022/10/4 TI - Collective Value Promotes the Willingness to Share Provaccination Messages on Social Media in China: Randomized Controlled Trial JO - JMIR Form Res SP - e35744 VL - 6 IS - 10 KW - individual value KW - collective value KW - vaccination KW - message-sharing willingness KW - perceived responsibility KW - misinformation KW - vaccine misinformation KW - public health KW - influenza vaccine KW - social media KW - COVID-19 N2 - Background: The proliferation of vaccine misinformation on social media has seriously corrupted the public?s confidence in vaccination. Proactively sharing provaccination messages on social media is a cost-effective way to enhance global vaccination rates and resist vaccine misinformation. However, few strategies for encouraging the public to proactively share vaccine-related knowledge on social media have been developed. Objective: This research examines the effect of value type (individual vs collective) and message framing (gain vs loss) on influenza vaccination intention (experiment 1) and the willingness to share provaccination messages (experiment 2) among Chinese adults during the COVID-19 pandemic. The primary aim was to evaluate whether messages that emphasized collective value were more effective in increasing the willingness to share than messages that emphasized individual value. Methods: We enrolled 450 Chinese adults for experiment 1 (n=250, 55.6%) and experiment 2 (n=200, 44.4%). Participants were randomly assigned to individual-gain, individual-loss, collective-gain, or collective-loss conditions with regard to the message in each experiment using the online survey platform?s randomization function. Experiment 1 also included a control group. The primary outcome was influenza vaccination intention in experiment 1 and the willingness to share provaccination messages in experiment 2. Results: The valid sample included 213 adults in experiment 1 (females: n=151, 70.9%; mean age 29 [SD 9] years; at least some college education: n=202, 94.8%; single: n=131, 61.5%) and 171 adults in experiment 2 (females: n=106, 62.0%; mean age 28 [SD 7] years; at least some college education: n=163, 95.3%; single: n=95, 55.6%). Influenza vaccination intention was stronger in the individual-value conditions than in the collective-value conditions (F3,166=4.96, P=.03, ?2=0.03). The reverse result was found for the willingness to share provaccination messages (F3,165=6.87, P=.01, ?2=0.04). Specifically, participants who received a message emphasizing collective value had a higher intention to share the message than participants who read a message emphasizing individual value (F3,165=6.87, P=.01, ?2=0.04), and the perceived responsibility for message sharing played a mediating role (indirect effect=0.23, 95% lower limit confidence interval [LLCI] 0.41, 95% upper limit confidence interval [ULCI] 0.07). In addition, gain framing facilitated influenza vaccination intention more than loss framing (F3,166=5.96, P=.02, ?2=0.04). However, experiment 2 did not find that message framing affected message-sharing willingness. Neither experiment found an interaction between value type and message framing. Conclusions: Strengthened individual value rather than collective value is more likely to persuade Chinese adults to vaccinate. However, these adults are more likely to share a message that emphasizes collective rather than individual value, and the perceived responsibility for message sharing plays a mediating role. UR - https://formative.jmir.org/2022/10/e35744 UR - http://dx.doi.org/10.2196/35744 UR - http://www.ncbi.nlm.nih.gov/pubmed/36067417 ID - info:doi/10.2196/35744 ER - TY - JOUR AU - Simonart, Thierry AU - Lam Hoai, Xuân-Lan AU - de Maertelaer, Viviane PY - 2022/10/4 TI - Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis JO - JMIR Dermatol SP - e35034 VL - 5 IS - 4 KW - big data KW - infodemiology KW - measles KW - varicella KW - rubella KW - hand KW - foot KW - mouth disease KW - skin infection KW - epidemic KW - wart KW - skin KW - dermatology KW - trend KW - Google search KW - web search KW - surveillance KW - vaccinable KW - incidence KW - viral epidemics KW - distribution N2 - Background: Most common viral skin infections are not reportable conditions. Studying the population dynamics of these viral epidemics using traditional field methods is costly and time-consuming, especially over wide geographical areas. Objective: This study aimed to explore the evolution, seasonality, and distribution of vaccinable and nonvaccinable viral skin infections through an analysis of Google Trends. Methods: Worldwide search trends from January 2004 through May 2021 for viral skin infections were extracted from Google Trends, quantified, and analyzed. Results: Time series decomposition showed that the total search term volume for warts; zoster; roseola; measles; hand, foot, and mouth disease (HFMD); varicella; and rubella increased worldwide over the study period, whereas the interest for Pityriasis rosea and herpes simplex decreased. Internet searches for HFMD, varicella, and measles exhibited the highest seasonal patterns. The interest for measles and rubella was more pronounced in African countries, whereas the interest for HFMD and roseola was more pronounced in East Asia. Conclusions: Harnessing data generated by web searches may increase the efficacy of traditional surveillance systems and strengthens the suspicion that the incidence of some vaccinable viral skin infections such as varicella, measles, and rubella may be globally increasing, whereas the incidence of common nonvaccinable skin infections remains stable. UR - https://derma.jmir.org/2022/4/e35034 UR - http://dx.doi.org/10.2196/35034 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632891 ID - info:doi/10.2196/35034 ER - TY - JOUR AU - You, Yueyue AU - Yang-Huang, Junwen AU - Raat, Hein AU - Van Grieken, Amy PY - 2022/10/4 TI - Social Media Use and Health-Related Quality of Life Among Adolescents: Cross-sectional Study JO - JMIR Ment Health SP - e39710 VL - 9 IS - 10 KW - adolescents KW - social media platforms KW - social media KW - health-related quality of life KW - EuroQol 5-dimension questionnaire, youth version N2 - Background: Using social media is a time-consuming activity of children and adolescents. Health authorities have warned that excessive use of social media can negatively affect adolescent social, physical, and psychological health. However, scientific findings regarding associations between time spent on social media and adolescent health-related quality of life (HRQoL) are not consistent. Adolescents typically use multiple social media platforms. Whether the use of multiple social media platforms impacts adolescent health is unclear. Objective: The aim of this study was to examine the relationship between social media use, including the number of social media platforms used and time spent on social media, and adolescent HRQoL. Methods: We analyzed the data of 3397 children (mean age 13.5, SD 0.4 years) from the Generation R Study, a population-based cohort study in the Netherlands. Children reported the number of social media platforms used and time spent on social media during weekdays and weekends separately. Children?s HRQoL was self-reported with the EuroQol 5-dimension questionnaire?youth version. Data on social media use and HRQoL were collected from 2015 to 2019. Multiple logistic and linear regressions were applied. Results: In this study, 72.6% (2466/3397) of the children used 3 or more social media platforms, and 37.7% (1234/3276) and 58.3% (1911/3277) of the children used social media at least 2 hours per day during weekdays and weekends, respectively. Children using more social media platforms (7 or more platforms) had a higher odds of reporting having some or a lot of problems on ?having pain or discomfort? (OR 1.55, 95% CI 1.20 to 1.99) and ?feeling worried, sad or unhappy? (OR 1.99, 95% CI 1.52 to 2.60) dimensions and reported lower self-rated health (? ?3.81, 95% CI ?5.54 to ?2.09) compared with children who used 0 to 2 social media platforms. Both on weekdays and weekends, children spent more time on social media were more likely to report having some or a lot of problems on ?doing usual activities,? ?having pain or discomfort,? ?feeling worried, sad or unhappy,? and report lower self-rated health (all P<.001). Conclusions: Our findings indicate that using more social media platforms and spending more time on social media were significantly related to lower HRQoL. We recommend future research to study the pathway between social media use and HRQoL among adolescents. UR - https://mental.jmir.org/2022/10/e39710 UR - http://dx.doi.org/10.2196/39710 UR - http://www.ncbi.nlm.nih.gov/pubmed/36194460 ID - info:doi/10.2196/39710 ER - TY - JOUR AU - Ferawati, Kiki AU - Liew, Kongmeng AU - Aramaki, Eiji AU - Wakamiya, Shoko PY - 2022/10/4 TI - Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study JO - JMIR Infodemiology SP - e39504 VL - 2 IS - 2 KW - COVID-19 KW - vaccine KW - COVID-19 vaccine KW - Pfizer KW - Moderna KW - vaccine side effects KW - side effects KW - Twitter KW - logistic regression N2 - Background: The year 2021 was marked by vaccinations against COVID-19, which spurred wider discussion among the general population, with some in favor and some against vaccination. Twitter, a popular social media platform, was instrumental in providing information about the COVID-19 vaccine and has been effective in observing public reactions. We focused on tweets from Japan and Indonesia, 2 countries with a large Twitter-using population, where concerns about side effects were consistently stated as a strong reason for vaccine hesitancy. Objective: This study aimed to investigate how Twitter was used to report vaccine-related side effects and to compare the mentions of these side effects from 2 messenger RNA (mRNA) vaccine types developed by Pfizer and Moderna, in Japan and Indonesia. Methods: We obtained tweet data from Twitter using Japanese and Indonesian keywords related to COVID-19 vaccines and their side effects from January 1, 2021, to December 31, 2021. We then removed users with a high frequency of tweets and merged the tweets from multiple users as a single sentence to focus on user-level analysis, resulting in a total of 214,165 users (Japan) and 12,289 users (Indonesia). Then, we filtered the data to select tweets mentioning Pfizer or Moderna only and removed tweets mentioning both. We compared the side effect counts to the public reports released by Pfizer and Moderna. Afterward, logistic regression models were used to compare the side effects for the Pfizer and Moderna vaccines for each country. Results: We observed some differences in the ratio of side effects between the public reports and tweets. Specifically, fever was mentioned much more frequently in tweets than would be expected based on the public reports. We also observed differences in side effects reported between Pfizer and Moderna vaccines from Japan and Indonesia, with more side effects reported for the Pfizer vaccine in Japanese tweets and more side effects with the Moderna vaccine reported in Indonesian tweets. Conclusions: We note the possible consequences of vaccine side effect surveillance on Twitter and information dissemination, in that fever appears to be over-represented. This could be due to fever possibly having a higher severity or measurability, and further implications are discussed. UR - https://infodemiology.jmir.org/2022/2/e39504 UR - http://dx.doi.org/10.2196/39504 UR - http://www.ncbi.nlm.nih.gov/pubmed/36277140 ID - info:doi/10.2196/39504 ER - TY - JOUR AU - Koss, Jonathan AU - Bohnet-Joschko, Sabine PY - 2022/10/3 TI - Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing JO - JMIR Form Res SP - e39582 VL - 6 IS - 10 KW - social media mining KW - drug repurposing KW - long-COVID KW - crowdsourcing KW - COVID-19 KW - Reddit KW - social media KW - content analysis KW - network analysis KW - recognition algorithm KW - treatment N2 - Background: Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called ?long-COVID.? Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social media. Objective: The aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users? self-reports to support hypothesis generation for drug repurposing, by incorporating patients? experiences. Methods: We used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the ?/r/covidlonghaulers? subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance clusters. Results: The named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. ?Histamine antagonists,? ?famotidine,? ?magnesium,? ?vitamins,? and ?steroids? were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication patterns. Conclusions: This feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed method could be used to support drug repurposing hypothesis development by prioritizing substances that are important to users. UR - https://formative.jmir.org/2022/10/e39582 UR - http://dx.doi.org/10.2196/39582 UR - http://www.ncbi.nlm.nih.gov/pubmed/36007131 ID - info:doi/10.2196/39582 ER - TY - JOUR AU - Nguyen, Jean Cassandra AU - Pham, Christian AU - Jackson, M. Alexandra AU - Ellison, Kamakahiolani Nicole Lee AU - Sinclair, Ka`imi PY - 2022/9/30 TI - Online Food Security Discussion Before and During the COVID-19 Pandemic in Native Hawaiian and Pacific Islander Community Groups and Organizations: Content Analysis of Facebook Posts JO - Asian Pac Isl Nurs J SP - e40436 VL - 6 IS - 1 KW - social media KW - oceanic ancestry group KW - food insecurity KW - social networking KW - COVID-19 KW - Facebook KW - community KW - Hawaiian KW - Pacific Islander KW - online KW - food KW - risk factor KW - disease KW - cardiometabolic KW - diabetes KW - hypertension KW - food security KW - digital KW - support KW - culture N2 - Background: The Native Hawaiian and Pacific Islander (NHPI) population experiences disproportionately higher rates of food insecurity, which is a risk factor for cardiometabolic diseases such as cardiovascular disease, type 2 diabetes, obesity, and hypertension, when compared to white individuals. Novel and effective approaches that address food insecurity are needed for the NHPI population, particularly in areas of the continental United States, which is a popular migration area for many NHPI families. Social media may serve as an opportune setting to reduce food insecurity and thus the risk factors for cardiometabolic diseases among NHPI people; however, it is unclear if and how food insecurity is discussed in online communities targeting NHPI individuals. Objective: The objective of this study was to characterize the quantity, nature, and audience engagement of messages related to food insecurity posted online in community groups and organizations that target NHPI audiences. Methods: Publicly accessible Facebook pages and groups focused on serving NHPI community members living in the states of Washington or Oregon served as the data source. Facebook posts between March and June 2019 (before the COVID-19 pandemic) and from March to June 2020 (during the COVID-19 pandemic) that were related to food security were identified using a set of 36 related keywords. Data on the post and any user engagement (ie, comments, shares, or digital reactions) were extracted for all relevant posts. A content analytical approach was used to identify and quantify the nature of the identified posts and any related comments. The codes resulting from the content analysis were described and compared by year, page type, and engagement. Results: Of the 1314 nonduplicated posts in the 7 relevant Facebook groups and pages, 88 were related to food security (8 in 2019 and 80 in 2020). The nature of posts was broadly classified into literature-based codes, food assistance (the most common), perspectives of food insecurity, community gratitude and support, and macrolevel contexts. Among the 88 posts, 74% (n=65) had some form of engagement, and posts reflecting community gratitude and support or culture had more engagement than others (mean 19.9, 95% CI 11.2-28.5 vs mean 6.1, 95% CI 1.7-10.4; and mean 26.8, 95% CI 12.7-40.9 vs mean 5.3, 95% CI 3.0-7.7, respectively). Conclusions: Food security?related posts in publicly accessible Facebook groups targeting NHPI individuals living in Washington and Oregon largely focused on food assistance, although cultural values of gratitude, maintaining NHPI culture, and supporting children were also reflected. Future work should capitalize on social media as a potential avenue to reach a unique cultural group in the United States experiencing inequitably high rates of food insecurity and risk of cardiometabolic diseases. UR - https://apinj.jmir.org/2022/1/e40436 UR - http://dx.doi.org/10.2196/40436 UR - http://www.ncbi.nlm.nih.gov/pubmed/36212246 ID - info:doi/10.2196/40436 ER - TY - JOUR AU - Hoque Tania, Marzia AU - Hossain, Razon Md AU - Jahanara, Nuzhat AU - Andreev, Ilya AU - Clifton, A. David PY - 2022/9/30 TI - Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work JO - JMIR Form Res SP - e30113 VL - 6 IS - 9 KW - work-related mental health KW - sentiment analysis KW - natural language processing KW - occupational health KW - Bayesian inference KW - machine learning KW - artificial intelligence KW - mobile phone N2 - Background: Millions of workers experience work-related ill health every year. The loss of working days often accounts for poor well-being because of discomfort and stress caused by the workplace. The ongoing pandemic and postpandemic shift in socioeconomic and work culture can continue to contribute to adverse work-related sentiments. Critically investigating state-of-the-art technologies, this study identifies the research gaps in recognizing workers? need for well-being support, and we aspire to understand how such evidence can be collected to transform the workforce and workplace. Objective: Building on recent advances in sentiment analysis, this study aims to closely examine the potential of social media as a tool to assess workers? emotions toward the workplace. Methods: This study collected a large Twitter data set comprising both pandemic and prepandemic tweets facilitated through a human-in-the-loop approach in combination with unsupervised learning and meta-heuristic optimization algorithms. The raw data preprocessed through natural language processing techniques were assessed using a generative statistical model and a lexicon-assisted rule-based model, mapping lexical features to emotion intensities. This study also assigned human annotations and performed work-related sentiment analysis. Results: A mixed methods approach, including topic modeling using latent Dirichlet allocation, identified the top topics from the corpus to understand how Twitter users engage with discussions on work-related sentiments. The sorted aspects were portrayed through overlapped clusters and low intertopic distances. However, further analysis comprising the Valence Aware Dictionary for Sentiment Reasoner suggested a smaller number of negative polarities among diverse subjects. By contrast, the human-annotated data set created for this study contained more negative sentiments. In this study, sentimental juxtaposition revealed through the labeled data set was supported by the n-gram analysis as well. Conclusions: The developed data set demonstrates that work-related sentiments are projected onto social media, which offers an opportunity to better support workers. The infrastructure of the workplace, the nature of the work, the culture within the industry and the particular organization, employers, colleagues, person-specific habits, and upbringing all play a part in the health and well-being of any working adult who contributes to the productivity of the organization. Therefore, understanding the origin and influence of the complex underlying factors both qualitatively and quantitatively can inform the next generation of workplaces to drive positive change by relying on empirically grounded evidence. Therefore, this study outlines a comprehensive approach to capture deeper insights into work-related health. UR - https://formative.jmir.org/2022/9/e30113 UR - http://dx.doi.org/10.2196/30113 UR - http://www.ncbi.nlm.nih.gov/pubmed/36178712 ID - info:doi/10.2196/30113 ER - TY - JOUR AU - Korshakova, Elena AU - Marsh, K. Jessecae AU - Kleinberg, Samantha PY - 2022/9/28 TI - Health Information Sourcing and Health Knowledge Quality: Repeated Cross-sectional Survey JO - JMIR Form Res SP - e39274 VL - 6 IS - 9 KW - health knowledge KW - health information seeking KW - information dissemination KW - COVID-19 KW - online health information KW - public health KW - health literacy KW - social media KW - information quality KW - infodemiology N2 - Background: People?s health-related knowledge influences health outcomes, as this knowledge may influence whether individuals follow advice from their doctors or public health agencies. Yet, little attention has been paid to where people obtain health information and how these information sources relate to the quality of knowledge. Objective: We aim to discover what information sources people use to learn about health conditions, how these sources relate to the quality of their health knowledge, and how both the number of information sources and health knowledge change over time. Methods: We surveyed 200 different individuals at 12 time points from March through September 2020. At each time point, we elicited participants? knowledge about causes, risk factors, and preventative interventions for 8 viral (Ebola, common cold, COVID-19, Zika) and nonviral (food allergies, amyotrophic lateral sclerosis [ALS], strep throat, stroke) illnesses. Participants were further asked how they learned about each illness and to rate how much they trust various sources of health information. Results: We found that participants used different information sources to obtain health information about common illnesses (food allergies, strep throat, stroke) compared to emerging illnesses (Ebola, common cold, COVID-19, Zika). Participants relied mainly on news media, government agencies, and social media for information about emerging illnesses, while learning about common illnesses from family, friends, and medical professionals. Participants relied on social media for information about COVID-19, with their knowledge accuracy of COVID-19 declining over the course of the pandemic. The number of information sources participants used was positively correlated with health knowledge quality, though there was no relationship with the specific source types consulted. Conclusions: Building on prior work on health information seeking and factors affecting health knowledge, we now find that people systematically consult different types of information sources by illness type and that the number of information sources people use affects the quality of individuals? health knowledge. Interventions to disseminate health information may need to be targeted to where individuals are likely to seek out information, and these information sources differ systematically by illness type. UR - https://formative.jmir.org/2022/9/e39274 UR - http://dx.doi.org/10.2196/39274 UR - http://www.ncbi.nlm.nih.gov/pubmed/35998198 ID - info:doi/10.2196/39274 ER - TY - JOUR AU - Kabir, Khubayeeb Muhammad AU - Islam, Maisha AU - Kabir, Binte Anika Nahian AU - Haque, Adiba AU - Rhaman, Khalilur Md PY - 2022/9/28 TI - Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques JO - JMIR Form Res SP - e36118 VL - 6 IS - 9 KW - mental health forums KW - natural language processing KW - severity KW - major depressive disorder KW - deep learning KW - machine learning KW - multiclass text classification N2 - Background: There are a myriad of language cues that indicate depression in written texts, and natural language processing (NLP) researchers have proven the ability of machine learning and deep learning approaches to detect these cues. However, to date, these approaches bridging NLP and the domain of mental health for Bengali literature are not comprehensive. The Bengali-speaking population can express emotions in their native language in greater detail. Objective: Our goal is to detect the severity of depression using Bengali texts by generating a novel Bengali corpus of depressive posts. We collaborated with mental health experts to generate a clinically sound labeling scheme and an annotated corpus to train machine learning and deep learning models. Methods: We conducted a study using Bengali text-based data from blogs and open source platforms. We constructed a procedure for annotated corpus generation and extraction of textual information from Bengali literature for predictive analysis. We developed our own structured data set and designed a clinically sound labeling scheme with the help of mental health professionals, adhering to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) during the process. We used 5 machine learning models for detecting the severity of depression: kernel support vector machine (SVM), random forest, logistic regression K-nearest neighbor (KNN), and complement naive Bayes (NB). For the deep learning approach, we used long short-term memory (LSTM) units and gated recurrent units (GRUs) coupled with convolutional blocks or self-attention layers. Finally, we aimed for enhanced outcomes by using state-of-the-art pretrained language models. Results: The independent recurrent neural network (RNN) models yielded the highest accuracies and weighted F1 scores. GRUs, in particular, produced 81% accuracy. The hybrid architectures could not surpass the RNNs in terms of performance. Kernel SVM with term frequency?inverse document frequency (TF-IDF) embeddings generated 78% accuracy on test data. We used validation and training loss curves to observe and report the performance of our architectures. Overall, the number of available data remained the limitation of our experiment. Conclusions: The findings from our experimental setup indicate that machine learning and deep learning models are fairly capable of assessing the severity of mental health issues from texts. For the future, we suggest more research endeavors to increase the volume of Bengali text data, in particular, so that modern architectures reach improved generalization capability. UR - https://formative.jmir.org/2022/9/e36118 UR - http://dx.doi.org/10.2196/36118 UR - http://www.ncbi.nlm.nih.gov/pubmed/36169989 ID - info:doi/10.2196/36118 ER - TY - JOUR AU - Hu, Mengke AU - Conway, Mike PY - 2022/9/27 TI - Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia JO - JMIR Infodemiology SP - e36941 VL - 2 IS - 2 KW - COVID-19 KW - social media KW - natural language processing KW - Reddit N2 - Background: Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging. Objective: The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit. Methods: We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19?related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country. Results: Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19?related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits. Conclusions: This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches. UR - https://infodemiology.jmir.org/2022/2/e36941 UR - http://dx.doi.org/10.2196/36941 UR - http://www.ncbi.nlm.nih.gov/pubmed/36196144 ID - info:doi/10.2196/36941 ER - TY - JOUR AU - Liang, Jing AU - Wang, Linlin AU - Song, Shijie AU - Dong, Man AU - Xu, Yidan AU - Zuo, Xinyu AU - Zhang, Jingyi AU - Adrian Sherif, Akil AU - Ehsan, Jafree AU - Ma, Jianjun AU - Li, Pengyang PY - 2022/9/26 TI - Quality and Audience Engagement of Takotsubo Syndrome?Related Videos on TikTok: Content Analysis JO - J Med Internet Res SP - e39360 VL - 24 IS - 9 KW - TikTok KW - short video apps KW - information quality KW - Takotsubo syndrome KW - patient education KW - social media KW - audience engagement N2 - Background: The incidence of Takotsubo syndrome (TTS), also known as the broken heart syndrome or stress cardiomyopathy, is increasing worldwide. The understanding of its prognosis has been progressively evolving and currently appears to be poorer than previously thought, which has attracted the attention of researchers. An attempt to recognize the awareness of this condition among the general population drove us to analyze the dissemination of this topic on TikTok, a popular short-video?based social media platform. We found a considerable number of videos on TTS on TikTok; however, the quality of the presented information remains unknown. Objective: The aim of this study was to analyze the quality and audience engagement of TTS-related videos on TikTok. Methods: Videos on the TikTok platform were explored on August 2, 2021 to identify those related to TTS by using 6 Chinese keywords. A total of 2549 videos were found, of which 80 met our inclusion criteria and were evaluated for their characteristics, content, quality, and reliability. The quality and reliability were rated using the DISCERN instrument and the Journal of the American Medical Association (JAMA) criteria by 2 reviewers independently, and a score was assigned. Descriptive statistics were generated, and the Kruskal-Wallis test was used for statistical analysis. Multiple linear regression was performed to evaluate the association between audience engagement and other factors such as video content, video quality, and author types. Results: The scores assigned to the selected video content were low with regard to the diagnosis (0.66/2) and management (0.34/2) of TTS. The evaluated videos were found to have an average score of 36.93 out of 80 on the DISCERN instrument and 1.51 out of 4 per the JAMA criteria. None of the evaluated videos met all the JAMA criteria. The quality of the relayed information varied by source (All P<.05). TTS-related videos made by health care professionals accounted for 28% (22/80) of all the evaluated videos and had the highest DISCERN scores with an average of 40.59 out of 80. Multiple linear regression analysis showed that author types that identified as health professionals (exponentiated regression coefficient 17.48, 95% CI 2.29-133.52; P=.006) and individual science communicators (exponentiated regression coefficient 13.38, 95% CI 1.83-97.88; P=.01) were significant and independent determinants of audience engagement (in terms of the number of likes). Other author types of videos, video content, and DISCERN document scores were not associated with higher likes. Conclusions: We found that the quality of videos regarding TTS for patient education on TikTok is poor. Patients should be cautious about health-related information on TikTok. The formulation of a measure for video quality review is necessary, especially when the purpose of the published content is to educate and increase awareness on a health-related topic. UR - https://www.jmir.org/2022/9/e39360 UR - http://dx.doi.org/10.2196/39360 UR - http://www.ncbi.nlm.nih.gov/pubmed/36155486 ID - info:doi/10.2196/39360 ER - TY - JOUR AU - Silver, Nathan AU - Kierstead, Elexis AU - Kostygina, Ganna AU - Tran, Hy AU - Briggs, Jodie AU - Emery, Sherry AU - Schillo, Barbara PY - 2022/9/22 TI - The Influence of Provaping ?Gatewatchers? on the Dissemination of COVID-19 Misinformation on Twitter: Analysis of Twitter Discourse Regarding Nicotine and the COVID-19 Pandemic JO - J Med Internet Res SP - e40331 VL - 24 IS - 9 KW - social media KW - tobacco KW - COVID-19 KW - nicotine KW - misinformation KW - Twitter KW - information KW - infodemiology KW - vaping KW - therapeutic KW - influence KW - environment KW - harmful KW - consequences N2 - Background: There is a lot of misinformation about a potential protective role of nicotine against COVID-19 spread on Twitter despite significant evidence to the contrary. We need to examine the role of vape advocates in the dissemination of such information through the lens of the gatewatching framework, which posits that top users can amplify and exert a disproportionate influence over the dissemination of certain content through curating, sharing, or, in the case of Twitter, retweeting it, serving more as a vector for misinformation rather than the source. Objective: This research examines the Twitter discourse at the intersection of COVID-19 and tobacco (1) to identify the extent to which the most outspoken contributors to this conversation self-identify as vaping advocates and (2) to understand how and to what extent these vape advocates serve as gatewatchers through disseminating content about a therapeutic role of tobacco, nicotine, or vaping against COVID-19. Methods: Tweets about tobacco, nicotine, or vaping and COVID-19 (N=1,420,271) posted during the first 9 months of the pandemic (January-September 2020) were identified from within a larger corpus of tobacco-related tweets using validated keyword filters. The top posters (ie, tweeters and retweeters) were identified and characterized, along with the most shared Uniform Resource Locators (URLs), most used hashtags, and the 1000 most retweeted posts. Finally, we examined the role of both top users and vape advocates in retweeting the most retweeted posts about the therapeutic role of nicotine, tobacco, or vaping against COVID-19. Results: Vape advocates comprised between 49.7% (n=81) of top 163 and 88% (n=22) of top 25 users discussing COVID-19 and tobacco on Twitter. Content about the ability of tobacco, nicotine, or vaping to treat or prevent COVID-19 was disseminated broadly, accounting for 22.5% (n=57) of the most shared URLs and 10% (n=107) of the most retweeted tweets. Finally, among top users, retweets comprised an average of 78.6% of the posts from vape advocates compared to 53.1% from others (z=3.34, P<.001). Vape advocates were also more likely to retweet the top tweeted posts about a therapeutic role of nicotine, with 63% (n=51) of vape advocates retweeting at least 1 post compared to 40.3% (n=29) of other top users (z=2.80, P=.01). Conclusions: Provaping users dominated discussions of tobacco use during the COVID-19 pandemic on Twitter and were instrumental in disseminating the most retweeted posts about a potential therapeutic role of tobacco use against the virus. Subsequent research is needed to better understand the extent of this influence and how to mitigate the influence of vape advocates over the broader narrative of tobacco regulation on Twitter. UR - https://www.jmir.org/2022/9/e40331 UR - http://dx.doi.org/10.2196/40331 UR - http://www.ncbi.nlm.nih.gov/pubmed/36070451 ID - info:doi/10.2196/40331 ER - TY - JOUR AU - Zhan, Kevin AU - Li, Yutong AU - Osmani, Rafay AU - Wang, Xiaoyu AU - Cao, Bo PY - 2022/9/22 TI - Data Exploration and Classification of News Article Reliability: Deep Learning Study JO - JMIR Infodemiology SP - e38839 VL - 2 IS - 2 KW - COVID-19 KW - deep learning KW - news article reliability KW - false information KW - infodemic KW - ensemble model N2 - Background: During the ongoing COVID-19 pandemic, we are being exposed to large amounts of information each day. This ?infodemic? is defined by the World Health Organization as the mass spread of misleading or false information during a pandemic. This spread of misinformation during the infodemic ultimately leads to misunderstandings of public health orders or direct opposition against public policies. Although there have been efforts to combat misinformation spread, current manual fact-checking methods are insufficient to combat the infodemic. Objective: We propose the use of natural language processing (NLP) and machine learning (ML) techniques to build a model that can be used to identify unreliable news articles online. Methods: First, we preprocessed the ReCOVery data set to obtain 2029 English news articles tagged with COVID-19 keywords from January to May 2020, which are labeled as reliable or unreliable. Data exploration was conducted to determine major differences between reliable and unreliable articles. We built an ensemble deep learning model using the body text, as well as features, such as sentiment, Empath-derived lexical categories, and readability, to classify the reliability. Results: We found that reliable news articles have a higher proportion of neutral sentiment, while unreliable articles have a higher proportion of negative sentiment. Additionally, our analysis demonstrated that reliable articles are easier to read than unreliable articles, in addition to having different lexical categories and keywords. Our new model was evaluated to achieve the following performance metrics: 0.906 area under the curve (AUC), 0.835 specificity, and 0.945 sensitivity. These values are above the baseline performance of the original ReCOVery model. Conclusions: This paper identified novel differences between reliable and unreliable news articles; moreover, the model was trained using state-of-the-art deep learning techniques. We aim to be able to use our findings to help researchers and the public audience more easily identify false information and unreliable media in their everyday lives. UR - https://infodemiology.jmir.org/2022/2/e38839 UR - http://dx.doi.org/10.2196/38839 UR - http://www.ncbi.nlm.nih.gov/pubmed/36193330 ID - info:doi/10.2196/38839 ER - TY - JOUR AU - Ahmed, Saifuddin AU - Rasul, Ehab Muhammad PY - 2022/9/20 TI - Social Media News Use and COVID-19 Misinformation Engagement: Survey Study JO - J Med Internet Res SP - e38944 VL - 24 IS - 9 KW - COVID-19 KW - misinformation KW - personality KW - cognitive ability KW - social media KW - Singapore N2 - Background: Social media is widely used as a source of news and information regarding COVID-19. However, the abundance of misinformation on social media platforms has raised concerns regarding the spreading infodemic. Accordingly, many have questioned the utility and impact of social media news use on users? engagement with (mis)information. Objective: This study offers a conceptual framework for how social media news use influences COVID-19 misinformation engagement. More specifically, we examined how news consumption on social media leads to COVID-19 misinformation sharing by inducing belief in such misinformation. We further explored if the effects of social media news use on COVID-19 misinformation engagement depend on individual differences in cognition and personality traits. Methods: We used data from an online survey panel administered by a survey agency (Qualtrics) in Singapore. The survey was conducted in March 2022, and 500 respondents answered the survey. All participants were older than 21 years and provided consent before taking part in the study. We used linear regression, mediation, and moderated mediation analyses to explore the proposed relationships between social media news use, cognitive ability, personality traits, and COVID-19 misinformation belief and sharing intentions. Results: The results suggested that those who frequently used social media for news consumption were more likely to believe COVID-19 misinformation and share it on social media. Further probing the mechanism suggested that social media news use translated into sharing intent via the perceived accuracy of misinformation. Simply put, social media news users shared COVID-19 misinformation because they believed it to be accurate. We also found that those with high levels of extraversion than those with low levels were more likely to perceive the misinformation to be accurate and share it. Those with high levels of neuroticism and openness than those with low levels were also likely to perceive the misinformation to be accurate. Finally, it was observed that personality traits did not significantly influence misinformation sharing at higher levels of cognitive ability, but low cognitive users largely drove misinformation sharing across personality traits. Conclusions: The reliance on social media platforms for news consumption during the COVID-19 pandemic has amplified, with dire consequences for misinformation sharing. This study shows that increased social media news consumption is associated with believing and sharing COVID-19 misinformation, with low cognitive users being the most vulnerable. We offer recommendations to newsmakers, social media moderators, and policymakers toward efforts in limiting COVID-19 misinformation propagation and safeguarding citizens. UR - https://www.jmir.org/2022/9/e38944 UR - http://dx.doi.org/10.2196/38944 UR - http://www.ncbi.nlm.nih.gov/pubmed/36067414 ID - info:doi/10.2196/38944 ER - TY - JOUR AU - Jo, Soojung AU - Pituch, A. Keenan AU - Howe, Nancy PY - 2022/9/20 TI - The Relationships Between Social Media and Human Papillomavirus Awareness and Knowledge: Cross-sectional Study JO - JMIR Public Health Surveill SP - e37274 VL - 8 IS - 9 KW - papillomavirus infections KW - vaccination KW - social media KW - health promotion KW - public reporting of health care data KW - human papillomavirus N2 - Background: Human papillomavirus (HPV) is the most common sexually transmitted infection. HPV can infect both females and males, and it can cause many cancers, including anal, cervical, vaginal, vulvar, and penile cancers. HPV vaccination rates are lower than vaccination rates within other national vaccination programs, despite its importance. Research literature indicates that people obtain health-related information from internet sources and social media; however, the association between such health-seeking behavior on social media and HPV-related behaviors has not been consistently demonstrated in the literature. Objective: This study aims to examine the association between social media usage and HPV knowledge and HPV awareness. Methods: This study analyzed public health data collected through the Health Information National Trends Survey (HINTS) conducted by the US National Cancer Institute. The analysis used data collected in 2020; in total, 2948 responses were included in the analysis. Six HPV-related questions were used to identify HPV awareness, HPV vaccine awareness, and HPV knowledge about HPV-related cancers. Four questions about social media usage and one question about online health information?seeking behavior were used to analyze the associations between social media usage and HPV-related behaviors. Initially, six logistic regressions were conducted using replicate weights. Based on the results, significant factors were included in a second set of regression analyses that also included demographic variables. Results: About half of the respondents were aware of HPV (68.40%), the HPV vaccine (64.04%), and the relationship between HPV and cervical cancer (48.00%). However, fewer respondents were knowledgeable about the relationships between HPV and penile cancer (19.18%), anal cancer (18.33%), and oral cancer (19.86%). Although social media usage is associated with HPV awareness, HPV vaccine awareness, and knowledge of cervical cancer, these associations were not significant after adjusting for demographic variables. Those less likely to report HPV awareness and knowledge included older participants, males, those with a household income of less than US $20,000, those with a formal education equal to or less than high school, or those who resided in a household where adults are not fluent in English. Conclusions: After adjusting for demographic variables, social media use was not related to HPV knowledge and awareness, and survey respondents were generally not aware that HPV can lead to specific types of cancer, other than cervical cancer. These results suggest that perhaps a lack of high-quality information on social media may impede HPV awareness and knowledge. Efforts to educate the public about HPV via social media might be improved by using techniques like storytelling or infographics, especially targeting vulnerable populations, such as older participants, males, those with low incomes, those with less formal education, or those who reside in the United States but are not fluent in English. UR - https://publichealth.jmir.org/2022/9/e37274 UR - http://dx.doi.org/10.2196/37274 UR - http://www.ncbi.nlm.nih.gov/pubmed/36125858 ID - info:doi/10.2196/37274 ER - TY - JOUR AU - Christensen, Bente AU - Laydon, Daniel AU - Chelkowski, Tadeusz AU - Jemielniak, Dariusz AU - Vollmer, Michaela AU - Bhatt, Samir AU - Krawczyk, Konrad PY - 2022/9/20 TI - Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study JO - JMIR Infodemiology SP - e35121 VL - 2 IS - 2 KW - data mining KW - COVID-19 KW - vaccine KW - text mining KW - change KW - coverage KW - communication KW - media KW - social media KW - news KW - outbreak KW - acceptance KW - hesitancy KW - understanding KW - knowledge KW - sentiment N2 - Background: Achieving herd immunity through vaccination depends upon the public?s acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear communication of often complex information and, increasingly, the countering of misinformation. The primary outlet shaping public understanding is mainstream online news media, where coverage of COVID-19 vaccines was widespread. Objective: We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage. Methods: We analyzed 28 million articles from 172 major news sources across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of overall articles devoted to vaccines. We performed topic detection using BERTopic and named entity recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all collated English-language articles. Results: The proportion of front-page articles mentioning vaccines increased from 0.1% to 4% with the outbreak of COVID-19. The number of negatively polarized articles increased from 6698 in 2015-2019 to 28,552 in 2020-2021. However, overall vaccine coverage before the COVID-19 pandemic was slightly negatively polarized (57% negative), whereas coverage during the pandemic was positively polarized (38% negative). Conclusions: Throughout the pandemic, vaccines have risen from a marginal to a widely discussed topic on the front pages of major news outlets. Mainstream online media has been positively polarized toward vaccines, compared with mainly negative prepandemic vaccine news. However, the pandemic was accompanied by an order-of-magnitude increase in vaccine news that, due to low prepandemic frequency, may contribute to a perceived negative sentiment. These results highlight important interactions between the volume of news and overall polarization. To the best of our knowledge, our work is the first systematic text mining study of front-page vaccine news headlines in the context of COVID-19. UR - https://infodemiology.jmir.org/2022/2/e35121 UR - http://dx.doi.org/10.2196/35121 UR - http://www.ncbi.nlm.nih.gov/pubmed/36348981 ID - info:doi/10.2196/35121 ER - TY - JOUR AU - Renner, Simon AU - Loussikian, Paul AU - Foulquié, Pierre AU - Arnould, Benoit AU - Marrel, Alexia AU - Barbier, Valentin AU - Mebarki, Adel AU - Schück, Stéphane AU - Bharmal, Murtuza PY - 2022/9/20 TI - Perceived Unmet Needs in Patients Living With Advanced Bladder Cancer and Their Caregivers: Infodemiology Study Using Data From Social Media in the United States JO - JMIR Cancer SP - e37518 VL - 8 IS - 3 KW - real-world evidence KW - unmet needs KW - quality of life KW - social media KW - bladder cancer KW - caregivers N2 - Background: Locally advanced or metastatic bladder cancer (BC), which is generally termed advanced BC (aBC), has a very poor prognosis, and in addition to its physical symptoms, it is associated with emotional and social challenges. However, few studies have assessed the unmet needs and burden of aBC from patient and caregiver perspectives. Infodemiology, that is, epidemiology based on internet health-related content, can help obtain more insights on patients? and caregivers? experiences with aBC. Objective: The study aimed to identify the main discussion themes and the unmet needs of patients with aBC and their caregivers through a mixed methods analysis of social media posts. Methods: Social media posts were collected between January 2015 and April 2021 from US geolocalized sites using specific keywords for aBC. Automatic natural language processing (regular expressions and machine learning) methods were used to filter out irrelevant content and identify verbatim posts from patients and caregivers. The verbatim posts were analyzed to identify main discussion themes using biterm topic modeling. Difficulties or unmet needs were further explored using qualitative research methods by 2 independent annotators until saturation of concepts. Results: A total of 688 posts from 262 patients and 1214 posts from 679 caregivers discussing aBC were identified. Analysis of 340 randomly selected patient posts and 423 randomly selected caregiver posts uncovered 33 unique unmet need categories among patients and 36 among caregivers. The main unmet patient needs were related to challenges regarding adverse events (AEs; 28/95, 29%) and the psychological impact of aBC (20/95, 21%). Other patient unmet needs identified were prognosis or diagnosis errors (9/95, 9%) and the need for better management of aBC symptoms (9/95, 9%). The main unmet caregiver needs were related to the psychological impacts of aBC (46/177, 26.0%), the need for support groups and to share experiences between peers (28/177, 15.8%), and the fear and management of patient AEs (22/177, 12.4%). Conclusions: The combination of manual and automatic methods allowed the extraction and analysis of several hundreds of social media posts from patients with aBC and their caregivers. The results highlighted the emotional burden of cancer for both patients and caregivers. Additional studies on patients with aBC and their caregivers are required to quantitatively explore the impact of this disease on quality of life. UR - https://cancer.jmir.org/2022/3/e37518 UR - http://dx.doi.org/10.2196/37518 UR - http://www.ncbi.nlm.nih.gov/pubmed/36125861 ID - info:doi/10.2196/37518 ER - TY - JOUR AU - Charbonneau, Esther AU - Mellouli, Sehl AU - Chouikh, Arbi AU - Couture, Laurie-Jane AU - Desroches, Sophie PY - 2022/9/16 TI - The Information Sharing Behaviors of Dietitians and Twitter Users in the Nutrition and COVID-19 Infodemic: Content Analysis Study of Tweets JO - JMIR Infodemiology SP - e38573 VL - 2 IS - 2 KW - nutrition KW - COVID-19 KW - dietitians KW - Twitter KW - public KW - themes KW - behavior KW - content accuracy KW - user engagement KW - content analysis KW - misinformation KW - disinformation KW - infodemic N2 - Background: The COVID-19 pandemic has generated an infodemic, an overabundance of online and offline information. In this context, accurate information as well as misinformation and disinformation about the links between nutrition and COVID-19 have circulated on Twitter since the onset of the pandemic. Objective: The purpose of this study was to compare tweets on nutrition in times of COVID-19 published by 2 groups, namely, a preidentified group of dietitians and a group of general users of Twitter, in terms of themes, content accuracy, use of behavior change factors, and user engagement, in order to contrast their information sharing behaviors during the pandemic. Methods: Public English-language tweets published between December 31, 2019, and December 31, 2020, by 625 dietitians from Canada and the United States, and Twitter users were collected using hashtags and keywords related to nutrition and COVID-19. After filtration, tweets were coded against an original codebook of themes and the Theoretical Domains Framework (TDF) for identifying behavior change factors, and were compared to reliable nutritional recommendations pertaining to COVID-19. The numbers of likes, replies, and retweets per tweet were also collected to determine user engagement. Results: In total, 2886 tweets (dietitians, n=1417; public, n=1469) were included in the analyses. Differences in frequency between groups were found in 11 out of 15 themes. Grocery (271/1417, 19.1%), and diets and dietary patterns (n=507, 34.5%) were the most frequently addressed themes by dietitians and the public, respectively. For 9 out of 14 TDF domains, there were differences in the frequency of usage between groups. ?Skills? was the most used domain by both groups, although they used it in different proportions (dietitians: 612/1417, 43.2% vs public: 529/1469, 36.0%; P<.001). A higher proportion of dietitians? tweets were accurate compared with the public?s tweets (532/575, 92.5% vs 250/382, 65.5%; P<.001). The results for user engagement were mixed. While engagement by likes varied between groups according to the theme, engagement by replies and retweets was similar across themes but varied according to the group. Conclusions: Differences in tweets between groups, notably ones related to content accuracy, themes, and engagement in the form of likes, shed light on potentially useful and relevant elements to include in timely social media interventions aiming at fighting the COVID-19?related infodemic or future infodemics. UR - https://infodemiology.jmir.org/2022/2/e38573 UR - http://dx.doi.org/10.2196/38573 UR - http://www.ncbi.nlm.nih.gov/pubmed/36188421 ID - info:doi/10.2196/38573 ER - TY - JOUR AU - Klein, Z. Ari AU - Magge, Arjun AU - O'Connor, Karen AU - Gonzalez-Hernandez, Graciela PY - 2022/9/16 TI - Automatically Identifying Twitter Users for Interventions to Support Dementia Family Caregivers: Annotated Data Set and Benchmark Classification Models JO - JMIR Aging SP - e39547 VL - 5 IS - 3 KW - natural language processing KW - social media KW - data mining KW - dementia KW - Alzheimer disease KW - caregivers N2 - Background: More than 6 million people in the United States have Alzheimer disease and related dementias, receiving help from more than 11 million family or other informal caregivers. A range of traditional interventions has been developed to support family caregivers; however, most of them have not been implemented in practice and remain largely inaccessible. While recent studies have shown that family caregivers of people with dementia use Twitter to discuss their experiences, methods have not been developed to enable the use of Twitter for interventions. Objective: The objective of this study is to develop an annotated data set and benchmark classification models for automatically identifying a cohort of Twitter users who have a family member with dementia. Methods: Between May 4 and May 20, 2021, we collected 10,733 tweets, posted by 8846 users, that mention a dementia-related keyword, a linguistic marker that potentially indicates a diagnosis, and a select familial relationship. Three annotators annotated 1 random tweet per user to distinguish those that indicate having a family member with dementia from those that do not. Interannotator agreement was 0.82 (Fleiss kappa). We used the annotated tweets to train and evaluate support vector machine and deep neural network classifiers. To assess the scalability of our approach, we then deployed automatic classification on unlabeled tweets that were continuously collected between May 4, 2021, and March 9, 2022. Results: A deep neural network classifier based on a BERT (bidirectional encoder representations from transformers) model pretrained on tweets achieved the highest F1-score of 0.962 (precision=0.946 and recall=0.979) for the class of tweets indicating that the user has a family member with dementia. The classifier detected 128,838 tweets that indicate having a family member with dementia, posted by 74,290 users between May 4, 2021, and March 9, 2022?that is, approximately 7500 users per month. Conclusions: Our annotated data set can be used to automatically identify Twitter users who have a family member with dementia, enabling the use of Twitter on a large scale to not only explore family caregivers? experiences but also directly target interventions at these users. UR - https://aging.jmir.org/2022/3/e39547 UR - http://dx.doi.org/10.2196/39547 UR - http://www.ncbi.nlm.nih.gov/pubmed/36112408 ID - info:doi/10.2196/39547 ER - TY - JOUR AU - Toussaint, A. Philipp AU - Renner, Maximilian AU - Lins, Sebastian AU - Thiebes, Scott AU - Sunyaev, Ali PY - 2022/9/15 TI - Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users' Comments JO - JMIR Infodemiology SP - e38749 VL - 2 IS - 2 KW - direct-to-consumer genetic testing KW - health information KW - social media KW - YouTube KW - sentiment analysis KW - topic modeling KW - content analysis KW - online health information KW - user discourse KW - infodemiology N2 - Background: With direct-to-consumer (DTC) genetic testing enabling self-responsible access to novel information on ancestry, traits, or health, consumers often turn to social media for assistance and discussion. YouTube, the largest social media platform for videos, offers an abundance of DTC genetic testing?related videos. Nevertheless, user discourse in the comments sections of these videos is largely unexplored. Objective: This study aims to address the lack of knowledge concerning user discourse in the comments sections of DTC genetic testing?related videos on YouTube by exploring topics discussed and users' attitudes toward these videos. Methods: We employed a 3-step research approach. First, we collected metadata and comments of the 248 most viewed DTC genetic testing?related videos on YouTube. Second, we conducted topic modeling using word frequency analysis, bigram analysis, and structural topic modeling to identify topics discussed in the comments sections of those videos. Finally, we employed Bing (binary), National Research Council Canada (NRC) emotion, and 9-level sentiment analysis to identify users' attitudes toward these DTC genetic testing?related videos, as expressed in their comments. Results: We collected 84,082 comments from the 248 most viewed DTC genetic testing?related YouTube videos. With topic modeling, we identified 6 prevailing topics on (1) general genetic testing, (2) ancestry testing, (3) relationship testing, (4) health and trait testing, (5) ethical concerns, and (6) YouTube video reaction. Further, our sentiment analysis indicates strong positive emotions (anticipation, joy, surprise, and trust) and a neutral-to-positive attitude toward DTC genetic testing?related videos. Conclusions: With this study, we demonstrate how to identify users' attitudes on DTC genetic testing by examining topics and opinions based on YouTube video comments. Shedding light on user discourse on social media, our findings suggest that users are highly interested in DTC genetic testing and related social media content. Nonetheless, with this novel market constantly evolving, service providers, content providers, or regulatory authorities may still need to adapt their services to users' interests and desires. UR - https://infodemiology.jmir.org/2022/2/e38749 UR - http://dx.doi.org/10.2196/38749 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113449 ID - info:doi/10.2196/38749 ER - TY - JOUR AU - Yousef, Murooj AU - Dietrich, Timo AU - Rundle-Thiele, Sharyn PY - 2022/9/15 TI - Actions Speak Louder Than Words: Sentiment and Topic Analysis of COVID-19 Vaccination on Twitter and Vaccine Uptake JO - JMIR Form Res SP - e37775 VL - 6 IS - 9 KW - COVID-19 KW - COVID-19 vaccination KW - sentiment analysis KW - public health campaigns KW - vaccine uptake KW - Twitter KW - social media KW - vaccines N2 - Background: The lack of trust in vaccines is a major contributor to vaccine hesitancy. To overcome vaccine hesitancy for the COVID-19 vaccine, the Australian government launched multiple public health campaigns to encourage vaccine uptake. This sentiment analysis examines the effect of public health campaigns and COVID-19?related events on sentiment and vaccine uptake. Objective: This study aims to examine the relationship between sentiment and COVID-19 vaccine uptake and government actions that impacted public sentiment about the vaccine. Methods: Using machine learning methods, we collected 137,523 publicly available English language tweets published in Australia between February and October 2021 that contained COVID-19 vaccine?related keywords. Machine learning methods were used to extract topics and sentiments relating to COVID-19 vaccination. The relationship between public vaccination sentiment on Twitter and vaccine uptake was examined. Results: The majority of collected tweets expressed negative (n=91,052, 66%) rather than positive (n=21,686, 16%) or neutral (n=24,785, 18%) sentiments. Topics discussed within the study time frame included the role of the government in the vaccination rollout, availability and accessibility of the vaccine, and vaccine efficacy. There was a significant positive correlation between negative sentiment and the number of vaccine doses administered daily (r267=.15, P<.05), with positive sentiment showing the inverse effect. Public health campaigns, lockdowns, and antivaccination protests were associated with increased negative sentiment, while vaccination mandates had no significant effect on sentiment. Conclusions: The study findings demonstrate that negative sentiment was more prevalent on Twitter during the Australian vaccination rollout but vaccine uptake remained high. Australians expressed anger at the slow rollout and limited availability of the vaccine during the study period. Public health campaigns, lockdowns, and antivaccination rallies increased negative sentiment. In contrast, news of increased vaccine availability for the public and government acquisition of more doses were key government actions that reduced negative sentiment. These findings can be used to inform government communication planning. UR - https://formative.jmir.org/2022/9/e37775 UR - http://dx.doi.org/10.2196/37775 UR - http://www.ncbi.nlm.nih.gov/pubmed/36007136 ID - info:doi/10.2196/37775 ER - TY - JOUR AU - Marcon, R. Alessandro AU - Wagner, N. Darren AU - Giles, Carly AU - Isenor, Cynthia PY - 2022/9/14 TI - Web-Based Perspectives of Deemed Consent Organ Donation Legislation in Nova Scotia: Thematic Analysis of Commentary in Facebook Groups JO - JMIR Infodemiology SP - e38242 VL - 2 IS - 2 KW - organ donation KW - organ transplantation KW - deemed consent KW - presumed consent KW - social media KW - Facebook KW - public perceptions KW - public policy KW - thematic analysis N2 - Background: The Canadian province of Nova Scotia recently became the first jurisdiction in North America to implement deemed consent organ donation legislation. Changing the consent models constituted one aspect of a larger provincial program to increase organ and tissue donation and transplantation rates. Deemed consent legislation can be controversial among the public, and public participation is integral to the successful implementation of the program. Objective: Social media constitutes key spaces where people express opinions and discuss topics, and social media discourse can influence public perceptions. This project aimed to examine how the public in Nova Scotia responded to legislative changes in Facebook groups. Methods: Using Facebook?s search engine, we searched for posts in public Facebook groups using the terms ?deemed consent,? ?presumed consent,? ?opt out,? or ?organ donation? and ?Nova Scotia,? appearing from January 1, 2020, to May 1, 2021. The finalized data set included 2337 comments on 26 relevant posts in 12 different public Nova Scotia?based Facebook groups. We conducted thematic and content analyses of the comments to determine how the public responded to the legislative changes and how the participants interacted with one another in the discussions. Results: Our thematic analysis revealed principal themes that supported and critiqued the legislation, raised specific issues, and reflected on the topic from a neutral perspective. Subthemes showed individuals presenting perspectives through a variety of themes, including compassion, anger, frustration, mistrust, and a range of argumentative tactics. The comments included personal narratives, beliefs about the government, altruism, autonomy, misinformation, and reflections on religion and death. Content analysis revealed that Facebook users reacted to popular comments with ?likes? more than other reactions. Comments with the most reactions included both negative and positive perspectives about the legislation. Personal donation and transplantation success stories, as well as attempts to correct misinformation, were some of the most ?liked? positive comments. Conclusions: The findings provide key insights into perspectives of individuals from Nova Scotia on deemed consent legislation, as well as organ donation and transplantation broadly. The insights derived from this analysis can contribute to public understanding, policy creation, and public outreach efforts that might occur in other jurisdictions considering the enactment of similar legislation. UR - https://infodemiology.jmir.org/2022/2/e38242 UR - http://dx.doi.org/10.2196/38242 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113450 ID - info:doi/10.2196/38242 ER - TY - JOUR AU - Abroms, C. Lorien AU - Yom-Tov, Elad PY - 2022/9/14 TI - The Role of Information Boxes in Search Engine Results for Symptom Searches: Analysis of Archival Data JO - JMIR Infodemiology SP - e37286 VL - 2 IS - 2 KW - health misinformation KW - search engine KW - internet search KW - information boxes KW - knowledge graph boxes KW - misinformation KW - health information KW - Microsoft KW - internet KW - data KW - symptoms KW - results KW - users KW - medical KW - Bing KW - USA KW - linear KW - logistic KW - regression KW - web KW - ads KW - behavior N2 - Background: Search engines provide health information boxes as part of search results to address information gaps and misinformation for commonly searched symptoms. Few prior studies have sought to understand how individuals who are seeking information about health symptoms navigate different types of page elements on search engine results pages, including health information boxes. Objective: Using real-world search engine data, this study sought to investigate how users searching for common health-related symptoms with Bing interacted with health information boxes (info boxes) and other page elements. Methods: A sample of searches (N=28,552 unique searches) was compiled for the 17 most common medical symptoms queried on Microsoft Bing by users in the United States between September and November 2019. The association between the page elements that users saw, their characteristics, and the time spent on elements or clicks was investigated using linear and logistic regression. Results: The number of searches ranged by symptom type from 55 searches for cramps to 7459 searches for anxiety. Users searching for common health-related symptoms saw pages with standard web results (n=24,034, 84%), itemized web results (n=23,354, 82%), ads (n=13,171, 46%), and info boxes (n=18,215, 64%). Users spent on average 22 (SD 26) seconds on the search engine results page. Users who saw all page elements spent 25% (7.1 s) of their time on the info box, 23% (6.1 s) on standard web results, 20% (5.7 s) on ads, and 10% (10 s) on itemized web results, with significantly more time on the info box compared to other elements and the least amount of time on itemized web results. Info box characteristics such as reading ease and appearance of related conditions were associated with longer time on the info box. Although none of the info box characteristics were associated with clicks on standard web results, info box characteristics such as reading ease and related searches were negatively correlated with clicks on ads. Conclusions: Info boxes were attended most by users compared with other page elements, and their characteristics may influence future web searching. Future studies are needed that further explore the utility of info boxes and their influence on real-world health-seeking behaviors. UR - https://infodemiology.jmir.org/2022/2/e37286 UR - http://dx.doi.org/10.2196/37286 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113445 ID - info:doi/10.2196/37286 ER - TY - JOUR AU - Kinoshita, Takuya AU - Matsumoto, Takehiro AU - Taura, Naota AU - Usui, Tetsuya AU - Matsuya, Nemu AU - Nishiguchi, Mayumi AU - Horita, Hozumi AU - Nakao, Kazuhiko PY - 2022/9/14 TI - Public Interest and Accessibility of Telehealth in Japan: Retrospective Analysis Using Google Trends and National Surveillance JO - JMIR Form Res SP - e36525 VL - 6 IS - 9 KW - COVID-19 KW - telehealth KW - telemedicine KW - public interest KW - mobile app KW - correlation KW - infodemiology, infoveillance KW - surveillance KW - Google Trends N2 - Background: Recently, the use of telehealth for patient treatment under the COVID-19 pandemic has gained interest around the world. As a result, many infodemiology and infoveillance studies using web-based sources such as Google Trends were reported, focusing on the first wave of the COVID-19 pandemic. Although public interest in telehealth has increased in many countries during this time, the long-term interest has remained unknown among people living in Japan. Moreover, various mobile telehealth apps have become available for remote areas in the COVID-19 era, but the accessibility of these apps in epidemic versus nonepidemic regions is unknown. Objective: We aimed to investigate the public interest in telehealth during the first pandemic wave and after the wave in the first part of this study, and the accessibility of medical institutions using telehealth in the epidemic and nonepidemic regions, in the second part. Methods: We examined and compared the first wave and after the wave with regards to severe cases, number of deaths, relative search volume (RSV) of telehealth and COVID-19, and the correlation between RSV and COVID-19 cases, using open sources such as Google Trends and the Japanese Ministry of Health, Labour and Welfare (JMHLW) data. The weekly mean and the week-over-week change rates of RSV and COVID-19 cases were used to examine the correlation coefficients. In the second part, the prevalence of COVID-19 cases, severe cases, number of deaths, and the telehealth accessibility rate were compared between epidemic regions and nonepidemic regions, using the JMHLW data. We also examined the regional correlation between telehealth accessibility and the prevalence of COVID-19 cases. Results: Among the 83 weeks with 5 pandemic waves, the overall mean for the RSV of telehealth and COVID-19 was 11.3 (95% CI 8.0-14.6) and 30.7 (95% CI 27.2-34.2), respectively. The proportion of severe cases (26.54% vs 18.16%; P<.001), deaths (5.33% vs 0.99%; P<.001), RSV of telehealth (mean 33.1, 95% CI 16.2-50.0 vs mean 7.3, 95% CI 6.7-8.0; P<.001), and RSV of COVID-19 (mean 52.1, 95% CI 38.3-65.9 vs mean 26.3, 95% CI 24.4-29.2; P<.001) was significantly higher in the first wave compared to after the wave. In the correlation analysis, the public interest in telehealth was 0.899 in the first wave and ?0.300 overall. In Japan, the accessibility of telehealth using mobile apps was significantly higher in epidemic regions compared to nonepidemic regions in both hospitals (3.8% vs 2.0%; P=.004) and general clinics (5.2% vs 3.1%; P<.001). In the regional correlation analysis, telehealth accessibility using mobile apps was 0.497 in hospitals and 0.629 in general clinics. Conclusions: Although there was no long-term correlation between the public interest in telehealth and COVID-19, there was a regional correlation between mobile telehealth app accessibility in Japan, especially for general clinics. We also revealed that epidemic regions had higher mobile telehealth app accessibility. Further studies about the actual use of telehealth and its effect after the COVID-19 pandemic are necessary. UR - https://formative.jmir.org/2022/9/e36525 UR - http://dx.doi.org/10.2196/36525 UR - http://www.ncbi.nlm.nih.gov/pubmed/36103221 ID - info:doi/10.2196/36525 ER - TY - JOUR AU - Ceretti, Elisabetta AU - Covolo, Loredana AU - Cappellini, Francesca AU - Nanni, Alberto AU - Sorosina, Sara AU - Beatini, Andrea AU - Taranto, Mirella AU - Gasparini, Arianna AU - De Castro, Paola AU - Brusaferro, Silvio AU - Gelatti, Umberto PY - 2022/9/13 TI - Evaluating the Effectiveness of Internet-Based Communication for Public Health: Systematic Review JO - J Med Internet Res SP - e38541 VL - 24 IS - 9 KW - internet-based communication KW - websites KW - social media KW - public health KW - efficacy KW - systematic review KW - communication KW - internet-based KW - health information KW - exchange KW - health care KW - web-based KW - campaigns N2 - Background: Communicating strategically is a key issue for health organizations. Over the past decade, health care communication via social media and websites has generated a great deal of studies examining different realities of communication strategies. However, when it comes to systematic reviews, there is fragmentary evidence on this type of communication. Objective: The aim of this systematic review was to summarize the evidence on web institutional health communication for public health authorities to evaluate possible aim-specific key points based on these existing studies. Methods: Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, we conducted a comprehensive review across 2 electronic databases (PubMed and Web of Science) from January 1, 2011, to October 7, 2021, searching for studies investigating institutional health communication. In total, 2 independent researchers (AN and SS) reviewed the articles for inclusion, and the assessment of methodological quality was based on the Kmet appraisal checklist. Results: A total of 78 articles were selected. Most studies (35/78, 45%) targeted health promotion and disease prevention, followed by crisis communication (24/78, 31%), general health (13/78, 17%), and misinformation correction and health promotion (6/78, 8%). Engagement and message framing were the most analyzed aspects. Few studies (14/78, 18%) focused on campaign effectiveness. Only 23% (18/78) of the studies had an experimental design. The Kmet evaluation was used to distinguish studies presenting a solid structure from lacking studies. In particular, considering the 0.75-point threshold, 36% (28/78) of the studies were excluded. Studies above this threshold were used to identify a series of aim-specific and medium-specific suggestions as the communication strategies used differed greatly. Conclusions: Overall, the findings suggest that no single strategy works best in the case of web-based health care communication. The extreme variability of outcomes and the lack of a unitary measure for assessing the end points of a specific campaign or study lead us to reconsider the tools we use to evaluate the efficacy of web-based health communication. UR - https://www.jmir.org/2022/9/e38541 UR - http://dx.doi.org/10.2196/38541 UR - http://www.ncbi.nlm.nih.gov/pubmed/36098994 ID - info:doi/10.2196/38541 ER - TY - JOUR AU - Stevens, Hannah AU - Rasul, Ehab Muhammad AU - Oh, Jung Yoo PY - 2022/9/13 TI - Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights JO - JMIR Infodemiology SP - e37635 VL - 2 IS - 2 KW - vaccine hesitancy KW - COVID-19 KW - vaccine mandates KW - natural language processing KW - incivility KW - LIWC KW - Linguistic Inquiry and Word Count KW - Twitter N2 - Background: Despite vaccine availability, vaccine hesitancy has inhibited public health officials? efforts to mitigate the COVID-19 pandemic in the United States. Although some US elected officials have responded by issuing vaccine mandates, others have amplified vaccine hesitancy by broadcasting messages that minimize vaccine efficacy. The politically polarized nature of COVID-19 information on social media has given rise to incivility, wherein health attitudes often hinge more on political ideology than science. Objective: To the best of our knowledge, incivility has not been studied in the context of discourse regarding COVID-19 vaccines and mandates. Specifically, there is little focus on the psychological processes that elicit uncivil vaccine discourse and behaviors. Thus, we investigated 3 psychological processes theorized to predict discourse incivility?namely, anxiety, anger, and sadness. Methods: We used 2 different natural language processing approaches: (1) the Linguistic Inquiry and Word Count computational tool and (2) the Google Perspective application programming interface (API) to analyze a data set of 8014 tweets containing terms related to COVID-19 vaccine mandates from September 14, 2021, to October 1, 2021. To collect the tweets, we used the Twitter API Tweet Downloader Tool (version 2). Subsequently, we filtered through a data set of 375,000 vaccine-related tweets using keywords to extract tweets explicitly focused on vaccine mandates. We relied on the Linguistic Inquiry and Word Count computational tool to measure the valence of linguistic anger, sadness, and anxiety in the tweets. To measure dimensions of post incivility, we used the Google Perspective API. Results: This study resolved discrepant operationalizations of incivility by introducing incivility as a multifaceted construct and explored the distinct emotional processes underlying 5 dimensions of discourse incivility. The findings revealed that 3 types of emotions?anxiety, anger, and sadness?were uniquely associated with dimensions of incivility (eg, toxicity, severe toxicity, insult, profanity, threat, and identity attacks). Specifically, the results showed that anger was significantly positively associated with all dimensions of incivility (all P<.001), whereas sadness was significantly positively related to threat (P=.04). Conversely, anxiety was significantly negatively associated with identity attack (P=.03) and profanity (P=.02). Conclusions: The results suggest that our multidimensional approach to incivility is a promising alternative to understanding and intervening in the psychological processes underlying uncivil vaccine discourse. Understanding specific emotions that can increase or decrease incivility such as anxiety, anger, and sadness can enable researchers and public health professionals to develop effective interventions against uncivil vaccine discourse. Given the need for real-time monitoring and automated responses to the spread of health information and misinformation on the web, social media platforms can harness the Google Perspective API to offer users immediate, automated feedback when it detects that a comment is uncivil. UR - https://infodemiology.jmir.org/2022/2/e37635 UR - http://dx.doi.org/10.2196/37635 UR - http://www.ncbi.nlm.nih.gov/pubmed/36188420 ID - info:doi/10.2196/37635 ER - TY - JOUR AU - Ottwell, Ryan AU - Cox, Katherine AU - Dobson, Taylor AU - Shah, Muneeb AU - Hartwell, Micah PY - 2022/9/12 TI - Evaluating the Public's Interest in Testicle Tanning: Observational Study JO - JMIR Dermatol SP - e39766 VL - 5 IS - 3 KW - general dermatology KW - google trends KW - testicle tanning KW - UV radiation KW - public trends KW - skin cancer KW - cancer KW - harmful KW - internet KW - health trends KW - tanning N2 - Background: A new and potentially dangerous health trend, testicle tanning, received extensive media attention following a popular television program where a health and fitness influencer touted that testicular tanning increases testosterone levels. It has been shown that the public has a particular interest in tanning wellness trends; thus, given the vague nomenclature of the practice, the abundance of misleading information and support for using UV light by other health influencers may lead to an increase in men exposing themselves to UV radiation and developing associated complications. Objective: The aim of this paper is to evaluate the public?s interest in testicle tanning. Methods: Relative search interest was collected from Google Trends, and daily tweet volume was collected using Twitter via Sprout Social. The search was filtered to observe internet activity between February 1, 2022, and August 18, 2022. Autoregressive integrated moving average models were applied to forecast the predicted values through April 30 to compare to the actual observed values immediately following the airing of the show. Results: We found that the relative search interest for testicle tanning peaked (100) on April 19, 2022, following a discussion of the topic on a television program. Compared to the forecasted relative search interest of 1.36 (95% CI ?3.29 to 6.01), had the topic not been discussed, it showed a 7252% increase in relative search interest. A similar spike was observed in the volume of tweets peaking on April 18 with 42,736. The expected number of tweets from the autoregressive integrated moving average model was 122 (95% CI ?154 to 397), representing a 35,053% increase. Conclusions: Our results show that the promotion of testicle tanning generated significant public interest in an evidence-lacking and potentially dangerous health trend. Dermatologists and other health care professionals should be aware of these new viral health trends to best counsel patients and combat health misinformation. UR - https://derma.jmir.org/2022/3/e39766 UR - http://dx.doi.org/10.2196/39766 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632896 ID - info:doi/10.2196/39766 ER - TY - JOUR AU - Matharaarachchi, Surani AU - Domaratzki, Mike AU - Katz, Alan AU - Muthukumarana, Saman PY - 2022/9/7 TI - Discovering Long COVID Symptom Patterns: Association Rule Mining and Sentiment Analysis in Social Media Tweets JO - JMIR Form Res SP - e37984 VL - 6 IS - 9 KW - COVID-19 KW - long COVID symptoms KW - social media analysis KW - association rule mining KW - bigram analysis KW - natural language processing KW - Twitter KW - content analysis KW - data mining KW - infodemiology KW - health information N2 - Background: The COVID-19 pandemic is a substantial public health crisis that negatively affects human health and well-being. As a result of being infected with the coronavirus, patients can experience long-term health effects called long COVID syndrome. Multiple symptoms characterize this syndrome, and it is crucial to identify these symptoms as they may negatively impact patients? day-to-day lives. Breathlessness, fatigue, and brain fog are the 3 most common continuing and debilitating symptoms that patients with long COVID have reported, often months after the onset of COVID-19. Objective: This study aimed to understand the patterns and behavior of long COVID symptoms reported by patients on the Twitter social media platform, which is vital to improving our understanding of long COVID. Methods: Long COVID?related Twitter data were collected from May 1, 2020, to December 31, 2021. We used association rule mining techniques to identify frequent symptoms and establish relationships between symptoms among patients with long COVID in Twitter social media discussions. The highest confidence level?based detection was used to determine the most significant rules with 10% minimum confidence and 0.01% minimum support with a positive lift. Results: Among the 30,327 tweets included in our study, the most frequent symptoms were brain fog (n=7812, 25.8%), fatigue (n=5284, 17.4%), breathing/lung issues (n=4750, 15.7%), heart issues (n=2900, 9.6%), flu symptoms (n=2824, 9.3%), depression (n=2256, 7.4%) and general pains (n=1786, 5.9%). Loss of smell and taste, cold, cough, chest pain, fever, headache, and arm pain emerged in 1.6% (n=474) to 5.3% (n=1616) of patients with long COVID. Furthermore, the highest confidence level?based detection successfully demonstrates the potential of association analysis and the Apriori algorithm to establish patterns to explore 57 meaningful relationship rules among long COVID symptoms. The strongest relationship revealed that patients with lung/breathing problems and loss of taste are likely to have a loss of smell with 77% confidence. Conclusions: There are very active social media discussions that could support the growing understanding of COVID-19 and its long-term impact. These discussions enable a potential field of research to analyze the behavior of long COVID syndrome. Exploratory data analysis using natural language processing methods revealed the symptoms and medical conditions related to long COVID discussions on the Twitter social media platform. Using Apriori algorithm?based association rules, we determined interesting and meaningful relationships between symptoms. UR - https://formative.jmir.org/2022/9/e37984 UR - http://dx.doi.org/10.2196/37984 UR - http://www.ncbi.nlm.nih.gov/pubmed/36069846 ID - info:doi/10.2196/37984 ER - TY - JOUR AU - Kong, Dexia AU - Chen, Anfan AU - Zhang, Jingwen AU - Xiang, Xiaoling AU - Lou, Vivian W. Q. AU - Kwok, Timothy AU - Wu, Bei PY - 2022/9/2 TI - Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts JO - J Med Internet Res SP - e39805 VL - 24 IS - 9 KW - dementia KW - public discourse KW - sentiment KW - Weibo KW - social media KW - machine learning KW - infodemiology KW - aging KW - elderly population KW - content analysis KW - topic modeling KW - thematic analysis KW - social support KW - sentiment analysis N2 - Background: Dementia is a global public health priority due to rapid growth of the aging population. As China has the world?s largest population with dementia, this debilitating disease has created tremendous challenges for older adults, family caregivers, and health care systems on the mainland nationwide. However, public awareness and knowledge of the disease remain limited in Chinese society. Objective: This study examines online public discourse and sentiment toward dementia among the Chinese public on a leading Chinese social media platform Weibo. Specifically, this study aims to (1) assess and examine public discourse and sentiment toward dementia among the Chinese public, (2) determine the extent to which dementia-related discourse and sentiment vary among different user groups (ie, government, journalists/news media, scientists/experts, and the general public), and (3) characterize temporal trends in public discourse and sentiment toward dementia among different user groups in China over the past decade. Methods: In total, 983,039 original dementia-related posts published by 347,599 unique users between 2010 and 2021, together with their user information, were analyzed. Machine learning analytical techniques, including topic modeling, sentiment analysis, and semantic network analyses, were used to identify salient themes/topics and their variations across different user groups (ie, government, journalists/news media, scientists/experts, and the general public). Results: Topic modeling results revealed that symptoms, prevention, and social support are the most prevalent dementia-related themes on Weibo. Posts about dementia policy/advocacy have been increasing in volume since 2018. Raising awareness is the least discussed topic over time. Sentiment analysis indicated that Weibo users generally attach negative attitudes/emotions to dementia, with the general public holding a more negative attitude than other user groups. Conclusions: Overall, dementia has received greater public attention on social media since 2018. In particular, discussions related to dementia advocacy and policy are gaining momentum in China. However, disparaging language is still used to describe dementia in China; therefore, a nationwide initiative is needed to alter the public discourse on dementia. The results contribute to previous research by providing a macrolevel understanding of the Chinese public?s discourse and attitudes toward dementia, which is essential for building national education and policy initiatives to create a dementia-friendly society. Our findings indicate that dementia is associated with negative sentiments, and symptoms and prevention dominate public discourse. The development of strategies to address unfavorable perceptions of dementia requires policy and public health attention. The results further reveal that an urgent need exists to increase public knowledge about dementia. Social media platforms potentially could be leveraged for future dementia education interventions to increase dementia awareness and promote positive attitudes. UR - https://www.jmir.org/2022/9/e39805 UR - http://dx.doi.org/10.2196/39805 UR - http://www.ncbi.nlm.nih.gov/pubmed/36053565 ID - info:doi/10.2196/39805 ER - TY - JOUR AU - Marcec, Robert AU - Stjepanovic, Josip AU - Likic, Robert PY - 2022/9/1 TI - Seasonality of Hashimoto Thyroiditis: Infodemiology Study of Google Trends Data JO - JMIR Bioinform Biotech SP - e38976 VL - 3 IS - 1 KW - Hashimoto disease KW - Hashimoto thyroiditis KW - infodemiology KW - search engine KW - Google Trends KW - seasonality KW - cosinor analysis KW - Google KW - thyroid N2 - Background: Hashimoto thyroiditis (HT) is an autoimmune thyroid disease and the leading cause of hypothyroidism in areas with sufficient iodine intake. The quality-of-life impact and financial burden of hypothyroidism and HT highlight the need for additional research investigating the disease etiology with the aim of revealing potential modifiable risk factors. Objective: Implementation of measures against such risk factors, once identified, has the potential to lessen the financial burden while also improving the quality of life of many individuals. Therefore, we aimed to examine the potential seasonality of HT in Europe using the Google Trends data to explore whether there is a seasonal characteristic of Google searches regarding HT, examine the potential impact of the countries? geographic location on the potential seasonality, and identify potential modifiable risk factors for HT, thereby inspiring future research on the topic. Methods: Monthly Google Trends data on the search topic ?Hashimoto thyroiditis? were retrieved in a 17-year time frame from January 2004 to December 2020 for 36 European countries. A cosinor model analysis was conducted to evaluate potential seasonality. Simple linear regression was used to estimate the potential effect of latitude and longitude on seasonal amplitude and phase of the model outputs. Results: Of 36 included European countries, significant seasonality was observed in 30 (83%) countries. Most phase peaks occurred in spring (14/30, 46.7%) and winter (8/30, 26.7%). A statistically significant effect was observed regarding the effect of geographical latitude on cosinor model amplitude (y = ?3.23 + 0.13 x; R2=0.29; P=.002). Seasonal increases in HT search volume may therefore be a consequence of an increased incidence or higher disease activity. It is particularly interesting that in most countries, a seasonal peak occurred in spring and winter months; when viewed in the context of the statistically significant impact of geographical latitude on seasonality amplitude, this may indicate the potential role of vitamin D levels in the seasonality of HT. Conclusions: Significant seasonality of HT Google Trends search volume was observed in our study, with seasonal peaks in most countries occurring in spring and winter and with a significant impact of latitude on seasonality amplitude. Further studies on the topic of seasonality in HT and factors impacting it are required. UR - https://bioinform.jmir.org/2022/1/e38976 UR - http://dx.doi.org/10.2196/38976 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/38976 ER - TY - JOUR AU - Tong, Chau AU - Margolin, Drew AU - Chunara, Rumi AU - Niederdeppe, Jeff AU - Taylor, Teairah AU - Dunbar, Natalie AU - King, J. Andy PY - 2022/8/30 TI - Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube JO - JMIR Med Inform SP - e37862 VL - 10 IS - 8 KW - health information retrieval KW - search term identification KW - social media KW - health communication KW - public health KW - computational textual analysis KW - natural language processing KW - NLP KW - word2vec KW - word embeddings KW - network analysis N2 - Background: Common methods for extracting content in health communication research typically involve using a set of well-established queries, often names of medical procedures or diseases, that are often technical or rarely used in the public discussion of health topics. Although these methods produce high recall (ie, retrieve highly relevant content), they tend to overlook health messages that feature colloquial language and layperson vocabularies on social media. Given how such messages could contain misinformation or obscure content that circumvents official medical concepts, correctly identifying (and analyzing) them is crucial to the study of user-generated health content on social media platforms. Objective: Health communication scholars would benefit from a retrieval process that goes beyond the use of standard terminologies as search queries. Motivated by this, this study aims to put forward a search term identification method to improve the retrieval of user-generated health content on social media. We focused on cancer screening tests as a subject and YouTube as a platform case study. Methods: We retrieved YouTube videos using cancer screening procedures (colonoscopy, fecal occult blood test, mammogram, and pap test) as seed queries. We then trained word embedding models using text features from these videos to identify the nearest neighbor terms that are semantically similar to cancer screening tests in colloquial language. Retrieving more YouTube videos from the top neighbor terms, we coded a sample of 150 random videos from each term for relevance. We then used text mining to examine the new content retrieved from these videos and network analysis to inspect the relations between the newly retrieved videos and videos from the seed queries. Results: The top terms with semantic similarities to cancer screening tests were identified via word embedding models. Text mining analysis showed that the 5 nearest neighbor terms retrieved content that was novel and contextually diverse, beyond the content retrieved from cancer screening concepts alone. Results from network analysis showed that the newly retrieved videos had at least one total degree of connection (sum of indegree and outdegree) with seed videos according to YouTube relatedness measures. Conclusions: We demonstrated a retrieval technique to improve recall and minimize precision loss, which can be extended to various health topics on YouTube, a popular video-sharing social media platform. We discussed how health communication scholars can apply the technique to inspect the performance of the retrieval strategy before investing human coding resources and outlined suggestions on how such a technique can be extended to other health contexts. UR - https://medinform.jmir.org/2022/8/e37862 UR - http://dx.doi.org/10.2196/37862 UR - http://www.ncbi.nlm.nih.gov/pubmed/36040760 ID - info:doi/10.2196/37862 ER - TY - JOUR AU - Bhagavathula, Srikanth Akshaya AU - Massey, M. Philip PY - 2022/8/29 TI - Google Trends on Human Papillomavirus Vaccine Searches in the United States From 2010 to 2021: Infodemiology Study JO - JMIR Public Health Surveill SP - e37656 VL - 8 IS - 8 KW - Google Trends KW - HPV vaccine KW - Google search KW - attitude KW - infodemiology KW - searches KW - United States of America N2 - Background: The human papillomavirus (HPV) vaccine is recommended for adolescents and young adults to prevent HPV-related cancers and genital warts. However, HPV vaccine uptake among the target age groups is suboptimal. Objective: The aim of this infodemiology study was to examine public online searches in the United States related to the HPV vaccine from January 2010 to December 2021. Methods: Google Trends (GT) was used to explore online searches related to the HPV vaccine from January 1, 2010, to December 31, 2021. Online searches and queries on the HPV vaccine were investigated using relative search volumes (RSVs). Analysis of variance was performed to investigate quarterly differences in HPV vaccine searches in each year from 2010 to 2021. A joinpoint regression was used to identify statistically significant changes over time; the ? level was set to .05. Results: The year-wise online search volume related to the HPV vaccine increased from 2010 to 2021, often following federal changes related to vaccine administration. Joinpoint regression analysis showed that HPV vaccine searches significantly increased on average by 8.6% (95% CI 5.9%-11.4%) across each year from 2010 to 2021. Moreover, HPV vaccine searches demonstrated a similar pattern across years, with search interest increasing through August nearly every year. At the state level, the highest 12-year mean RSV was observed in California (59.9, SD 14.3) and the lowest was observed in Wyoming (17.4, SD 8.5) during the period of 2010-2021. Conclusions: Online searches related to the HPV vaccine increased by an average of 8.6% across each year from 2010 to 2021, with noticeable spikes corresponding to key changes in vaccine recommendations. We identified patterns across years and differences at the state level in the online search interest related to the HPV vaccine. Public health organizations can use GT as a tool to characterize the public interest in and promote the HPV vaccine in the United States. UR - https://publichealth.jmir.org/2022/8/e37656 UR - http://dx.doi.org/10.2196/37656 UR - http://www.ncbi.nlm.nih.gov/pubmed/36036972 ID - info:doi/10.2196/37656 ER - TY - JOUR AU - Chen, Jiarui AU - Xue, Siyu AU - Xie, Zidian AU - Li, Dongmei PY - 2022/8/29 TI - Perceptions and Discussions of Snus on Twitter: Observational Study JO - JMIR Med Inform SP - e38174 VL - 10 IS - 8 KW - snus KW - Twitter KW - sentiment KW - topic modeling KW - smokeless tobacco products N2 - Background: With the increasing popularity of snus, it is essential to understand the public perception of this oral tobacco product. Twitter?a popular social media platform that is being used to share personal experiences and opinions?provides an ideal data source for studying the public perception of snus. Objective: This study aims to examine public perceptions and discussions of snus on Twitter. Methods: Twitter posts (tweets) about snus were collected through the Twitter streaming application programming interface from March 11, 2021, to February 26, 2022. A temporal analysis was conducted to examine the change in number of snus-related tweets over time. A sentiment analysis was conducted to examine the sentiments of snus-related tweets. Topic modeling was applied to tweets to determine popular topics. Finally, a keyword search and hand-coding were used to understand the health symptoms mentioned in snus-related tweets. Results: The sentiment analysis showed that the proportion of snus-related tweets with a positive sentiment was significantly higher than the proportion of negative sentiment tweets (4341/11,631, 37.32% vs 3094/11,631, 26.60%; P<.001). The topic modeling analysis revealed that positive tweets focused on snus?s harm reduction and snus use being an alternative to smoking, while negative tweets focused on health concerns related to snus. Mouth and respiratory symptoms were the most mentioned health symptoms in snus-related tweets. Conclusions: This study examined the public perception of snus and popular snus-related topics discussed on Twitter, thus providing a guide for policy makers with regard to the future formulation and adjustment of tobacco regulation policies. UR - https://medinform.jmir.org/2022/8/e38174 UR - http://dx.doi.org/10.2196/38174 UR - http://www.ncbi.nlm.nih.gov/pubmed/36036970 ID - info:doi/10.2196/38174 ER - TY - JOUR AU - Suarez-Lledo, Victor AU - Alvarez-Galvez, Javier PY - 2022/8/25 TI - Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism JO - J Med Internet Res SP - e36085 VL - 24 IS - 8 KW - infodemics KW - social media KW - misinformation KW - epidemics KW - outbreaks KW - COVID-19 KW - infodemiology KW - health promotion KW - pandemic KW - chatbot KW - social media bot KW - Twitter stream KW - Botometer KW - peer support N2 - Background: Social media has changed the way we live and communicate, as well as offering unprecedented opportunities to improve many aspects of our lives, including health promotion and disease prevention. However, there is also a darker side to social media that is not always as evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation. Objective: This study aimed to study the role of social media bots during the COVID-19 outbreak. Methods: The Twitter streaming API was used to collect tweets regarding COVID-19 during the early stages of the outbreak. The Botometer tool was then used to obtain the likelihood of whether each account is a bot or not. Bot classification and topic-modeling techniques were used to interpret the Twitter conversation. Finally, the sentiment associated with the tweets was compared depending on the source of the tweet. Results: Regarding the conversation topics, there were notable differences between the different accounts. The content of nonbot accounts was associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content consisted mainly of news, such as the existence of diagnostic tests, the evolution of the pandemic, and scientific findings. Finally, in the case of bots, the content was mostly political. Above all, there was a general overriding tone of criticism and disagreement. In relation to the sentiment analysis, the main differences were associated with the tone of the conversation. In the case of self-declared bots, this tended to be neutral, whereas the conversation of normal users scored positively. In contrast, bots tended to score negatively. Conclusions: By classifying the accounts according to their likelihood of being bots and performing topic modeling, we were able to segment the Twitter conversation regarding COVID-19. Bot accounts tended to criticize the measures imposed to curb the pandemic, express disagreement with politicians, or question the veracity of the information shared on social media. UR - https://www.jmir.org/2022/8/e36085 UR - http://dx.doi.org/10.2196/36085 UR - http://www.ncbi.nlm.nih.gov/pubmed/35839385 ID - info:doi/10.2196/36085 ER - TY - JOUR AU - Kolluri, Nikhil AU - Liu, Yunong AU - Murthy, Dhiraj PY - 2022/8/25 TI - COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic JO - JMIR Infodemiology SP - e38756 VL - 2 IS - 2 KW - COVID-19 KW - misinformation KW - machine learning KW - fact-checking KW - infodemiology KW - infodemic management KW - model performance KW - model accuracy KW - content analysis N2 - Background: The volume of COVID-19?related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning?based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19?related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed. Objective: The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response. Methods: We evaluated three strategies for training a machine-learning model to determine the highest model performance: (1) COVID-19?related fact-checked data only, (2) general fact-checked data only, and (3) combined COVID-19 and general fact-checked data. We created two COVID-19?related misinformation data sets from fact-checked ?false? content combined with programmatically retrieved ?true? content. The first set contained ~7000 entries from July to August 2020, and the second contained ~31,000 entries from January 2020 to June 2022. We crowdsourced 31,441 votes to human label the first data set. Results: The models achieved an accuracy of 96.55% and 94.56% on the first and second external validation data set, respectively. Our best-performing model was developed using COVID-19?specific content. We were able to successfully develop combined models that outperformed human votes of misinformation. Specifically, when we blended our model predictions with human votes, the highest accuracy we achieved on the first external validation data set was 99.1%. When we considered outputs where the machine-learning model agreed with human votes, we achieved accuracies up to 98.59% on the first validation data set. This outperformed human votes alone with an accuracy of only 73%. Conclusions: External validation accuracies of 96.55% and 94.56% are evidence that machine learning can produce superior results for the difficult task of classifying the veracity of COVID-19 content. Pretrained language models performed best when fine-tuned on a topic-specific data set, while other models achieved their best accuracy when fine-tuned on a combination of topic-specific and general-topic data sets. Crucially, our study found that blended models, trained/fine-tuned on general-topic content with crowdsourced data, improved our models? accuracies up to 99.7%. The successful use of crowdsourced data can increase the accuracy of models in situations when expert-labeled data are scarce. The 98.59% accuracy on a ?high-confidence? subsection comprised of machine-learned and human labels suggests that crowdsourced votes can optimize machine-learned labels to improve accuracy above human-only levels. These results support the utility of supervised machine learning to deter and combat future health-related disinformation. UR - https://infodemiology.jmir.org/2022/2/e38756 UR - http://dx.doi.org/10.2196/38756 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113446 ID - info:doi/10.2196/38756 ER - TY - JOUR AU - Gore, Ross AU - Lynch, J. Christopher AU - Jordan, A. Craig AU - Collins, Andrew AU - Robinson, Michael R. AU - Fuller, Gabrielle AU - Ames, Pearson AU - Keerthi, Prateek AU - Kandukuri, Yash PY - 2022/8/24 TI - Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison JO - JMIR Public Health Surveill SP - e37379 VL - 8 IS - 8 KW - bicycle paths KW - pedestrian paths KW - bicycling KW - walking KW - diabetes KW - high blood pressure KW - physical health KW - factor analysis KW - digital neighborhoods KW - data analysis N2 - Background: Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions. Objective: Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question ?To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents? health outcomes over time?? Methods: We conducted a factor analysis of data from the American Community Survey, Center for Disease Control 500 Cities project, Strava, and bicycle and pedestrian path location and use data from two different cities (Norfolk, Virginia, and San Francisco, California). We constructed 2 city-specific factor models and used an algorithm to predict the expected mean improvement that a specified number of bicycle and pedestrian path miles contributes to the identified health outcomes. Results: We show that given a factor model constructed from data from 2011 to 2015, the number of additional bicycle and pedestrian path miles in 2016, and a specific census tract, our models forecast health outcome improvements in 2020 more accurately than 2 alternative approaches for both Norfolk, Virginia, and San Francisco, California. Furthermore, for each city, we show that the additional accuracy is a statistically significant improvement (P<.001 in every case) when compared with the alternate approaches. For Norfolk, Virginia (n=31 census tracts), our approach estimated, on average, the percentage of individuals with high blood pressure in the census tract within 1.49% (SD 0.85%), the percentage of individuals with diabetes in the census tract within 1.63% (SD 0.59%), and the percentage of individuals who had >2 weeks of poor physical health days in the census tract within 1.83% (SD 0.57%). For San Francisco (n=49 census tracts), our approach estimates, on average, that the percentage of individuals who had a stroke in the census tract is within 1.81% (SD 0.52%), and the percentage of individuals with diabetes in the census tract is within 1.26% (SD 0.91%). Conclusions: We propose and evaluate a methodology to enable decision makers to weigh the extent to which 2 bicycle and pedestrian paths of equal cost, which were proposed in different census tracts, improve residents? health outcomes; identify areas where bicycle and pedestrian paths are unlikely to be effective interventions and other strategies should be used; and quantify the minimum amount of additional bicycle path miles needed to maximize health outcome improvements. Our methodology shows statistically significant improvements, compared with alternative approaches, in historical accuracy for 2 large cities (for 2016) within different geographic areas and with different demographics. UR - https://publichealth.jmir.org/2022/8/e37379 UR - http://dx.doi.org/10.2196/37379 UR - http://www.ncbi.nlm.nih.gov/pubmed/36001362 ID - info:doi/10.2196/37379 ER - TY - JOUR AU - Palmer, Tanner AU - Benson, Scott L. AU - Porucznik, Christina AU - Gren, H. Lisa PY - 2022/8/24 TI - Impact of COVID-19 Social Distancing Mandates on Gastrointestinal Pathogen Positivity: Secondary Data Analysis JO - JMIR Public Health Surveill SP - e34757 VL - 8 IS - 8 KW - social distancing KW - gastrointestinal KW - COVID-19 KW - gastroenteritis KW - surveillance KW - epidemiology KW - pathogen transmission KW - respiratory pathogen KW - public health KW - pathogen outbreak KW - outbreak KW - surveillance tool KW - diagnostic database N2 - Background: Acute gastrointestinal (GI) illnesses are of the most common problems evaluated by physicians and some of the most preventable. There is evidence of GI pathogen transmission when people are in close contact. The COVID-19 pandemic led to the sudden implementation of widespread social distancing measures in the United States. There is strong evidence that social distancing measures impact the spread of SARS-CoV-2, and a growing body of research indicates that these measures also decrease the transmission of other respiratory pathogens. Objective: This study aims to investigate the impact of COVID-19 social distancing mandates on the GI pathogen positivity rates. Methods: Deidentified GI Panel polymerase chain reaction test results from a routinely collected diagnostic database from January 1, 2019, through August 31, 2020, were analyzed for the GI pathogen positivity percentage. An interrupted time series analysis was performed, using social distancing mandate issue dates as the intervention date. The following 3 target organisms were chosen for the final analysis to represent different primary transmission routes: adenovirus F40 and 41, norovirus GI and GII, and Escherichia coli O157. Results: In total, 84,223 test results from 9 states were included in the final data set. With the exception of E coli O157 in Kansas, Michigan, and Nebraska, we observed an immediate decrease in positivity percentage during the week of social distancing mandates for all other targets and states. Norovirus GI and GII showed the most notable drop in positivity, whereas E coli O157 appeared to be least impacted by social distancing mandates. Although we acknowledge the analysis has a multiple testing problem, the majority of our significant results showed significance even below the .01 level. Conclusions: This study aimed to investigate the impact of social distancing mandates for COVID-19 on GI pathogen positivity, and we discovered that social distancing measures in fact decreased GI pathogen positivity initially. The use of similar measures may prove useful in GI pathogen outbreaks. The use of a unique diagnostic database in this study exhibits the potential for its use as a public health surveillance tool. UR - https://publichealth.jmir.org/2022/8/e34757 UR - http://dx.doi.org/10.2196/34757 UR - http://www.ncbi.nlm.nih.gov/pubmed/35507923 ID - info:doi/10.2196/34757 ER - TY - JOUR AU - Buller, David AU - Walkosz, Barbara AU - Henry, Kimberly AU - Woodall, Gill W. AU - Pagoto, Sherry AU - Berteletti, Julia AU - Kinsey, Alishia AU - Divito, Joseph AU - Baker, Katie AU - Hillhouse, Joel PY - 2022/8/23 TI - Promoting Social Distancing and COVID-19 Vaccine Intentions to Mothers: Randomized Comparison of Information Sources in Social Media Messages JO - JMIR Infodemiology SP - e36210 VL - 2 IS - 2 KW - social media KW - COVID-19 KW - vaccination KW - nonpharmaceutical interventions KW - information source KW - misinformation KW - vaccine KW - public health KW - COVID-19 prevention KW - health promotion N2 - Background: Social media disseminated information and spread misinformation during the COVID-19 pandemic that affected prevention measures, including social distancing and vaccine acceptance. Objective: In this study, we aimed to test the effect of a series of social media posts promoting COVID-19 nonpharmaceutical interventions (NPIs) and vaccine intentions and compare effects among 3 common types of information sources: government agency, near-peer parents, and news media. Methods: A sample of mothers of teen daughters (N=303) recruited from a prior trial were enrolled in a 3 (information source) × 4 (assessment period) randomized factorial trial from January to March 2021 to evaluate the effects of information sources in a social media campaign addressing NPIs (ie, social distancing), COVID-19 vaccinations, media literacy, and mother?daughter communication about COVID-19. Mothers received 1 social media post per day in 3 randomly assigned Facebook private groups, Monday-Friday, covering all 4 topics each week, plus 1 additional post on a positive nonpandemic topic to promote engagement. Posts in the 3 groups had the same messages but differed by links to information from government agencies, near-peer parents, or news media in the post. Mothers reported on social distancing behavior and COVID-19 vaccine intentions for self and daughter, theoretic mediators, and covariates in baseline and 3-, 6-, and 9-week postrandomization assessments. Views, reactions, and comments related to each post were counted to measure engagement with the messages. Results: Nearly all mothers (n=298, 98.3%) remained in the Facebook private groups throughout the 9-week trial period, and follow-up rates were high (n=276, 91.1%, completed the 3-week posttest; n=273, 90.1%, completed the 6-week posttest; n=275, 90.8%, completed the 9-week posttest; and n=244, 80.5%, completed all assessments). In intent-to-treat analyses, social distancing behavior by mothers (b=?0.10, 95% CI ?0.12 to ?0.08, P<.001) and daughters (b=?0.10, 95% CI ?0.18 to ?0.03, P<.001) decreased over time but vaccine intentions increased (mothers: b=0.34, 95% CI 0.19-0.49, P<.001; daughters: b=0.17, 95% CI 0.04-0.29, P=.01). Decrease in social distancing by daughters was greater in the near-peer source group (b=?0.04, 95% CI ?0.07 to 0.00, P=.03) and lesser in the government agency group (b=0.05, 95% CI 0.02-0.09, P=.003). The higher perceived credibility of the assigned information source increased social distancing (mothers: b=0.29, 95% CI 0.09-0.49, P<.01; daughters: b=0.31, 95% CI 0.11-0.51, P<.01) and vaccine intentions (mothers: b=4.18, 95% CI 1.83-6.53, P<.001; daughters: b=3.36, 95% CI 1.67-5.04, P<.001). Mothers? intentions to vaccinate self may have increased when they considered the near-peer source to be not credible (b=?0.50, 95% CI ?0.99 to ?0.01, P=.05). Conclusions: Decreasing case counts, relaxation of government restrictions, and vaccine distribution during the study may explain the decreased social distancing and increased vaccine intentions. When promoting COVID-19 prevention, campaign planners may be more effective when selecting information sources that audiences consider credible, as no source was more credible in general. Trial Registration: ClinicalTrials.gov NCT02835807; https://clinicaltrials.gov/ct2/show/NCT02835807 UR - https://infodemiology.jmir.org/2022/2/e36210 UR - http://dx.doi.org/10.2196/36210 UR - http://www.ncbi.nlm.nih.gov/pubmed/36039372 ID - info:doi/10.2196/36210 ER - TY - JOUR AU - Burgess, Raquel AU - Feliciano, T. Josemari AU - Lizbinski, Leonardo AU - Ransome, Yusuf PY - 2022/8/11 TI - Trends and Characteristics of #HIVPrevention Tweets Posted Between 2014 and 2019: Retrospective Infodemiology Study JO - JMIR Public Health Surveill SP - e35937 VL - 8 IS - 8 KW - HIV KW - social media KW - Twitter KW - prevention KW - infodemiology N2 - Background: Twitter is becoming an increasingly important avenue for people to seek information about HIV prevention. Tweets about HIV prevention may reflect or influence current norms about the acceptability of different HIV prevention methods. Therefore, it may be useful to empirically investigate trends in the level of attention paid to different HIV prevention topics on Twitter over time. Objective: The primary objective of this study was to investigate temporal trends in the frequency of tweets about different HIV prevention topics on Twitter between 2014 and 2019. Methods: We used the Twitter application programming interface to obtain English-language tweets employing #HIVPrevention between January 1, 2014, and December 31, 2019 (n=69,197, globally). Using iterative qualitative content analysis on samples of tweets, we developed a keyword list to categorize the tweets into 10 prevention topics (eg, condom use, preexposure prophylaxis [PrEP]) and compared the frequency of tweets mentioning each topic over time. We assessed the overall change in the proportions of #HIVPrevention tweets mentioning each prevention topic in 2019 as compared with 2014 using chi-square and Fisher exact tests. We also conducted descriptive analyses to identify the accounts posting the most original tweets, the accounts retweeted most frequently, the most frequently used word pairings, and the spatial distribution of tweets in the United States compared with the number of state-level HIV cases. Results: PrEP (13,895 tweets; 20.08% of all included tweets) and HIV testing (7688, 11.11%) were the most frequently mentioned topics, whereas condom use (2941, 4.25%) and postexposure prophylaxis (PEP; 823, 1.19%) were mentioned relatively less frequently. The proportions of tweets mentioning PrEP (327/2251, 14.53%, in 2014, 5067/12,971, 39.1%, in 2019; P?.001), HIV testing (208/2251, 9.24%, in 2014, 2193/12,971, 16.91% in 2019; P?.001), and PEP (25/2251, 1.11%, in 2014, 342/12,971, 2.64%, in 2019; P?.001) were higher in 2019 compared with 2014, whereas the proportions of tweets mentioning abstinence, condom use, circumcision, harm reduction, and gender inequity were lower in 2019 compared with 2014. The top retweeted accounts were mostly UN-affiliated entities; celebrities and HIV advocates were also represented. Geotagged #HIVPrevention tweets in the United States between 2014 and 2019 (n=514) were positively correlated with the number of state-level HIV cases in 2019 (r=0.81, P?.01). Conclusions: Twitter may be a useful source for identifying HIV prevention trends. During our evaluation period (2014-2019), the most frequently mentioned prevention topics were PrEP and HIV testing in tweets using #HIVPrevention. Strategic responses to these tweets that provide information about where to get tested or how to obtain PrEP may be potential approaches to reduce HIV incidence. UR - https://publichealth.jmir.org/2022/8/e35937 UR - http://dx.doi.org/10.2196/35937 UR - http://www.ncbi.nlm.nih.gov/pubmed/35969453 ID - info:doi/10.2196/35937 ER - TY - JOUR AU - van Woudenberg, Thabo AU - Buijzen, Moniek AU - Hendrikx, Roy AU - van Weert, Julia AU - van den Putte, Bas AU - Kroese, Floor AU - Bouman, Martine AU - de Bruin, Marijn AU - Lambooij, Mattijs PY - 2022/8/11 TI - Physical Distancing and Social Media Use in Emerging Adults and Adults During the COVID-19 Pandemic: Large-scale Cross-sectional and Longitudinal Survey Study JO - JMIR Infodemiology SP - e33713 VL - 2 IS - 2 KW - COVID-19 KW - physical distancing KW - compliance KW - emerging adults KW - social media N2 - Background: Although emerging adults play a role in the spread of COVID-19, they are less likely to develop severe symptoms after infection. Emerging adults? relatively high use of social media as a source of information raises concerns regarding COVID-19?related behavioral compliance (ie, physical distancing) in this age group. Objective: This study aimed to investigate physical distancing among emerging adults in comparison with adults and examine the role of using social media for COVID-19 news and information in this regard. In addition, this study explored the relationship between physical distancing and using different social media platforms and sources. Methods: The secondary data of a large-scale longitudinal national survey (N=123,848) between April and November 2020 were used. Participants indicated, ranging from 1 to 8 waves, how often they were successful in keeping a 1.5-m distance on a 7-point Likert scale. Participants aged between 18 and 24 years were considered emerging adults, and those aged >24 years were considered adults. In addition, a dummy variable was created to indicate per wave whether participants used social media for COVID-19 news and information. A subset of participants received follow-up questions to determine which platforms they used and what sources of news and information they had seen on social media. All preregistered hypotheses were tested with linear mixed-effects models and random intercept cross-lagged panel models. Results: Emerging adults reported fewer physical distancing behaviors than adults (?=?.08, t86,213.83=?26.79; P<.001). Moreover, emerging adults were more likely to use social media for COVID-19 news and information (b=2.48; odds ratio 11.93 [95% CI=9.72-14.65]; SE 0.11; Wald=23.66; P<.001), which mediated the association with physical distancing but only to a small extent (indirect effect: b=?0.03, 95% CI ?0.04 to ?0.02). Contrary to our hypothesis, the longitudinal random intercept cross-lagged panel model showed no evidence that physical distancing was not influenced by social media use in the previous wave. However, evidence indicated that social media use affects subsequent physical distancing behavior. Moreover, additional analyses showed that the use of most social media platforms (ie, YouTube, Facebook, and Instagram) and interpersonal communication were negatively associated with physical distancing, whereas other platforms (ie, LinkedIn and Twitter) and government messages had no or small positive associations with physical distancing. Conclusions: In conclusion, we should be vigilant with regard to the physical distancing of emerging adults, but the study results did not indicate concerns regarding the role of social media for COVID-19 news and information. However, as the use of some social media platforms and sources showed negative associations with physical distancing, future studies should more carefully examine these factors to better understand the associations between social media use for news and information and behavioral interventions in times of crisis. UR - https://infodemiology.jmir.org/2022/2/e33713 UR - http://dx.doi.org/10.2196/33713 UR - http://www.ncbi.nlm.nih.gov/pubmed/35996459 ID - info:doi/10.2196/33713 ER - TY - JOUR AU - Yin, Dean-Chen Jason PY - 2022/8/10 TI - Media Data and Vaccine Hesitancy: Scoping Review JO - JMIR Infodemiology SP - e37300 VL - 2 IS - 2 KW - review KW - social media KW - traditional media KW - vaccine hesitancy KW - natural language processing KW - digital epidemiology N2 - Background: Media studies are important for vaccine hesitancy research, as they analyze how the media shapes risk perceptions and vaccine uptake. Despite the growth in studies in this field owing to advances in computing and language processing and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology. Objective: This review aimed to identify and illustrate the media platforms and methods used to study vaccine hesitancy and how they build or contribute to the study of the media?s influence on vaccine hesitancy and public health. Methods: This study followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A search was conducted on PubMed and Scopus for any studies that used media data (social media or traditional media), had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, or stance), were written in English, and were published after 2010. Studies were screened by only 1 reviewer and extracted for media platform, analysis method, the theoretical models used, and outcomes. Results: In total, 125 studies were included, of which 71 (56.8%) used traditional research methods and 54 (43.2%) used computational methods. Of the traditional methods, most used content analysis (43/71, 61%) and sentiment analysis (21/71, 30%) to analyze the texts. The most common platforms were newspapers, print media, and web-based news. The computational methods mostly used sentiment analysis (31/54, 57%), topic modeling (18/54, 33%), and network analysis (17/54, 31%). Fewer studies used projections (2/54, 4%) and feature extraction (1/54, 2%). The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. The following five major categories of studies arose: antivaccination themes centered on the distrust of institutions, civil liberties, misinformation, conspiracy theories, and vaccine-specific concerns; provaccination themes centered on ensuring vaccine safety using scientific literature; framing being important and health professionals and personal stories having the largest impact on shaping vaccine opinion; the coverage of vaccination-related data mostly identifying negative vaccine content and revealing deeply fractured vaccine communities and echo chambers; and the public reacting to and focusing on certain signals?in particular cases, deaths, and scandals?which suggests a more volatile period for the spread of information. Conclusions: The heterogeneity in the use of media to study vaccines can be better consolidated through theoretical grounding. Areas of suggested research include understanding how trust in institutions is associated with vaccine uptake, how misinformation and information signaling influence vaccine uptake, and the evaluation of government communications on vaccine rollouts and vaccine-related events. The review ends with a statement that media data analyses, though groundbreaking in approach, should supplement?not supplant?current practices in public health research. UR - https://infodemiology.jmir.org/2022/2/e37300 UR - http://dx.doi.org/10.2196/37300 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113443 ID - info:doi/10.2196/37300 ER - TY - JOUR AU - Hsu, Tze-Hou Jerome AU - Tsai, Tzong-Han Richard PY - 2022/8/9 TI - Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis JO - J Med Internet Res SP - e38776 VL - 24 IS - 8 KW - natural language processing KW - lockdown KW - online aggression KW - infoveillance KW - causal relationship KW - social media KW - neural networks KW - computer KW - pandemic KW - COVID-19 KW - emotions KW - internet KW - sentiment analysis KW - Twitter KW - content analysis KW - infodemiology N2 - Background: The COVID-19 pandemic caused a critical public health crisis worldwide, and policymakers are using lockdowns to control the virus. However, there has been a noticeable increase in aggressive social behaviors that threaten social stability. Lockdown measures might negatively affect mental health and lead to an increase in aggressive emotions. Discovering the relationship between lockdown and increased aggression is crucial for formulating appropriate policies that address these adverse societal effects. We applied natural language processing (NLP) technology to internet data, so as to investigate the social and emotional impacts of lockdowns. Objective: This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States. Methods: We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship. Results: In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations. Conclusions: Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort. UR - https://www.jmir.org/2022/8/e38776 UR - http://dx.doi.org/10.2196/38776 UR - http://www.ncbi.nlm.nih.gov/pubmed/35943771 ID - info:doi/10.2196/38776 ER - TY - JOUR AU - Cui, Bin AU - Wang, Jian AU - Lin, Hongfei AU - Zhang, Yijia AU - Yang, Liang AU - Xu, Bo PY - 2022/8/9 TI - Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation JO - JMIR Med Inform SP - e37818 VL - 10 IS - 8 KW - depression detection KW - emotional semantic features KW - social media KW - sentence-level attention KW - emotion-based reinforcement N2 - Background: Depression detection has recently received attention in the field of natural language processing. The task aims to detect users with depression based on their historical posts on social media. However, existing studies in this area use the entire historical posts of the users and select depression indicator posts. Moreover, these methods fail to effectively extract deep emotional semantic features or simply concatenate emotional representation. To solve this problem, we propose a model to extract deep emotional semantic features and select depression indicator posts based on the emotional states. Objective: This study aims to develop an emotion-based reinforcement attention network for depression detection of users on social media. Methods: The proposed model is composed of 2 components: the emotion extraction network, which is used to capture deep emotional semantic information, and the reinforcement learning (RL) attention network, which is used to select depression indicator posts based on the emotional states. Finally, we concatenated the output of these 2 parts and send them to the classification layer for depression detection. Results: Experimental results of our model on the multimodal depression data set outperform the state-of-the-art baselines. Specifically, the proposed model achieved accuracy, precision, recall, and F1-score of 90.6%, 91.2%, 89.7%, and 90.4%, respectively. Conclusions: The proposed model utilizes historical posts of users to effectively identify users? depression tendencies. The experimental results show that the emotion extraction network and the RL selection layer based on emotional states can effectively improve the accuracy of detection. In addition, sentence-level attention layer can capture core posts. UR - https://medinform.jmir.org/2022/8/e37818 UR - http://dx.doi.org/10.2196/37818 UR - http://www.ncbi.nlm.nih.gov/pubmed/35943770 ID - info:doi/10.2196/37818 ER - TY - JOUR AU - Royan, Regina AU - Pendergrast, Rae Tricia AU - Del Rios, Marina AU - Rotolo, M. Shannon AU - Trueger, Seth N. AU - Bloomgarden, Eve AU - Behrens, Deanna AU - Jain, Shikha AU - Arora, M. Vineet PY - 2022/7/22 TI - Use of Twitter Amplifiers by Medical Professionals to Combat Misinformation During the COVID-19 Pandemic JO - J Med Internet Res SP - e38324 VL - 24 IS - 7 KW - social media KW - combating disinformation KW - misinformation KW - infodemic KW - amplifier KW - COVID-19 KW - advocacy KW - public health communication KW - disinformation KW - medical information KW - health professional amplifier KW - healthcare profession KW - health care profession KW - Twitter KW - public communication KW - health information KW - health promotion UR - https://www.jmir.org/2022/7/e38324 UR - http://dx.doi.org/10.2196/38324 UR - http://www.ncbi.nlm.nih.gov/pubmed/35839387 ID - info:doi/10.2196/38324 ER - TY - JOUR AU - Gillis, Timber AU - Garrison, Scott PY - 2022/7/19 TI - Confounding Effect of Undergraduate Semester?Driven ?Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration JO - JMIR Infodemiology SP - e34464 VL - 2 IS - 2 KW - Google Trends KW - seasonality KW - Fast Fourier transform KW - FFT KW - pathogenic bacteria KW - depression KW - Google search KW - Google KW - health information KW - health information seeking KW - internet search N2 - Background: Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year?driven internet search patterns of health care students. Objective: This study aimed to (1) demonstrate that artificial ?academic cycling? of Google Trends? search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples. Methods: We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook. Results: Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term [?Staphylococcus?], for which academic cycling explained 73.8% of the variability in search volume (using the squared Spearman rank correlation coefficient, P<.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) [?Aeromonas? + ?Plesiomonas?] (nosocomial infections that were searched for more frequently during the summer), (2) [?Ehrlichia?] (a tick-borne pathogen that was searched for more frequently during late spring), (3) [?Moraxella?] and [?Haemophilus?] (respiratory infections that were searched for more frequently during late winter), (4) [?Legionella?] (searched for more frequently during midsummer), and (5) [?Vibrio?] (which spiked for 2 months during midsummer). The terms [?myocardial infarction?] and [?hypertension?] lacked any obvious seasonal cycling after filtering, whereas [?depression?] maintained an annual cycling pattern. Conclusions: Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends? internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to filter out academic cycling is a potential means to establish whether additional seasonality is present. UR - https://infodemiology.jmir.org/2022/2/e34464 UR - http://dx.doi.org/10.2196/34464 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113451 ID - info:doi/10.2196/34464 ER - TY - JOUR AU - Al-Rawi, Ahmed AU - Zemenchik, Kiana PY - 2022/7/14 TI - Sex Workers? Lived Experiences With COVID-19 on Social Media: Content Analysis of Twitter Posts JO - JMIR Form Res SP - e36268 VL - 6 IS - 7 KW - sex work KW - social media KW - COVID-19 KW - pandemic KW - Twitter KW - infodemiology KW - social stigma KW - sex worker KW - risk KW - public health N2 - Background: The COVID-19 pandemic has drawn attention to various inequalities in global societies, highlighting discrepancies in terms of safety, accessibility, and overall health. In particular, sex workers are disproportionately at risk due to the nature of their work and the social stigma that comes alongside it. Objective: This study examines how public social media can be used as a tool of professional and personal expression by sex workers during the COVID-19 pandemic. We aimed to explore an underresearched topic by focusing on sex workers? experiences with the ongoing COVID-19 pandemic on the social media platform Twitter. In particular, we aimed to find the main issues that sex workers discuss on social media in relation to the COVID-19 pandemic. Methods: A literature review followed by a qualitative analysis of 1458 (re)tweets from 22 sex worker Twitter accounts was used for this study. The tweets were qualitatively coded by theme through the use of intercoder reliability. Empirical, experimental, and observational studies were included in this review to provide context and support for our findings. Results: In total, 5 major categories were identified as a result of the content analysis used for this study: concerns (n=542, 37.2%), solicitation (n=336, 23.0%), herd mentality (n=231, 15.8%), humor (n=190, 13.0%), and blame (n=146, 10.0%). The concerns category was the most prominent category, which could be due to its multifaceted nature of including individual concerns, health issues, concerns for essential workers and businesses, as well as concerns about inequalities or intersectionality. When using gender as a control factor, the majority of the results were not noteworthy, save for the blame category, in which sexual and gender minorities (SGMs) were more likely to post content. Conclusions: Though there has been an increase in the literature related to the experiences of sex workers, this paper recommends that future studies could benefit from further examining these 5 major categories through mixed methods research. Examining this phenomenon could recognize the challenges unique to this working community during the COVID-19 pandemic and potentially reduce the widespread stigma associated with sex work in general. UR - https://formative.jmir.org/2022/7/e36268 UR - http://dx.doi.org/10.2196/36268 UR - http://www.ncbi.nlm.nih.gov/pubmed/35767693 ID - info:doi/10.2196/36268 ER - TY - JOUR AU - Leung, Tong Yue AU - Khalvati, Farzad PY - 2022/7/13 TI - Exploring COVID-19?Related Stressors: Topic Modeling Study JO - J Med Internet Res SP - e37142 VL - 24 IS - 7 KW - COVID-19 KW - natural language processing KW - public health informatics KW - topic modeling N2 - Background: The COVID-19 pandemic has affected the lives of people globally for over 2 years. Changes in lifestyles due to the pandemic may cause psychosocial stressors for individuals and could lead to mental health problems. To provide high-quality mental health support, health care organizations need to identify COVID-19?specific stressors and monitor the trends in the prevalence of those stressors. Objective: This study aims to apply natural language processing (NLP) techniques to social media data to identify the psychosocial stressors during the COVID-19 pandemic and to analyze the trend in the prevalence of these stressors at different stages of the pandemic. Methods: We obtained a data set of 9266 Reddit posts from the subreddit \rCOVID19_support, from February 14, 2020, to July 19, 2021. We used the latent Dirichlet allocation (LDA) topic model to identify the topics that were mentioned on the subreddit and analyzed the trends in the prevalence of the topics. Lexicons were created for each of the topics and were used to identify the topics of each post. The prevalences of topics identified by the LDA and lexicon approaches were compared. Results: The LDA model identified 6 topics from the data set: (1) ?fear of coronavirus,? (2) ?problems related to social relationships,? (3) ?mental health symptoms,? (4) ?family problems,? (5) ?educational and occupational problems,? and (6) ?uncertainty on the development of pandemic.? According to the results, there was a significant decline in the number of posts about the ?fear of coronavirus? after vaccine distribution started. This suggests that the distribution of vaccines may have reduced the perceived risks of coronavirus. The prevalence of discussions on the uncertainty about the pandemic did not decline with the increase in the vaccinated population. In April 2021, when the Delta variant became prevalent in the United States, there was a significant increase in the number of posts about the uncertainty of pandemic development but no obvious effects on the topic of fear of the coronavirus. Conclusions: We created a dashboard to visualize the trend in the prevalence of topics about COVID-19?related stressors being discussed on a social media platform (Reddit). Our results provide insights into the prevalence of pandemic-related stressors during different stages of the COVID-19 pandemic. The NLP techniques leveraged in this study could also be applied to analyze event-specific stressors in the future. UR - https://www.jmir.org/2022/7/e37142 UR - http://dx.doi.org/10.2196/37142 UR - http://www.ncbi.nlm.nih.gov/pubmed/35731966 ID - info:doi/10.2196/37142 ER - TY - JOUR AU - Lohiniva, Anna-Leena AU - Nurzhynska, Anastasiya AU - Hudi, Al-hassan AU - Anim, Bridget AU - Aboagye, Costa Da PY - 2022/7/12 TI - Infodemic Management Using Digital Information and Knowledge Cocreation to Address COVID-19 Vaccine Hesitancy: Case Study From Ghana JO - JMIR Infodemiology SP - e37134 VL - 2 IS - 2 KW - COVID-19 KW - infodemic management KW - misinformation KW - disinformation KW - social listening KW - pandemic preparedness KW - infodemiology KW - social media KW - Ghana KW - vaccination KW - qualitative methods N2 - Background: Infodemic management is an integral part of pandemic management. Ghana Health Services (GHS) together with the UNICEF (United Nations International Children's Emergency Fund) Country Office have developed a systematic process that effectively identifies, analyzes, and responds to COVID-19 and vaccine-related misinformation in Ghana. Objective: This paper describes an infodemic management system workflow based on digital data collection, qualitative methodology, and human-centered systems to support the COVID-19 vaccine rollout in Ghana with examples of system implementation. Methods: The infodemic management system was developed by the Health Promotion Division of the GHS and the UNICEF Country Office. It uses Talkwalker, a social listening software platform, to collect misinformation on the web. The methodology relies on qualitative data analysis and interpretation as well as knowledge cocreation to verify the findings. Results: A multi-sectoral National Misinformation Task Force was established to implement and oversee the misinformation management system. Two members of the task force were responsible for carrying out the analysis. They used Talkwalker to find posts that include the keywords related to COVID-19 vaccine?related discussions. They then assessed the significance of the posts on the basis of the engagement rate and potential reach of the posts, negative sentiments, and contextual factors. The process continues by identifying misinformation within the posts, rating the risk of identified misinformation posts, and developing proposed responses to address them. The results of the analysis are shared weekly with the Misinformation Task Force for their review and verification to ensure that the risk assessment and responses are feasible, practical, and acceptable in the context of Ghana. Conclusions: The paper describes an infodemic management system workflow in Ghana based on qualitative data synthesis that can be used to manage real-time infodemic responses. UR - https://infodemiology.jmir.org/2022/2/e37134 UR - http://dx.doi.org/10.2196/37134 UR - http://www.ncbi.nlm.nih.gov/pubmed/35854815 ID - info:doi/10.2196/37134 ER - TY - JOUR AU - Gao, Yankun AU - Xie, Zidian AU - Li, Dongmei PY - 2022/7/8 TI - Investigating the Impact of the New York State Flavor Ban on e-Cigarette?Related Discussions on Twitter: Observational Study JO - JMIR Public Health Surveill SP - e34114 VL - 8 IS - 7 KW - New York State flavor ban KW - e-cigarettes KW - twitter KW - topic modeling KW - sentiment analysis N2 - Background: On May 18, 2020, the New York State Department of Health implemented a statewide flavor ban to prohibit the sales of all flavored vapor products, except for tobacco or any other authorized flavor. Objective: This study aims to investigate the discussion changes in e-cigarette?related tweets over time with the implementation of the New York State flavor ban. Methods: Through the Twitter streaming application programming interface, 59,883 e-cigarette?related tweets were collected within the New York State from February 6, 2020, to May 17, 2020 (period 1, before the implementation of the flavor ban), May 18, 2020-June 30, 2020 (period 2, between the implementation of the flavor ban and the online sales ban), July 1, 2020-September 15, 2020 (period 3, the short term after the online sales ban), and September 16, 2020-November 30, 2020 (period 4, the long term after the online sales ban). Sentiment analysis and topic modeling were conducted to investigate the changes in public attitudes and discussions in e-cigarette?related tweets. The popularity of different e-cigarette flavor categories was compared before and after the implementation of the New York State flavor ban. Results: Our results showed that the proportion of e-cigarette?related tweets with negative sentiment significantly decreased (4305/13,246, 32.5% vs 3855/14,455, 26.67%, P<.001), and tweets with positive sentiment significantly increased (5246/13,246, 39.6% vs 7038/14,455, 48.69%, P<.001) in period 4 compared to period 3. ?Teens and nicotine products? was the most frequently discussed e-cigarette?related topic in the negative tweets. In contrast, ?nicotine products and quitting? was more prevalent in positive tweets. The proportion of tweets mentioning mint and menthol flavors significantly increased right after the flavor ban and decreased to lower levels over time. The proportions of fruit and sweet flavors were most frequently mentioned in period 1, decreased in period 2, and dominated again in period 4. Conclusions: The proportion of e-cigarette?related tweets with different attitudes and frequently discussed flavor categories changed over time after the implementation of the New York State ban of flavored vaping products. This change indicated a potential impact of the flavor ban on public discussions of flavored e-cigarettes. UR - https://publichealth.jmir.org/2022/7/e34114 UR - http://dx.doi.org/10.2196/34114 UR - http://www.ncbi.nlm.nih.gov/pubmed/35802417 ID - info:doi/10.2196/34114 ER - TY - JOUR AU - Ngai, Bik Cindy Sing AU - Singh, Gill Rita AU - Yao, Le PY - 2022/7/6 TI - Impact of COVID-19 Vaccine Misinformation on Social Media Virality: Content Analysis of Message Themes and Writing Strategies JO - J Med Internet Res SP - e37806 VL - 24 IS - 7 KW - antivaccine misinformation KW - content themes KW - writing strategies KW - COVID-19 KW - virality KW - social media KW - content analysis N2 - Background: Vaccines serve an integral role in containing pandemics, yet vaccine hesitancy is prevalent globally. One key reason for this hesitancy is the pervasiveness of misinformation on social media. Although considerable research attention has been drawn to how exposure to misinformation is closely associated with vaccine hesitancy, little scholarly attention has been given to the investigation or robust theorizing of the various content themes pertaining to antivaccine misinformation about COVID-19 and the writing strategies in which these content themes are manifested. Virality of such content on social media exhibited in the form of comments, shares, and reactions has practical implications for COVID-19 vaccine hesitancy. Objective: We investigated whether there were differences in the content themes and writing strategies used to disseminate antivaccine misinformation about COVID-19 and their impact on virality on social media. Methods: We constructed an antivaccine misinformation database from major social media platforms during September 2019-August 2021 to examine how misinformation exhibited in the form of content themes and how these themes manifested in writing were associated with virality in terms of likes, comments, and shares. Antivaccine misinformation was retrieved from two globally leading and widely cited fake news databases, COVID Global Misinformation Dashboard and International Fact-Checking Network Corona Virus Facts Alliance Database, which aim to track and debunk COVID-19 misinformation. We primarily focused on 140 Facebook posts, since most antivaccine misinformation posts on COVID-19 were found on Facebook. We then employed quantitative content analysis to examine the content themes (ie, safety concerns, conspiracy theories, efficacy concerns) and manifestation strategies of misinformation (ie, mimicking of news and scientific reports in terms of the format and language features, use of a conversational style, use of amplification) in these posts and their association with virality of misinformation in the form of likes, comments, and shares. Results: Our study revealed that safety concern was the most prominent content theme and a negative predictor of likes and shares. Regarding the writing strategies manifested in content themes, a conversational style and mimicking of news and scientific reports via the format and language features were frequently employed in COVID-19 antivaccine misinformation, with the latter being a positive predictor of likes. Conclusions: This study contributes to a richer research-informed understanding of which concerns about content theme and manifestation strategy need to be countered on antivaccine misinformation circulating on social media so that accurate information on COVID-19 vaccines can be disseminated to the public, ultimately reducing vaccine hesitancy. The liking of COVID-19 antivaccine posts that employ language features to mimic news or scientific reports is perturbing since a large audience can be reached on social media, potentially exacerbating the spread of misinformation and hampering global efforts to combat the virus. UR - https://www.jmir.org/2022/7/e37806 UR - http://dx.doi.org/10.2196/37806 UR - http://www.ncbi.nlm.nih.gov/pubmed/35731969 ID - info:doi/10.2196/37806 ER - TY - JOUR AU - Deiner, S. Michael AU - Kaur, Gurbani AU - McLeod, D. Stephen AU - Schallhorn, M. Julie AU - Chodosh, James AU - Hwang, H. Daniel AU - Lietman, M. Thomas AU - Porco, C. Travis PY - 2022/7/5 TI - A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study JO - J Med Internet Res SP - e27310 VL - 24 IS - 7 KW - diurnal eye conditions KW - hebdomadal KW - online search KW - web-based search KW - eye conditions KW - infodemiology KW - dry eye KW - conjunctivitis KW - pink eye KW - information seeking KW - vision N2 - Background: Studies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients? eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations. Objective: To investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other. Methods: We designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google?s search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant. Results: Distinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, ?pink eye? showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, ?dry eyes? had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning. Conclusions: The frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions. UR - https://www.jmir.org/2022/7/e27310 UR - http://dx.doi.org/10.2196/27310 UR - http://www.ncbi.nlm.nih.gov/pubmed/35537041 ID - info:doi/10.2196/27310 ER - TY - JOUR AU - Sigalo, Nekabari AU - St Jean, Beth AU - Frias-Martinez, Vanessa PY - 2022/7/5 TI - Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets JO - JMIR Public Health Surveill SP - e34285 VL - 8 IS - 7 KW - social media KW - Twitter KW - food deserts KW - food insecurity N2 - Background: The issue of food insecurity is becoming increasingly important to public health practitioners because of the adverse health outcomes and underlying racial disparities associated with insufficient access to healthy foods. Prior research has used data sources such as surveys, geographic information systems, and food store assessments to identify regions classified as food deserts but perhaps the individuals in these regions unknowingly provide their own accounts of food consumption and food insecurity through social media. Social media data have proved useful in answering questions related to public health; therefore, these data are a rich source for identifying food deserts in the United States. Objective: The aim of this study was to develop, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States using the linguistic constructs found in food-related tweets. Methods: Twitter?s streaming application programming interface was used to collect a random 1% sample of public geolocated tweets across 25 major cities from March 2020 to December 2020. A total of 60,174 geolocated food-related tweets were collected across the 25 cities. Each geolocated tweet was mapped to its respective census tract using point-to-polygon mapping, which allowed us to develop census tract?level features derived from the linguistic constructs found in food-related tweets, such as tweet sentiment and average nutritional value of foods mentioned in the tweets. These features were then used to examine the associations between food desert status and the food ingestion language and sentiment of tweets in a census tract and to determine whether food-related tweets can be used to infer census tract?level food desert status. Results: We found associations between a census tract being classified as a food desert and an increase in the number of tweets in a census tract that mentioned unhealthy foods (P=.03), including foods high in cholesterol (P=.02) or low in key nutrients such as potassium (P=.01). We also found an association between a census tract being classified as a food desert and an increase in the proportion of tweets that mentioned healthy foods (P=.03) and fast-food restaurants (P=.01) with positive sentiment. In addition, we found that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance compared with baseline models that only include socioeconomic characteristics. Conclusions: Social media data have been increasingly used to answer questions related to health and well-being. Using Twitter data, we found that food-related tweets can be used to develop models for predicting census tract food desert status with high accuracy and improve over baseline models. Food ingestion language found in tweets, such as census tract?level measures of food sentiment and healthiness, are associated with census tract?level food desert status. UR - https://publichealth.jmir.org/2022/7/e34285 UR - http://dx.doi.org/10.2196/34285 UR - http://www.ncbi.nlm.nih.gov/pubmed/35788108 ID - info:doi/10.2196/34285 ER - TY - JOUR AU - Yashpal, Shahen AU - Raghunath, Ananditha AU - Gencerliler, Nihan AU - Burns, E. Lorel PY - 2022/7/1 TI - Exploring Public Perceptions of Dental Care Affordability in the United States: Mixed Method Analysis via Twitter JO - JMIR Form Res SP - e36315 VL - 6 IS - 7 KW - dentistry KW - oral health KW - social media KW - access to care KW - healthcare reform KW - COVID-19 KW - dental care KW - health care service KW - twitter KW - public health KW - health communication KW - dental treatment KW - health policy KW - dental professional KW - thematic analysis N2 - Background: Dental care expenses are reported to present higher financial barriers than any other type of health care service in the United States. Social media platforms such as Twitter have become a source of public health communication and surveillance. Previous studies have demonstrated the usefulness of Twitter in exploring public opinion on aspects of dental care. To date, no studies have leveraged Twitter to examine public sentiments regarding dental care affordability in the United States. Objective: The aim of this study is to understand public perceptions of dental care affordability in the United States on the social media site, Twitter. Methods: Tweets posted between September 1, 2017, and September 30, 2021, were collected using the Snscrape application. Query terms were selected a priori to represent dentistry and financial aspects associated with dental treatment. Data were analyzed qualitatively using both deductive and inductive approaches. In total, 8% (440/5500) of all included tweets were coded to identify prominent themes and subthemes. The entire sample of included tweets were then independently coded into thematic categories. Quantitative data analyses included geographic distribution of tweets by state, volume analysis of tweets over time, and distribution of tweets by content theme. Results: A final sample of 5314 tweets were included in the study. Thematic analysis identified the following prominent themes: (1) general sentiments (1614 tweets, 30.4%); (2) delaying or forgoing dental care (1190 tweets, 22.4%); (3) payment strategies (1019 tweets, 19.2%); (4) insurance (767 tweets, 14.4%); and (5) policy statements (724 tweets, 13.6%). Geographic distributions of the tweets established California, Texas, Florida, and New York as the states with the most tweets. Qualitative analysis revealed barriers faced by individuals to accessing dental care, strategies taken to cope with dental pain, and public perceptions on aspects of dental care policy. The volume and thematic trends of the tweets corresponded to relevant societal events, including the COVID-19 pandemic and debates on health care policy resulting from the election of President Joseph R. Biden. Conclusions: The findings illustrate the real-time sentiment of social media users toward the cost of dental treatment and suggest shortcomings in funding that may be representative of greater systemic failures in the provision of dental care. Thus, this study provides insights for policy makers and dental professionals who strive to increase access to dental care. UR - https://formative.jmir.org/2022/7/e36315 UR - http://dx.doi.org/10.2196/36315 UR - http://www.ncbi.nlm.nih.gov/pubmed/35658090 ID - info:doi/10.2196/36315 ER - TY - JOUR AU - Hagen, Loni AU - Fox, Ashley AU - O'Leary, Heather AU - Dyson, DeAndre AU - Walker, Kimberly AU - Lengacher, A. Cecile AU - Hernandez, Raquel PY - 2022/6/30 TI - The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding JO - JMIR Infodemiology SP - e34231 VL - 2 IS - 1 KW - COVID-19, vaccine hesitancy, social media, influential actors KW - influencer KW - Twitter N2 - Background: Since COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers have indicated a polarized social media presence contributing to the spread of mis- or disinformation as being responsible for these growing partisan gaps in uptake. Objective: The major aim of this study was to investigate the role of influential actors in the context of the community structures and discourse related to COVID-19 vaccine conversations on Twitter that emerged prior to the vaccine rollout to the general population and discuss implications for vaccine promotion and policy. Methods: We collected tweets on COVID-19 between July 1, 2020, and July 31, 2020, a time when attitudes toward the vaccines were forming but before the vaccines were widely available to the public. Using network analysis, we identified different naturally emerging Twitter communities based on their internal information sharing. A PageRank algorithm was used to quantitively measure the level of ?influentialness? of Twitter accounts and identifying the ?influencers,? followed by coding them into different actor categories. Inductive coding was conducted to describe discourses shared in each of the 7 communities. Results: Twitter vaccine conversations were highly polarized, with different actors occupying separate ?clusters.? The antivaccine cluster was the most densely connected group. Among the 100 most influential actors, medical experts were outnumbered both by partisan actors and by activist vaccine skeptics or conspiracy theorists. Scientists and medical actors were largely absent from the conservative network, and antivaccine sentiment was especially salient among actors on the political right. Conversations related to COVID-19 vaccines were highly polarized along partisan lines, with ?trust? in vaccines being manipulated to the political advantage of partisan actors. Conclusions: These findings are informative for designing improved vaccine information communication strategies to be delivered on social media especially by incorporating influential actors. Although polarization and echo chamber effect are not new in political conversations in social media, it was concerning to observe these in health conversations on COVID-19 vaccines during the vaccine development process. UR - https://infodemiology.jmir.org/2022/1/e34231 UR - http://dx.doi.org/10.2196/34231 UR - http://www.ncbi.nlm.nih.gov/pubmed/35814809 ID - info:doi/10.2196/34231 ER - TY - JOUR AU - Klein, Z. Ari AU - O'Connor, Karen AU - Levine, D. Lisa AU - Gonzalez-Hernandez, Graciela PY - 2022/6/30 TI - Using Twitter Data for Cohort Studies of Drug Safety in Pregnancy: Proof-of-concept With ?-Blockers JO - JMIR Form Res SP - e36771 VL - 6 IS - 6 KW - natural language processing KW - social media KW - data mining KW - pregnancy KW - pharmacoepidemiology N2 - Background: Despite the fact that medication is taken during more than 90% of pregnancies, the fetal risk for most medications is unknown, and the majority of medications have no data regarding safety in pregnancy. Objective: Using ?-blockers as a proof-of-concept, the primary objective of this study was to assess the utility of Twitter data for a cohort study design?in particular, whether we could identify (1) Twitter users who have posted tweets reporting that they took medication during pregnancy and (2) their associated pregnancy outcomes. Methods: We searched for mentions of ?-blockers in 2.75 billion tweets posted by 415,690 users who announced their pregnancy on Twitter. We manually reviewed the matching tweets to first determine if the user actually took the ?-blocker mentioned in the tweet. Then, to help determine if the ?-blocker was taken during pregnancy, we used the time stamp of the tweet reporting intake and drew upon an automated natural language processing (NLP) tool that estimates the date of the user?s prenatal time period. For users who posted tweets indicating that they took or may have taken the ?-blocker during pregnancy, we drew upon additional NLP tools to help identify tweets that report their pregnancy outcomes. Adverse pregnancy outcomes included miscarriage, stillbirth, birth defects, preterm birth (<37 weeks gestation), low birth weight (<5 pounds and 8 ounces at delivery), and neonatal intensive care unit (NICU) admission. Normal pregnancy outcomes included gestational age ?37 weeks and birth weight ?5 pounds and 8 ounces. Results: We retrieved 5114 tweets, posted by 2339 users, that mention a ?-blocker, and manually identified 2332 (45.6%) tweets, posted by 1195 (51.1%) of the users, that self-report taking the ?-blocker. We were able to estimate the date of the prenatal time period for 356 pregnancies among 334 (27.9%) of these 1195 users. Among these 356 pregnancies, we identified 257 (72.2%) during which the ?-blocker was or may have been taken. We manually verified an adverse pregnancy outcome?preterm birth, NICU admission, low birth weight, birth defects, or miscarriage?for 38 (14.8%) of these 257 pregnancies. We manually verified a gestational age ?37 weeks for 198 (90.4%) and a birth weight ?5 pounds and 8 ounces for 50 (22.8%) of the 219 pregnancies for which we did not identify an adverse pregnancy outcome. Conclusions: Our ability to detect pregnancy outcomes for Twitter users who posted tweets reporting that they took or may have taken a ?-blocker during pregnancy suggests that Twitter can be a complementary resource for cohort studies of drug safety in pregnancy. UR - https://formative.jmir.org/2022/6/e36771 UR - http://dx.doi.org/10.2196/36771 UR - http://www.ncbi.nlm.nih.gov/pubmed/35771614 ID - info:doi/10.2196/36771 ER - TY - JOUR AU - Albalawi, Yahya AU - Nikolov, S. Nikola AU - Buckley, Jim PY - 2022/6/29 TI - Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study JO - JMIR Form Res SP - e34834 VL - 6 IS - 6 KW - social media KW - machine learning KW - pretrained language models KW - bidirectional encoder representations from transformers KW - BERT KW - deep learning KW - health information KW - infodemiology KW - tweets KW - language model KW - health informatics KW - misinformation N2 - Background: In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic. Objective: The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models. Methods: We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9% (623/779 tweets) of the data set and tested on 20% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F1 score, and macroaverage of the F1 score. Results: We constructed a data set of labeled tweets, 38% (296/779) of which were labeled as inaccurate health information, and 62% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F1 score of 87%, followed by AraBERT version 2?large and AraBERTv0.2-base. Conclusions: Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results. UR - https://formative.jmir.org/2022/6/e34834 UR - http://dx.doi.org/10.2196/34834 UR - http://www.ncbi.nlm.nih.gov/pubmed/35767322 ID - info:doi/10.2196/34834 ER - TY - JOUR AU - Saini, Vipin AU - Liang, Li-Lin AU - Yang, Yu-Chen AU - Le, Mai Huong AU - Wu, Chun-Ying PY - 2022/6/27 TI - The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model JO - JMIR Infodemiology SP - e37077 VL - 2 IS - 1 KW - COVID-19 KW - Twitter KW - provaccine KW - antivaccine KW - elaboration likelihood model KW - infodemiology KW - dissemination KW - content analysis KW - emotional valence KW - social media N2 - Background: Messages on one?s stance toward vaccination on microblogging sites may affect the reader?s decision on whether to receive a vaccine. Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial; however, studies on this topic have remained limited. Objective: This study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message. Methods: English-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user. Results: Content-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively; emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03; IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001). Conclusions: The dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics. UR - https://infodemiology.jmir.org/2022/1/e37077 UR - http://dx.doi.org/10.2196/37077 UR - http://www.ncbi.nlm.nih.gov/pubmed/35783451 ID - info:doi/10.2196/37077 ER - TY - JOUR AU - Tripathi, D. Sanidhya AU - Parker, D. Pearman AU - Prabhu, V. Arpan AU - Thomas, Kevin AU - Rodriguez, Analiz PY - 2022/6/22 TI - An Examination of Patients and Caregivers on Reddit Navigating Brain Cancer: Content Analysis of the Brain Tumor Subreddit JO - JMIR Cancer SP - e35324 VL - 8 IS - 2 KW - brain tumor KW - internet KW - social media KW - Reddit KW - cancer KW - emotional support KW - self-management N2 - Background: Occurring in up to 40% of all patients with cancer, the incidence of brain tumors has caused limited survival, a high psychosocial burden, and an increase in the loss of decision-making capability for the unique population. Although specific symptoms depend on the type of brain tumor, a clinical team of physicians, nurses, and other individuals commonly assist patients and their caregivers with how to tackle the upcoming challenges of their diagnosis. Despite the support from clinical team members, many patients and caregivers may still seek outside support through social media to process their emotions and seek comfort outside of the clinical setting. Specifically, online resources such as Reddit are used where users are provided with the anonymity they need to show their true behavior without fear of judgment. In this study, we aimed to examine trends from Reddit discussion threads on brain tumors to identify areas of need in patient care. Objective: Our primary aims were to determine the type of Reddit user posting, classify the specific brain tumors that were the subject of the posts, and examine the content of the original posts. Methods: We used a qualitative descriptive design to understand patients? and caregivers? unmet and met needs. We selected posts from the top-rated 100 posts from the r/braincancer subreddit from February 2017 to June 2020 to identify common themes using content analysis. Results: The qualitative content analysis revealed how Reddit users primarily used the forum as a method to understand and process the emotions surrounding a brain tumor diagnosis. Three major topic areas from content analysis emerged as prominent themes, including (1) harnessing hope, (2) moving through the grief process, and (3) expressing gratitude toward other Reddit users. Most of the authors of the posts were patients with brain tumors (32/88, 36%) who used Reddit as a reflective journaling tool to process the associated emotions of a challenging diagnosis. Conclusions: This study shows the potential of Reddit to serve as a unique group therapy platform for patients affected by brain tumors. Our results highlight the support provided by the Reddit community members as a unique mechanism to assist cancer survivors and caregivers with the emotional processing of living with brain tumors. Additionally, the results highlight the importance of recommending Reddit as a therapeutic virtual community and the need for implementing online resources as a part of a health care professional?s repertoire to understand the level of support they can give their patients. UR - https://cancer.jmir.org/2022/2/e35324 UR - http://dx.doi.org/10.2196/35324 UR - http://www.ncbi.nlm.nih.gov/pubmed/35731559 ID - info:doi/10.2196/35324 ER - TY - JOUR AU - Xue, Haoning AU - Gong, Xuanjun AU - Stevens, Hannah PY - 2022/6/21 TI - COVID-19 Vaccine Fact-Checking Posts on Facebook: Observational Study JO - J Med Internet Res SP - e38423 VL - 24 IS - 6 KW - COVID-19 vaccine KW - fact checking KW - misinformation correction KW - sentiment analysis KW - social media KW - COVID-19 KW - vaccination KW - misinformation KW - health information KW - online information KW - infodemic KW - public sentiment N2 - Background: Effective interventions aimed at correcting COVID-19 vaccine misinformation, known as fact-checking messages, are needed to combat the mounting antivaccine infodemic and alleviate vaccine hesitancy. Objective: This work investigates (1) the changes in the public's attitude toward COVID-19 vaccines over time, (2) the effectiveness of COVID-19 vaccine fact-checking information on social media engagement and attitude change, and (3) the emotional and linguistic features of the COVID-19 vaccine fact-checking information ecosystem. Methods: We collected a data set of 12,553 COVID-19 vaccine fact-checking Facebook posts and their associated comments (N=122,362) from January 2020 to March 2022 and conducted a series of natural language processing and statistical analyses to investigate trends in public attitude toward the vaccine in COVID-19 vaccine fact-checking posts and comments, and emotional and linguistic features of the COVID-19 fact-checking information ecosystem. Results: The percentage of fact-checking posts relative to all COVID-19 vaccine posts peaked in May 2020 and then steadily decreased as the pandemic progressed (r=?0.92, df=21, t=?10.94, 95% CI ?0.97 to ?0.82, P<.001). The salience of COVID-19 vaccine entities was significantly lower in comments (mean 0.03, SD 0.03, t=39.28, P<.001) than in posts (mean 0.09, SD 0.11). Third-party fact checkers have been playing a more important role in more fact-checking over time (r=0.63, df=25, t=4.06, 95% CI 0.33-0.82, P<.001). COVID-19 vaccine fact-checking posts continued to be more analytical (r=0.81, df=25, t=6.88, 95% CI 0.62-0.91, P<.001) and more confident (r=0.59, df=25, t=3.68, 95% CI 0.27-0.79, P=.001) over time. Although comments did not exhibit a significant increase in confidence over time, tentativeness in comments significantly decreased (r=?0.62, df=25, t=?3.94, 95% CI ?0.81 to ?0.31, P=.001). In addition, although hospitals receive less engagement than other information sources, the comments expressed more positive attitudinal valence in comments compared to other information sources (b=0.06, 95% CI 0.00-0.12, t=2.03, P=.04). Conclusions: The percentage of fact-checking posts relative to all posts about the vaccine steadily decreased after May 2020. As the pandemic progressed, third-party fact checkers played a larger role in posting fact-checking COVID-19 vaccine posts. COVID-19 vaccine fact-checking posts continued to be more analytical and more confident over time, reflecting increased confidence in posts. Similarly, tentativeness in comments decreased; this likewise suggests that public uncertainty diminished over time. COVID-19 fact-checking vaccine posts from hospitals yielded more positive attitudes toward vaccination than other information sources. At the same time, hospitals received less engagement than other information sources. This suggests that hospitals should invest more in generating engaging public health campaigns on social media. UR - https://www.jmir.org/2022/6/e38423 UR - http://dx.doi.org/10.2196/38423 UR - http://www.ncbi.nlm.nih.gov/pubmed/35671409 ID - info:doi/10.2196/38423 ER - TY - JOUR AU - Ritschl, Valentin AU - Eibensteiner, Fabian AU - Mosor, Erika AU - Omara, Maisa AU - Sperl, Lisa AU - Nawaz, A. Faisal AU - Siva Sai, Chandragiri AU - Cenanovic, Merisa AU - Devkota, Prasad Hari AU - Hribersek, Mojca AU - De, Ronita AU - Klager, Elisabeth AU - Schaden, Eva AU - Kletecka-Pulker, Maria AU - Völkl-Kernstock, Sabine AU - Willschke, Harald AU - Aufricht, Christoph AU - Atanasov, G. Atanas AU - Stamm, Tanja PY - 2022/6/21 TI - Mandatory Vaccination Against COVID-19: Twitter Poll Analysis on Public Health Opinion JO - JMIR Form Res SP - e35754 VL - 6 IS - 6 KW - COVID-19 KW - SARS-CoV-2 KW - vaccine KW - vaccination KW - Twitter KW - survey KW - mandatory vaccination KW - vaccination hesitancy KW - coronavirus KW - hesitancy KW - social media KW - questionnaire KW - mandatory KW - support KW - poll KW - opinion KW - public health KW - perception N2 - Background: On January 30, 2020, the World Health Organization Emergency Committee declared the rapid worldwide spread of COVID-19 a global health emergency. By December 2020, the safety and efficacy of the first COVID-19 vaccines had been demonstrated. However, international vaccination coverage rates have remained below expectations (in Europe at the time of manuscript submission). Controversial mandatory vaccination is currently being discussed and has already been introduced in some countries (Austria, Greece, and Italy). We used the Twitter survey system as a viable method to quickly and comprehensively gather international public health insights on mandatory vaccination against COVID-19. Objective: The purpose of this study was to better understand the public?s perception of mandatory COVID-19 vaccination in real time using Twitter polls. Methods: Two Twitter polls were developed (in the English language) to seek the public?s opinion on the possibility of mandatory vaccination. The polls were pinned to the Digital Health and Patient Safety Platform?s (based in Vienna, Austria) Twitter timeline for 1 week in mid-November 2021, 3 days after the official public announcement of mandatory COVID-19 vaccination in Austria. Twitter users were asked to participate and retweet the polls to reach the largest possible audience. Results: Our Twitter polls revealed two extremes on the topic of mandatory vaccination against COVID-19. Almost half of the 2545 respondents (n=1246, 49%) favor mandatory vaccination, at least in certain areas. This attitude contrasts with the 45.7% (n=1162) who categorically reject mandatory vaccination. Over one-quarter (n=621, 26.3%) of participating Twitter users said they would never get vaccinated, as reflected by the current Western European and North American vaccination coverage rate. Concatenating interpretation of these two polls should be done cautiously as participating populations might substantially differ. Conclusions: Mandatory vaccination against COVID-19 (in at least certain areas) is favored by less than 50%, whereas it is opposed by almost half of the surveyed Twitter users. Since (social) media strongly influences public perceptions and views, and social media discussions and surveys are specifically susceptible to the ?echo chamber effect,? the results should be interpreted as a momentary snapshot. Therefore, the results of this study need to be complemented by long-term surveys to maintain their validity. UR - https://formative.jmir.org/2022/6/e35754 UR - http://dx.doi.org/10.2196/35754 UR - http://www.ncbi.nlm.nih.gov/pubmed/35617671 ID - info:doi/10.2196/35754 ER - TY - JOUR AU - Zhao, Yuehua AU - Zhu, Sicheng AU - Wan, Qiang AU - Li, Tianyi AU - Zou, Chun AU - Wang, Hao AU - Deng, Sanhong PY - 2022/6/20 TI - Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses JO - J Med Internet Res SP - e37623 VL - 24 IS - 6 KW - health misinformation KW - COVID-19 KW - social media KW - misinformation spread KW - infodemiology KW - global health crisis KW - misinformation KW - theoretical model KW - medical information KW - epidemic KW - pandemic N2 - Background: During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media. Objective: We propose an elaboration likelihood model?based theoretical model to understand the persuasion process of COVID-19?related misinformation on social media. Methods: The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19?related misinformation feature includes five topics: medical information, social issues and people?s livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic?related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns. Results: Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination. Conclusions: Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics. UR - https://www.jmir.org/2022/6/e37623 UR - http://dx.doi.org/10.2196/37623 UR - http://www.ncbi.nlm.nih.gov/pubmed/35671411 ID - info:doi/10.2196/37623 ER - TY - JOUR AU - Piltch-Loeb, Rachael AU - Su, Max AU - Hughes, Brian AU - Testa, Marcia AU - Goldberg, Beth AU - Braddock, Kurt AU - Miller-Idriss, Cynthia AU - Maturo, Vanessa AU - Savoia, Elena PY - 2022/6/20 TI - Testing the Efficacy of Attitudinal Inoculation Videos to Enhance COVID-19 Vaccine Acceptance: Quasi-Experimental Intervention Trial JO - JMIR Public Health Surveill SP - e34615 VL - 8 IS - 6 KW - attitudinal inoculation KW - intervention KW - COVID-19 vaccine KW - vaccine hesitancy KW - COVID-19 KW - vaccine KW - vaccination KW - public health KW - health intervention KW - misinformation KW - infodemiology KW - vaccine misinformation N2 - Background: Over the course of the COVID-19 pandemic, a variety of COVID-19-related misinformation has spread and been amplified online. The spread of misinformation can influence COVID-19 beliefs and protective actions, including vaccine hesitancy. Belief in vaccine misinformation is associated with lower vaccination rates and higher vaccine resistance. Attitudinal inoculation is a preventative approach to combating misinformation and disinformation, which leverages the power of narrative, rhetoric, values, and emotion. Objective: This study seeks to test inoculation messages in the form of short video messages to promote resistance against persuasion by COVID-19 vaccine misinformation. Methods: We designed a series of 30-second inoculation videos and conducted a quasi-experimental study to test the use of attitudinal inoculation in a population of individuals who were unvaccinated (N=1991). The 3 intervention videos were distinguished by their script design, with intervention video 1 focusing on narrative/rhetorical (?Narrative?) presentation of information, intervention video 2 focusing on delivering a fact-based information (?Fact?), and intervention video 3 using a hybrid design (?Hybrid?). Analysis of covariance (ANCOVA) models were used to compare the main effect of the intervention on the 3 outcome variables: ability to recognize misinformation tactics (?Recognize?), willingness to share misinformation (?Share?), and willingness to take the COVID-19 vaccine (?Willingness?). Results: There were significant effects across all 3 outcome variables comparing inoculation intervention groups to controls. For the Recognize outcome, the ability to recognize rhetorical strategies, there was a significant intervention group effect (P<.001). For the Share outcome, support for sharing the mis- and disinformation, the intervention group main effect was statistically significant (P=.02). For the Willingness outcome, there was a significant intervention group effect; intervention groups were more willing to get the COVID-19 vaccine compared to controls (P=.01). Conclusions: Across all intervention groups, inoculated individuals showed greater resistance to misinformation than their noninoculated counterparts. Relative to those who were not inoculated, inoculated participants showed significantly greater ability to recognize and identify rhetorical strategies used in misinformation, were less likely to share false information, and had greater willingness to get the COVID-19 vaccine. Attitudinal inoculation delivered through short video messages should be tested in public health messaging campaigns to counter mis- and disinformation. UR - https://publichealth.jmir.org/2022/6/e34615 UR - http://dx.doi.org/10.2196/34615 UR - http://www.ncbi.nlm.nih.gov/pubmed/35483050 ID - info:doi/10.2196/34615 ER - TY - JOUR AU - Oh, Jimin AU - Bonett, Stephen AU - Kranzler, C. Elissa AU - Saconi, Bruno AU - Stevens, Robin PY - 2022/6/17 TI - User- and Message-Level Correlates of Endorsement and Engagement for HIV-Related Messages on Twitter: Cross-sectional Study JO - JMIR Public Health Surveill SP - e32718 VL - 8 IS - 6 KW - HIV prevention KW - social media KW - public health KW - young adults KW - LASSO KW - HIV KW - Twitter KW - digital health N2 - Background: Youth and young adults continue to experience high rates of HIV and are also frequent users of social media. Social media platforms such as Twitter can bolster efforts to promote HIV prevention for these individuals, and while HIV-related messages exist on Twitter, little is known about the impact or reach of these messages for this population. Objective: This study aims to address this gap in the literature by identifying user and message characteristics that are associated with tweet endorsement (favorited) and engagement (retweeted) among youth and young men (aged 13-24 years). Methods: In a secondary analysis of data from a study of HIV-related messages posted by young men on Twitter, we used model selection techniques to examine user and tweet-level factors associated with tweet endorsement and engagement. Results: Tweets from personal user accounts garnered greater endorsement and engagement than tweets from institutional users (aOR 3.27, 95% CI 2.75-3.89; P<.001). High follower count was associated with increased endorsement and engagement (aOR 1.05, 95% CI 1.04-1.06; P<.001); tweets that discussed STIs garnered lower endorsement and engagement (aOR 0.59, 95% CI 0.47-1.74; P<.001). Conclusions: Findings suggest practitioners should partner with youth to design and disseminate HIV prevention messages on social media, incorporate content that resonates with youth audiences, and work to challenge stigma and foster social norms conducive to open conversation about sex, sexuality, and health. UR - https://publichealth.jmir.org/2022/6/e32718 UR - http://dx.doi.org/10.2196/32718 UR - http://www.ncbi.nlm.nih.gov/pubmed/35713945 ID - info:doi/10.2196/32718 ER - TY - JOUR AU - Li, Jingwei AU - Huang, Wei AU - Sia, Ling Choon AU - Chen, Zhuo AU - Wu, Tailai AU - Wang, Qingnan PY - 2022/6/16 TI - Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries JO - JMIR Public Health Surveill SP - e35266 VL - 8 IS - 6 KW - SARS-CoV-2 KW - COVID 19 KW - epidemic forecasting KW - disease surveillance KW - infectious disease epidemiology KW - social medial KW - online news KW - search query KW - autoregression model N2 - Background: The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. Objective: The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. Methods: We first used core terms and symptom-related keyword-based methods to extract COVID-19?related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis. Results: Our proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=?8.722, P<.001; model 2, t198=?5.000, P<.001, model 3, t198=?1.882, P=.06; model 4, t198=?4.644, P<.001; model 5, t198=?4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=?1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks. Conclusions: Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics. UR - https://publichealth.jmir.org/2022/6/e35266 UR - http://dx.doi.org/10.2196/35266 UR - http://www.ncbi.nlm.nih.gov/pubmed/35507921 ID - info:doi/10.2196/35266 ER - TY - JOUR AU - Lotto, Matheus AU - Santana Jorge, Olivia AU - Sá Menezes, Tamires AU - Ramalho, Maria Ana AU - Marchini Oliveira, Thais AU - Bevilacqua, Fernando AU - Cruvinel, Thiago PY - 2022/6/16 TI - Psychophysiological Reactions of Internet Users Exposed to Fluoride Information and Disinformation: Protocol for a Randomized Controlled Trial JO - JMIR Res Protoc SP - e39133 VL - 11 IS - 6 KW - fluoride KW - disinformation KW - randomized controlled trial KW - social media KW - internet N2 - Background: False messages on the internet continually propagate possible adverse effects of fluoridated oral care products and water, despite their essential role in preventing and controlling dental caries. Objective: This study aims to evaluate the patterns of psychophysiological reactions of adults after the consumption of internet-based fluoride-related information and disinformation. Methods: A 2-armed, single-blinded, parallel, and randomized controlled trial will be conducted with 58 parents or caregivers of children who attend the Clinics of Pediatric Dentistry at the Bauru School of Dentistry, considering an attrition of 10% and a significance level of 5%. The participants will be randomized into test and intervention groups, being respectively exposed to fluoride-related information and disinformation presented on a computer with simultaneous monitoring of their psychophysiological reactions, including analysis of their heart rates (HRs) and 7 facial features (mouth outer, mouth corner, eye area, eyebrow activity, face area, face motion, and facial center of mass). Then, participants will respond to questions about the utility and truthfulness of content, their emotional state after the experiment, eHealth literacy, oral health knowledge, and socioeconomic characteristics. The Shapiro-Wilk and Levene tests will be used to determine the normality and homogeneity of the data, which could lead to further statistical analyses for elucidating significant differences between groups, using parametric (Student t test) or nonparametric (Mann-Whitney U test) analyses. Moreover, multiple logistic regression models will be developed to evaluate the association of distinct variables with the psychophysiological aspects. Only factors with significant Wald statistics in the simple analysis will be included in the multiple models (P<.2). Furthermore, receiver operating characteristic curve analysis will be performed to determine the accuracy of the remote HR with respect to the measured HR. For all analyses, P<.05 will be considered significant. Results: From June 2022, parents and caregivers who frequent the Clinics of Pediatric Dentistry at the Bauru School of Dentistry will be invited to participate in the study and will be randomized into 1 of the 2 groups (control or intervention). Data collection is expected to be completed in December 2023. Subsequently, the authors will analyze the data and publish the findings of the clinical trial by June 2024. Conclusions: This randomized controlled trial aims to elucidate differences between psychophysiological patterns of adults exposed to true or false oral health content. This evidence may support the development of further studies and digital strategies, such as neural network models to automatically detect disinformation available on the internet. Trial Registration: Brazilian Clinical Trials Registry (RBR-7q4ymr2) U1111-1263-8227; https://tinyurl.com/2kf73t3d International Registered Report Identifier (IRRID): PRR1-10.2196/39133 UR - https://www.researchprotocols.org/2022/6/e39133 UR - http://dx.doi.org/10.2196/39133 UR - http://www.ncbi.nlm.nih.gov/pubmed/35708767 ID - info:doi/10.2196/39133 ER - TY - JOUR AU - Basch, H. Corey AU - Hillyer, C. Grace AU - Jacques, T. Erin PY - 2022/6/15 TI - News Coverage of Colorectal Cancer on Google News: Descriptive Study JO - JMIR Cancer SP - e39180 VL - 8 IS - 2 KW - colorectal cancer KW - internet KW - online news KW - screening KW - disparities KW - infodemiology KW - online health information KW - content analysis KW - public awareness KW - health news KW - cancer screening KW - health video KW - video content analysis N2 - Background: Colorectal cancer (CRC) is one of the leading causes of cancer death in the United States. The incidence and prevalence of CRC have historically increased with age. Although rates of CRC in the United States have been decreasing over the past decades among those aged ?65 years, there has been an uptick among those in younger age brackets. Google News is one of the biggest traffic drivers to top news sites. It aggregates and shares news highlights from multiple sources worldwide and organizes them by content type. Despite the widespread use of Google News, research is lacking on the type of CRC content represented in this news source.  Objective: The purpose of this study was to analyze content related to CRC screening and prevention in Google News articles published during National Colorectal Cancer Awareness Month (March 2022). Methods: Data collection for this cross-sectional study was conducted in March 2022?National Colorectal Cancer Awareness Month. Using the term colorectal cancer, 100 English-language Google News articles were extracted and coded for content. A combined approach?deductive and inductive coding?was utilized. Descriptive analyses were conducted, and frequency distributions were reported. Univariable analyses were performed to assess differences between articles that mentioned CRC screening and those that did not via chi-square tests. Results: Of the 100 articles reviewed, nearly half (n=49, 49%) were created by health news organizations, and another 27% (n=27) were created by television news services. The predominant themes in the content included age at the onset of disease (n=59, 59%), mortality related to CRC (n=57, 57%), and the severity of disease (n=50, 50%). Only 18% (n=18) of articles discussed CRC disparities, 23% (n=23) mentioned that there are hereditary forms of the disease, 36% (n=36) spoke of colonoscopy to screen for the disease, and 37% (n=37) mentioned how the disease is treated. Although most articles mentioned CRC screening (n=61, 61%), it was striking that sex was only mentioned in 34% (21/61) of these articles, colonoscopy was mentioned in 46% (28/61), and diet was mentioned in 30% (18/61). Conclusions: Heightening the public?s awareness of this disease is important, but it is critical that messages related to how preventable this cancer is, who is the most likely to develop CRC, and what can be done to detect it in the early stages when the disease is the most curable be the critical elements of dialogue, particularly during National Colorectal Cancer Awareness Month. There is a need to disseminate information about early-onset CRC and the importance of screening, especially among populations with low rates of uptake. Web-based news is potentially an underutilized communication mechanism for promoting CRC screenings as secondary prevention measures for high-risk groups. UR - https://cancer.jmir.org/2022/2/e39180 UR - http://dx.doi.org/10.2196/39180 UR - http://www.ncbi.nlm.nih.gov/pubmed/35704377 ID - info:doi/10.2196/39180 ER - TY - JOUR AU - Ren, Ningjun AU - Li, Yuansheng AU - Wang, Ruolan AU - Zhang, Wenxin AU - Chen, Run AU - Xiao, Ticheng AU - Chen, Hang AU - Li, Ailing AU - Fan, Song PY - 2022/6/14 TI - The Distribution of HIV and AIDS Cases in Luzhou, China, From 2011 to 2020: Bayesian Spatiotemporal Analysis JO - JMIR Public Health Surveill SP - e37491 VL - 8 IS - 6 KW - HIV and AIDS KW - reported incidence KW - Bayesian model KW - spatio-temporal distribution N2 - Background: The vastly increasing number of reported HIV and AIDS cases in Luzhou, China, in recent years, coupled with the city?s unique geographical location at the intersection of 4 provinces, makes it particularly important to conduct a spatiotemporal analysis of HIV and AIDS cases. Objective: The aim of this study is to understand the spatiotemporal distribution of HIV and the factors influencing this distribution in Luzhou, China, from 2011 to 2020. Methods: Data on the incidence of HIV and AIDS in Luzhou from 2011 to 2020 were obtained from the AIDS Information Management System of the Luzhou Center for Disease Control and Prevention. ArcGIS was used to visualize the spatiotemporal distribution of HIV and AIDS cases. The Bayesian spatiotemporal model was used to investigate factors affecting the spatiotemporal distribution of HIV and AIDS, including the gross domestic product (GDP) per capita, urbanization rate, number of hospital beds, population density, and road mileage. Results: The reported incidence of HIV and AIDS rose from 8.50 cases per 100,000 population in 2011 to 49.25 cases per 100,000 population in 2020?an increase of 578.87%. In the first 5 years, hotspots were concentrated in Jiangyang district, Longmatan district, and Luxian county. After 2016, Luzhou?s high HIV incidence areas gradually shifted eastward, with Hejiang county having the highest average prevalence rate (41.68 cases per 100,000 population) from 2011 to 2020, being 2.28 times higher than that in Gulin county (18.30 cases per 100,000), where cold spots were concentrated. The risk for the incidence of HIV and AIDS was associated with the urbanization rate, population density, and GDP per capita. For every 1% increase in the urbanization rate, the relative risk (RR) increases by 1.3%, while an increase of 100 people per square kilometer would increase the RR by 8.7%; for every 1000 Yuan (US $148.12) increase in GDP per capita, the RR decreases by 1.5%. Conclusions: In Luzhou, current HIV and AIDS prevention and control efforts must be focused on the location of each district or county government; we suggest the region balance urban development and HIV and AIDS prevention. Moreover, more attention should be paid to economically disadvantaged areas. UR - https://publichealth.jmir.org/2022/6/e37491 UR - http://dx.doi.org/10.2196/37491 UR - http://www.ncbi.nlm.nih.gov/pubmed/35700022 ID - info:doi/10.2196/37491 ER - TY - JOUR AU - Riddell, A. Corinne AU - Neumann, Krista AU - Santaularia, Jeanie N. AU - Farkas, Kriszta AU - Ahern, Jennifer AU - Mason, M. Susan PY - 2022/6/13 TI - Excess Google Searches for Child Abuse and Intimate Partner Violence During the COVID-19 Pandemic: Infoveillance Approach JO - J Med Internet Res SP - e36445 VL - 24 IS - 6 KW - child abuse KW - household violence KW - infoveillance KW - violence KW - domestic violence KW - abuse KW - Google KW - COVID-19 N2 - Background: The COVID-19 pandemic has created environments with increased risk factors for household violence, such as unemployment and financial uncertainty. At the same time, it led to the introduction of policies to mitigate financial uncertainty. Further, it hindered traditional measurements of household violence. Objective: Using an infoveillance approach, our goal was to determine if there were excess Google searches related to exposure to child abuse, intimate partner violence (IPV), and child-witnessed IPV during the COVID-19 pandemic and if any excesses are temporally related to shelter-in-place and economic policies. Methods: Data on relative search volume for each violence measure was extracted using the Google Health Trends application programming interface for each week from 2017 to 2020 for the United States. Using linear regression with restricted cubic splines, we analyzed data from 2017 to 2019 to characterize the seasonal variation shared across prepandemic years. Parameters from prepandemic years were used to predict the expected number of Google searches and 95% prediction intervals (PI) for each week in 2020. Weeks with searches above the upper bound of the PI are in excess of the model?s prediction. Results: Relative search volume for exposure to child abuse was greater than expected in 2020, with 19% (10/52) of the weeks falling above the upper bound of the PI. These excesses in searches began a month after the Pandemic Unemployment Compensation program ended. Relative search volume was also heightened in 2020 for child-witnessed IPV, with 33% (17/52) of the weeks falling above the upper bound of the PI. This increase occurred after the introduction of shelter-in-place policies. Conclusions: Social and financial disruptions, which are common consequences of major disasters such as the COVID-19 pandemic, may increase risks for child abuse and child-witnessed IPV. UR - https://www.jmir.org/2022/6/e36445 UR - http://dx.doi.org/10.2196/36445 UR - http://www.ncbi.nlm.nih.gov/pubmed/35700024 ID - info:doi/10.2196/36445 ER - TY - JOUR AU - Ferrell, DaJuan AU - Campos-Castillo, Celeste PY - 2022/6/13 TI - Factors Affecting Physicians? Credibility on Twitter When Sharing Health Information: Online Experimental Study JO - JMIR Infodemiology SP - e34525 VL - 2 IS - 1 KW - source credibility KW - user engagement KW - social media KW - health communication KW - misinformation KW - Twitter N2 - Background: Largely absent from research on how users appraise the credibility of professionals as sources for the information they find on social media is work investigating factors shaping credibility within a specific profession, such as physicians. Objective: We address debates about how physicians can show their credibility on social media depending on whether they employ a formal or casual appearance in their profile picture. Using prominence-interpretation theory, we posit that formal appearance will affect perceived credibility based on users' social context?specifically, whether they have a regular health care provider. Methods: For this experiment, we recruited 205 social media users using Amazon Mechanical Turk. We asked participants if they had a regular health care provider and then randomly assigned them to read 1 of 3 Twitter posts that varied only in the profile picture of the physician offering health advice. Next, we tasked participants with assessing the credibility of the physician and their likelihood of engaging with the tweet and the physician on Twitter. We used path analysis to assess whether participants having a regular health care provider impacted how the profile picture affected their ratings of the physician?s credibility and their likelihood to engage with the tweet and physician on Twitter. Results: We found that the profile picture of a physician posting health advice in either formal or casual attire did not elicit significant differences in credibility, with ratings comparable to those having no profile image. Among participants assigned the formal appearance condition, those with a regular provider rated the physician higher on a credibility than those without, which led to stronger intentions to engage with the tweet and physician. Conclusions: The findings add to existing research by showing how the social context of information seeking on social media shapes the credibility of a given professional. Practical implications for professionals engaging with the public on social media and combating false information include moving past debates about casual versus formal appearances and toward identifying ways to segment audiences based on factors like their backgrounds (eg, experiences with health care providers). UR - https://infodemiology.jmir.org/2022/1/e34525 UR - http://dx.doi.org/10.2196/34525 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113807 ID - info:doi/10.2196/34525 ER - TY - JOUR AU - Chauhan, Jyoti AU - Aasaithambi, Sathyaraj AU - Márquez-Rodas, Iván AU - Formisano, Luigi AU - Papa, Sophie AU - Meyer, Nicolas AU - Forschner, Andrea AU - Faust, Guy AU - Lau, Mike AU - Sagkriotis, Alexandros PY - 2022/6/13 TI - Understanding the Lived Experiences of Patients With Melanoma: Real-World Evidence Generated Through a European Social Media Listening Analysis JO - JMIR Cancer SP - e35930 VL - 8 IS - 2 KW - melanoma KW - social media KW - social media listening KW - real-world evidence KW - patient journey KW - cancer KW - mortality rate KW - health information N2 - Background: Cutaneous melanoma is an aggressive malignancy that is proposed to account for 90% of skin cancer?related mortality. Individuals with melanoma experience both physical and psychological impacts associated with their diagnosis and treatment. Health-related information is being increasingly accessed and shared by stakeholders on social media platforms. Objective: This study aimed to assess how individuals living with melanoma across 14 European countries use social media to discuss their needs and provide their perceptions of the disease. Methods: Social media sources including Twitter, forums, and blogs were searched using predefined search strings of keywords relating to melanoma. Manual and automated relevancy approaches filtered the extracted data for content that provided patient-centric insights. This contextualized data was then mined for insightful concepts around the symptoms, diagnosis, treatment, impacts, and lived experiences of melanoma. Results: A total of 182,400 posts related to melanoma were identified between November 2018 and November 2020. Following exclusion of irrelevant posts and using random sampling methodology, 864 posts were identified as relevant to the study objectives. Of the social media channels included, Twitter was the most commonly used, followed by forums and blogs. Most posts originated from the United Kingdom (n=328, 38%) and Spain (n=138, 16%). Of the relevant posts, 62% (n=536) were categorized as originating from individuals with melanoma. The most frequently discussed melanoma-related topics were treatment (436/792, 55%), diagnosis and tests (261/792, 33%), and remission (190/792, 24%). The majority of treatment discussions were about surgery (292/436, 67%), followed by immunotherapy (52/436, 12%). In total, 255 posts discussed the impacts of melanoma, which included emotional burden (n=179, 70%), physical impacts (n=61, 24%), effects on social life (n=43, 17%), and financial impacts (n=10, 4%). Conclusions: Findings from this study highlight how melanoma stakeholders discuss key concepts associated with the condition on social media, adding to the conceptual model of the patient journey. This social media listening approach is a powerful tool for exploring melanoma stakeholder perspectives, providing insights that can be used to corroborate existing data and inform future studies. UR - https://cancer.jmir.org/2022/2/e35930 UR - http://dx.doi.org/10.2196/35930 UR - http://www.ncbi.nlm.nih.gov/pubmed/35699985 ID - info:doi/10.2196/35930 ER - TY - JOUR AU - Niu, Qian AU - Liu, Junyu AU - Kato, Masaya AU - Nagai-Tanima, Momoko AU - Aoyama, Tomoki PY - 2022/6/9 TI - The Effect of Fear of Infection and Sufficient Vaccine Reservation Information on Rapid COVID-19 Vaccination in Japan: Evidence From a Retrospective Twitter Analysis JO - J Med Internet Res SP - e37466 VL - 24 IS - 6 KW - COVID-19 KW - vaccine hesitancy KW - Japan KW - social media KW - text mining N2 - Background: The global public health and socioeconomic impacts of the COVID-19 pandemic have been substantial, rendering herd immunity by COVID-19 vaccination an important factor for protecting people and retrieving the economy. Among all the countries, Japan became one of the countries with the highest COVID-19 vaccination rates in several months, although vaccine confidence in Japan is the lowest worldwide. Objective: We attempted to find the reasons for rapid COVID-19 vaccination in Japan given its lowest vaccine confidence levels worldwide, through Twitter analysis.  Methods: We downloaded COVID-19?related Japanese tweets from a large-scale public COVID-19 Twitter chatter data set within the timeline of February 1 and September 30, 2021. The daily number of vaccination cases was collected from the official website of the Prime Minister?s Office of Japan. After preprocessing, we applied unigram and bigram token analysis and then calculated the cross-correlation and Pearson correlation coefficient (r) between the term frequency and daily vaccination cases. We then identified vaccine sentiments and emotions of tweets and used the topic modeling to look deeper into the dominant emotions.  Results: We selected 190,697 vaccine-related tweets after filtering. Through n-gram token analysis, we discovered the top unigrams and bigrams over the whole period. In all the combinations of the top 6 unigrams, tweets with both keywords ?reserve? and ?venue? showed the largest correlation with daily vaccination cases (r=0.912; P<.001). On sentiment analysis, negative sentiment overwhelmed positive sentiment, and fear was the dominant emotion across the period. For the latent Dirichlet allocation model on tweets with fear emotion, the two topics were identified as ?infect? and ?vaccine confidence.? The expectation of the number of tweets generated from topic ?infect? was larger than that generated from topic ?vaccine confidence.? Conclusions: Our work indicates that awareness of the danger of COVID-19 might increase the willingness to get vaccinated. With a sufficient vaccine supply, effective delivery of vaccine reservation information may be an important factor for people to get vaccinated. We did not find evidence for increased vaccine confidence in Japan during the period of our study. We recommend policy makers to share accurate and prompt information about the infectious diseases and vaccination and to make efforts on smoother delivery of vaccine reservation information. UR - https://www.jmir.org/2022/6/e37466 UR - http://dx.doi.org/10.2196/37466 UR - http://www.ncbi.nlm.nih.gov/pubmed/35649182 ID - info:doi/10.2196/37466 ER - TY - JOUR AU - Kulkarni, Vishnutheertha AU - Okoye, A. Ginette AU - Garza, A. Luis AU - Wongvibulsin, Shannon PY - 2022/6/9 TI - Geospatial Heterogeneity of Hidradenitis Suppurativa Searches in the United States: Infodemiology Study of Google Search Data JO - JMIR Dermatol SP - e34594 VL - 5 IS - 2 KW - hidradenitis suppurativa KW - infodemiology KW - internet KW - digital dermatoepidemiology KW - epidemiology KW - big data KW - dermatology UR - https://derma.jmir.org/2022/2/e34594 UR - http://dx.doi.org/10.2196/34594 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632873 ID - info:doi/10.2196/34594 ER - TY - JOUR AU - Lundberg, L. Alexander AU - Lorenzo-Redondo, Ramon AU - Hultquist, F. Judd AU - Hawkins, A. Claudia AU - Ozer, A. Egon AU - Welch, B. Sarah AU - Prasad, Vara P. V. AU - Achenbach, J. Chad AU - White, I. Janine AU - Oehmke, F. James AU - Murphy, L. Robert AU - Havey, J. Robert AU - Post, A. Lori PY - 2022/6/3 TI - Overlapping Delta and Omicron Outbreaks During the COVID-19 Pandemic: Dynamic Panel Data Estimates JO - JMIR Public Health Surveill SP - e37377 VL - 8 IS - 6 KW - Omicron variant of concern KW - Delta KW - COVID-19 KW - SARS-CoV-2 KW - B.1.1.529 KW - outbreak KW - Arellano-Bond estimator KW - dynamic panel data KW - stringency index KW - surveillance KW - disease transmission metrics N2 - Background: The Omicron variant of SARS-CoV-2 is more transmissible than prior variants of concern (VOCs). It has caused the largest outbreaks in the pandemic, with increases in mortality and hospitalizations. Early data on the spread of Omicron were captured in countries with relatively low case counts, so it was unclear how the arrival of Omicron would impact the trajectory of the pandemic in countries already experiencing high levels of community transmission of Delta. Objective: The objective of this study is to quantify and explain the impact of Omicron on pandemic trajectories and how they differ between countries that were or were not in a Delta outbreak at the time Omicron occurred. Methods: We used SARS-CoV-2 surveillance and genetic sequence data to classify countries into 2 groups: those that were in a Delta outbreak (defined by at least 10 novel daily transmissions per 100,000 population) when Omicron was first sequenced in the country and those that were not. We used trend analysis, survival curves, and dynamic panel regression models to compare outbreaks in the 2 groups over the period from November 1, 2021, to February 11, 2022. We summarized the outbreaks in terms of their peak rate of SARS-CoV-2 infections and the duration of time the outbreaks took to reach the peak rate. Results: Countries that were already in an outbreak with predominantly Delta lineages when Omicron arrived took longer to reach their peak rate and saw greater than a twofold increase (2.04) in the average apex of the Omicron outbreak compared to countries that were not yet in an outbreak. Conclusions: These results suggest that high community transmission of Delta at the time of the first detection of Omicron was not protective, but rather preluded larger outbreaks in those countries. Outbreak status may reflect a generally susceptible population, due to overlapping factors, including climate, policy, and individual behavior. In the absence of strong mitigation measures, arrival of a new, more transmissible variant in these countries is therefore more likely to lead to larger outbreaks. Alternately, countries with enhanced surveillance programs and incentives may be more likely to both exist in an outbreak status and detect more cases during an outbreak, resulting in a spurious relationship. Either way, these data argue against herd immunity mitigating future outbreaks with variants that have undergone significant antigenic shifts. UR - https://publichealth.jmir.org/2022/6/e37377 UR - http://dx.doi.org/10.2196/37377 UR - http://www.ncbi.nlm.nih.gov/pubmed/35500140 ID - info:doi/10.2196/37377 ER - TY - JOUR AU - Watanabe, Tomomi AU - Yada, Shuntaro AU - Aramaki, Eiji AU - Yajima, Hiroshi AU - Kizaki, Hayato AU - Hori, Satoko PY - 2022/6/3 TI - Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs JO - JMIR Cancer SP - e37840 VL - 8 IS - 2 KW - breast neoplasm KW - cancer KW - natural language processing KW - NLP KW - artificial intelligence KW - model KW - machine learning KW - content analysis KW - text mining KW - sentiment analysis KW - oncology KW - quality of life KW - social media KW - social support KW - breast cancer KW - BERT model KW - peer support KW - blog post KW - patient data N2 - Background: Patients with breast cancer have a variety of worries and need multifaceted information support. Their accumulated posts on social media contain rich descriptions of their daily worries concerning issues such as treatment, family, and finances. It is important to identify these issues to help patients with breast cancer to resolve their worries and obtain reliable information. Objective: This study aimed to extract and classify multiple worries from text generated by patients with breast cancer using Bidirectional Encoder Representations From Transformers (BERT), a context-aware natural language processing model. Methods: A total of 2272 blog posts by patients with breast cancer in Japan were collected. Five worry labels, ?treatment,? ?physical,? ?psychological,? ?work/financial,? and ?family/friends,? were defined and assigned to each post. Multiple labels were allowed. To assess the label criteria, 50 blog posts were randomly selected and annotated by two researchers with medical knowledge. After the interannotator agreement had been assessed by means of Cohen kappa, one researcher annotated all the blogs. A multilabel classifier that simultaneously predicts five worries in a text was developed using BERT. This classifier was fine-tuned by using the posts as input and adding a classification layer to the pretrained BERT. The performance was evaluated for precision using the average of 5-fold cross-validation results. Results: Among the blog posts, 477 included ?treatment,? 1138 included ?physical,? 673 included ?psychological,? 312 included ?work/financial,? and 283 included ?family/friends.? The interannotator agreement values were 0.67 for ?treatment,? 0.76 for ?physical,? 0.56 for ?psychological,? 0.73 for ?work/financial,? and 0.73 for ?family/friends,? indicating a high degree of agreement. Among all blog posts, 544 contained no label, 892 contained one label, and 836 contained multiple labels. It was found that the worries varied from user to user, and the worries posted by the same user changed over time. The model performed well, though prediction performance differed for each label. The values of precision were 0.59 for ?treatment,? 0.82 for ?physical,? 0.64 for ?psychological,? 0.67 for ?work/financial,? and 0.58 for ?family/friends.? The higher the interannotator agreement and the greater the number of posts, the higher the precision tended to be. Conclusions: This study showed that the BERT model can extract multiple worries from text generated from patients with breast cancer. This is the first application of a multilabel classifier using the BERT model to extract multiple worries from patient-generated text. The results will be helpful to identify breast cancer patients? worries and give them timely social support. UR - https://cancer.jmir.org/2022/2/e37840 UR - http://dx.doi.org/10.2196/37840 UR - http://www.ncbi.nlm.nih.gov/pubmed/35657664 ID - info:doi/10.2196/37840 ER - TY - JOUR AU - Couture, Alexia AU - Iuliano, Danielle A. AU - Chang, H. Howard AU - Patel, N. Neha AU - Gilmer, Matthew AU - Steele, Molly AU - Havers, P. Fiona AU - Whitaker, Michael AU - Reed, Carrie PY - 2022/6/2 TI - Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model: Modeling Study JO - JMIR Public Health Surveill SP - e34296 VL - 8 IS - 6 KW - COVID-19 KW - SARS-CoV-2 KW - hospitalization KW - Bayesian KW - COVID-NET KW - extrapolation KW - hospital KW - estimation KW - prediction KW - United States KW - surveillance KW - data KW - model KW - modeling KW - hierarchical KW - rate KW - novel KW - framework KW - monitoring N2 - Background: In the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important. Objective: We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. Methods: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ?85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. Results: We estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ?85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states. Conclusions: Our novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data. UR - https://publichealth.jmir.org/2022/6/e34296 UR - http://dx.doi.org/10.2196/34296 UR - http://www.ncbi.nlm.nih.gov/pubmed/35452402 ID - info:doi/10.2196/34296 ER - TY - JOUR AU - Gomaa, Basma AU - Houghton, F. Rebecca AU - Crocker, Nicole AU - Walsh-Buhi, R. Eric PY - 2022/6/2 TI - Skin Cancer Narratives on Instagram: Content Analysis JO - JMIR Infodemiology SP - e34940 VL - 2 IS - 1 KW - digital health KW - social media KW - skin cancer KW - Instagram KW - melanoma KW - oncology KW - cancer KW - skin KW - content analysis KW - narrative KW - information sharing KW - online platform N2 - Background: Skin cancer is among the deadliest forms of cancer in the United States. The American Cancer Society reported that 3 million skin cancer cases could be avoided every year if individuals are more aware of the risk factors related to sun exposure and prevention. Social media platforms may serve as potential intervention modalities that can be used to raise public awareness of several diseases and health conditions, including skin cancer. Social media platforms are efficient, cost-effective tools for health-related content that can reach a broad number of individuals who are already using these spaces in their day-to-day personal lives. Instagram was launched in 2010, and it is now used by 1 billion users, of which 90% are under the age of 35 years. Despite previous research highlighting the potential of image-based platforms in skin cancer prevention and leveraging Instagram?s popularity among the priority population to raise awareness, there is still a lack of studies describing skin cancer?related content on Instagram. Objective: This study aims to describe skin cancer?related content on Instagram, including the type of account; the characteristics of the content, such as the kind of media used; and the type of skin cancer discussed. This study also seeks to reveal content themes in terms of skin cancer risks, treatment, and prevention. Methods: Through CrowdTangle, a Facebook-owned tool, we retrieved content from publicly available accounts on Instagram for the 30 days preceding May 14, 2021. Out of 2932 posts, we randomly selected 1000 posts for review. Of the 1000 posts, 592 (59.2%) met the following inclusion criteria: (1) content was focused on human skin cancer, (2) written in English language only, and (3) originated from the United States. Guided by previous research and through an iterative process, 2 undergraduate students independently coded the remaining posts. The 2 coders and a moderator met several times to refine the codebook. Results: Of the 592 posts, profiles representing organizations (n=321, 54.2%) were slightly more common than individual accounts (n=256, 43.2%). The type of media included in the posts varied, with posts containing photos occurring more frequently (n=315, 53.2%) than posts containing infographics (n=233, 39.4%) or videos (n=85, 14.4%). Melanoma was the most mentioned type of skin cancer (n=252, 42.6%). Prevention methods (n=404, 68.2%) were discussed in Instagram posts more often than risk factors (n=271, 45.8%). Only 81 out of 592 (13.7%) posts provided a citation. Conclusions: This study?s findings highlight the potential role of Instagram as a platform for improving awareness of skin cancer risks and the benefits of prevention practices. We believe that social media is the most promising venue for researchers and dermatologists to dedicate their efforts and presence that can widely reach the public to educate about skin cancer and empower prevention. UR - https://infodemiology.jmir.org/2022/1/e34940 UR - http://dx.doi.org/10.2196/34940 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113805 ID - info:doi/10.2196/34940 ER - TY - JOUR AU - Silenou, C. Bernard AU - Verset, Carolin AU - Kaburi, B. Basil AU - Leuci, Olivier AU - Ghozzi, Stéphane AU - Duboudin, Cédric AU - Krause, Gérard PY - 2022/5/31 TI - A Novel Tool for Real-time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment JO - JMIR Public Health Surveill SP - e34438 VL - 8 IS - 5 KW - COVID-19 KW - disease outbreak KW - contact tracing KW - serial interval KW - basic reproduction number KW - infectious disease incubation period KW - superspreading events KW - telemedicine KW - public health KW - epidemiology KW - surveillance tool KW - outbreak response KW - pandemic KW - digital health application KW - response strategy N2 - Background: The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in their epidemic response. It consists of the documentation, linkage, and follow-up of cases, contacts, and events. To allow SORMAS users to visualize data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application. Objective: This study aims to describe the essential visualizations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrate the application of SORMAS-Stats in response to the COVID-19 outbreak. Methods: Based on findings from a rapid review and SORMAS user requests, we included the following visualization and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter k, and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI computed from the transmission network data. Results: Using COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63,570 nodes. The network comprises 1.75% (1115/63,570) events, 19.59% (12,452/63,570) case persons, and 78.66% (50,003/63,570) exposed persons, including 1238 infector-infectee pairs and 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with the best fit to the observed SI data was a lognormal distribution with a mean of 4.30 (95% CI 4.09-4.51) days. We estimated a dispersion parameter k of 21.11 (95% CI 7.57-34.66) and an effective reproduction number R of 0.9 (95% CI 0.58-0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61. Conclusions: We provide an application for real-time estimation of epidemiological parameters, which is essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. The SORMAS-Stats application could greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualizations and computation of surveillance indicators. UR - https://publichealth.jmir.org/2022/5/e34438 UR - http://dx.doi.org/10.2196/34438 UR - http://www.ncbi.nlm.nih.gov/pubmed/35486812 ID - info:doi/10.2196/34438 ER - TY - JOUR AU - McMann, J. Tiana AU - Calac, Alec AU - Nali, Matthew AU - Cuomo, Raphael AU - Maroulis, James AU - Mackey, K. Tim PY - 2022/5/31 TI - Synthetic Cannabinoids in Prisons: Content Analysis of TikToks JO - JMIR Infodemiology SP - e37632 VL - 2 IS - 1 KW - social media KW - substance use disorder KW - synthetic drugs KW - prison KW - cannabinoid KW - synthetic KW - psychoactive KW - illicit KW - video KW - substance use KW - harmful KW - K2/Spice KW - TikTok N2 - Background: Synthetic cannabinoids are a significant public health concern, especially among incarcerated populations due to increased reports of abuse. Recent news reports have highlighted the severe consequences of K2/Spice, a synthetic cannabinoid, among the prison population in the United States. Despite regulations against cell phone use, inmates use TikTok to post K2/Spice-related content. Objective: This study aimed to examine TikTok posts for use and illicit distribution of psychoactive substances (eg, K2/Spice) among incarcerated populations. Methods: The study collected TikTok videos associated with the #k2spice hashtag and used a data collection approach similar to snowball sampling. Inductive coding was used to conduct content analysis of video characteristics. Videos were manually annotated to generate binary classifications related to the use of K2/Spice as well as selling and buying activities associated with it. Statistical analysis was used to determine associations between a video?s user engagement and an intent to buy or sell K2/Spice. Results: A total of 89 TikTok videos with the hashtag #k2spice were manually coded, with 40% (n=36) identified as displaying the use, solicitation, or adverse effects of K2/Spice among the prison population. Of them, 44.44% (n=16) were in a prison-based setting documenting adverse effects including possible overdose. Videos with higher user engagement were positively correlated with comments indicating an intent to buy or sell K2/Spice. Conclusions: K2/Spice is a drug subject to abuse among prison inmates in the United States, including depictions of its harmful effects being recorded and shared on TikTok. Lack of policy enforcement on TikTok and the need for better access to treatment services within the prison system may be exacerbating substance use among this highly vulnerable population. Minimizing the potential individual harm of this content on the incarcerated population should be a priority for social media platforms and the criminal justice system alike. UR - https://infodemiology.jmir.org/2022/1/e37632 UR - http://dx.doi.org/10.2196/37632 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113804 ID - info:doi/10.2196/37632 ER - TY - JOUR AU - Li, Peiyi AU - Chen, Bo AU - Deveaux, Genevieve AU - Luo, Yunmei AU - Tao, Wenjuan AU - Li, Weimin AU - Wen, Jin AU - Zheng, Yuan PY - 2022/5/31 TI - Cross-Verification of COVID-19 Information Obtained From Unofficial Social Media Accounts and Associated Changes in Health Behaviors: Web-Based Questionnaire Study Among Chinese Netizens JO - JMIR Public Health Surveill SP - e33577 VL - 8 IS - 5 KW - COVID-19 KW - pandemic KW - social media KW - behavior change KW - information cross-verification KW - eHealth literacy N2 - Background: As social media platforms have become significant sources of information during the pandemic, a significant volume of both factual and inaccurate information related to the prevention of COVID-19 has been disseminated through social media. Thus, disparities in COVID-19 information verification across populations have the potential to promote the dissemination of misinformation among clustered groups of people with similar characteristics. Objective: This study aimed to identify the characteristics of social media users who obtained COVID-19 information through unofficial social media accounts and were (1) most likely to change their health behaviors according to web-based information and (2) least likely to actively verify the accuracy of COVID-19 information, as these individuals may be susceptible to inaccurate prevention measures and may exacerbate transmission. Methods: An online questionnaire consisting of 17 questions was disseminated by West China Hospital via its official online platforms, between May 18, 2020, and May 31, 2020. The questionnaire collected the sociodemographic information of 14,509 adults, and included questions surveying Chinese netizens? knowledge about COVID-19, personal social media use, health behavioral change tendencies, and cross-verification behaviors for web-based information during the pandemic. Multiple stepwise regression models were used to examine the relationships between social media use, behavior changes, and information cross-verification. Results: Respondents who were most likely to change their health behaviors after obtaining web-based COVID-19 information from celebrity sources had the following characteristics: female sex (P=.004), age ?50 years (P=.009), higher COVID-19 knowledge and health literacy (P=.045 and P=.03, respectively), non?health care professional (P=.02), higher frequency of searching on social media (P<.001), better health conditions (P<.001), and a trust rating score of more than 3 for information released by celebrities on social media (P=.005). Furthermore, among participants who were most likely to change their health behaviors according to social media information released by celebrities, female sex (P<.001), living in a rural residence rather than first-tier city (P<.001), self-reported medium health status and lower health care literacy (P=.007 and P<.001, respectively), less frequent search for COVID-19 information on social media (P<.001), and greater level of trust toward celebrities? social media accounts with a trust rating score greater than 1 (P?.04) were associated with a lack of cross-verification of information. Conclusions: The findings suggest that governments, health care agencies, celebrities, and technicians should combine their efforts to decrease the risk in vulnerable groups that are inclined to change health behaviors according to web-based information but do not perform any fact-check verification of the accuracy of the unofficial information. Specifically, it is necessary to correct the false information related to COVID-19 on social media, appropriately apply celebrities? star power, and increase Chinese netizens? awareness of information cross-verification and eHealth literacy for evaluating the veracity of web-based information. UR - https://publichealth.jmir.org/2022/5/e33577 UR - http://dx.doi.org/10.2196/33577 UR - http://www.ncbi.nlm.nih.gov/pubmed/35486529 ID - info:doi/10.2196/33577 ER - TY - JOUR AU - Powell, Leigh AU - Nour, Radwa AU - Zidoun, Youness AU - Kaladhara, Sreelekshmi AU - Al Suwaidi, Hanan AU - Zary, Nabil PY - 2022/5/30 TI - A Web-Based Public Health Intervention for Addressing Vaccine Misinformation: Protocol for Analyzing Learner Engagement and Impacts on the Hesitancy to Vaccinate JO - JMIR Res Protoc SP - e38034 VL - 11 IS - 5 KW - public health KW - population health KW - education KW - gamification KW - COVID-19 KW - vaccination KW - misinformation KW - infodemic KW - vaccine hesitancy KW - web-based health KW - web-based intervention KW - learning design KW - dissemination N2 - Background: A barrier to successful COVID-19 vaccine campaigns is the ongoing misinformation pandemic, or infodemic, which is contributing to vaccine hesitancy. Web-based population health interventions have been shown to impact health behaviors positively. For web-based interventions to be successful, they must use effective learning design strategies that seek to address known issues with learner engagement and retention. To know if an intervention successfully addresses vaccine hesitancy, there must be some embedded measure for comparing learners preintervention and postintervention. Objective: This protocol aims to describe a study on the effectiveness of a web-based population health intervention that is designed to address vaccine misinformation and hesitancy. The study will examine learner analytics to understand what aspects of the learning design for the intervention were effective and implement a validated instrument?the Adult Vaccine Hesitancy Scale?to measure if any changes in vaccine hesitancy were observed preintervention and postintervention. Methods: We developed a fully web-based population health intervention to help learners identify misinformation concerning COVID-19 and share the science behind vaccinations. Intervention development involves using a design-based research approach to output more effective interventions in which data can be analyzed to improve future health interventions. The study will use a quasi-experimental design in which a pre-post survey will be provided and compared statistically. Learning analytics will also be generated based on the engagement and retention data collected through the intervention to understand what aspects of our learning design are effective. Results: The web-based intervention was released to the public in September 2021, and data collection is ongoing. No external marketing or advertising has been done to market the course, making our current population of 486 participants our pilot study population. An analysis of this initial population will enable the revision of the intervention, which will then be marketed to a broader audience. Study outcomes are expected to be published by August 2022. We anticipate the release of the revised intervention by May 2022. Conclusions: Disseminating accurate information to the public during pandemic situations is vital to contributing to positive health outcomes, such as those among people getting vaccinated. Web-based interventions are valuable, as they can reach people anytime and anywhere. However, web-based interventions must use sound learning design to help incentivize engagement and motivate learners to learn and must provide a means of evaluating the intervention to determine its impact. Our study will examine both the learning design and the effectiveness of the intervention by using the analytics collected within the intervention and a statistical analysis of a validated instrument to determine if learners had a change in vaccine hesitancy as a result of what they learned. International Registered Report Identifier (IRRID): DERR1-10.2196/38034 UR - https://www.researchprotocols.org/2022/5/e38034 UR - http://dx.doi.org/10.2196/38034 UR - http://www.ncbi.nlm.nih.gov/pubmed/35451967 ID - info:doi/10.2196/38034 ER - TY - JOUR AU - Hussain, Zain AU - Sheikh, Zakariya AU - Tahir, Ahsen AU - Dashtipour, Kia AU - Gogate, Mandar AU - Sheikh, Aziz AU - Hussain, Amir PY - 2022/5/27 TI - Artificial Intelligence?Enabled Social Media Analysis for Pharmacovigilance of COVID-19 Vaccinations in the United Kingdom: Observational Study JO - JMIR Public Health Surveill SP - e32543 VL - 8 IS - 5 KW - COVID-19 KW - artificial intelligence KW - deep learning KW - Facebook KW - health informatics KW - natural language processing KW - public health KW - sentiment analysis KW - social media KW - Twitter KW - infodemiology KW - vaccination N2 - Background:  The rollout of vaccines for COVID-19 in the United Kingdom started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalizations, and deaths among vaccinated individuals. However, vaccine hesitancy remains a concern, in particular relating to adverse effects following immunization (AEFIs). Social media analysis has the potential to inform policy makers about AEFIs being discussed by the public as well as public attitudes toward the national immunization campaign. Objective:  We sought to assess the frequency and nature of AEFI-related mentions on social media in the United Kingdom and to provide insights on public sentiments toward COVID-19 vaccines. Methods:  We extracted and analyzed over 121,406 relevant Twitter and Facebook posts, from December 8, 2020, to April 30, 2021. These were thematically filtered using a 2-step approach, initially using COVID-19?related keywords and then using vaccine- and manufacturer-related keywords. We identified AEFI-related keywords and modeled their word frequency to monitor their trends over 2-week periods. We also adapted and utilized our recently developed hybrid ensemble model, which combines state-of-the-art lexicon rule?based and deep learning?based approaches, to analyze sentiment trends relating to the main vaccines available in the United Kingdom. Results:  Our COVID-19 AEFI search strategy identified 46,762 unique Facebook posts by 14,346 users and 74,644 tweets (excluding retweets) by 36,446 users over the 4-month period. We identified an increasing trend in the number of mentions for each AEFI on social media over the study period. The most frequent AEFI mentions were found to be symptoms related to appetite (n=79,132, 14%), allergy (n=53,924, 9%), injection site (n=56,152, 10%), and clots (n=43,907, 8%). We also found some rarely reported AEFIs such as Bell palsy (n=11,909, 2%) and Guillain-Barre syndrome (n=9576, 2%) being discussed as frequently as more well-known side effects like headache (n=10,641, 2%), fever (n=12,707, 2%), and diarrhea (n=16,559, 3%). Overall, we found public sentiment toward vaccines and their manufacturers to be largely positive (58%), with a near equal split between negative (22%) and neutral (19%) sentiments. The sentiment trend was relatively steady over time and had minor variations, likely based on political and regulatory announcements and debates. Conclusions:  The most frequently discussed COVID-19 AEFIs on social media were found to be broadly consistent with those reported in the literature and by government pharmacovigilance. We also detected potential safety signals from our analysis that have been detected elsewhere and are currently being investigated. As such, we believe our findings support the use of social media analysis to provide a complementary data source to conventional knowledge sources being used for pharmacovigilance purposes. UR - https://publichealth.jmir.org/2022/5/e32543 UR - http://dx.doi.org/10.2196/32543 UR - http://www.ncbi.nlm.nih.gov/pubmed/35144240 ID - info:doi/10.2196/32543 ER - TY - JOUR AU - Boukobza, Adrien AU - Burgun, Anita AU - Roudier, Bertrand AU - Tsopra, Rosy PY - 2022/5/25 TI - Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set JO - JMIR Med Inform SP - e34306 VL - 10 IS - 5 KW - neural network KW - deep learning KW - COVID-19 KW - explainable artificial intelligence KW - decision support KW - natural language processing N2 - Background: Public engagement is a key element for mitigating pandemics, and a good understanding of public opinion could help to encourage the successful adoption of public health measures by the population. In past years, deep learning has been increasingly applied to the analysis of text from social networks. However, most of the developed approaches can only capture topics or sentiments alone but not both together. Objective: Here, we aimed to develop a new approach, based on deep neural networks, for simultaneously capturing public topics and sentiments and applied it to tweets sent just after the announcement of the COVID-19 pandemic by the World Health Organization (WHO). Methods: A total of 1,386,496 tweets were collected, preprocessed, and split with a ratio of 80:20 into training and validation sets, respectively. We combined lexicons and convolutional neural networks to improve sentiment prediction. The trained model achieved an overall accuracy of 81% and a precision of 82% and was able to capture simultaneously the weighted words associated with a predicted sentiment intensity score. These outputs were then visualized via an interactive and customizable web interface based on a word cloud representation. Using word cloud analysis, we captured the main topics for extreme positive and negative sentiment intensity scores. Results: In reaction to the announcement of the pandemic by the WHO, 6 negative and 5 positive topics were discussed on Twitter. Twitter users seemed to be worried about the international situation, economic consequences, and medical situation. Conversely, they seemed to be satisfied with the commitment of medical and social workers and with the collaboration between people. Conclusions: We propose a new method based on deep neural networks for simultaneously extracting public topics and sentiments from tweets. This method could be helpful for monitoring public opinion during crises such as pandemics. UR - https://medinform.jmir.org/2022/5/e34306 UR - http://dx.doi.org/10.2196/34306 UR - http://www.ncbi.nlm.nih.gov/pubmed/35533390 ID - info:doi/10.2196/34306 ER - TY - JOUR AU - Lotto, Matheus AU - Sá Menezes, Tamires AU - Zakir Hussain, Irfhana AU - Tsao, Shu-Feng AU - Ahmad Butt, Zahid AU - P Morita, Plinio AU - Cruvinel, Thiago PY - 2022/5/19 TI - Characterization of False or Misleading Fluoride Content on Instagram: Infodemiology Study JO - J Med Internet Res SP - e37519 VL - 24 IS - 5 KW - eHealth KW - fluorides KW - infodemiology KW - information seeking behavior KW - internet KW - misinformation KW - social media KW - infoveillance KW - health outcome KW - dental caries KW - health information KW - dental health N2 - Background: Online false or misleading oral health?related content has been propagated on social media to deceive people against fluoride?s economic and health benefits to prevent dental caries. Objective: The aim of this study was to characterize the false or misleading fluoride-related content on Instagram. Methods: A total of 3863 posts ranked by users? total interaction and published between August 2016 and August 2021 were retrieved by CrowdTangle, of which 641 were screened to obtain 500 final posts. Subsequently, two independent investigators analyzed posts qualitatively to define their authors? interests, profile characteristics, content type, and sentiment. Latent Dirichlet allocation analysis topic modeling was then applied to find salient terms and topics related to false or misleading content, and their similarity was calculated through an intertopic distance map. Data were evaluated by descriptive analysis, the Mann-Whitney U test, the Cramer V test, and multiple logistic regression models. Results: Most of the posts were categorized as misinformation and political misinformation. The overperforming score was positively associated with older messages (odds ratio [OR]=3.293, P<.001) and professional/political misinformation (OR=1.944, P=.05). In this context, time from publication, negative/neutral sentiment, author?s profile linked to business/dental office/news agency, and social and political interests were related to the increment of performance of messages. Although political misinformation with negative/neutral sentiments was typically published by regular users, misinformation was linked to positive commercial posts. Overall messages focused on improving oral health habits, side effects, dentifrice containing natural ingredients, and fluoride-free products propaganda. Conclusions: False or misleading fluoride-related content found on Instagram was predominantly produced by regular users motivated by social, psychological, and/or financial interests. However, higher engagement and spreading metrics were associated with political misinformation. Most of the posts were related to the toxicity of fluoridated water and products frequently motivated by financial interests. UR - https://www.jmir.org/2022/5/e37519 UR - http://dx.doi.org/10.2196/37519 UR - http://www.ncbi.nlm.nih.gov/pubmed/35588055 ID - info:doi/10.2196/37519 ER - TY - JOUR AU - Lloret-Pineda, Amanda AU - He, Yuelu AU - Haro, Maria Josep AU - Cristóbal-Narváez, Paula PY - 2022/5/19 TI - Types of Racism and Twitter Users? Responses Amid the COVID-19 Outbreak: Content Analysis JO - JMIR Form Res SP - e29183 VL - 6 IS - 5 KW - COVID-19 KW - racism KW - Chinese KW - advocacy KW - Twitter N2 - Background: When the first COVID-19 cases were noticed in China, many racist comments against Chinese individuals spread. As there is a huge need to better comprehend why all of these targeted comments and opinions developed specifically at the start of the outbreak, we sought to carefully examine racism and advocacy efforts on Twitter in the first quarter of 2020 (January 15 to March 3, 2020). Objective: The first research question aimed to understand the main type of racism displayed on Twitter during the first quarter of 2020. The second research question focused on evaluating Twitter users? positive and negative responses regarding racism toward Chinese individuals. Methods: Content analysis of tweets was utilized to address the two research questions. Using the NCapture browser link and NVivo software, tweets in English and Spanish were pulled from the Twitter data stream from January 15 to March 3, 2020. A total of 19,150 tweets were captured using the advanced Twitter search engine with the keywords and hashtags #nosoyunvirus, #imNotAVirus, #ChineseDon?tComeToJapan, #racism, ?No soy un virus,? and ?Racismo Coronavirus.? After cleaning the data, a total of 402 tweets were codified and analyzed. Results: The data confirmed clear sentiments of racism against Chinese individuals during the first quarter of 2020. The tweets displayed individual, cultural, and institutional racism. Individual racism was the most commonly reported form of racism, specifically displaying physical and verbal aggression. As a form of resistance, Twitter users created spaces for advocacy and activism. The hashtag ?I am not a virus? helped to break stereotypes, prejudice, and discrimination on Twitter. Conclusions: Advocacy efforts were enormous both inside and outside the Chinese community; an allyship sentiment was fostered by some white users, and an identification with the oppression experienced by the Chinese population was expressed in the Black and Muslim worldwide communities. Activism through social media manifested through art, food sharing, and community support. UR - https://formative.jmir.org/2022/5/e29183 UR - http://dx.doi.org/10.2196/29183 UR - http://www.ncbi.nlm.nih.gov/pubmed/35446780 ID - info:doi/10.2196/29183 ER - TY - JOUR AU - García-Martínez, Claudia AU - Oliván-Blázquez, Bárbara AU - Fabra, Javier AU - Martínez-Martínez, Belén Ana AU - Pérez-Yus, Cruz María AU - López-Del-Hoyo, Yolanda PY - 2022/5/17 TI - Exploring the Risk of Suicide in Real Time on Spanish Twitter: Observational Study JO - JMIR Public Health Surveill SP - e31800 VL - 8 IS - 5 KW - suicide KW - prevention KW - social media KW - Twitter KW - emotional analysis KW - eHealth KW - big data KW - content analysis KW - emotional content KW - risk factors KW - mental health KW - public health KW - suicide prevention N2 - Background: Social media is now a common context wherein people express their feelings in real time. These platforms are increasingly showing their potential to detect the mental health status of the population. Suicide prevention is a global health priority and efforts toward early detection are starting to develop, although there is a need for more robust research. Objective: We aimed to explore the emotional content of Twitter posts in Spanish and their relationships with severity of the risk of suicide at the time of writing the tweet. Methods: Tweets containing a specific lexicon relating to suicide were filtered through Twitter's public application programming interface. Expert psychologists were trained to independently evaluate these tweets. Each tweet was evaluated by 3 experts. Tweets were filtered by experts according to their relevance to the risk of suicide. In the tweets, the experts evaluated: (1) the severity of the general risk of suicide and the risk of suicide at the time of writing the tweet (2) the emotional valence and intensity of 5 basic emotions; (3) relevant personality traits; and (4) other relevant risk variables such as helplessness, desire to escape, perceived social support, and intensity of suicidal ideation. Correlation and multivariate analyses were performed. Results: Of 2509 tweets, 8.61% (n=216) were considered to indicate suicidality by most experts. Severity of the risk of suicide at the time was correlated with sadness (?=0.266; P<.001), joy (?=?0.234; P=.001), general risk (?=0.908; P<.001), and intensity of suicidal ideation (?=0.766; P<.001). The severity of risk at the time of the tweet was significantly higher in people who expressed feelings of defeat and rejection (P=.003), a desire to escape (P<.001), a lack of social support (P=.03), helplessness (P=.001), and daily recurrent thoughts (P=.007). In the multivariate analysis, the intensity of suicide ideation was a predictor for the severity of suicidal risk at the time (?=0.311; P=.001), as well as being a predictor for fear (?=?0.009; P=.01) and emotional valence (?=0.007; P=.009). The model explained 75% of the variance. Conclusions: These findings suggest that it is possible to identify emotional content and other risk factors in suicidal tweets with a Spanish sample. Emotional analysis and, in particular, the detection of emotional variations may be key for real-time suicide prevention through social media. UR - https://publichealth.jmir.org/2022/5/e31800 UR - http://dx.doi.org/10.2196/31800 UR - http://www.ncbi.nlm.nih.gov/pubmed/35579921 ID - info:doi/10.2196/31800 ER - TY - JOUR AU - Sauvayre, Romy AU - Vernier, Jessica AU - Chauvičre, Cédric PY - 2022/5/17 TI - An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach JO - JMIR Med Inform SP - e37831 VL - 10 IS - 5 KW - social media KW - natural language processing KW - public health KW - vaccine KW - machine learning KW - CamemBERT language model KW - method KW - epistemology KW - COVID-19 KW - disinformation KW - language model N2 - Background: As the COVID-19 pandemic progressed, disinformation, fake news, and conspiracy theories spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media posts is particularly important, but the large amount of data exchanged on social media platforms requires specific methods. This is why machine learning and natural language processing models are increasingly applied to social media data. Objective: The aim of this study is to examine the capability of the CamemBERT French-language model to faithfully predict the elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic, or irrelevant to the studied topic. Methods: A total of 901,908 unique French-language tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using Twitter?s application programming interface (version 2; Twitter Inc). Approximately 2000 randomly selected tweets were labeled with 2 types of categorizations: (1) arguments for (pros) or against (cons) vaccination (health measures included) and (2) type of content (scientific, political, social, or vaccination status). The CamemBERT model was fine-tuned and tested for the classification of French-language tweets. The model?s performance was assessed by computing the F1-score, and confusion matrices were obtained. Results: The accuracy of the applied machine learning reached up to 70.6% for the first classification (pro and con tweets) and up to 90% for the second classification (scientific and political tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio 1.86; 95% CI 1.20-2.86). Conclusions: The accuracy of the model is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the model drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy by selecting tweets using a new method based on tweet length. UR - https://medinform.jmir.org/2022/5/e37831 UR - http://dx.doi.org/10.2196/37831 UR - http://www.ncbi.nlm.nih.gov/pubmed/35512274 ID - info:doi/10.2196/37831 ER - TY - JOUR AU - Westmaas, Lee J. AU - Masters, Matthew AU - Bandi, Priti AU - Majmundar, Anuja AU - Asare, Samuel AU - Diver, Ryan W. PY - 2022/5/16 TI - COVID-19 and Tweets About Quitting Cigarette Smoking: Topic Model Analysis of Twitter Posts 2018-2020 JO - JMIR Infodemiology SP - e36215 VL - 2 IS - 1 KW - COVID-19 KW - machine learning KW - pandemic KW - quit smoking KW - topic model analysis KW - Twitter KW - social media KW - smoking cessation KW - latent Dirichlet allocation KW - tweet KW - public health N2 - Background: The risk of infection and severity of illness by SARS-CoV-2 infection is elevated for people who smoke cigarettes and may motivate quitting. Organic public conversations on Twitter about quitting smoking could provide insight into quitting motivations or behaviors associated with the pandemic. Objective: This study explored key topics of conversation about quitting cigarette smoking and examined their trajectory during 2018-2020. Methods: Topic model analysis with latent Dirichlet allocation (LDA) identified themes in US tweets with the term ?quit smoking.? The model was trained on posts from 2018 and was then applied to tweets posted in 2019 and 2020. Analysis of variance and follow-up pairwise tests were used to compare the daily frequency of tweets within and across years by quarter. Results: The mean numbers of daily tweets on quitting smoking in 2018, 2019, and 2020 were 133 (SD 36.2), 145 (SD 69.4), and 127 (SD 32.6), respectively. Six topics were extracted: (1) need to quit, (2) personal experiences, (3) electronic cigarettes (e-cigarettes), (4) advice/success, (5) quitting as a component of general health behavior change, and (6) clinics/services. Overall, the pandemic was not associated with changes in posts about quitting; instead, New Year?s resolutions and the 2019 e-cigarette or vaping use?associated lung injury (EVALI) epidemic were more plausible explanations for observed changes within and across years. Fewer second-quarter posts in 2020 for the topic e-cigarettes may reflect lower pandemic-related quitting interest, whereas fourth-quarter increases in 2020 for other topics pointed to a late-year upswing. Conclusions: Twitter posts suggest that the pandemic did not generate greater interest in quitting smoking, but possibly a decrease in motivation when the rate of infections was increasing in the second quarter of 2020. Public health authorities may wish to craft messages for specific Twitter audiences (eg, using hashtags) to motivate quitting during pandemics. UR - https://infodemiology.jmir.org/2022/1/e36215 UR - http://dx.doi.org/10.2196/36215 UR - http://www.ncbi.nlm.nih.gov/pubmed/35611092 ID - info:doi/10.2196/36215 ER - TY - JOUR AU - Chidambaram, Swathikan AU - Maheswaran, Yathukulan AU - Chan, Calvin AU - Hanna, Lydia AU - Ashrafian, Hutan AU - Markar, R. Sheraz AU - Sounderajah, Viknesh AU - Alverdy, C. John AU - Darzi, Ara PY - 2022/5/16 TI - Misinformation About the Human Gut Microbiome in YouTube Videos: Cross-sectional Study JO - JMIR Form Res SP - e37546 VL - 6 IS - 5 KW - microbiome KW - social media KW - YouTube KW - misinformation KW - content analysis KW - gut health KW - public N2 - Background: Social media platforms such as YouTube are integral tools for disseminating information about health and wellness to the public. However, anecdotal reports have cited that the human gut microbiome has been a particular focus of dubious, misleading, and, on occasion, harmful media content. Despite these claims, there have been no published studies investigating this phenomenon within popular social media platforms. Objective: The aim of this study is to (1) evaluate the accuracy and reliability of the content in YouTube videos related to the human gut microbiome and (2) investigate the correlation between content engagement metrics and video quality, as defined by validated criteria. Methods: In this cross-sectional study, videos about the human gut microbiome were searched for on the United Kingdom version of YouTube on September 20, 2021. The 600 most-viewed videos were extracted and screened for relevance. The contents and characteristics of the videos were extracted and independently rated using the DISCERN quality criteria by 2 researchers. Results: Overall, 319 videos accounting for 62,354,628 views were included. Of the 319 videos, 73.4% (n=234) were produced in North America and 78.7% (n=251) were uploaded between 2019 and 2021. A total of 41.1% (131/319) of videos were produced by nonprofit organizations. Of the videos, 16.3% (52/319) included an advertisement for a product or promoted a health-related intervention for financial purposes. Videos by nonmedical education creators had the highest total and preferred viewership. Daily viewership was the highest for videos by internet media sources. The average DISCERN and Health on the Net Foundation Code of Conduct scores were 49.5 (SE 0.68) out of 80 and 5.05 (SE 2.52) out of 8, respectively. DISCERN scores for videos by medical professionals (mean 53.2, SE 0.17) were significantly higher than for videos by independent content creators (mean 39.1, SE 5.58; P<.001). Videos including promotional materials had significantly lower DISCERN scores than videos without any advertisements or product promotion (P<.001). There was no correlation between DISCERN scores and total viewership, daily viewership, or preferred viewership (number of likes). Conclusions: The overall quality and reliability of information about the human gut microbiome on YouTube is generally poor. Moreover, there was no correlation between the quality of a video and the level of public engagement. The significant disconnect between reliable sources of information and the public suggests that there is an immediate need for cross-sector initiatives to safeguard vulnerable viewers from the potentially harmful effects of misinformation. UR - https://formative.jmir.org/2022/5/e37546 UR - http://dx.doi.org/10.2196/37546 UR - http://www.ncbi.nlm.nih.gov/pubmed/35576578 ID - info:doi/10.2196/37546 ER - TY - JOUR AU - Portelli, Beatrice AU - Scaboro, Simone AU - Tonino, Roberto AU - Chersoni, Emmanuele AU - Santus, Enrico AU - Serra, Giuseppe PY - 2022/5/13 TI - Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets JO - J Med Internet Res SP - e35115 VL - 24 IS - 5 KW - adverse drug events KW - COVID-19 KW - digital pharmacovigilance KW - opinion mining KW - vaccines KW - social media KW - machine learning KW - deep learning KW - learning models KW - sentiment analysis KW - Twitter analysis KW - Twitter KW - web portal KW - public health N2 - Background: In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments. Objective: Monitoring social media for the early identification of side effects, and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective of this study was to create a web portal to monitor the opinion of social media users on COVID-19 vaccines, which can offer a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign. Methods: We developed a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among other techniques, a state-of-the-art system for the identification of adverse drug events on social media; natural language processing models for sentiment analysis; statistical tools; and open-source databases to visualize the trending hashtags, news articles, and their factuality. All modules of the system are displayed through an open web portal. Results: A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot?related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. Conclusions: We present a tool connected with a web portal to monitor and display some key aspects of the public?s reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model. UR - https://www.jmir.org/2022/5/e35115 UR - http://dx.doi.org/10.2196/35115 UR - http://www.ncbi.nlm.nih.gov/pubmed/35446781 ID - info:doi/10.2196/35115 ER - TY - JOUR AU - Faust, Guy AU - Booth, Alison AU - Merinopoulou, Evie AU - Halhol, Sonia AU - Tosar, Heena AU - Nawaz, Amir AU - Szlachetka, Magdalena AU - Chiu, Gavin PY - 2022/5/13 TI - The Experiences of Patients With Adjuvant and Metastatic Melanoma Using Disease-Specific Social Media Communities in the Advent of Novel Therapies (Excite Project): Social Media Listening Study JO - JMIR Cancer SP - e34073 VL - 8 IS - 2 KW - health-related social media KW - patient-centric KW - melanoma KW - adjuvant KW - metastatic KW - immunotherapy KW - targeted therapy KW - natural language processing KW - patient experience KW - cancer KW - cancer therapy KW - patient perspective KW - social media KW - caregiver experience N2 - Background: Immunotherapy and targeted therapy treatments are novel treatments available for patients with metastatic and adjuvant melanoma. As recently approved treatments, information surrounding the patients? and caregivers? experience with these therapies, perceptions of treatments, and the effect the treatments have on their day-to-day life are lacking. Such insights would be valuable for any future decision-making with regard to treatment options. Objective: This study aims to use health-related social media data to understand the experience of patients with adjuvant and metastatic melanoma who are receiving either immunotherapy or targeted therapies. This study also included caregivers? perspectives. Methods: Publicly available social media forum posts by patients with self-reported adjuvant or metastatic melanoma (and their caregivers) between January 2014 to October 2019 were programmatically extracted, deidentified, cleaned, and analyzed using a combination of natural language processing and qualitative data analyses. This study identified spontaneously reported symptoms and their impacts, symptom duration, and the impact of treatment for both treatment groups. Results: Overall, 1037 users (9023 posts) and 114 users (442 posts) were included in the metastatic group and adjuvant group, respectively. The most identified symptoms in both groups were fatigue, pain, or exanthema (identified in 5%-43% of patients dependent on the treatment group). Symptom impacts reported by both groups were physical impacts, impacts on family, and impacts on work. Positive treatment impacts were reported in both groups and covered the areas of work, social and family life, and general health and quality of life. Conclusions: This study explored health-related social media to better understand the experience and perspectives of patients with melanoma receiving immunotherapy or targeted therapy treatments as well as the experience of their caregivers. This exploratory work uncovered the most discussed concerns among patients and caregivers on the forums including symptoms and their impacts, thus contributing to a deeper understanding of the patient/caregiver experience. UR - https://cancer.jmir.org/2022/2/e34073 UR - http://dx.doi.org/10.2196/34073 UR - http://www.ncbi.nlm.nih.gov/pubmed/35559986 ID - info:doi/10.2196/34073 ER - TY - JOUR AU - Guendelman, Sylvia AU - Pleasants, Elizabeth AU - Cheshire, Coye AU - Kong, Ashley PY - 2022/5/12 TI - Exploring Google Searches for Out-of-Clinic Medication Abortion in the United States During 2020: Infodemiology Approach Using Multiple Samples JO - JMIR Infodemiology SP - e33184 VL - 2 IS - 1 KW - abortion KW - abortion access KW - internet KW - online information KW - Google searches KW - infodemiology N2 - Background: As access barriers to in-person abortion care increase due to legal restrictions and COVID-19?related disruptions, individuals may be turning to the internet for information and services on out-of-clinic medication abortions. Google searches allow us to explore timely population-level interest in this topic and assess its implications. Objective: We examined the extent to which people searched for out-of-clinic medication abortions in the United States in 2020 through 3 initial search terms: home abortion, self abortion, and buy abortion pill online. Methods: Using the Google Trends website, we estimated the relative search index (RSI)?a comparative measure of search popularity?for each initial search term and determined trends and its peak value between January 1, 2020, and January 1, 2021. RSI scores also helped to identify the 10 states where these searches were most popular. We developed a master list of top search queries for each of the initial search terms using the Google Trends application programming interface (API). We estimated the relative search volume (RSV)?the search volume of each query relative to other associated terms?for each of the top queries using the Google Health Trends API. We calculated average RSIs and RSVs from multiple samples to account for low-frequency data. Using the Custom Search API, we determined the top webpages presented to people searching for each of the initial search terms, contextualizing the information found when searching them on Google. Results: Searches for home abortion had average RSIs that were 3 times higher than self abortion and almost 4 times higher than buy abortion pill online. Interest in home abortion peaked in November 2020, during the third pandemic wave, at a time when providers could dispense medication abortion using telemedicine and by mail. Home abortion was most frequently queried by searching for Planned Parenthood, abortion pill, and abortion clinic, presumably denoting varying degrees of clinical support. Consistently lower search popularity for self abortion and buy abortion pill online reflect less population interest in mostly or completely self-managed out-of-clinic abortions. We observed the highest interest for home abortion and self abortion in states hostile to abortion, suggesting that state restrictions encourage these online searches. Top webpages provided limited evidence-based clinical content on self-management of abortions, and several antiabortion sites presented health-related disinformation. Conclusions: During the pandemic in the United States, there has been considerably more interest in home abortions than in minimally or nonclinically supported self-abortions. While our study was mainly descriptive, showing how infrequent abortion-related search data can be analyzed through multiple resampling, future studies should explore correlations between the keywords denoting interest in out-of-clinic abortion and abortion care measures and test models that allow for improved monitoring and surveillance of abortion concerns in our rapidly evolving policy context. UR - https://infodemiology.jmir.org/2022/1/e33184 UR - http://dx.doi.org/10.2196/33184 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113801 ID - info:doi/10.2196/33184 ER - TY - JOUR AU - Gangireddy, Rakshith AU - Chakraborty, Stuti AU - Pakenham-Walsh, Neil AU - Nagarajan, Branavan AU - Krishan, Prerna AU - McGuire, Richard AU - Vaghela, Gladson AU - Sriharan, Abi PY - 2022/5/11 TI - Themes Surrounding COVID-19 and Its Infodemic: Qualitative Analysis of the COVID-19 Discussion on the Multidisciplinary Healthcare Information for All Health Forum JO - JMIR Infodemiology SP - e30167 VL - 2 IS - 1 KW - infodemic KW - infodemiology KW - COVID-19 KW - pandemic KW - misinformation KW - health information KW - theme KW - public health KW - qualitative study KW - global health N2 - Background: Healthcare Information for All (HIFA) is a multidisciplinary global campaign consisting of more than 20,000 members worldwide committed to improving the availability and use of health care information in low- and middle-income countries (LMICs). During the COVID-19 pandemic, online HIFA forums saw a tremendous amount of discussion regarding the lack of information about COVID-19, the spread of misinformation, and the pandemic?s impact on different communities. Objective: This study aims to analyze the themes and perspectives shared in the COVID-19 discussion on English HIFA forums. Methods: Over a period of 8 months, a qualitative thematic content analysis of the COVID-19 discussion on English HIFA forums was conducted. In total, 865 posts between January 24 and October 31, 2020, from 246 unique study participants were included and analyzed. Results: In total, 6 major themes were identified: infodemic, health system, digital health literacy, economic consequences, marginalized peoples, and mental health. The geographical distribution of study participants involved in the discussion spanned across 46 different countries in every continent except Antarctica. Study participants? professions included public health workers, health care providers, and researchers, among others. Study participants? affiliation included nongovernment organizations (NGOs), commercial organizations, academic institutions, the United Nations (UN), the World Health Organization (WHO), and others. Conclusions: The themes that emerged from this analysis highlight personal recounts, reflections, suggestions, and evidence around addressing COVID-19 related misinformation and might also help to understand the timeline of information evolution, focus, and needs surrounding the COVID-19 pandemic. UR - https://infodemiology.jmir.org/2022/1/e30167 UR - http://dx.doi.org/10.2196/30167 UR - http://www.ncbi.nlm.nih.gov/pubmed/35586197 ID - info:doi/10.2196/30167 ER - TY - JOUR AU - Niu, Qian AU - Liu, Junyu AU - Kato, Masaya AU - Shinohara, Yuki AU - Matsumura, Natsuki AU - Aoyama, Tomoki AU - Nagai-Tanima, Momoko PY - 2022/5/9 TI - Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis JO - JMIR Infodemiology SP - e32335 VL - 2 IS - 1 KW - COVID-19 KW - Japan KW - vaccine KW - Twitter KW - sentiment KW - latent dirichlet allocation KW - natural language processing N2 - Background: COVID-19 vaccines are considered one of the most effective ways for containing the COVID-19 pandemic, but Japan lagged behind other countries in vaccination in the early stages. A deeper understanding of the slow progress of vaccination in Japan can be instructive for COVID-19 booster vaccination and vaccinations during future pandemics. Objective: This retrospective study aims to analyze the slow progress of early-stage vaccination in Japan by exploring opinions and sentiment toward the COVID-19 vaccine in Japanese tweets before and at the beginning of vaccination. Methods: We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords between August 1, 2020, and June 30, 2021. We visualized the trend of the tweets and sentiments and identified the critical events that may have triggered the surges. Correlations between sentiments and the daily infection, death, and vaccination cases were calculated. The latent dirichlet allocation model was applied to identify topics of negative tweets from the beginning of vaccination. We also conducted an analysis of vaccine brands (Pfizer, Moderna, AstraZeneca) approved in Japan. Results: The daily number of tweets continued with accelerating growth after the start of large-scale vaccinations in Japan. The sentiments of around 85% of the tweets were neutral, and negative sentiment overwhelmed the positive sentiment in the other tweets. We identified 6 public-concerned topics related to the negative sentiment at the beginning of the vaccination process. Among the vaccines from the 3 manufacturers, the attitude toward Moderna was the most positive, and the attitude toward AstraZeneca was the most negative. Conclusions: Negative sentiment toward vaccines dominated positive sentiment in Japan, and the concerns about side effects might have outweighed fears of infection at the beginning of the vaccination process. Topic modeling on negative tweets indicated that the government and policy makers should take prompt actions in building a safe and convenient vaccine reservation and rollout system, which requires both flexibility of the medical care system and the acceleration of digitalization in Japan. The public showed different attitudes toward vaccine brands. Policy makers should provide more evidence about the effectiveness and safety of vaccines and rebut fake news to build vaccine confidence. UR - https://infodemiology.jmir.org/2022/1/e32335 UR - http://dx.doi.org/10.2196/32335 UR - http://www.ncbi.nlm.nih.gov/pubmed/35578643 ID - info:doi/10.2196/32335 ER - TY - JOUR AU - Golder, Su AU - Stevens, Robin AU - O'Connor, Karen AU - James, Richard AU - Gonzalez-Hernandez, Graciela PY - 2022/4/29 TI - Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review JO - J Med Internet Res SP - e35788 VL - 24 IS - 4 KW - twitter KW - social media KW - race KW - ethnicity N2 - Background: A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. Objective: This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. Methods: We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both. Results: Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown. Conclusions: There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice. UR - https://www.jmir.org/2022/4/e35788 UR - http://dx.doi.org/10.2196/35788 UR - http://www.ncbi.nlm.nih.gov/pubmed/35486433 ID - info:doi/10.2196/35788 ER - TY - JOUR AU - Kyabaggu, Ramona AU - Marshall, Deneice AU - Ebuwei, Patience AU - Ikenyei, Uche PY - 2022/4/28 TI - Health Literacy, Equity, and Communication in the COVID-19 Era of Misinformation: Emergence of Health Information Professionals in Infodemic Management JO - JMIR Infodemiology SP - e35014 VL - 2 IS - 1 KW - COVID-19 KW - social media KW - infodemiology KW - infoveillance KW - equity KW - health literacy KW - digital literacy KW - health information management KW - pandemic KW - health information KW - public policy KW - infodemic UR - https://infodemiology.jmir.org/2022/1/e35014 UR - http://dx.doi.org/10.2196/35014 UR - http://www.ncbi.nlm.nih.gov/pubmed/35529308 ID - info:doi/10.2196/35014 ER - TY - JOUR AU - Han, Joseph AU - Kamat, Samir AU - Agarwal, Aneesh AU - O'Hagan, Ross AU - Tukel, Connor AU - Owji, Shayan AU - Ghalili, Sabrina AU - Luu, Yen AU - Dautriche Svidzinski, Cula AU - Abittan, J. Brian AU - Ungar, Jonathan AU - Gulati, Nicholas PY - 2022/4/27 TI - Correlation Between Interest in COVID-19 Hair Loss and COVID-19 Surges: Analysis of Google Trends JO - JMIR Dermatol SP - e37271 VL - 5 IS - 2 KW - COVID-19 KW - SARS-CoV-2 virus KW - pandemic KW - hair loss KW - telogen effluvium KW - Google Trends KW - omicron KW - omicron variant KW - delta variant KW - public interest KW - stress KW - dermatology KW - public perception KW - social media KW - online health KW - digital dermatology UR - https://derma.jmir.org/2022/2/e37271 UR - http://dx.doi.org/10.2196/37271 UR - http://www.ncbi.nlm.nih.gov/pubmed/35505684 ID - info:doi/10.2196/37271 ER - TY - JOUR AU - Tahamtan, Iman AU - Potnis, Devendra AU - Mohammadi, Ehsan AU - Singh, Vandana AU - Miller, E. Laura PY - 2022/4/26 TI - The Mutual Influence of the World Health Organization (WHO) and Twitter Users During COVID-19: Network Agenda-Setting Analysis JO - J Med Internet Res SP - e34321 VL - 24 IS - 4 KW - COVID-19 KW - agenda setting KW - network agenda setting KW - Twitter KW - social media KW - public opinion KW - content analysis KW - public health KW - WHO N2 - Background: Little is known about the role of the World Health Organization (WHO) in communicating with the public on social media during a global health emergency. More specifically, there is no study about the relationship between the agendas of the WHO and Twitter users during the COVID-19 pandemic. Objective: This study utilizes the network agenda-setting model to investigate the mutual relationship between the agenda of the WHO?s official Twitter account and the agenda of 7.5 million of its Twitter followers regarding COVID-19. Methods: Content analysis was applied to 7090 tweets posted by the WHO on Twitter from January 1, 2020, to July 31, 2020, to identify the topics of tweets. The quadratic assignment procedure (QAP) was used to investigate the relationship between the WHO agenda network and the agenda network of the 6 Twitter user categories, including ?health care professionals,? ?academics,? ?politicians,? ?print and electronic media,? ?legal professionals,? and the ?private sector.? Additionally, 98 Granger causality statistical tests were performed to determine which topic in the WHO agenda had an effect on the corresponding topic in each Twitter user category and vice versa. Results: Content analysis revealed 7 topics that reflect the WHO agenda related to the COVID-19 pandemic, including ?prevention,? ?solidarity,? ?charity,? ?teamwork,? ?ill-effect,? ?surveillance,? and ?credibility.? Results of the QAP showed significant and strong correlations between the WHO agenda network and the agenda network of each Twitter user category. These results provide evidence that WHO had an overall effect on different types of Twitter users on the identified topics. For instance, the Granger causality tests indicated that the WHO tweets influenced politicians and print and electronic media about ?surveillance.? The WHO tweets also influenced academics and the private sector about ?credibility? and print and electronic media about ?ill-effect.? Additionally, Twitter users affected some topics in the WHO. For instance, WHO followers affected ?charity? and ?prevention? in the WHO. Conclusions: This paper extends theorizing on agenda setting by providing empirical evidence that agenda-setting effects vary by topic and types of Twitter users. Although prior studies showed that network agenda setting is a ?one-way? model, the novel findings of this research confirm a ?2-way? or ?multiway? effect of agenda setting on social media due to the interactions between the content creators and audiences. The WHO can determine which topics should be promoted on social media during different phases of a pandemic and collaborate with other public health gatekeepers to collectively make them salient in the public. UR - https://www.jmir.org/2022/4/e34321 UR - http://dx.doi.org/10.2196/34321 UR - http://www.ncbi.nlm.nih.gov/pubmed/35275836 ID - info:doi/10.2196/34321 ER - TY - JOUR AU - Chen, Yen-Pin AU - Chen, Yi-Ying AU - Yang, Kai-Chou AU - Lai, Feipei AU - Huang, Chien-Hua AU - Chen, Yun-Nung AU - Tu, Yi-Chin PY - 2022/4/26 TI - The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media JO - J Med Internet Res SP - e36830 VL - 24 IS - 4 KW - misinformation KW - vaccine hesitancy KW - vaccination KW - infodemic KW - infodemiology KW - COVID-19 KW - public immunity KW - social media KW - fake news N2 - Background: Vaccination is an important intervention to prevent the incidence and spread of serious diseases. Many factors including information obtained from the internet influence individuals? decisions to vaccinate. Misinformation is a critical issue and can be hard to detect, although it can change people's minds, opinions, and decisions. The impact of misinformation on public health and vaccination hesitancy is well documented, but little research has been conducted on the relationship between the size of the population reached by misinformation and the vaccination decisions made by that population. A number of fact-checking services are available on the web, including the Islander news analysis system, a free web service that provides individuals with real-time judgment on web news. In this study, we used such services to estimate the amount of fake news available and used Google Trends levels to model the spread of fake news. We quantified this relationship using official public data on COVID-19 vaccination in Taiwan. Objective: In this study, we aimed to quantify the impact of the magnitude of the propagation of fake news on vaccination decisions. Methods: We collected public data about COVID-19 infections and vaccination from Taiwan's official website and estimated the popularity of searches using Google Trends. We indirectly collected news from 26 digital media sources, using the news database of the Islander system. This system crawls the internet in real time, analyzes the news, and stores it. The incitement and suspicion scores of the Islander system were used to objectively judge news, and a fake news percentage variable was produced. We used multivariable linear regression, chi-square tests, and the Johnson-Neyman procedure to analyze this relationship, using weekly data. Results: A total of 791,183 news items were obtained over 43 weeks in 2021. There was a significant increase in the proportion of fake news in 11 of the 26 media sources during the public vaccination stage. The regression model revealed a positive adjusted coefficient (?=0.98, P=.002) of vaccine availability on the following week's vaccination doses, and a negative adjusted coefficient (?=?3.21, P=.04) of the interaction term on the fake news percentage with the Google Trends level. The Johnson-Neiman plot of the adjusted effect for the interaction term showed that the Google Trends level had a significant negative adjustment effect on vaccination doses for the following week when the proportion of fake news exceeded 39.3%. Conclusions: There was a significant relationship between the amount of fake news to which the population was exposed and the number of vaccination doses administered. Reducing the amount of fake news and increasing public immunity to misinformation will be critical to maintain public health in the internet age. UR - https://www.jmir.org/2022/4/e36830 UR - http://dx.doi.org/10.2196/36830 UR - http://www.ncbi.nlm.nih.gov/pubmed/35380546 ID - info:doi/10.2196/36830 ER - TY - JOUR AU - Trevino, Jesus AU - Malik, Sanjeev AU - Schmidt, Michael PY - 2022/4/22 TI - Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study JO - JMIR Infodemiology SP - e32386 VL - 2 IS - 1 KW - infodemiology KW - patient volume forecasting KW - emergency medicine KW - digital health KW - Google Trends KW - infoveillance KW - social media KW - prediction models KW - emergency department N2 - Background: The search for health information from web-based resources raises opportunities to inform the service operations of health care systems. Google Trends search query data have been used to study public health topics, such as seasonal influenza, suicide, and prescription drug abuse; however, there is a paucity of literature using Google Trends data to improve emergency department patient-volume forecasting. Objective: We assessed the ability of Google Trends search query data to improve the performance of adult emergency department daily volume prediction models. Methods: Google Trends search query data related to chief complaints and health care facilities were collected from Chicago, Illinois (July 2015 to June 2017). We calculated correlations between Google Trends search query data and emergency department daily patient volumes from a tertiary care adult hospital in Chicago. A baseline multiple linear regression model of emergency department daily volume with traditional predictors was augmented with Google Trends search query data; model performance was measured using mean absolute error and mean absolute percentage error. Results: There were substantial correlations between emergency department daily volume and Google Trends ?hospital? (r=0.54), combined terms (r=0.50), and ?Northwestern Memorial Hospital? (r=0.34) search query data. The final Google Trends data?augmented model included the predictors Combined 3-day moving average and Hospital 3-day moving average and performed better (mean absolute percentage error 6.42%) than the final baseline model (mean absolute percentage error 6.67%)?an improvement of 3.1%. Conclusions: The incorporation of Google Trends search query data into an adult tertiary care hospital emergency department daily volume prediction model modestly improved model performance. Further development of advanced models with comprehensive search query terms and complementary data sources may improve prediction performance and could be an avenue for further research. UR - https://infodemiology.jmir.org/2022/1/e32386 UR - http://dx.doi.org/10.2196/32386 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113800 ID - info:doi/10.2196/32386 ER - TY - JOUR AU - Grigsby-Toussaint, Diana AU - Champagne, Ashley AU - Uhr, Justin AU - Silva, Elizabeth AU - Noh, Madeline AU - Bradley, Adam AU - Rashleigh, Patrick PY - 2022/4/20 TI - US Black Maternal Health Advocacy Topics and Trends on Twitter: Temporal Infoveillance Study JO - JMIR Infodemiology SP - e30885 VL - 2 IS - 1 KW - Black maternal health KW - disparity KW - COVID-19 KW - Twitter KW - topic modeling KW - digital humanities KW - infoveillance KW - maternal health KW - minority KW - women KW - advocacy KW - social media KW - model KW - trend KW - feasibility N2 - Background: Black women in the United States disproportionately suffer adverse pregnancy and birth outcomes compared to White women. Economic adversity and implicit bias during clinical encounters may lead to physiological responses that place Black women at higher risk for adverse birth outcomes. The novel coronavirus disease of 2019 (COVID-19) further exacerbated this risk, as safety protocols increased social isolation in clinical settings, thereby limiting opportunities to advocate for unbiased care. Twitter, 1 of the most popular social networking sites, has been used to study a variety of issues of public interest, including health care. This study considers whether posts on Twitter accurately reflect public discourse during the COVID-19 pandemic and are being used in infodemiology studies by public health experts. Objective: This study aims to assess the feasibility of Twitter for identifying public discourse related to social determinants of health and advocacy that influence maternal health among Black women across the United States and to examine trends in sentiment between 2019 and 2020 in the context of the COVID-19 pandemic. Methods: Tweets were collected from March 1 to July 13, 2020, from 21 organizations and influencers and from 4 hashtags that focused on Black maternal health. Additionally, tweets from the same organizations and hashtags were collected from the year prior, from March 1 to July 13, 2019. Twint, a Python programming library, was used for data collection and analysis. We gathered the text of approximately 17,000 tweets, as well as all publicly available metadata. Topic modeling and k-means clustering were used to analyze the tweets. Results: A variety of trends were observed when comparing the 2020 data set to the 2019 data set from the same period. The percentages listed for each topic are probabilities of that topic occurring in our corpus. In our topic models, tweets on reproductive justice, maternal mortality crises, and patient care increased by 67.46% in 2020 versus 2019. Topics on community, advocacy, and health equity increased by over 30% in 2020 versus 2019. In contrast, tweet topics that decreased in 2020 versus 2019 were as follows: tweets on Medicaid and medical coverage decreased by 27.73%, and discussions about creating space for Black women decreased by just under 30%. Conclusions: The results indicate that the COVID-19 pandemic may have spurred an increased focus on advocating for improved reproductive health and maternal health outcomes among Black women in the United States. Further analyses are needed to capture a longer time frame that encompasses more of the pandemic, as well as more diverse voices to confirm the robustness of the findings. We also concluded that Twitter is an effective source for providing a snapshot of relevant topics to guide Black maternal health advocacy efforts. UR - https://infodemiology.jmir.org/2022/1/e30885 UR - http://dx.doi.org/10.2196/30885 UR - http://www.ncbi.nlm.nih.gov/pubmed/35578642 ID - info:doi/10.2196/30885 ER - TY - JOUR AU - Rovetta, Alessandro PY - 2022/4/19 TI - Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis JO - JMIRx Med SP - e35356 VL - 3 IS - 2 KW - COVID-19 KW - epidemiology KW - Google Trends KW - infodemiology KW - infoveillance KW - Italy KW - public health KW - SARS-CoV-2 KW - vaccinations KW - vaccines KW - social media analysis KW - social media N2 - Background: Google Trends is an infoveillance tool widely used by the scientific community to investigate different user behaviors related to COVID-19. However, several limitations regarding its adoption are reported in the literature. Objective: This paper aims to provide an effective and efficient approach to investigating vaccine adherence against COVID-19 via Google Trends. Methods: Through the cross-correlational analysis of well-targeted hypotheses, we investigate the predictive capacity of web searches related to COVID-19 toward vaccinations in Italy from November 2020 to November 2021. The keyword ?vaccine reservation? query (VRQ) was chosen as it reflects a real intention of being vaccinated (V). Furthermore, the impact of the second most read Italian newspaper (vaccine-related headlines [VRH]) on vaccine-related web searches was investigated to evaluate the role of the mass media as a confounding factor. Fisher r-to-z transformation (z) and percentage difference (?) were used to compare Spearman coefficients. A regression model V=f(VRH, VRQ) was built to validate the results found. The Holm-Bonferroni correction was adopted (P*). SEs are reported. Results: Simple and generic keywords are more likely to identify the actual web interest in COVID-19 vaccines than specific and elaborated keywords. Cross-correlations between VRQ and V were very strong and significant (min r˛=0.460, P*<.001, lag 0 weeks; max r˛=0.903, P*<.001, lag 6 weeks). The remaining cross-correlations have been markedly lower (?>55.8%; z>5.8; P*<.001). The regression model confirmed the greater significance of VRQ versus VRH (P*<.001 vs P=.03, P*=.29). Conclusions: This research provides preliminary evidence in favor of using Google Trends as a surveillance and prediction tool for vaccine adherence against COVID-19 in Italy. Further research is needed to establish the appropriate use and limits of Google Trends for vaccination tracking. However, these findings prove that the search for suitable keywords is a fundamental step to reduce confounding factors. Additionally, targeting hypotheses helps diminish the likelihood of spurious correlations. It is recommended that Google Trends be leveraged as a complementary infoveillance tool by government agencies to monitor and predict vaccine adherence in this and future crises by following the methods proposed in this paper. UR - https://med.jmirx.org/2022/2/e35356 UR - http://dx.doi.org/10.2196/35356 UR - http://www.ncbi.nlm.nih.gov/pubmed/35481982 ID - info:doi/10.2196/35356 ER - TY - JOUR AU - Chandrasekaran, Ranganathan AU - Desai, Rashi AU - Shah, Harsh AU - Kumar, Vivek AU - Moustakas, Evangelos PY - 2022/4/15 TI - Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts JO - JMIR Infodemiology SP - e33909 VL - 2 IS - 1 KW - coronavirus KW - infoveillance KW - COVID-19 KW - vaccination KW - social media KW - Twitter study KW - text mining KW - sentiment analysis KW - topic modeling KW - tweets KW - content analysis N2 - Background: A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine?related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users. Objective: The aim of this study was to examine key themes and topics from COVID-19 vaccine?related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments. Methods: We gathered and assessed a corpus of 2.94 million COVID-19 vaccine?related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination. Results: Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94%), followed by vaccine hesitancy (12.63%) and postvaccination symptoms and effects (10.44%) Average compound sentiment scores were negative throughout the 16 weeks for the topics postvaccination symptoms and side effects and hoax/conspiracy. However, consistent positive sentiment scores were observed for the topics vaccination disclosure, vaccine efficacy, clinical trials and approvals, affordability, regulation, distribution and shortage, travel, appointment and scheduling, vaccination sites, advocacy, opinion leaders and endorsement, and gratitude toward health care workers. Reversal in sentiment scores in a few weeks was observed for the topics vaccination eligibility and hesitancy. Conclusions: Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts. UR - https://infodemiology.jmir.org/2022/1/e33909 UR - http://dx.doi.org/10.2196/33909 UR - http://www.ncbi.nlm.nih.gov/pubmed/35462735 ID - info:doi/10.2196/33909 ER - TY - JOUR AU - Mohammadi, Ehsan AU - Tahamtan, Iman AU - Mansourian, Yazdan AU - Overton, Holly PY - 2022/4/13 TI - Identifying Frames of the COVID-19 Infodemic: Thematic Analysis of Misinformation Stories Across Media JO - JMIR Infodemiology SP - e33827 VL - 2 IS - 1 KW - COVID-19 KW - pandemic KW - misinformation KW - fake news KW - framing theory KW - social media KW - infodemic KW - thematic analysis KW - theme KW - pattern KW - prevalence N2 - Background: The word ?infodemic? refers to the deluge of false information about an event, and it is a global challenge for today?s society. The sheer volume of misinformation circulating during the COVID-19 pandemic has been harmful to people around the world. Therefore, it is important to study different aspects of misinformation related to the pandemic. Objective: This paper aimed to identify the main subthemes related to COVID-19 misinformation on various platforms, from traditional outlets to social media. This paper aimed to place these subthemes into categories, track the changes, and explore patterns in prevalence, over time, across different platforms and contexts. Methods: From a theoretical perspective, this research was rooted in framing theory; it also employed thematic analysis to identify the main themes and subthemes related to COVID-19 misinformation. The data were collected from 8 fact-checking websites that formed a sample of 127 pieces of false COVID-19 news published from January 1, 2020 to March 30, 2020. Results: The findings revealed 4 main themes (attribution, impact, protection and solutions, and politics) and 19 unique subthemes within those themes related to COVID-19 misinformation. Governmental and political organizations (institutional level) and administrators and politicians (individual level) were the 2 most frequent subthemes, followed by origination and source, home remedies, fake statistics, treatments, drugs, and pseudoscience, among others. Results indicate that the prevalence of misinformation subthemes had altered over time between January 2020 and March 2020. For instance, false stories about the origin and source of the virus were frequent initially (January). Misinformation regarding home remedies became a prominent subtheme in the middle (February), while false information related to government organizations and politicians became popular later (March). Although conspiracy theory web pages and social media outlets were the primary sources of misinformation, surprisingly, results revealed trusted platforms such as official government outlets and news organizations were also avenues for creating COVID-19 misinformation. Conclusions: The identified themes in this study reflect some of the information attitudes and behaviors, such as denial, uncertainty, consequences, and solution-seeking, that provided rich information grounds to create different types of misinformation during the COVID-19 pandemic. Some themes also indicate that the application of effective communication strategies and the creation of timely content were used to persuade human minds with false stories in different phases of the crisis. The findings of this study can be beneficial for communication officers, information professionals, and policy makers to combat misinformation in future global health crises or related events. UR - https://infodemiology.jmir.org/2022/1/e33827 UR - http://dx.doi.org/10.2196/33827 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113806 ID - info:doi/10.2196/33827 ER - TY - JOUR AU - Gunasekeran, Visva Dinesh AU - Chew, Alton AU - Chandrasekar, K. Eeshwar AU - Rajendram, Priyanka AU - Kandarpa, Vasundhara AU - Rajendram, Mallika AU - Chia, Audrey AU - Smith, Helen AU - Leong, Kit Choon PY - 2022/4/11 TI - The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review JO - J Med Internet Res SP - e33680 VL - 24 IS - 4 KW - digital health KW - social media KW - big data KW - population health KW - blockchain KW - COVID-19 KW - review KW - benefit KW - challenge KW - public health N2 - Background:  Social media platforms have numerous potential benefits and drawbacks on public health, which have been described in the literature. The COVID-19 pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions. Objective: This review aims to highlight a brief history of social media in health care and report its potential negative and positive public health impacts, which have been characterized in the literature. Methods:  We searched electronic bibliographic databases including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020. We screened the title and abstracts and selected relevant reports for review of full text and reference lists. These were analyzed thematically and consolidated into applications of social media platforms for public health. Results:  The positive and negative impact of social media platforms on public health are catalogued on the basis of recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence. However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy. Conclusions:  Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring, and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health. UR - https://www.jmir.org/2022/4/e33680 UR - http://dx.doi.org/10.2196/33680 UR - http://www.ncbi.nlm.nih.gov/pubmed/35129456 ID - info:doi/10.2196/33680 ER - TY - JOUR AU - Li, Ang AU - Jiao, Dongdong AU - Zhu, Tingshao PY - 2022/4/8 TI - Stigmatizing Attitudes Across Cybersuicides and Offline Suicides: Content Analysis of Sina Weibo JO - J Med Internet Res SP - e36489 VL - 24 IS - 4 KW - stigma KW - cybersuicide KW - livestreamed suicide KW - linguistic analysis KW - social media N2 - Background: The new reality of cybersuicide raises challenges to ideologies about the traditional form of suicide that does not involve the internet (offline suicide), which may lead to changes in audience?s attitudes. However, knowledge on whether stigmatizing attitudes differ between cybersuicides and offline suicides remains limited. Objective: This study aims to consider livestreamed suicide as a typical representative of cybersuicide and use social media data (Sina Weibo) to investigate the differences in stigmatizing attitudes across cybersuicides and offline suicides in terms of attitude types and linguistic characteristics. Methods: A total of 4393 cybersuicide-related and 2843 offline suicide-related Weibo posts were collected and analyzed. First, human coders were recruited and trained to perform a content analysis on the collected posts to determine whether each of them reflected stigma. Second, a text analysis tool was used to automatically extract a number of psycholinguistic features from each post. Subsequently, based on the selected features, a series of classification models were constructed for different purposes: differentiating the general stigma of cybersuicide from that of offline suicide and differentiating the negative stereotypes of cybersuicide from that of offline suicide. Results: In terms of attitude types, cybersuicide was observed to carry more stigma than offline suicide (?21=179.8; P<.001). Between cybersuicides and offline suicides, there were significant differences in the proportion of posts associated with five different negative stereotypes, including stupid and shallow (?21=28.9; P<.001), false representation (?21=144.4; P<.001), weak and pathetic (?21=20.4; P<.001), glorified and normalized (?21=177.6; P<.001), and immoral (?21=11.8; P=.001). Similar results were also found for different genders and regions. In terms of linguistic characteristics, the F-measure values of the classification models ranged from 0.81 to 0.85. Conclusions: The way people perceive cybersuicide differs from how they perceive offline suicide. The results of this study have implications for reducing the stigma against suicide. UR - https://www.jmir.org/2022/4/e36489 UR - http://dx.doi.org/10.2196/36489 UR - http://www.ncbi.nlm.nih.gov/pubmed/35394437 ID - info:doi/10.2196/36489 ER - TY - JOUR AU - Rovetta, Alessandro AU - Bhagavathula, Srikanth Akshaya PY - 2022/4/7 TI - The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends JO - JMIR Public Health Surveill SP - e36022 VL - 8 IS - 4 KW - COVID-19 KW - deniers KW - excess deaths KW - epidemiology KW - infodemic KW - infodemiology KW - Italy KW - longitudinal analysis KW - mortality KW - time series KW - pandemic KW - public health N2 - Background: Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy. Objective: This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series. Methods: We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis. Results: Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals? using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006). Conclusions: Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19?related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends. UR - https://publichealth.jmir.org/2022/4/e36022 UR - http://dx.doi.org/10.2196/36022 UR - http://www.ncbi.nlm.nih.gov/pubmed/35238784 ID - info:doi/10.2196/36022 ER - TY - JOUR AU - Wang, Alex AU - McCarron, Robert AU - Azzam, Daniel AU - Stehli, Annamarie AU - Xiong, Glen AU - DeMartini, Jeremy PY - 2022/3/31 TI - Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study JO - JMIR Ment Health SP - e35253 VL - 9 IS - 3 KW - depression KW - epidemiology KW - internet KW - google trends KW - big data KW - mental health N2 - Background: The epidemiology of mental health disorders has important theoretical and practical implications for health care service and planning. The recent increase in big data storage and subsequent development of analytical tools suggest that mining search databases may yield important trends on mental health, which can be used to support existing population health studies. Objective: This study aimed to map depression search intent in the United States based on internet-based mental health queries. Methods: Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: ?feeling sad,? ?depressed,? ?depression,? ?empty,? ?insomnia,? ?fatigue,? ?guilty,? ?feeling guilty,? and ?suicide.? Multivariable regression models were created based on geographic and environmental factors and normalized to the following control terms: ?sports,? ?news,? ?google,? ?youtube,? ?facebook,? and ?netflix.? Heat maps of population depression were generated based on search intent. Results: Depression search intent grew 67% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P<.001) and early spring months (adjusted P<.001), relative to summer months. Geographic location correlated with depression search intent with states in the Northeast (adjusted P=.01) having higher search intent than states in the South. Conclusions: The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map depression prevalence in the United States. UR - https://mental.jmir.org/2022/3/e35253 UR - http://dx.doi.org/10.2196/35253 UR - http://www.ncbi.nlm.nih.gov/pubmed/35357320 ID - info:doi/10.2196/35253 ER - TY - JOUR AU - Marshall, Christopher AU - Lanyi, Kate AU - Green, Rhiannon AU - Wilkins, C. Georgina AU - Pearson, Fiona AU - Craig, Dawn PY - 2022/3/31 TI - Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study JO - JMIR Infodemiology SP - e32449 VL - 2 IS - 1 KW - Twitter KW - mental health KW - COVID-19 KW - sentiment KW - lockdown KW - soft intelligence KW - artificial intelligence KW - machine learning KW - natural language processing N2 - Background: There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making. Objective: The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic. Methods: A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter?s advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized. Results: We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25% and 49%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people?s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. Conclusions: Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people?s mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis. UR - https://infodemiology.jmir.org/2022/1/e32449 UR - http://dx.doi.org/10.2196/32449 UR - http://www.ncbi.nlm.nih.gov/pubmed/36406146 ID - info:doi/10.2196/32449 ER - TY - JOUR AU - Bacsu, Juanita-Dawne AU - Fraser, Sarah AU - Chasteen, L. Alison AU - Cammer, Allison AU - Grewal, S. Karl AU - Bechard, E. Lauren AU - Bethell, Jennifer AU - Green, Shoshana AU - McGilton, S. Katherine AU - Morgan, Debra AU - O?Rourke, M. Hannah AU - Poole, Lisa AU - Spiteri, J. Raymond AU - O'Connell, E. Megan PY - 2022/3/31 TI - Using Twitter to Examine Stigma Against People With Dementia During COVID-19: Infodemiology Study JO - JMIR Aging SP - e35677 VL - 5 IS - 1 KW - coronavirus 2019 KW - social media KW - stigma KW - dementia KW - ageism KW - COVID-19 KW - Twitter KW - bias KW - infodemiology KW - attention KW - risk KW - impact KW - misinformation KW - belief KW - cognition KW - cognitive impairment N2 - Background: During the pandemic, there has been significant social media attention focused on the increased COVID-19 risks and impacts for people with dementia and their care partners. However, these messages can perpetuate misconceptions, false information, and stigma. Objective: This study used Twitter data to understand stigma against people with dementia propagated during the COVID-19 pandemic. Methods: We collected 1743 stigma-related tweets using the GetOldTweets application in Python from February 15 to September 7, 2020. Thematic analysis was used to analyze the tweets. Results: Based on our analysis, 4 main themes were identified: (1) ageism and devaluing the lives of people with dementia, (2) misinformation and false beliefs about dementia and COVID-19, (3) dementia used as an insult for political ridicule, and (4) challenging stigma against dementia. Social media has been used to spread stigma, but it can also be used to challenge negative beliefs, stereotypes, and false information. Conclusions: Dementia education and awareness campaigns are urgently needed on social media to address COVID-19-related stigma. When stigmatizing discourse on dementia is widely shared and consumed amongst the public, it has public health implications. How we talk about dementia shapes how policymakers, clinicians, and the public value the lives of people with dementia. Stigma perpetuates misinformation, pejorative language, and patronizing attitudes that can lead to discriminatory actions, such as the limited provision of lifesaving supports and health services for people with dementia during the pandemic. COVID-19 policies and public health messages should focus on precautions and preventive measures rather than labeling specific population groups. UR - https://aging.jmir.org/2022/1/e35677 UR - http://dx.doi.org/10.2196/35677 UR - http://www.ncbi.nlm.nih.gov/pubmed/35290197 ID - info:doi/10.2196/35677 ER - TY - JOUR AU - Purushothaman, Vidya AU - McMann, Tiana AU - Nali, Matthew AU - Li, Zhuoran AU - Cuomo, Raphael AU - Mackey, K. Tim PY - 2022/3/30 TI - Content Analysis of Nicotine Poisoning (Nic Sick) Videos on TikTok: Retrospective Observational Infodemiology Study JO - J Med Internet Res SP - e34050 VL - 24 IS - 3 KW - nic sick KW - vaping KW - tobacco KW - social media KW - TikTok KW - content analysis KW - smoking KW - nicotine KW - e-cigarette KW - adverse effects KW - public health KW - infodemiology N2 - Background: TikTok is a microvideo social media platform currently experiencing rapid growth and with 60% of its monthly users between the ages of 16 and 24 years. Increased exposure to e-cigarette content on social media may influence patterns of use, including the risk of overconsumption and possible nicotine poisoning, when users engage in trending challenges online. However, there is limited research assessing the characteristics of nicotine poisoning?related content posted on social media. Objective: We aimed to assess the characteristics of content on TikTok that is associated with a popular nicotine poisoning?related hashtag. Methods: We collected TikTok posts associated with the hashtag #nicsick, using a Python programming package (Selenium) and used an inductive coding approach to analyze video content and characteristics of interest. Videos were manually annotated to generate a codebook of the nicotine sickness?related themes. Statistical analysis was used to compare user engagement characteristics and video length in content with and without active nicotine sickness TikTok topics. Results: A total of 132 TikTok videos associated with the hashtag #nicsick were manually coded, with 52.3% (69/132) identified as discussing firsthand and secondhand reports of suspected nicotine poisoning symptoms and experiences. More than one-third of nicotine poisoning?related content (26/69, 37.68%) portrayed active vaping by users, which included content with vaping behavior such as vaping tricks and overconsumption, and 43% (30/69) of recorded users self-reported experiencing nicotine sickness, poisoning, or adverse events such as vomiting following nicotine consumption. The average follower count of users posting content related to nicotine sickness was significantly higher than that for users posting content unrelated to nicotine sickness (W=2350.5, P=.03). Conclusions: TikTok users openly discuss experiences, both firsthand and secondhand, with nicotine adverse events via the #nicsick hashtag including reports of overconsumption resulting in sickness. These study results suggest that there is a need to assess the utility of digital surveillance on emerging social media platforms for vaping adverse events, particularly on sites popular among youth and young adults. As vaping product use-patterns continue to evolve, digital adverse event detection likely represents an important tool to supplement traditional methods of public health surveillance (such as poison control center prevalence numbers). UR - https://www.jmir.org/2022/3/e34050 UR - http://dx.doi.org/10.2196/34050 UR - http://www.ncbi.nlm.nih.gov/pubmed/35353056 ID - info:doi/10.2196/34050 ER - TY - JOUR AU - Lu, Xinyi AU - Sun, Li AU - Xie, Zidian AU - Li, Dongmei PY - 2022/3/29 TI - Perception of the Food and Drug Administration Electronic Cigarette Flavor Enforcement Policy on Twitter: Observational Study JO - JMIR Public Health Surveill SP - e25697 VL - 8 IS - 3 KW - electronic cigarette KW - FDA flavor enforcement policy KW - Twitter KW - Food and Drug Administration KW - enforcement KW - policy KW - e-cigarettes KW - e-cigarette flavor KW - tobacco flavors KW - prohibit KW - sale N2 - Background: On January 2, 2020, the US Food and Drug Administration (FDA) released the electronic cigarette (e-cigarette) flavor enforcement policy to prohibit the sale of all flavored cartridge?based e-cigarettes, except for menthol and tobacco flavors. Objective: This research aimed to examine the public perception of this FDA flavor enforcement policy and its impact on the public perception of e-cigarettes on Twitter. Methods: A total of 2,341,660 e-cigarette?related tweets and 190,490 FDA flavor enforcement policy?related tweets in the United States were collected from Twitter before (between June 13 and August 22, 2019) and after (between January 2 and March 30, 2020) the announcement of the FDA flavor enforcement policy. Sentiment analysis was conducted to detect the changes in the public perceptions of the policy and e-cigarettes on Twitter. Topic modeling was used for finding frequently discussed topics about e-cigarettes. Results: The proportion of negative sentiment tweets about e-cigarettes significantly increased after the announcement of the FDA flavor enforcement policy compared with before the announcement of the policy. In contrast, the overall sentiment toward the FDA flavor enforcement policy became less negative. The FDA flavor enforcement policy was the most popular topic associated with e-cigarettes after the announcement of the FDA flavor enforcement policy. Twitter users who discussed about e-cigarettes started to talk about other alternative ways of getting e-cigarettes after the FDA flavor enforcement policy. Conclusions: Twitter users? perceptions of e-cigarettes became more negative after the announcement of the FDA flavor enforcement policy. UR - https://publichealth.jmir.org/2022/3/e25697 UR - http://dx.doi.org/10.2196/25697 UR - http://www.ncbi.nlm.nih.gov/pubmed/35348461 ID - info:doi/10.2196/25697 ER - TY - JOUR AU - Jang, Hyeju AU - Rempel, Emily AU - Roe, Ian AU - Adu, Prince AU - Carenini, Giuseppe AU - Janjua, Zafar Naveed PY - 2022/3/29 TI - Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis JO - J Med Internet Res SP - e35016 VL - 24 IS - 3 KW - COVID-19 KW - vaccination KW - Twitter KW - aspect-based sentiment analysis KW - Canada KW - social media KW - pandemic KW - content analysis KW - vaccine rollout KW - sentiment analysis KW - public sentiment KW - public health KW - health promotion KW - vaccination promotion N2 - Background: The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity. Objective: We aim to investigate Twitter users? attitudes toward COVID-19 vaccination in Canada after vaccine rollout. Methods: We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination?related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward ?vaccination? changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects. Results: After applying the ABSA system, we obtained 170 aspect terms (eg, ?immunity? and ?pfizer?) and 6775 opinion terms (eg, ?trustworthy? for the positive sentiment and ?jeopardize? for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to ?vaccine distribution,? ?side effects,? ?allergy,? ?reactions,? and ?anti-vaxxer,? and positive sentiments related to ?vaccine campaign,? ?vaccine candidates,? and ?immune response.? These results indicate that the Twitter users express concerns about the safety of vaccines but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines: the ?anti-vaxxer? population that used negative sentiments as a means to discourage vaccination and the ?Covid Zero? population that used negative sentiments to encourage vaccinations while critiquing the public health response. Conclusions: Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination. UR - https://www.jmir.org/2022/3/e35016 UR - http://dx.doi.org/10.2196/35016 UR - http://www.ncbi.nlm.nih.gov/pubmed/35275835 ID - info:doi/10.2196/35016 ER - TY - JOUR AU - Cresswell, Liam AU - Espin-Noboa, Lisette AU - Murphy, Q. Malia S. AU - Ramlawi, Serine AU - Walker, C. Mark AU - Karsai, Márton AU - Corsi, J. Daniel PY - 2022/3/29 TI - The Volume and Tone of Twitter Posts About Cannabis Use During Pregnancy: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e34421 VL - 11 IS - 3 KW - cannabis KW - pregnancy KW - health information KW - social media KW - Twitter N2 - Background: Cannabis use has increased in Canada since its legalization in 2018, including among pregnant women who may be motivated to use cannabis to reduce symptoms of nausea and vomiting. However, a growing body of research suggests that cannabis use during pregnancy may harm the developing fetus. As a result, patients increasingly seek medical advice from online sources, but these platforms may also spread anecdotal descriptions or misinformation. Given the possible disconnect between online messaging and evidence-based research about the effects of cannabis use during pregnancy, there is a potential for advice taken from social media to affect the health of mothers and their babies. Objective: This study aims to quantify the volume and tone of English language posts related to cannabis use in pregnancy from January 2012 to December 2021. Methods: Modeling published frameworks for scoping reviews, we will collect publicly available posts from Twitter that mention cannabis use during pregnancy and use the Twitter Application Programming Interface for Academic Research to extract data from tweets, including public metrics such as the number of likes, retweets, and quotes, as well as health effect mentions, sentiment, location, and users? interests. These data will be used to quantify how cannabis use during pregnancy is discussed on Twitter and to build a qualitative profile of supportive and opposing posters. Results: The CHEO Research Ethics Board reviewed our project and granted an exemption in May 2021. As of December 2021, we have gained approval to use the Twitter Application Programming Interface for Academic Research and have developed a preliminary search strategy that returns over 3 million unique tweets posted between 2012 and 2021. Conclusions: Understanding how Twitter is being used to discuss cannabis use during pregnancy will help public health agencies and health care providers assess the messaging patients may be receiving and develop communication strategies to counter misinformation, especially in geographical regions where legalization is recent or imminent. Most importantly, we foresee that our findings will assist expecting families in making informed choices about where they choose to access advice about using cannabis during pregnancy. Trial Registration: Open Science Framework 10.17605/OSF.IO/BW8DA; www.osf.io/6fb2e International Registered Report Identifier (IRRID): PRR1-10.2196/34421 UR - https://www.researchprotocols.org/2022/3/e34421 UR - http://dx.doi.org/10.2196/34421 UR - http://www.ncbi.nlm.nih.gov/pubmed/35348465 ID - info:doi/10.2196/34421 ER - TY - JOUR AU - Chen, Xi AU - Lin, Fen AU - Cheng, W. Edmund PY - 2022/3/22 TI - Stratified Impacts of the Infodemic During the COVID-19 Pandemic: Cross-sectional Survey in 6 Asian Jurisdictions JO - J Med Internet Res SP - e31088 VL - 24 IS - 3 KW - infodemic KW - information overload KW - psychological distress KW - protective behavior KW - cross-national survey KW - Asia KW - COVID-19 N2 - Background: Although timely and accurate information during the COVID-19 pandemic is essential for containing the disease and reducing mental distress, an infodemic, which refers to an overabundance of information, may trigger unpleasant emotions and reduce compliance. Prior research has shown the negative consequences of an infodemic during the pandemic; however, we know less about which subpopulations are more exposed to the infodemic and are more vulnerable to the adverse psychological and behavioral effects. Objective: This study aimed to examine how sociodemographic factors and information-seeking behaviors affect the perceived information overload during the COVID-19 pandemic. We also investigated the effect of perceived information overload on psychological distress and protective behavior and analyzed the socioeconomic differences in the effects. Methods: The data for this study were obtained from a cross-national survey of residents in 6 jurisdictions in Asia in May 2020. The survey targeted residents aged 18 years or older. A probability-based quota sampling strategy was adopted to ensure that the selected samples matched the population?s geographical and demographic characteristics released by the latest available census in each jurisdiction. The final sample included 10,063 respondents. Information overload about COVID-19 was measured by asking the respondents to what extent they feel overwhelmed by news related to COVID-19. The measure of psychological distress was adapted from the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5). Protective behaviors included personal hygienic behavior and compliance with social distancing measures. Results: Younger respondents and women (b=0.20, 95% CI 0.14 to 0.26) were more likely to perceive information overload. Participants self-perceived as upper or upper-middle class (b=0.19, 95% CI 0.09 to 0.30) and those with full-time jobs (b=0.11, 95% CI 0.04 to 0.17) tended to perceive higher information overload. Respondents who more frequently sought COVID-19 information from newspapers (b=0.12, 95% CI 0.11 to 0.14), television (b=0.07, 95% CI 0.05 to 0.09), and family and friends (b=0.11, 95% CI 0.09 to 0.14) were more likely to feel overwhelmed. In contrast, obtaining COVID-19 information from online news outlets and social media was not associated with perceived information overload. There was a positive relationship between perceived information overload and psychological distress (b=2.18, 95% CI 2.09 to 2.26). Such an association was stronger among urban residents, full-time employees, and those living in privately owned housing. The effect of perceived information overload on protective behavior was not significant. Conclusions: Our findings revealed that respondents who were younger, were female, had a higher socioeconomic status (SES), and had vulnerable populations in the household were more likely to feel overwhelmed by COVID-19 information. Perceived information overload tended to increase psychological distress, and people with higher SES were more vulnerable to this adverse psychological consequence. Effective policies and interventions should be promoted to target vulnerable populations who are more susceptible to the occurrence and negative psychological influence of perceived information overload. UR - https://www.jmir.org/2022/3/e31088 UR - http://dx.doi.org/10.2196/31088 UR - http://www.ncbi.nlm.nih.gov/pubmed/35103601 ID - info:doi/10.2196/31088 ER - TY - JOUR AU - Metzler, Matthias Julian AU - Kalaitzopoulos, Rafail Dimitrios AU - Burla, Laurin AU - Schaer, Gabriel AU - Imesch, Patrick PY - 2022/3/18 TI - Examining the Influence on Perceptions of Endometriosis via Analysis of Social Media Posts: Cross-sectional Study JO - JMIR Form Res SP - e31135 VL - 6 IS - 3 KW - endometriosis KW - social media KW - Facebook KW - Instagram KW - influencer KW - engagement N2 - Background: Social media platforms, such as Facebook and Instagram, are increasingly being used to share health-related information by ?influencers,? regular users, and institutions alike. While patients may benefit in various ways from these interactions, little is known about the types of endometriosis-related information published on social media. As digital opinion leaders influence the perceptions of their followers, physicians need to be aware about ideas and beliefs that are available online, in order to address possible misconceptions and provide optimal patient care. Objective: The aim of this study was to identify and analyze frequent endometriosis-related discussion topics on social media in order to offer caregivers insight into commonly discussed subject matter and aspects. Methods: We performed a systematic search using predefined parameters. Using the term ?endometriosis? in Facebook?s search function and a social media search engine, a list of Facebook pages was generated. A list of Instagram accounts was generated using the terms ?endometriosis? and ?endo? in Instagram?s search function. Pages and accounts in English with 5000 or more followers or likes were included. Nonpublic, unrelated, or inactive pages and accounts were excluded. For each account, the most recent 10 posts were identified and categorized by two independent examiners using qualitative content analysis. User engagement was calculated using the numbers of interactions (ie, shares, likes, and comments) for each post, stratified by the number of followers. Results: A total of 39 Facebook pages and 43 Instagram accounts with approximately 1.4 million followers were identified. Hospitals and medical centers made up 15% (6/39) of the Facebook pages and 5% (2/43) of the Instagram accounts. Top accounts had up to 111,600 (Facebook) and 41,400 (Instagram) followers. A total of 820 posts were analyzed. On Facebook, most posts were categorized as ?awareness? (101/390, 25.9% of posts), ?education and research? (71/390, 18.2%), and ?promotion? (64/390, 16.4%). On Instagram, the top categories were ?inspiration and support? (120/430, 27.9% of posts), ?awareness? (72/430, 16.7%), and ?personal story? (72/430, 16.7%). The frequency of most categories differed significantly between platforms. User engagement was higher on Instagram than on Facebook (3.20% vs 0.97% of followers per post). On Instagram, the highest percentage of users engaged with posts categorized as ?humor? (mean 4.19%, SD 4.53%), ?personal story? (mean 3.02%, SD 4.95%), and ?inspiration and support? (mean 2.83%, SD 3.08%). On Facebook, posts in the categories ?awareness? (mean 2.05%, SD 15.56%), ?humor? (mean 0.91%, SD 1.07%), and ?inspiration and support? (mean 0.56%, SD 1.37%) induced the most user engagement. Posts made by hospitals and medical centers generated higher user engagement than posts by regular accounts on Facebook (mean 1.44%, SD 1.11% vs mean 0.88%, SD 2.71% of followers per post) and Instagram (mean 3.33%, SD 1.21% vs mean 3.19%, SD 2.52% of followers per post). Conclusions: Facebook and Instagram are widely used to share endometriosis-related information among a large number of users. Most posts offer inspiration or support, spread awareness about the disease, or cover personal issues. Followers mostly engage with posts with a humoristic, supportive, and awareness-generating nature. Health care providers should be aware about the topics discussed online, as this may lead to an increased understanding of the needs and demands of digitally proficient patients with endometriosis. UR - https://formative.jmir.org/2022/3/e31135 UR - http://dx.doi.org/10.2196/31135 UR - http://www.ncbi.nlm.nih.gov/pubmed/35302501 ID - info:doi/10.2196/31135 ER - TY - JOUR AU - Deiner, S. Michael AU - Seitzman, D. Gerami AU - Kaur, Gurbani AU - McLeod, D. Stephen AU - Chodosh, James AU - Lietman, M. Thomas AU - Porco, C. Travis PY - 2022/3/16 TI - Sustained Reductions in Online Search Interest for Communicable Eye and Other Conditions During the COVID-19 Pandemic: Infodemiology Study JO - JMIR Infodemiology SP - e31732 VL - 2 IS - 1 KW - COVID-19 KW - pandemic KW - communicable disease KW - social distancing KW - infodemiology KW - Google Trends KW - influenza KW - conjunctivitis KW - ocular symptoms KW - seasonality KW - trend KW - online health information KW - information-seeking N2 - Background: In a prior study at the start of the pandemic, we reported reduced numbers of Google searches for the term ?conjunctivitis? in the United States in March and April 2020 compared with prior years. As one explanation, we conjectured that reduced information-seeking may have resulted from social distancing reducing contagious conjunctivitis cases. Here, after 1 year of continued implementation of social distancing, we asked if there have been persistent reductions in searches for ?conjunctivitis,? and similarly for other communicable disease terms, compared to control terms. Objective: The aim of this study was to determine if reduction in searches in the United States for terms related to conjunctivitis and other common communicable diseases occurred in the spring-winter season of the COVID-19 pandemic, and to compare this outcome to searches for terms representing noncommunicable conditions, COVID-19, and to seasonality. Methods: Weekly relative search frequency volume data from Google Trends for 68 search terms in English for the United States were obtained for the weeks of March 2011 through February 2021. Terms were classified a priori as 16 terms related to COVID-19, 29 terms representing communicable conditions, and 23 terms representing control noncommunicable conditions. To reduce bias, all analyses were performed while masked to term names, classifications, and locations. To test for the significance of changes during the pandemic, we detrended and compared postpandemic values to those expected based on prepandemic trends, per season, computing one- and two-sided P values. We then compared these P values between term groups using Wilcoxon rank-sum and Fisher exact tests to assess if non-COVID-19 terms representing communicable diseases were more likely to show significant reductions in searches in 2020-2021 than terms not representing such diseases. We also assessed any relationship between a term?s seasonality and a reduced search trend for the term in 2020-2021 seasons. P values were subjected to false discovery rate correction prior to reporting. Data were then unmasked. Results: Terms representing conjunctivitis and other communicable conditions showed a sustained reduced search trend in the first 4 seasons of the 2020-2021 COVID-19 pandemic compared to prior years. In comparison, the search for noncommunicable condition terms was significantly less reduced (Wilcoxon and Fisher exact tests, P<.001; summer, autumn, winter). A significant correlation was also found between reduced search for a term in 2020-2021 and seasonality of that term (Theil-Sen, P<.001; summer, autumn, winter). Searches for COVID-19?related conditions were significantly elevated compared to those in prior years, and searches for influenza-related terms were significantly lower than those for prior years in winter 2020-2021 (P<.001). Conclusions: We demonstrate the low-cost and unbiased use of online search data to study how a wide range of conditions may be affected by large-scale interventions or events such as social distancing during the COVID-19 pandemic. Our findings support emerging clinical evidence implicating social distancing and the COVID-19 pandemic in the reduction of communicable disease and on ocular conditions. UR - https://infodemiology.jmir.org/2022/1/e31732 UR - http://dx.doi.org/10.2196/31732 UR - http://www.ncbi.nlm.nih.gov/pubmed/35320981 ID - info:doi/10.2196/31732 ER - TY - JOUR AU - Calac, J. Alec AU - Haupt, R. Michael AU - Li, Zhuoran AU - Mackey, Tim PY - 2022/3/16 TI - Spread of COVID-19 Vaccine Misinformation in the Ninth Inning: Retrospective Observational Infodemic Study JO - JMIR Infodemiology SP - e33587 VL - 2 IS - 1 KW - infoveillance KW - infodemiology KW - COVID-19 KW - vaccine KW - Twitter KW - social listening KW - social media KW - misinformation KW - spread KW - observational KW - hesitancy KW - communication KW - discourse N2 - Background: Shortly after Pfizer and Moderna received emergency use authorizations from the Food and Drug Administration, there were increased reports of COVID-19 vaccine-related deaths in the Vaccine Adverse Event Reporting System (VAERS). In January 2021, Major League Baseball legend and Hall of Famer, Hank Aaron, passed away at the age of 86 years from natural causes, just 2 weeks after he received the COVID-19 vaccine. Antivaccination groups attempted to link his death to the Moderna vaccine, similar to other attempts misrepresenting data from the VAERS to spread COVID-19 misinformation. Objective: This study assessed the spread of misinformation linked to erroneous claims about Hank Aaron?s death on Twitter and then characterized different vaccine misinformation and hesitancy themes generated from users who interacted with this misinformation discourse. Methods: An initial sample of tweets from January 31, 2021, to February 6, 2021, was queried from the Twitter Search Application Programming Interface using the keywords ?Hank Aaron? and ?vaccine.? The sample was manually annotated for misinformation, reporting or news media, and public reaction. Nonmedia user accounts were also classified if they were verified by Twitter. A second sample of tweets, representing direct comments or retweets to misinformation-labeled content, was also collected. User sentiment toward misinformation, positive (agree) or negative (disagree), was recorded. The Strategic Advisory Group of Experts Vaccine Hesitancy Matrix from the World Health Organization was used to code the second sample of tweets for factors influencing vaccine confidence. Results: A total of 436 tweets were initially sampled from the Twitter Search Application Programming Interface. Misinformation was the most prominent content type (n=244, 56%) detected, followed by public reaction (n=122, 28%) and media reporting (n=69, 16%). No misinformation-related content reviewed was labeled as misleading by Twitter at the time of the study. An additional 1243 comments on misinformation-labeled tweets from 973 unique users were also collected, with 779 comments deemed relevant to study aims. Most of these comments expressed positive sentiment (n=612, 78.6%) to misinformation and did not refute it. Based on the World Health Organization Strategic Advisory Group of Experts framework, the most common vaccine hesitancy theme was individual or group influences (n=508, 65%), followed by vaccine or vaccination-specific influences (n=110, 14%) and contextual influences (n=93, 12%). Common misinformation themes observed included linking the death of Hank Aaron to ?suspicious? elderly deaths following vaccination, claims about vaccines being used for depopulation, death panels, federal officials targeting Black Americans, and misinterpretation of VAERS reports. Four users engaging with or posting misinformation were verified on Twitter at the time of data collection. Conclusions: Our study found that the death of a high-profile ethnic minority celebrity led to the spread of misinformation on Twitter. This misinformation directly challenged the safety and effectiveness of COVID-19 vaccines at a time when ensuring vaccine coverage among minority populations was paramount. Misinformation targeted at minority groups and echoed by other verified Twitter users has the potential to generate unwarranted vaccine hesitancy at the expense of people such as Hank Aaron who sought to promote public health and community immunity. UR - https://infodemiology.jmir.org/2022/1/e33587 UR - http://dx.doi.org/10.2196/33587 UR - http://www.ncbi.nlm.nih.gov/pubmed/35320982 ID - info:doi/10.2196/33587 ER - TY - JOUR AU - Quinn, K. Emma AU - Fenton, Shelby AU - Ford-Sahibzada, A. Chelsea AU - Harper, Andrew AU - Marcon, R. Alessandro AU - Caulfield, Timothy AU - Fazel, S. Sajjad AU - Peters, E. Cheryl PY - 2022/3/14 TI - COVID-19 and Vitamin D Misinformation on YouTube: Content Analysis JO - JMIR Infodemiology SP - e32452 VL - 2 IS - 1 KW - COVID-19 KW - vitamin D KW - misinformation KW - YouTube KW - content analysis KW - social media KW - video KW - infodemic KW - risk KW - prevention KW - health information KW - immunity KW - immune system KW - supplements KW - natural medicine N2 - Background: The ?infodemic? accompanying the SARS-CoV-2 virus pandemic has the potential to increase avoidable spread as well as engagement in risky health behaviors. Although social media platforms, such as YouTube, can be an inexpensive and effective method of sharing accurate health information, inaccurate and misleading information shared on YouTube can be dangerous for viewers. The confusing nature of data and claims surrounding the benefits of vitamin D, particularly in the prevention or cure of COVID-19, influences both viewers and the general ?immune boosting? commercial interest. Objective: The aim of this study was to ascertain how information on vitamin D and COVID-19 was presented on YouTube in 2020. Methods: YouTube video results for the search terms ?COVID,? ?coronavirus,? and ?vitamin D? were collected and analyzed for content themes and deemed useful or misleading based on the accuracy or inaccuracy of the content. Qualitative content analysis and simple statistical analysis were used to determine the prevalence and frequency of concerning content, such as confusing correlation with causation regarding vitamin D benefits. Results: In total, 77 videos with a combined 10,225,763 views (at the time of data collection) were included in the analysis, with over three-quarters of them containing misleading content about COVID-19 and vitamin D. In addition, 45 (58%) of the 77 videos confused the relationship between vitamin D and COVID-19, with 46 (85%) of 54 videos stating that vitamin D has preventative or curative abilities. The major contributors to these videos were medical professionals with YouTube accounts. Vitamin D recommendations that do not align with the current literature were frequently suggested, including taking supplementation higher than the recommended safe dosage or seeking intentional solar UV radiation exposure. Conclusions: The spread of misinformation is particularly alarming when spread by medical professionals, and existing data suggesting vitamin D has immune-boosting abilities can add to viewer confusion or mistrust in health information. Further, the suggestions made in the videos may increase the risks of other poor health outcomes, such as skin cancer from solar UV radiation. UR - https://infodemiology.jmir.org/2022/1/e32452 UR - http://dx.doi.org/10.2196/32452 UR - http://www.ncbi.nlm.nih.gov/pubmed/35310014 ID - info:doi/10.2196/32452 ER - TY - JOUR AU - Okunoye, Babatunde AU - Ning, Shaoyang AU - Jemielniak, Dariusz PY - 2022/3/11 TI - Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends JO - JMIR Form Res SP - e29819 VL - 6 IS - 3 KW - HIV/AIDS KW - web search KW - big data KW - public health KW - Wikipedia KW - information seeking behavior KW - online behavior KW - online health information KW - Google Trends N2 - Background: AIDS, caused by HIV, is a leading cause of mortality in Africa. HIV/AIDS is among the greatest public health challenges confronting health authorities, with South Africa having the greatest prevalence of the disease in the world. There is little research into how Africans meet their health information needs on HIV/AIDS online, and this research gap impacts programming and educational responses to the HIV/AIDS pandemic. Objective: This paper reports on how, in general, interest in the search terms ?HIV? and ?AIDS? mirrors the increase in people living with HIV and the decline in AIDS cases in South Africa. Methods: Data on search trends for HIV and AIDS for South Africa were found using the search terms ?HIV? and ?AIDS? (categories: health, web search) on Google Trends. This was compared with data on estimated adults and children living with HIV, and AIDS-related deaths in South Africa, from the Joint United Nations Programme on HIV/AIDS, and also with search interest in the topics ?HIV? and ?AIDS? on Wikipedia Afrikaans, the most developed local language Wikipedia service in South Africa. Nonparametric statistical tests were conducted to support the trends and associations identified in the data. Results: Google Trends shows a statistically significant decline (P<.001) in search interest for AIDS relative to HIV in South Africa. This trend mirrors progress on the ground in South Africa and is significantly associated (P<.001) with a decline in AIDS-related deaths and people living longer with HIV. This trend was also replicated on Wikipedia Afrikaans, where there was a greater interest in HIV than AIDS. Conclusions: This statistically significant (P<.001) association between interest in the search terms ?HIV? and ?AIDS? in South Africa (2004-2019) and the number of people living with HIV and AIDS in the country (2004-2019) might be an indicator that multilateral efforts at combating HIV/AIDS?particularly through awareness raising and behavioral interventions in South Africa?are bearing fruit, and this is not only evident on the ground, but is also reflected in the online information seeking on the HIV/AIDS pandemic. We acknowledge the limitation that in studying the association between Google search interests on HIV/AIDS and cases/deaths, causal relationships should not be drawn due to the limitations of the data. UR - https://formative.jmir.org/2022/3/e29819 UR - http://dx.doi.org/10.2196/29819 UR - http://www.ncbi.nlm.nih.gov/pubmed/35275080 ID - info:doi/10.2196/29819 ER - TY - JOUR AU - Blane, T. Janice AU - Bellutta, Daniele AU - Carley, M. Kathleen PY - 2022/3/7 TI - Social-Cyber Maneuvers During the COVID-19 Vaccine Initial Rollout: Content Analysis of Tweets JO - J Med Internet Res SP - e34040 VL - 24 IS - 3 KW - social cybersecurity KW - social-cyber maneuvers KW - social network analysis KW - disinformation KW - BEND maneuvers KW - COVID-19 KW - coronavirus KW - social media KW - vaccine KW - anti-vaccine KW - pro-vaccine KW - ORA-PRO KW - cybersecurity KW - security KW - Twitter KW - community KW - communication KW - health information KW - manipulation KW - belief N2 - Background: During the time surrounding the approval and initial distribution of Pfizer-BioNTech?s COVID-19 vaccine, large numbers of social media users took to using their platforms to voice opinions on the vaccine. They formed pro- and anti-vaccination groups toward the purpose of influencing behaviors to vaccinate or not to vaccinate. The methods of persuasion and manipulation for convincing audiences online can be characterized under a framework for social-cyber maneuvers known as the BEND maneuvers. Previous studies have been conducted on the spread of COVID-19 vaccine disinformation. However, these previous studies lacked comparative analyses over time on both community stances and the competing techniques of manipulating both the narrative and network structure to persuade target audiences. Objective: This study aimed to understand community response to vaccination by dividing Twitter data from the initial Pfizer-BioNTech COVID-19 vaccine rollout into pro-vaccine and anti-vaccine stances, identifying key actors and groups, and evaluating how the different communities use social-cyber maneuvers, or BEND maneuvers, to influence their target audiences and the network as a whole. Methods: COVID-19 Twitter vaccine data were collected using the Twitter application programming interface (API) for 1-week periods before, during, and 6 weeks after the initial Pfizer-BioNTech rollout (December 2020 to January 2021). Bot identifications and linguistic cues were derived for users and tweets, respectively, to use as metrics for evaluating social-cyber maneuvers. Organization Risk Analyzer (ORA)-PRO software was then used to separate the vaccine data into pro-vaccine and anti-vaccine communities and to facilitate identification of key actors, groups, and BEND maneuvers for a comparative analysis between each community and the entire network. Results: Both the pro-vaccine and anti-vaccine communities used combinations of the 16 BEND maneuvers to persuade their target audiences of their particular stances. Our analysis showed how each side attempted to build its own community while simultaneously narrowing and neglecting the opposing community. Pro-vaccine users primarily used positive maneuvers such as excite and explain messages to encourage vaccination and backed leaders within their group. In contrast, anti-vaccine users relied on negative maneuvers to dismay and distort messages with narratives on side effects and death and attempted to neutralize the effectiveness of the leaders within the pro-vaccine community. Furthermore, nuking through platform policies showed to be effective in reducing the size of the anti-vaccine online community and the quantity of anti-vaccine messages. Conclusions: Social media continues to be a domain for manipulating beliefs and ideas. These conversations can ultimately lead to real-world actions such as to vaccinate or not to vaccinate against COVID-19. Moreover, social media policies should be further explored as an effective means for curbing disinformation and misinformation online. UR - https://www.jmir.org/2022/3/e34040 UR - http://dx.doi.org/10.2196/34040 UR - http://www.ncbi.nlm.nih.gov/pubmed/35044302 ID - info:doi/10.2196/34040 ER - TY - JOUR AU - Li, Chuqin AU - Ademiluyi, Adesoji AU - Ge, Yaorong AU - Park, Albert PY - 2022/3/7 TI - Using Social Media to Understand Web-Based Social Factors Concerning Obesity: Systematic Review JO - JMIR Public Health Surveill SP - e25552 VL - 8 IS - 3 KW - obesity KW - web-based social factors KW - systematic review KW - social-ecological model N2 - Background: Evidence in the literature surrounding obesity suggests that social factors play a substantial role in the spread of obesity. Although social ties with a friend who is obese increase the probability of becoming obese, the role of social media in this dynamic remains underexplored in obesity research. Given the rapid proliferation of social media in recent years, individuals socialize through social media and share their health-related daily routines, including dieting and exercising. Thus, it is timely and imperative to review previous studies focused on social factors in social media and obesity. Objective: This study aims to examine web-based social factors in relation to obesity research. Methods: We conducted a systematic review. We searched PubMed, Association for Computing Machinery, and ScienceDirect for articles published by July 5, 2019. Web-based social factors that are related to obesity behaviors were studied and analyzed. Results: In total, 1608 studies were identified from the selected databases. Of these 1608 studies, 50 (3.11%) studies met the eligibility criteria. In total, 10 types of web-based social factors were identified, and a socioecological model was adopted to explain their potential impact on an individual from varying levels of web-based social structure to social media users? connection to the real world. Conclusions: We found 4 levels of interaction in social media. Gender was the only factor found at the individual level, and it affects user?s web-based obesity-related behaviors. Social support was the predominant factor identified, which benefits users in their weight loss journey at the interpersonal level. Some factors, such as stigma were also found to be associated with a healthy web-based social environment. Understanding the effectiveness of these factors is essential to help users create and maintain a healthy lifestyle. UR - https://publichealth.jmir.org/2022/3/e25552 UR - http://dx.doi.org/10.2196/25552 UR - http://www.ncbi.nlm.nih.gov/pubmed/35254279 ID - info:doi/10.2196/25552 ER - TY - JOUR AU - Gabarron, Elia AU - Dechsling, Anders AU - Skafle, Ingjerd AU - Nordahl-Hansen, Anders PY - 2022/3/7 TI - Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter JO - JMIR Form Res SP - e32752 VL - 6 IS - 3 KW - social media KW - autism spectrum disorder KW - health literacy KW - famous persons KW - Asperger KW - Elon Musk KW - twitter KW - tweets KW - mental health KW - autism KW - sentiment analysis N2 - Background: On May 8, 2021, Elon Musk, a well-recognized entrepreneur and business magnate, revealed on a popular television show that he has Asperger syndrome. Research has shown that people?s perceptions of a condition are modified when influential individuals in society publicly disclose their diagnoses. It was anticipated that Musk's disclosure would contribute to discussions on the internet about the syndrome, and also to a potential change in the perception of this condition. Objective: The objective of this study was to compare the types of information contained in popular tweets about Asperger syndrome as well as their engagement and sentiment before and after Musk?s disclosure. Methods: We extracted tweets that were published 1 week before and after Musk's disclosure that had received >30 likes and included the terms ?Aspergers? or ?Aspie.? The content of each post was classified by 2 independent coders as to whether the information provided was valid, contained misinformation, or was neutral. Furthermore, we analyzed the engagement on these posts and the expressed sentiment by using the AFINN sentiment analysis tool. Results: We extracted a total of 227 popular tweets (34 posted the week before Musk?s announcement and 193 posted the week after). We classified 210 (92.5%) of the tweets as neutral, 13 (5.7%) tweets as informative, and 4 (1.8%) as containing misinformation. Both informative and misinformative tweets were posted after Musk?s disclosure. Popular tweets posted before Musk?s disclosure were significantly more engaging (received more comments, retweets, and likes) than the tweets posted the week after. We did not find a significant difference in the sentiment expressed in the tweets posted before and after the announcement. Conclusions: The use of social media platforms by health authorities, autism associations, and other stakeholders has the potential to increase the awareness and acceptance of knowledge about autism and Asperger syndrome. When prominent figures disclose their diagnoses, the number of posts about their particular condition tends to increase and thus promote a potential opportunity for greater outreach to the general public about that condition. UR - https://formative.jmir.org/2022/3/e32752 UR - http://dx.doi.org/10.2196/32752 UR - http://www.ncbi.nlm.nih.gov/pubmed/35254265 ID - info:doi/10.2196/32752 ER - TY - JOUR AU - Young, D. Sean AU - Zhang, Qingpeng AU - Zeng, Dajun Daniel AU - Zhan, Yongcheng AU - Cumberland, William PY - 2022/3/3 TI - Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: Content Analysis JO - J Med Internet Res SP - e24787 VL - 24 IS - 3 KW - internet KW - social media KW - health informatics KW - tool KW - monitor KW - adherence KW - COVID-19 KW - public health KW - guidelines KW - content analysis KW - policy N2 - Background: Innovative surveillance methods are needed to assess adherence to COVID-19 recommendations, especially methods that can provide near real-time or highly geographically targeted data. Use of location-based social media image data (eg, Instagram images) is one possible approach that could be explored to address this problem. Objective: We seek to evaluate whether publicly available near real-time social media images might be used to monitor COVID-19 health policy adherence. Methods: We collected a sample of 43,487 Instagram images in New York from February 7 to April 11, 2020, from the following location hashtags: #Centralpark (n=20,937), #Brooklyn Bridge (n=14,875), and #Timesquare (n=7675). After manually reviewing images for accuracy, we counted and recorded the frequency of valid daily posts at each of these hashtag locations over time, as well as rated and counted whether the individuals in the pictures at these location hashtags were social distancing (ie, whether the individuals in the images appeared to be distanced from others vs next to or touching each other). We analyzed the number of images posted over time and the correlation between trends among hashtag locations. Results: We found a statistically significant decline in the number of posts over time across all regions, with an approximate decline of 17% across each site (P<.001). We found a positive correlation between hashtags (#Centralpark and #Brooklynbridge: r=0.40; #BrooklynBridge and #Timesquare: r=0.41; and #Timesquare and #Centralpark: r=0.33; P<.001 for all correlations). The logistic regression analysis showed a mild statistically significant increase in the proportion of posts over time with people appearing to be social distancing at Central Park (P=.004) and Brooklyn Bridge (P=.02) but not for Times Square (P=.16). Conclusions: Results suggest the potential of using location-based social media image data as a method for surveillance of COVID-19 health policy adherence. Future studies should further explore the implementation and ethical issues associated with this approach. UR - https://www.jmir.org/2022/3/e24787 UR - http://dx.doi.org/10.2196/24787 UR - http://www.ncbi.nlm.nih.gov/pubmed/34995205 ID - info:doi/10.2196/24787 ER - TY - JOUR AU - Cai, Owen AU - Sousa-Pinto, Bernardo PY - 2022/3/3 TI - United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study JO - JMIR Public Health Surveill SP - e32364 VL - 8 IS - 3 KW - COVID-19 KW - influenza KW - surveillance KW - media coverage KW - Google Trends KW - infodemiology KW - monitoring KW - trend KW - United States KW - information-seeking KW - online health information N2 - Background: The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends. Objective: We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States. Methods: We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data. Results: We observed a nonsignificant weak correlation (?= ?0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models?for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ?=0.643; 2019-2020: ?=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ?=0.746; 2019-2020: ?=0.707). Conclusions: Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool. UR - https://publichealth.jmir.org/2022/3/e32364 UR - http://dx.doi.org/10.2196/32364 UR - http://www.ncbi.nlm.nih.gov/pubmed/34878996 ID - info:doi/10.2196/32364 ER - TY - JOUR AU - Jalali, Niloofar AU - Tran, Ken N. AU - Sen, Anindya AU - Morita, Pelegrini Plinio PY - 2022/3/3 TI - Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties JO - JMIR Infodemiology SP - e31813 VL - 2 IS - 1 KW - COVID-19 KW - cases KW - deaths KW - mobility KW - Google mobility data KW - clustering N2 - Background: The spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient nonpharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility. Objective: This study used publicly available Google data and machine learning to investigate population mobility across a sample of US counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates. Methods: Daily Google population mobility data for 1085 US counties from March 1 to December 31, 2020, were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths. Results: Our results grouped US counties into 4 Google mobility clusters. Clusters with more population mobility had a higher percentage of the population aged 65 years and over, a greater population share of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 presidential election. Furthermore, clusters with greater population mobility experienced a sharp increase in per capita COVID-19 case and death rates from November to December 2020. Conclusions: Republican-leaning counties that are characterized by certain demographic characteristics had higher increases in social mobility and ultimately experienced a more significant incidence of COVID-19 during the latter part of 2020. UR - https://infodemiology.jmir.org/2022/1/e31813 UR - http://dx.doi.org/10.2196/31813 UR - http://www.ncbi.nlm.nih.gov/pubmed/35287305 ID - info:doi/10.2196/31813 ER - TY - JOUR AU - Arshonsky, Josh AU - Krawczyk, Noa AU - Bunting, M. Amanda AU - Frank, David AU - Friedman, R. Samuel AU - Bragg, A. Marie PY - 2022/3/3 TI - Informal Coping Strategies Among People Who Use Opioids During COVID-19: Thematic Analysis of Reddit Forums JO - JMIR Form Res SP - e32871 VL - 6 IS - 3 KW - opioid use KW - Reddit KW - coping strategies KW - COVID-19 KW - opioid KW - drug KW - coping KW - social media KW - strategy KW - content analysis KW - abstain KW - addiction KW - data mining KW - support N2 - Background: The COVID-19 pandemic has transformed how people seeking to reduce opioid use access treatment services and navigate efforts to abstain from using opioids. Social distancing policies have drastically reduced access to many forms of social support, but they may have also upended some perceived barriers to reducing or abstaining from opioid use. Objective: This qualitative study aims to identify informal coping strategies for reducing and abstaining from opioid use among Reddit users who have posted in opioid-related subreddits at the beginning of the COVID-19 pandemic. Methods: We extracted data from 2 major opioid-related subreddits. Thematic data analysis was used to evaluate subreddit posts dated from March 5 to May 13, 2020, that referenced COVID-19 and opioid use, resulting in a final sample of 300 posts that were coded and analyzed. Results: Of the 300 subreddit posts, 100 (33.3%) discussed at least 1 type of informal coping strategy. Those strategies included psychological and behavioral coping skills, adoption of healthy habits, and use of substances to manage withdrawal symptoms. In addition, 12 (4%) subreddit posts explicitly mentioned using social distancing as an opportunity for cessation of or reduction in opioid use. Conclusions: Reddit discussion forums have provided a community for people to share strategies for reducing opioid use and support others during the COVID-19 pandemic. Future research needs to assess the impact of COVID-19 on opioid use behaviors, especially during periods of limited treatment access and isolation, as these can inform future efforts in curbing the opioid epidemic and other substance-related harms. UR - https://formative.jmir.org/2022/3/e32871 UR - http://dx.doi.org/10.2196/32871 UR - http://www.ncbi.nlm.nih.gov/pubmed/35084345 ID - info:doi/10.2196/32871 ER - TY - JOUR AU - Moon, Hana AU - Lee, Ho Geon AU - Cho, Jeong Yoon PY - 2022/3/3 TI - Readability of Korean-Language COVID-19 Information from the South Korean National COVID-19 Portal Intended for the General Public: Cross-sectional Infodemiology Study JO - JMIR Form Res SP - e30085 VL - 6 IS - 3 KW - COVID-19 KW - health literacy KW - readability KW - public health KW - health equity KW - consumer health information KW - information dissemination KW - health education KW - eHealth KW - online KW - social media KW - pandemic KW - infodemic N2 - Background: The coronavirus pandemic has increased reliance on the internet as a tool for disseminating information; however, information is useful only when it can be understood. Prior research has shown that web-based health information is not always easy to understand. It is not yet known whether the Korean-language COVID-19 information from the internet is easy for the general public to understand. Objective: We aimed to evaluate the readability of Korean-language COVID-19 information intended for the general public from the national COVID-19 portal of South Korea. Methods: A total of 122 publicly available COVID-19 information documents written in Korean were obtained from the South Korean national COVID-19 portal. We determined the level of readability (at or below ninth grade, 10th to 12th grade, college, or professional) of each document using a readability tool for Korean-language text. We measured the reading time, character count, word count, sentence count, and paragraph count for each document. We also evaluated the characteristics of difficult-to-read documents to modify the readability from difficult to easy. Results: The median readability level was at a professional level; 90.2% (110/122) of the information was difficult to read. In all 4 topics, few documents were easy to read (overview: 5/12, 41.7%; prevention: 6/97, 6.2%; test: 0/5, 0%; treatment: 1/8, 12.5%; P=.006), with a median 11th-grade readability level for overview, a median professional readability level for prevention, and median college readability levels for test and treatment. Difficult-to-read information had the following characteristics in common: literacy style, medical jargon, and unnecessary detail. Conclusions: In all 4 topics, most of the Korean-language COVID-19 web-based information intended for the general public provided by the national COVID-19 portal of South Korea was difficult to read; the median readability levels exceeded the recommended ninth-grade level. Readability should be a key consideration in developing public health documents, which play an important role in disease prevention and health promotion. UR - https://formative.jmir.org/2022/3/e30085 UR - http://dx.doi.org/10.2196/30085 UR - http://www.ncbi.nlm.nih.gov/pubmed/35072633 ID - info:doi/10.2196/30085 ER - TY - JOUR AU - MacLeod, Spencer AU - Singh, Paul Nikhi AU - Boyd, Joseph Carter PY - 2022/3/2 TI - The Unclear Role of the Physician on Social Media During the COVID-19 Pandemic. Comment on ?Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study? JO - J Med Internet Res SP - e34870 VL - 24 IS - 3 KW - COVID-19 pandemic KW - emergency medicine KW - disaster medicine KW - crisis standards of care KW - latent Dirichlet allocation KW - topic modeling KW - Twitter KW - sentiment analysis KW - surge capacity KW - physician wellness KW - social media KW - internet KW - infodemiology KW - COVID-19 UR - https://www.jmir.org/2022/3/e34870 UR - http://dx.doi.org/10.2196/34870 UR - http://www.ncbi.nlm.nih.gov/pubmed/35120018 ID - info:doi/10.2196/34870 ER - TY - JOUR AU - Hasan, Abul AU - Levene, Mark AU - Weston, David AU - Fromson, Renate AU - Koslover, Nicolas AU - Levene, Tamara PY - 2022/2/28 TI - Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach JO - J Med Internet Res SP - e30397 VL - 24 IS - 2 KW - COVID-19 KW - conditional random fields KW - disease detection and surveillance KW - medical social media KW - natural language processing KW - severity and prevalence KW - support vector machines KW - triage and diagnosis N2 - Background: The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. Objective: This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. Methods: The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients? posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. Results: We reported macro- and microaveraged F1 scores in the range of 71%-96% and 61%-87%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. Conclusions: Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems. UR - https://www.jmir.org/2022/2/e30397 UR - http://dx.doi.org/10.2196/30397 UR - http://www.ncbi.nlm.nih.gov/pubmed/35142636 ID - info:doi/10.2196/30397 ER - TY - JOUR AU - Kreps, Sarah AU - George, Julie AU - Watson, Noah AU - Cai, Gloria AU - Ding, Keyi PY - 2022/2/24 TI - (Mis)Information on Digital Platforms: Quantitative and Qualitative Analysis of Content From Twitter and Sina Weibo in the COVID-19 Pandemic JO - JMIR Infodemiology SP - e31793 VL - 2 IS - 1 KW - internet KW - social media KW - misinformation KW - COVID-19 KW - Twitter KW - Weibo KW - prevalence KW - discourse KW - content KW - communication KW - public health KW - context KW - content analysis N2 - Background: Misinformation about COVID-19 on social media has presented challenges to public health authorities during the pandemic. This paper leverages qualitative and quantitative content analysis on cross-platform, cross-national discourse and misinformation in the context of COVID-19. Specifically, we investigated COVID-19-related content on Twitter and Sina Weibo?the largest microblogging sites in the United States and China, respectively. Objective: Using data from 2 prominent microblogging platform, Twitter, based in the United States, and Sina Weibo, based in China, we compared the content and relative prevalence of misinformation to better understand public discourse of public health issues across social media and cultural contexts. Methods: A total of 3,579,575 posts were scraped from both Sina Weibo and Twitter, focusing on content from January 30, 2020, within 24 hours of when WHO declared COVID-19 a ?public health emergency of international concern,? and a week later, on February 6, 2020. We examined how the use and engagement measured by keyword frequencies and hashtags differ across the 2 platforms. A 1% random sample of tweets that contained both the English keywords ?coronavirus? and ?covid-19? and the equivalent Chinese characters was extracted and analyzed based on changes in the frequencies of keywords and hashtags and the Viterbi algorithm. We manually coded a random selection of 5%-7% of the content to identify misinformation on each platform and compared posts using the WHO fact-check page to adjudicate accuracy of content. Results: Both platforms posted about the outbreak and transmission, but posts on Sina Weibo were less likely to reference topics such as WHO, Hong Kong, and death and more likely to cite themes of resisting, fighting, and cheering against coronavirus. Misinformation constituted 1.1% of Twitter content and 0.3% of Sina Weibo content?almost 4 times as much on Twitter compared to Sina Weibo. Conclusions: Quantitative and qualitative analysis of content on both platforms points to lower degrees of misinformation, more content designed to bolster morale, and less reference to topics such as WHO, death, and Hong Kong on Sina Weibo than on Twitter. UR - https://infodemiology.jmir.org/2022/1/e31793 UR - http://dx.doi.org/10.2196/31793 UR - http://www.ncbi.nlm.nih.gov/pubmed/36406147 ID - info:doi/10.2196/31793 ER - TY - JOUR AU - Cummins, Alexander Jack PY - 2022/2/23 TI - Getting a Vaccine, Jab, or Vax Is More Than a Regular Expression. Comment on ?COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis? JO - J Med Internet Res SP - e31978 VL - 24 IS - 2 KW - COVID-19 KW - vaccine KW - vaccination KW - Twitter KW - infodemiology KW - infoveillance KW - topic KW - sentiment KW - opinion KW - discussion KW - communication KW - social media KW - perception KW - concern KW - emotion KW - natural language processing UR - https://www.jmir.org/2022/2/e31978 UR - http://dx.doi.org/10.2196/31978 UR - http://www.ncbi.nlm.nih.gov/pubmed/35195531 ID - info:doi/10.2196/31978 ER - TY - JOUR AU - Santarossa, Sara AU - Rapp, Ashley AU - Sardinas, Saily AU - Hussein, Janine AU - Ramirez, Alex AU - Cassidy-Bushrow, E. Andrea AU - Cheng, Philip AU - Yu, Eunice PY - 2022/2/22 TI - Understanding the #longCOVID and #longhaulers Conversation on Twitter: Multimethod Study JO - JMIR Infodemiology SP - e31259 VL - 2 IS - 1 KW - COVID-19 KW - postacute sequela of COVID-19 KW - PASC KW - patient-centered care KW - social media KW - social network analysis KW - long term KW - symptom KW - Twitter KW - communication KW - insight KW - perception KW - experience KW - patient-centered N2 - Background: The scientific community is just beginning to uncover the potential long-term effects of COVID-19, and one way to start gathering information is by examining the present discourse on the topic. The conversation about long COVID-19 on Twitter provides insight into related public perception and personal experiences. Objective: The aim of this study was to investigate the #longCOVID and #longhaulers conversations on Twitter by examining the combined effects of topic discussion and social network analysis for discovery on long COVID-19. Methods: A multipronged approach was used to analyze data (N=2500 records from Twitter) about long COVID-19 and from people experiencing long COVID-19. A text analysis was performed by both human coders and Netlytic, a cloud-based text and social networks analyzer. The social network analysis generated Name and Chain networks that showed connections and interactions between Twitter users. Results: Among the 2010 tweets about long COVID-19 and 490 tweets by COVID-19 long haulers, 30,923 and 7817 unique words were found, respectively. For both conversation types, ?#longcovid? and ?covid? were the most frequently mentioned words; however, through visually inspecting the data, words relevant to having long COVID-19 (ie, symptoms, fatigue, pain) were more prominent in tweets by COVID-19 long haulers. When discussing long COVID-19, the most prominent frames were ?support? (1090/1931, 56.45%) and ?research? (435/1931, 22.53%). In COVID-19 long haulers conversations, ?symptoms? (297/483, 61.5%) and ?building a community? (152/483, 31.5%) were the most prominent frames. The social network analysis revealed that for both tweets about long COVID-19 and tweets by COVID-19 long haulers, networks are highly decentralized, fragmented, and loosely connected. Conclusions: This study provides a glimpse into the ways long COVID-19 is framed by social network users. Understanding these perspectives may help generate future patient-centered research questions. UR - https://infodemiology.jmir.org/2022/1/e31259 UR - http://dx.doi.org/10.2196/31259 UR - http://www.ncbi.nlm.nih.gov/pubmed/35229074 ID - info:doi/10.2196/31259 ER - TY - JOUR AU - Engel-Rebitzer, Eden AU - Stokes, C. Daniel AU - Meisel, F. Zachary AU - Purtle, Jonathan AU - Doyle, Rebecca AU - Buttenheim, M. Alison PY - 2022/2/18 TI - Partisan Differences in Legislators? Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis JO - JMIR Infodemiology SP - e32372 VL - 2 IS - 1 KW - social media KW - Twitter KW - vaccination KW - partisanship KW - COVID-19 KW - vaccine KW - natural language processing KW - NLP KW - hesitancy KW - politicization KW - communication KW - linguistic KW - pattern N2 - Background: The COVID-19 era has been characterized by the politicization of health-related topics. This is especially concerning given evidence that politicized discussion of vaccination may contribute to vaccine hesitancy. No research, however, has examined the content and politicization of legislator communication with the public about vaccination during the COVID-19 era. Objective: The aim of this study was to examine vaccine-related tweets produced by state and federal legislators during the COVID-19 era to (1) describe the content of vaccine-related tweets; (2) examine the differences in vaccine-related tweet content between Democrats and Republicans; and (3) quantify (and describe trends over time in) partisan differences in vaccine-related communication. Methods: We abstracted all vaccine-related tweets produced by state and federal legislators between February 01, 2020, and December 11, 2020. We used latent Dirichlet allocation to define the tweet topics and used descriptive statistics to describe differences by party in the use of topics and changes in political polarization over time. Results: We included 14,519 tweets generated by 1463 state legislators and 521 federal legislators. Republicans were more likely to use words (eg, ?record time,? ?launched,? and ?innovation?) and topics (eg, Operation Warp Speed success) that were focused on the successful development of a SARS-CoV-2 vaccine. Democrats used a broader range of words (eg, ?anti-vaxxers,? ?flu,? and ?free?) and topics (eg, vaccine prioritization, influenza, and antivaxxers) that were more aligned with public health messaging related to the vaccine. Polarization increased over most of the study period. Conclusions: Republican and Democratic legislators used different language in their Twitter conversations about vaccination during the COVID-19 era, leading to increased political polarization of vaccine-related tweets. These communication patterns have the potential to contribute to vaccine hesitancy. UR - https://infodemiology.jmir.org/2022/1/e32372 UR - http://dx.doi.org/10.2196/32372 UR - http://www.ncbi.nlm.nih.gov/pubmed/35229075 ID - info:doi/10.2196/32372 ER - TY - JOUR AU - Spitale, Giovanni AU - Biller-Andorno, Nikola AU - Germani, Federico PY - 2022/2/16 TI - Concerns Around Opposition to the Green Pass in Italy: Social Listening Analysis by Using a Mixed Methods Approach JO - J Med Internet Res SP - e34385 VL - 24 IS - 2 KW - green pass KW - COVID-19 KW - COVID-19 pandemic KW - vaccines KW - vaccination hesitancy KW - freedom KW - social listening KW - social media KW - infodemic KW - bioethics KW - telegram N2 - Background: The recent introduction of COVID-19 certificates in several countries, including the introduction of the European green pass, has been met with protests and concerns by a fraction of the population. In Italy, the green pass has been used as a nudging measure to incentivize vaccinations because a valid green pass is needed to enter restaurants, bars, museums, or stadiums. As of December 2021, a valid green pass can be obtained by being fully vaccinated with an approved vaccine, recovered from COVID-19, or tested. However, a green pass obtained with a test has a short validity (48 hours for the rapid test, 72 hours for the polymerase chain reaction test) and does not allow access to several indoor public places. Objective: This study aims to understand and describe the concerns of individuals opposed to the green pass in Italy, the main arguments of their discussions, and their characterization. Methods: We collected data from Telegram chats and analyzed the arguments and concerns that were raised by the users by using a mixed methods approach. Results: Most individuals opposing the green pass share antivaccine views, but doubts and concerns about vaccines are generally not among the arguments raised to oppose the green pass. Instead, the discussion revolves around the legal aspects and the definition of personal freedom. We explain the differences and similarities between antivaccine and anti?green pass discourses, and we discuss the ethical ramifications of our research, focusing on the use of Telegram chats as a social listening tool for public health. Conclusions: A large portion of individuals opposed to the green pass share antivaccine views. We suggest public health and political institutions to provide a legal explanation and a context for the use of the green pass, as well as to continue focusing on vaccine communication to inform vaccine-hesitant individuals. Further work is needed to define a consensual ethical framework for social listening for public health. UR - https://www.jmir.org/2022/2/e34385 UR - http://dx.doi.org/10.2196/34385 UR - http://www.ncbi.nlm.nih.gov/pubmed/35156930 ID - info:doi/10.2196/34385 ER - TY - JOUR AU - Sarabadani, Sarah AU - Baruah, Gaurav AU - Fossat, Yan AU - Jeon, Jouhyun PY - 2022/2/16 TI - Longitudinal Changes of COVID-19 Symptoms in Social Media: Observational Study JO - J Med Internet Res SP - e33959 VL - 24 IS - 2 KW - COVID-19 KW - symptom KW - diagnosis KW - treatment KW - social media KW - Reddit KW - longitudinal KW - observational KW - machine learning N2 - Background: In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Many studies have been conducted to understand the clinical characteristics of COVID-19, and recently postinfection sequelae of this disease have begun to be investigated. However, there is little consensus on the longitudinal changes of lasting physical or psychological symptoms from prior COVID-19 infection. Objective: This study aims to investigate and analyze public social media data from Reddit to understand the longitudinal impact of COVID-19 symptoms before and after recovery from COVID-19. Methods: We collected 22,890 Reddit posts that were generated by 14,401 authors from March 14 to December 16, 2020. Using active learning and intensive manual inspection, 292 (2.03%) active authors, who were infected by COVID-19 and frequently reported disease progress on Reddit, along with their 2213 (9.67%) longitudinal posts, were identified. Machine learning tools to extract biomedical information were applied to identify COVID-19 symptoms mentioned in the Reddit posts. We then examined longitudinal changes in individual physiological and psychological characteristics before and after recovery from COVID-19 infection. Results: In total, 58 physiological and 3 psychological symptoms were identified in social media before and after recovery from COVID-19 infection. From the analyses, we found that symptoms of patients with COVID-19 lasted 2.5 months. On average, symptoms appeared around a month before recovery and remained for 1.5 months after recovery. Well-known COVID-19 symptoms, such as fever, cough, and chest congestion, appeared relatively earlier in patient journeys and were frequently observed before recovery from COVID-19. Meanwhile, mental discomfort or distress, such as brain fog or stress, fatigue, and manifestations on toes or fingers, were frequently mentioned after recovery and remained as intermediate- and longer-term sequelae. Conclusions: In this study, we showed the dynamic changes in COVID-19 symptoms during the infection and recovery phases of the disease. Our findings suggest the feasibility of using social media data for investigating disease states and understanding the evolution of the physiological and psychological characteristics of COVID-19 infection over time. UR - https://www.jmir.org/2022/2/e33959 UR - http://dx.doi.org/10.2196/33959 UR - http://www.ncbi.nlm.nih.gov/pubmed/35076400 ID - info:doi/10.2196/33959 ER - TY - JOUR AU - Shakeri Hossein Abad, Zahra AU - Butler, P. Gregory AU - Thompson, Wendy AU - Lee, Joon PY - 2022/2/14 TI - Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research JO - JMIR Public Health Surveill SP - e32355 VL - 8 IS - 2 KW - digital public health surveillance KW - social media analysis KW - physical activity KW - sedentary behavior KW - sleep KW - machine learning KW - online health information KW - infodemiology KW - public health database N2 - Background: Advances in automated data processing and machine learning (ML) models, together with the unprecedented growth in the number of social media users who publicly share and discuss health-related information, have made public health surveillance (PHS) one of the long-lasting social media applications. However, the existing PHS systems feeding on social media data have not been widely deployed in national surveillance systems, which appears to stem from the lack of practitioners and the public?s trust in social media data. More robust and reliable data sets over which supervised ML models can be trained and tested reliably is a significant step toward overcoming this hurdle. The health implications of daily behaviors (physical activity, sedentary behavior, and sleep [PASS]), as an evergreen topic in PHS, are widely studied through traditional data sources such as surveillance surveys and administrative databases, which are often several months out-of-date by the time they are used, costly to collect, and thus limited in quantity and coverage. Objective: The main objective of this study is to present a large-scale, multicountry, longitudinal, and fully labeled data set to enable and support digital PASS surveillance research in PHS. To support high-quality surveillance research using our data set, we have conducted further analysis on the data set to supplement it with additional PHS-related metadata. Methods: We collected the data of this study from Twitter using the Twitter livestream application programming interface between November 28, 2018, and June 19, 2020. To obtain PASS-related tweets for manual annotation, we iteratively used regular expressions, unsupervised natural language processing, domain-specific ontologies, and linguistic analysis. We used Amazon Mechanical Turk to label the collected data to self-reported PASS categories and implemented a quality control pipeline to monitor and manage the validity of crowd-generated labels. Moreover, we used ML, latent semantic analysis, linguistic analysis, and label inference analysis to validate the different components of the data set. Results: LPHEADA (Labelled Digital Public Health Dataset) contains 366,405 crowd-generated labels (3 labels per tweet) for 122,135 PASS-related tweets that originated in Australia, Canada, the United Kingdom, or the United States, labeled by 708 unique annotators on Amazon Mechanical Turk. In addition to crowd-generated labels, LPHEADA provides details about the three critical components of any PHS system: place, time, and demographics (ie, gender and age range) associated with each tweet. Conclusions: Publicly available data sets for digital PASS surveillance are usually isolated and only provide labels for small subsets of the data. We believe that the novelty and comprehensiveness of the data set provided in this study will help develop, evaluate, and deploy digital PASS surveillance systems. LPHEADA will be an invaluable resource for both public health researchers and practitioners. UR - https://publichealth.jmir.org/2022/2/e32355 UR - http://dx.doi.org/10.2196/32355 UR - http://www.ncbi.nlm.nih.gov/pubmed/35156938 ID - info:doi/10.2196/32355 ER - TY - JOUR AU - Stevens, Hannah AU - Palomares, A. Nicholas PY - 2022/2/10 TI - Constituents? Inferences of Local Governments? Goals and the Relationship Between Political Party and Belief in COVID-19 Misinformation: Cross-sectional Survey of Twitter Followers of State Public Health Departments JO - JMIR Infodemiology SP - e29246 VL - 2 IS - 1 KW - COVID-19 KW - outbreak KW - mass communication KW - Twitter KW - goal inferences KW - political agendas KW - misinformation KW - infodemic KW - partisanship KW - health information N2 - Background: Amid the COVID-19 pandemic, social media have influenced the circulation of health information. Public health agencies often use Twitter to disseminate and amplify the propagation of such information. Still, exposure to local government?endorsed COVID-19 public health information does not make one immune to believing misinformation. Moreover, not all health information on Twitter is accurate, and some users may believe misinformation and disinformation just as much as those who endorse more accurate information. This situation is complicated, given that elected officials may pursue a political agenda of re-election by downplaying the need for COVID-19 restrictions. The politically polarized nature of information and misinformation on social media in the United States has fueled a COVID-19 infodemic. Because pre-existing political beliefs can both facilitate and hinder persuasion, Twitter users? belief in COVID-19 misinformation is likely a function of their goal inferences about their local government agencies? motives for addressing the COVID-19 pandemic. Objective: We shed light on the cognitive processes of goal understanding that underlie the relationship between partisanship and belief in health misinformation. We investigate how the valence of Twitter users? goal inferences of local governments? COVID-19 efforts predicts their belief in COVID-19 misinformation as a function of their political party affiliation. Methods: We conducted a web-based cross-sectional survey of US Twitter users who followed their state?s official Department of Public Health Twitter account (n=258) between August 10 and December 23, 2020. Inferences about local governments? goals, demographics, and belief in COVID-19 misinformation were measured. State political affiliation was controlled. Results: Participants from all 50 states were included in the sample. An interaction emerged between political party affiliation and goal inference valence for belief in COVID-19 misinformation (?R2=0.04; F8,249=4.78; P<.001); positive goal inference valence predicted increased belief in COVID-19 misinformation among Republicans (?=.47; t249=2.59; P=.01) but not among Democrats (?=.07; t249=0.84; P=.40). Conclusions: Our results reveal that favorable inferences about local governments? COVID-19 efforts can accelerate belief in misinformation among Republican-identifying constituents. In other words, accurate COVID-19 transmission knowledge is a function of constituents' sentiment toward politicians rather than science, which has significant implications on public health efforts for minimizing the spread of the disease, as convincing misinformed constituents to practice safety measures might be a political issue just as much as it is a health one. Our work suggests that goal understanding processes matter for misinformation about COVID-19 among Republicans. Those responsible for future COVID-19 public health messaging aimed at increasing belief in valid information about COVID-19 should recognize the need to test persuasive appeals that address partisans? pre-existing political views in order to prevent individuals? goal inferences from interfering with public health messaging. UR - https://infodemiology.jmir.org/2022/1/e29246 UR - http://dx.doi.org/10.2196/29246 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113808 ID - info:doi/10.2196/29246 ER - TY - JOUR AU - Sullivan, Sean Patrick AU - Woodyatt, R. Cory AU - Kouzouian, Oskian AU - Parrish, J. Kristen AU - Taussig, Jennifer AU - Conlan, Chris AU - Phillips, Harold PY - 2022/2/10 TI - America?s HIV Epidemic Analysis Dashboard: Protocol for a Data Resource to Support Ending the HIV Epidemic in the United States JO - JMIR Public Health Surveill SP - e33522 VL - 8 IS - 2 KW - HIV KW - dashboard KW - data KW - data dashboard KW - infectious disease KW - infodemiology KW - surveillance KW - public health KW - United States KW - monitoring N2 - Background: The Ending the HIV Epidemic (EHE) plan aims to end the HIV epidemic in the United States by 2030. Having timely and accessible data to assess progress toward EHE goals at the local level is a critical resource to achieve this goal. Objective: The aim of this paper was to introduce America?s HIV Epidemic Analysis Dashboard (AHEAD), a data visualization tool that displays relevant data on the 6 HIV indicators provided by the Centers for Disease Control and Prevention. AHEAD can be used to monitor progress toward ending the HIV epidemic in local communities across the United States. Its objective is to make data available to stakeholders, which can be used to measure national and local progress toward 2025 and 2030 EHE goals and to help jurisdictions make local decisions that are grounded in high-quality data. Methods: AHEAD displays data from public health data systems (eg, surveillance systems and census data), organized around the 6 EHE indicators (HIV incidence, knowledge of HIV status, HIV diagnoses, linkage to HIV medical care, viral HIV suppression, and preexposure prophylaxis coverage). Data are displayed for each of the EHE priority areas (48 counties in Washington, District of Columbia, and San Juan, Puerto Rico) which accounted for more than 50% of all US HIV diagnoses in 2016 and 2017 and 7 primarily southern states with high rates of HIV in rural communities. AHEAD also displays data for the 43 remaining states for which data are available. Data features prioritize interactive data visualization tools that allow users to compare indicator data stratified by sex at birth, race or ethnicity, age, and transmission category within a jurisdiction (when available) or compare data on EHE indicators between jurisdictions. Results: AHEAD was launched on August 14, 2020. In the 11 months since its launch, the Dashboard has been visited 26,591 times by 17,600 unique users. About one-quarter of all users returned to the Dashboard at least once. On average, users engaged with 2.4 pages during their visit to the Dashboard, indicating that the average user goes beyond the informational landing page to engage with 1 or more pages of data and content. The most frequently visited content pages are the jurisdiction webpages. Conclusions: The Ending the HIV Epidemic plan is described as a ?whole of society? effort. Societal public health initiatives require objective indicators and require that all societal stakeholders have transparent access to indicator data at the level of the health jurisdictions responsible for meeting the goals of the plan. Data transparency empowers local stakeholders to track movement toward EHE goals, identify areas with needs for improvement, and make data-informed adjustments to deploy the expertise and resources required to locally tailor and implement strategies to end the HIV epidemic in their jurisdiction. UR - https://publichealth.jmir.org/2022/2/e33522 UR - http://dx.doi.org/10.2196/33522 UR - http://www.ncbi.nlm.nih.gov/pubmed/35142639 ID - info:doi/10.2196/33522 ER - TY - JOUR AU - Lwin, O. May AU - Sheldenkar, Anita AU - Lu, Jiahui AU - Schulz, Johannes Peter AU - Shin, Wonsun AU - Panchapakesan, Chitra AU - Gupta, Kumar Raj AU - Yang, Yinping PY - 2022/2/10 TI - The Evolution of Public Sentiments During the COVID-19 Pandemic: Case Comparisons of India, Singapore, South Korea, the United Kingdom, and the United States JO - JMIR Infodemiology SP - e31473 VL - 2 IS - 1 KW - COVID-19 KW - public sentiment KW - Twitter KW - crisis communication KW - cross-country comparison KW - sentiment KW - social media KW - communication KW - public health KW - health information KW - emotion KW - perception KW - health literacy KW - information literacy KW - digital literacy KW - community health N2 - Background: Public sentiments are an important indicator of crisis response, with the need to balance exigency without adding to panic or projecting overconfidence. Given the rapid spread of the COVID-19 pandemic, governments have enacted various nationwide measures against the disease with social media platforms providing the previously unparalleled communication space for the global populations. Objective: This research aims to examine and provide a macro-level narrative of the evolution of public sentiments on social media at national levels, by comparing Twitter data from India, Singapore, South Korea, the United Kingdom, and the United States during the current pandemic. Methods: A total of 67,363,091 Twitter posts on COVID-19 from January 28, 2020, to April 28, 2021, were analyzed from the 5 countries with ?wuhan,? ?corona,? ?nCov,? and ?covid? as search keywords. Change in sentiments (?very negative,? ?negative,? ?neutral or mixed,? ?positive,? ?very positive?) were compared between countries in connection with disease milestones and public health directives. Results: Country-specific assessments show that negative sentiments were predominant across all 5 countries during the initial period of the global pandemic. However, positive sentiments encompassing hope, resilience, and support arose at differing intensities across the 5 countries, particularly in Asian countries. In the next stage of the pandemic, India, Singapore, and South Korea faced escalating waves of COVID-19 cases, resulting in negative sentiments, but positive sentiments appeared simultaneously. In contrast, although negative sentiments in the United Kingdom and the United States increased substantially after the declaration of a national public emergency, strong parallel positive sentiments were slow to surface. Conclusions: Our findings on sentiments across countries facing similar outbreak concerns suggest potential associations between government response actions both in terms of policy and communications, and public sentiment trends. Overall, a more concerted approach to government crisis communication appears to be associated with more stable and less volatile public sentiments over the evolution of the COVID-19 pandemic. UR - https://infodemiology.jmir.org/2022/1/e31473 UR - http://dx.doi.org/10.2196/31473 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113803 ID - info:doi/10.2196/31473 ER - TY - JOUR AU - Huangfu, Luwen AU - Mo, Yiwen AU - Zhang, Peijie AU - Zeng, Dajun Daniel AU - He, Saike PY - 2022/2/8 TI - COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment?Based Topic Modeling JO - J Med Internet Res SP - e31726 VL - 24 IS - 2 KW - COVID-19 KW - COVID-19 vaccine KW - sentiment evolution KW - topic modeling KW - social media KW - text mining N2 - Background: COVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public?s conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public?s vaccine awareness through sentiment?based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines. Objective: In this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved. Methods: We collected 1,122,139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter?s application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857,128 tweets. We then applied sentiment?based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns. Results: Overall, 398,661 (46.51%) were positive, 204,084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405,560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17,154/205,592, 8.34%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign. Conclusions: To the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment?based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign. UR - https://www.jmir.org/2022/2/e31726 UR - http://dx.doi.org/10.2196/31726 UR - http://www.ncbi.nlm.nih.gov/pubmed/34783665 ID - info:doi/10.2196/31726 ER - TY - JOUR AU - Chen, Emily AU - Jiang, Julie AU - Chang, Herbert Ho-Chun AU - Muric, Goran AU - Ferrara, Emilio PY - 2022/2/8 TI - Charting the Information and Misinformation Landscape to Characterize Misinfodemics on Social Media: COVID-19 Infodemiology Study at a Planetary Scale JO - JMIR Infodemiology SP - e32378 VL - 2 IS - 1 KW - social media KW - social networks KW - Twitter KW - COVID-19 KW - infodemics KW - misinfodemics KW - infodemiology KW - misinformation N2 - Background: The novel coronavirus, also known as SARS-CoV-2, has come to define much of our lives since the beginning of 2020. During this time, countries around the world imposed lockdowns and social distancing measures. The physical movements of people ground to a halt, while their online interactions increased as they turned to engaging with each other virtually. As the means of communication shifted online, information consumption also shifted online. Governing authorities and health agencies have intentionally shifted their focus to use social media and online platforms to spread factual and timely information. However, this has also opened the gate for misinformation, contributing to and accelerating the phenomenon of misinfodemics. Objective: We carried out an analysis of Twitter discourse on over 1 billion tweets related to COVID-19 over a year to identify and investigate prevalent misinformation narratives and trends. We also aimed to describe the Twitter audience that is more susceptible to health-related misinformation and the network mechanisms driving misinfodemics. Methods: We leveraged a data set that we collected and made public, which contained over 1 billion tweets related to COVID-19 between January 2020 and April 2021. We created a subset of this larger data set by isolating tweets that included URLs with domains that had been identified by Media Bias/Fact Check as being prone to questionable and misinformation content. By leveraging clustering and topic modeling techniques, we identified major narratives, including health misinformation and conspiracies, which were present within this subset of tweets. Results: Our focus was on a subset of 12,689,165 tweets that we determined were representative of COVID-19 misinformation narratives in our full data set. When analyzing tweets that shared content from domains known to be questionable or that promoted misinformation, we found that a few key misinformation narratives emerged about hydroxychloroquine and alternative medicines, US officials and governing agencies, and COVID-19 prevention measures. We further analyzed the misinformation retweet network and found that users who shared both questionable and conspiracy-related content were clustered more closely in the network than others, supporting the hypothesis that echo chambers can contribute to the spread of health misinfodemics. Conclusions: We presented a summary and analysis of the major misinformation discourse surrounding COVID-19 and those who promoted and engaged with it. While misinformation is not limited to social media platforms, we hope that our insights, particularly pertaining to health-related emergencies, will help pave the way for computational infodemiology to inform health surveillance and interventions. UR - https://infodemiology.jmir.org/2022/1/e32378 UR - http://dx.doi.org/10.2196/32378 UR - http://www.ncbi.nlm.nih.gov/pubmed/35190798 ID - info:doi/10.2196/32378 ER - TY - JOUR AU - Neely, Stephen AU - Eldredge, Christina AU - Sanders, Ronald PY - 2022/2/4 TI - Authors? Reply: Understanding the Impact of Social Media Information and Misinformation Producers on Health Information Seeking. Comment on ?Health Information Seeking Behaviors on Social Media During the COVID-19 Pandemic Among American Social Networking Site Users: Survey Study? JO - J Med Internet Res SP - e31569 VL - 24 IS - 2 KW - social media KW - internet KW - communication KW - public health KW - COVID-19 KW - usage KW - United States KW - information seeking KW - web-based health information KW - online health information KW - survey KW - mistrust KW - vaccination KW - misinformation UR - https://www.jmir.org/2022/2/e31569 UR - http://dx.doi.org/10.2196/31569 UR - http://www.ncbi.nlm.nih.gov/pubmed/35119376 ID - info:doi/10.2196/31569 ER - TY - JOUR AU - Boudreau, Hunter AU - Singh, Nikhi AU - Boyd, J. Carter PY - 2022/2/4 TI - Understanding the Impact of Social Media Information and Misinformation Producers on Health Information Seeking. Comment on ?Health Information Seeking Behaviors on Social Media During the COVID-19 Pandemic Among American Social Networking Site Users: Survey Study? JO - J Med Internet Res SP - e31415 VL - 24 IS - 2 KW - social media KW - internet KW - communication KW - public health KW - COVID-19 KW - usage KW - United States KW - information seeking KW - web-based health information KW - online health information KW - survey KW - mistrust KW - vaccination KW - misinformation UR - https://www.jmir.org/2022/2/e31415 UR - http://dx.doi.org/10.2196/31415 UR - http://www.ncbi.nlm.nih.gov/pubmed/35076408 ID - info:doi/10.2196/31415 ER - TY - JOUR AU - Sun, Li AU - Lu, Xinyi AU - Xie, Zidian AU - Li, Dongmei PY - 2022/2/3 TI - Public Reactions to the New York State Policy on Flavored Electronic Cigarettes on Twitter: Observational Study JO - JMIR Public Health Surveill SP - e25216 VL - 8 IS - 2 KW - New York State policy KW - flavored e-cigarettes KW - Twitter KW - social media KW - vaping KW - e-cigarette N2 - Background: Flavored electronic cigarettes (e-cigarettes) have become popular in recent years, especially among youth and young adults. To address the epidemic of e-cigarettes, New York State approved a ban on sales of most flavored vaping products other than tobacco and menthol flavors on September 17, 2019. Objective: This study aims to examine the attitude of Twitter users to the policy on flavored e-cigarettes in New York State and the impact of this policy on public perceptions of e-cigarettes. This study also compares the attitudes and topics between New York Twitter users and Twitter users from other states who were not directly affected by this policy. Methods: Tweets related to e-cigarettes and the New York State policy on flavored e-cigarettes were collected using the Twitter streaming application programming interface from June 2019 to December 2019. Tweets from New York State and those from other states that did not have a flavored e-cigarette policy were extracted. Sentiment analysis was applied to analyze the proportion of negative and positive tweets about e-cigarettes or the flavor policy. Topic modeling was applied to e-cigarette?related data sets and New York flavor policy?related data sets to identify the most frequent topics before and after the announcement of the New York State policy. Results: We found that the average number of tweets related to e-cigarettes and the New York State policy on flavored e-cigarettes increased in both New York State and other states after the flavor policy announcement. Sentiment analysis revealed that after the announcement of the New York State flavor policy, in both New York State and other states, the proportion of negative tweets on e-cigarettes increased from 34.07% (4531/13,299) to 44.58% (18,451/41,390) and from 32.48% (14,320/44,090) to 44.40% (64,262/144,734), respectively, while positive tweets decreased significantly from 39.03% (5191/13,299) to 32.86% (13,601/41,390) and from 42.78% (18,863/44,090) to 33.93% (49,105/144,734), respectively. The majority of tweets related to the New York State flavor policy were negative both before and after the announcement of this policy in both New York (87/98, 89% and 3810/4565, 83.46%, respectively) and other states (200/255, 78.4% and 12,695/15,569, 81.54%, respectively), while New York State had a higher proportion of negative tweets than other states. Topic modeling results demonstrated that teenage vaping and health problems were the most discussed topics associated with e-cigarettes. Conclusions: Public attitudes toward e-cigarettes became more negative on Twitter after New York State announced the policy on flavored e-cigarettes. Twitter users in other states that did not have such a policy on flavored e-cigarettes paid close attention to the New York State flavor policy. This study provides some valuable information about the potential impact of the flavored e-cigarettes policy in New York State on public attitudes toward flavored e-cigarettes. UR - https://publichealth.jmir.org/2022/2/e25216 UR - http://dx.doi.org/10.2196/25216 UR - http://www.ncbi.nlm.nih.gov/pubmed/35113035 ID - info:doi/10.2196/25216 ER - TY - JOUR AU - Wakamiya, Shoko AU - Morimoto, Osamu AU - Omichi, Katsuhiro AU - Hara, Hideyuki AU - Kawase, Ichiro AU - Koshiba, Ryuji AU - Aramaki, Eiji PY - 2022/2/2 TI - Exploring Relationships Between Tweet Numbers and Over-the-counter Drug Sales for Allergic Rhinitis: Retrospective Analysis JO - JMIR Form Res SP - e33941 VL - 6 IS - 2 KW - infoveillance KW - social media KW - Twitter KW - over-the-counter drugs KW - allergic rhinitis KW - hay fever KW - drug KW - treatment KW - allergy KW - immunology KW - surveillance KW - monitoring KW - prevalence KW - motivation KW - Japan KW - symptom N2 - Background: Health-related social media data are increasingly being used in disease surveillance studies. In particular, surveillance of infectious diseases such as influenza has demonstrated high correlations between the number of social media posts mentioning the disease and the number of patients who went to the hospital and were diagnosed with the disease. However, the prevalence of some diseases, such as allergic rhinitis, cannot be estimated based on the number of patients alone. Specifically, individuals with allergic rhinitis typically self-medicate by taking over-the-counter (OTC) medications without going to the hospital. Although allergic rhinitis is not a life-threatening disease, it represents a major social problem because it reduces people?s quality of life, making it essential to understand its prevalence and people?s motives for self-medication behavior. Objective: This study aims to explore the relationship between the number of social media posts mentioning the main symptoms of allergic rhinitis and the sales volume of OTC rhinitis medications in Japan. Methods: We collected tweets over 4 years (from 2017 to 2020) that included keywords corresponding to the main nasal symptoms of allergic rhinitis: ?sneezing,? ?runny nose,? and ?stuffy nose.? We also obtained the sales volume of OTC drugs, including oral medications and nasal sprays, for the same period. We then calculated the Pearson correlation coefficient between time series data on the number of tweets per week and time series data on the sales volume of OTC drugs per week. Results: The results showed a much higher correlation (r=0.8432) between the time series data on the number of tweets mentioning ?stuffy nose? and the time series data on the sales volume of nasal sprays than for the other two symptoms. There was also a high correlation (r=0.9317) between the seasonal components of these time series data. Conclusions: We investigated the relationships between social media data and behavioral patterns, such as OTC drug sales volume. Exploring these relationships can help us understand the prevalence of allergic rhinitis and the motives for self-care treatment using social media data, which would be useful as a marketing indicator to reduce the number of out-of-stocks in stores, provide (sell) rhinitis medicines to consumers in a stable manner, and reduce the loss of sales opportunities. In the future, in-depth investigations are required to estimate sales volume using social media data, and future research could investigate other diseases and countries. UR - https://formative.jmir.org/2022/2/e33941 UR - http://dx.doi.org/10.2196/33941 UR - http://www.ncbi.nlm.nih.gov/pubmed/35107434 ID - info:doi/10.2196/33941 ER - TY - JOUR AU - Gisondi, A. Michael AU - Barber, Rachel AU - Faust, Samuel Jemery AU - Raja, Ali AU - Strehlow, C. Matthew AU - Westafer, M. Lauren AU - Gottlieb, Michael PY - 2022/2/1 TI - A Deadly Infodemic: Social Media and the Power of COVID-19 Misinformation JO - J Med Internet Res SP - e35552 VL - 24 IS - 2 KW - COVID-19 KW - social media KW - misinformation KW - disinformation KW - infodemic KW - ethics KW - vaccination KW - vaccine hesitancy KW - infoveillance KW - vaccine UR - https://www.jmir.org/2022/2/e35552 UR - http://dx.doi.org/10.2196/35552 UR - http://www.ncbi.nlm.nih.gov/pubmed/35007204 ID - info:doi/10.2196/35552 ER - TY - JOUR AU - Jung, Soyoung AU - Jung, Sooin PY - 2022/1/31 TI - The Impact of the COVID-19 Infodemic on Depression and Sleep Disorders: Focusing on Uncertainty Reduction Strategies and Level of Interpretation Theory JO - JMIR Form Res SP - e32552 VL - 6 IS - 1 KW - COVID-19 KW - social media KW - infodemic KW - construal level theory KW - uncertainty reduction strategy KW - depression KW - sleep disorder KW - preventive actions, affective reaction KW - infodemiology KW - misinformation KW - uncertainty KW - strategy KW - mental health KW - sleep KW - prevention KW - survey KW - usage KW - behavior N2 - Background: During the COVID-19 pandemic, information diffusion about the COVID-19 has attracted public attention through social media. The World Health Organization declared an infodemic of COVID-19 on February 15, 2020. Misinformation and disinformation, including overwhelming amounts of information about COVID-19 on social media, could promote adverse psychological effects. Objective: This study used the Psychological Distance and Level of Construal theory (CLT) to predict peoples? negative psychological symptoms from social media usage. In this study, the CLT intended to show peoples? psychological proximity to objects and events with respect to the COVID-19 pandemic. Furthermore, this study links the uncertainty reduction strategy (URS) and CLT for COVID-19?related preventive behaviors and affective reactions to assess their effects on mental health problems. Methods: A path model was tested (N=297) with data from a web-based survey to examine how social media usage behaviors are associated with URS and psychological distance with COVID-19 (based on the CLT), leading to preventive behaviors and affective reactions. Finally, the path model was used to examine how preventive behaviors and affective reactions are associated with mental health problems including anxiety and sleep disorder. Results: After measuring participants? social media usage behavior, we found that an increase in general social media usage led to higher use of the URS and lower construal level on COVID-19. The URS is associated with preventive behaviors, but the CLT did not show any association with preventive behaviors; however, it increases affective reactions. Moreover, increased preventive behavior showed negative associations with symptoms of mental health problems; that is, depression and sleep disorder. However, the affective reaction tends to be positively associated with depression and sleep disorder. Owing to the infodemic of COVID-19, the psychological perception of the pandemic negatively influenced users? mental health problems. Conclusions: Our results imply that the information from social media usage heightened concerns and had a lower construal level; this does not facilitate taking preventive actions but rather reinforces the negative emotional reaction and mental health problems. Thus, higher URS usage is desirable. UR - https://formative.jmir.org/2022/1/e32552 UR - http://dx.doi.org/10.2196/32552 UR - http://www.ncbi.nlm.nih.gov/pubmed/34870609 ID - info:doi/10.2196/32552 ER - TY - JOUR AU - Lekkas, Damien AU - Gyorda, A. Joseph AU - Price, D. George AU - Wortzman, Zoe AU - Jacobson, C. Nicholas PY - 2022/1/27 TI - Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior Across the United States: Integrated Sentiment Analysis and the Circumplex Model of Affect JO - J Med Internet Res SP - e32731 VL - 24 IS - 1 KW - affect KW - sentiment KW - circumplex KW - news KW - mental health KW - online search behavior KW - generalized mixed models KW - natural language processing KW - anxiety KW - depression KW - coronavirus KW - internet KW - information seeking KW - behavior KW - online health information KW - COVID-19 N2 - Background: The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent COVID-19 pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation. Objective: Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States. Methods: Using COVID-19?related news headlines from a database of online news stories in conjunction with mental health?related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID-19 news coverage across the United States from January 23, 2020, to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and nonspecific depression and anxiety symptoms to model the broad impact of news coverage on mental health. Results: Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first 9 months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed effects modeling implicated increased consistency in affective tone (SpinVA ?=?.207; P<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA ?=.221; P<.001) contributing generally to an increase in online mental health search term frequency. Conclusions: This study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health?related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data. UR - https://www.jmir.org/2022/1/e32731 UR - http://dx.doi.org/10.2196/32731 UR - http://www.ncbi.nlm.nih.gov/pubmed/34932494 ID - info:doi/10.2196/32731 ER - TY - JOUR AU - Dashtian, Hassan AU - Murthy, Dhiraj AU - Kong, Grace PY - 2022/1/27 TI - An Exploration of e-Cigarette?Related Search Items on YouTube: Network Analysis JO - J Med Internet Res SP - e30679 VL - 24 IS - 1 KW - electronic nicotine delivery systems KW - vaping KW - social media KW - search engine KW - natural language processing KW - social network analysis N2 - Background: e-Cigarette use among youth is high, which may be due in part to pro?e-cigarette content on social media such as YouTube. YouTube is also a valuable resource for learning about e-cigarette use, trends, marketing, and e-cigarette user perceptions. However, there is a lack of understanding on how similar e-cigarette?related search items result in similar or relatively mutually exclusive search results. This study uses novel methods to evaluate the relationship between e-cigarette?related search items and results. Objective: The aim of this study is to apply network modeling and rule-based classification to characterize the relationships between e-cigarette?related search items on YouTube and gauge the level of importance of each search item as part of an e-cigarette information network on YouTube. Methods: We used 16 fictitious YouTube profiles to retrieve 4201 distinct videos from 18 keywords related to e-cigarettes. We used network modeling to represent the relationships between the search items. Moreover, we developed a rule-based classification approach to classify videos. We used betweenness centrality (BC) and correlations between nodes (ie, search items) to help us gain knowledge of the underlying structure of the information network. Results: By modeling search items and videos as a network, we observed that broad search items such as e-cig had the most connections to other search items, and specific search items such as cigalike had the least connections. Search items with similar words (eg, vape and vaping) and search items with similar meaning (eg, e-liquid and e-juice) yielded a high degree of connectedness. We also found that each node had 18 (SD 34.8) connections (common videos) on average. BC indicated that general search items such as electronic cigarette and vaping had high importance in the network (BC=0.00836). Our rule-based classification sorted videos into four categories: e-cigarette devices (34%-57%), cannabis vaping (16%-28%), e-liquid (14%-37%), and other (8%-22%). Conclusions: Our findings indicate that search items on YouTube have unique relationships that vary in strength and importance. Our methods can not only be used to successfully identify the important, overlapping, and unique e-cigarette?related search items but also help determine which search items are more likely to act as a gateway to e-cigarette?related content. UR - https://www.jmir.org/2022/1/e30679 UR - http://dx.doi.org/10.2196/30679 UR - http://www.ncbi.nlm.nih.gov/pubmed/35084353 ID - info:doi/10.2196/30679 ER - TY - JOUR AU - Chejfec-Ciociano, Matias Jonathan AU - Martínez-Herrera, Pablo Juan AU - Parra-Guerra, Darianna Alexa AU - Chejfec, Ricardo AU - Barbosa-Camacho, José Francisco AU - Ibarrola-Peńa, Carlos Juan AU - Cervantes-Guevara, Gabino AU - Cervantes-Cardona, Alonso Guillermo AU - Fuentes-Orozco, Clotilde AU - Cervantes-Pérez, Enrique AU - García-Reyna, Benjamín AU - González-Ojeda, Alejandro PY - 2022/1/27 TI - Misinformation About and Interest in Chlorine Dioxide During the COVID-19 Pandemic in Mexico Identified Using Google Trends Data: Infodemiology Study JO - JMIR Infodemiology SP - e29894 VL - 2 IS - 1 KW - coronavirus KW - COVID-19 KW - Google Trends KW - chlorine dioxide KW - COVID-19 misinformation KW - public health surveillance KW - infodemiology KW - internet behavior KW - digital epidemiology KW - internet KW - mHealth KW - mobile health KW - pandemic KW - tele-epidemiology N2 - Background: The COVID-19 pandemic has prompted the increasing popularity of several emerging therapies or preventives that lack scientific evidence or go against medical directives. One such therapy involves the consumption of chlorine dioxide, which is commonly used in the cleaning industry and is available commercially as a mineral solution. This substance has been promoted as a preventive or treatment agent for several diseases, including SARS-CoV-2 infection. As interest in chlorine dioxide has grown since the start of the pandemic, health agencies, institutions, and organizations worldwide have tried to discourage and restrict the consumption of this substance. Objective: The aim of this study is to analyze search engine trends in Mexico to evaluate changes in public interest in chlorine dioxide since the beginning of the COVID-19 pandemic. Methods: We retrieved public query data for the Spanish equivalent of the term ?chlorine dioxide? from the Google Trends platform. The location was set to Mexico, and the time frame was from March 3, 2019, to February 21, 2021. A descriptive analysis was performed. The Kruskal-Wallis and Dunn tests were used to identify significant changes in search volumes for this term between four consecutive time periods, each of 13 weeks, from March 1, 2020, to February 27, 2021. Results: From the start of the pandemic in Mexico (February 2020), an upward trend was observed in the number of searches compared with that in 2019. Maximum volume trends were recorded during the week of July 19-25, 2020. The search volumes declined between September and November 2020, but another peak was registered in December 2020 through February 2021, which reached a maximum value on January 10. Percentage change from the first to the fourth time periods was +312.85, ?71.35, and +228.18, respectively. Pairwise comparisons using the Kruskal-Wallis and Dunn tests showed significant differences between the four periods (P<.001). Conclusions: Misinformation is a public health risk because it can lower compliance with the recommended measures and encourage the use of therapies that have not been proven safe. The ingestion of chlorine dioxide presents a danger to the population, and several adverse reactions have been reported. Programs should be implemented to direct those interested in this substance to accurate medical information. UR - https://infodemiology.jmir.org/2022/1/e29894 UR - http://dx.doi.org/10.2196/29894 UR - http://www.ncbi.nlm.nih.gov/pubmed/35155994 ID - info:doi/10.2196/29894 ER - TY - JOUR AU - Gonzalez, Gabriela AU - Vaculik, Kristina AU - Khalil, Carine AU - Zektser, Yuliya AU - Arnold, Corey AU - Almario, V. Christopher AU - Spiegel, Brennan AU - Anger, Jennifer PY - 2022/1/25 TI - Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis JO - J Med Internet Res SP - e26781 VL - 24 IS - 1 KW - female urology KW - urinary tract infections KW - health services research KW - social media KW - online community KW - online forum KW - latent Dirichlet allocation KW - data mining KW - digital ethnography N2 - Background: Current qualitative literature about the experiences of women dealing with urinary tract infections (UTIs) is limited to patients recruited from tertiary centers and medical clinics. However, traditional focus groups and interviews may limit what patients share. Using digital ethnography, we analyzed free-range conversations of an online community. Objective: This study aimed to investigate and characterize the patient perspectives of women dealing with UTIs using digital ethnography. Methods: A data-mining service was used to identify online posts. A thematic analysis was conducted on a subset of the identified posts. Additionally, a latent Dirichlet allocation (LDA) probabilistic topic modeling method was applied to review the entire data set using a semiautomatic approach. Each identified topic was generated as a discrete distribution over the words in the collection, which can be thought of as a word cloud. We also performed a thematic analysis of the word cloud topic model results. Results: A total of 83,589 posts by 53,460 users from 859 websites were identified. Our hand-coding inductive analysis yielded the following 7 themes: quality-of-life impact, knowledge acquisition, support of the online community, health care utilization, risk factors and prevention, antibiotic treatment, and alternative therapies. Using the LDA topic model method, 105 themes were identified and consolidated into 9 categories. Of the LDA-derived themes, 25.7% (27/105) were related to online community support, and 22% (23/105) focused on UTI risk factors and prevention strategies. Conclusions: Our large-scale social media analysis supports the importance and reproducibility of using online data to comprehend women?s UTI experience. This inductive thematic analysis highlights patient behavior, self-empowerment, and online media utilization by women to address their health concerns in a safe, anonymous way. UR - https://www.jmir.org/2022/1/e26781 UR - http://dx.doi.org/10.2196/26781 UR - http://www.ncbi.nlm.nih.gov/pubmed/35076404 ID - info:doi/10.2196/26781 ER - TY - JOUR AU - Hudson, Georgie AU - Jansli, M. Sonja AU - Erturk, Sinan AU - Morris, Daniel AU - Odoi, M. Clarissa AU - Clayton-Turner, Angela AU - Bray, Vanessa AU - Yourston, Gill AU - Clouden, Doreen AU - Proudfoot, David AU - Cornwall, Andrew AU - Waldron, Claire AU - Wykes, Til AU - Jilka, Sagar PY - 2022/1/24 TI - Investigation of Carers? Perspectives of Dementia Misconceptions on Twitter: Focus Group Study JO - JMIR Aging SP - e30388 VL - 5 IS - 1 KW - patient and public involvement KW - dementia KW - co-production KW - misconceptions KW - stigma KW - Twitter KW - social media KW - Alzheimer?s Disease N2 - Background: Dementia misconceptions on social media are common, with negative effects on people with the condition, their carers, and those who know them. This study codeveloped a thematic framework with carers to understand the forms these misconceptions take on Twitter. Objective: The aim of this study is to identify and analyze types of dementia conversations on Twitter using participatory methods. Methods: A total of 3 focus groups with dementia carers were held to develop a framework of dementia misconceptions based on their experiences. Dementia-related tweets were collected from Twitter?s official application programming interface using neutral and negative search terms defined by the literature and by carers (N=48,211). A sample of these tweets was selected with equal numbers of neutral and negative words (n=1497), which was validated in individual ratings by carers. We then used the framework to analyze, in detail, a sample of carer-rated negative tweets (n=863). Results: A total of 25.94% (12,507/48,211) of our tweet corpus contained negative search terms about dementia. The carers? framework had 3 negative and 3 neutral categories. Our thematic analysis of carer-rated negative tweets found 9 themes, including the use of weaponizing language to insult politicians (469/863, 54.3%), using dehumanizing or outdated words or statements about members of the public (n=143, 16.6%), unfounded claims about the cures or causes of dementia (n=11, 1.3%), or providing armchair diagnoses of dementia (n=21, 2.4%). Conclusions: This is the first study to use participatory methods to develop a framework that identifies dementia misconceptions on Twitter. We show that misconceptions and stigmatizing language are not rare. They manifest through minimizing and underestimating language. Web-based campaigns aiming to reduce discrimination and stigma about dementia could target those who use negative vocabulary and reduce the misconceptions that are being propagated, thus improving general awareness. UR - https://aging.jmir.org/2022/1/e30388 UR - http://dx.doi.org/10.2196/30388 UR - http://www.ncbi.nlm.nih.gov/pubmed/35072637 ID - info:doi/10.2196/30388 ER - TY - JOUR AU - Zhang, Chunyan AU - Xu, Songhua AU - Li, Zongfang AU - Liu, Ge AU - Dai, Duwei AU - Dong, Caixia PY - 2022/1/21 TI - The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study JO - J Med Internet Res SP - e32394 VL - 24 IS - 1 KW - COVID-19 KW - vaccine KW - attitude KW - Twitter KW - data mining KW - pandemic KW - population group KW - evolution KW - disparity N2 - Background: Due to the urgency caused by the COVID-19 pandemic worldwide, vaccine manufacturers have to shorten and parallel the development steps to accelerate COVID-19 vaccine production. Although all usual safety and efficacy monitoring mechanisms remain in place, varied attitudes toward the new vaccines have arisen among different population groups. Objective: This study aimed to discern the evolution and disparities of attitudes toward COVID-19 vaccines among various population groups through the study of large-scale tweets spanning over a whole year. Methods: We collected over 1.4 billion tweets from June 2020 to July 2021, which cover some critical phases concerning the development and inoculation of COVID-19 vaccines worldwide. We first developed a data mining model that incorporates a series of deep learning algorithms for inferring a range of individual characteristics, both in reality and in cyberspace, as well as sentiments and emotions expressed in tweets. We further conducted an observational study, including an overall analysis, a longitudinal study, and a cross-sectional study, to collectively explore the attitudes of major population groups. Results: Our study derived 3 main findings. First, the whole population?s attentiveness toward vaccines was strongly correlated (Pearson r=0.9512) with official COVID-19 statistics, including confirmed cases and deaths. Such attentiveness was also noticeably influenced by major vaccine-related events. Second, after the beginning of large-scale vaccine inoculation, the sentiments of all population groups stabilized, followed by a considerably pessimistic trend after June 2021. Third, attitude disparities toward vaccines existed among population groups defined by 8 different demographic characteristics. By crossing the 2 dimensions of attitude, we found that among population groups carrying low sentiments, some had high attentiveness ratios, such as males and individuals aged ?40 years, while some had low attentiveness ratios, such as individuals aged ?18 years, those with occupations of the 3rd category, those with account age <5 years, and those with follower number <500. These findings can be used as a guide in deciding who should be given more attention and what kinds of help to give to alleviate the concerns about vaccines. Conclusions: This study tracked the year-long evolution of attitudes toward COVID-19 vaccines among various population groups defined by 8 demographic characteristics, through which significant disparities in attitudes along multiple dimensions were revealed. According to these findings, it is suggested that governments and public health organizations should provide targeted interventions to address different concerns, especially among males, older people, and other individuals with low levels of education, low awareness of news, low income, and light use of social media. Moreover, public health authorities may consider cooperating with Twitter users having high levels of social influence to promote the acceptance of COVID-19 vaccines among all population groups. UR - https://www.jmir.org/2022/1/e32394 UR - http://dx.doi.org/10.2196/32394 UR - http://www.ncbi.nlm.nih.gov/pubmed/34878410 ID - info:doi/10.2196/32394 ER - TY - JOUR AU - Shakeri Hossein Abad, Zahra AU - Butler, P. Gregory AU - Thompson, Wendy AU - Lee, Joon PY - 2022/1/18 TI - Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk JO - J Med Internet Res SP - e28749 VL - 24 IS - 1 KW - crowdsourcing KW - machine learning KW - digital public health surveillance KW - public health database KW - social media analysis N2 - Background: Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems. Objective: This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems. Methods: We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning?based classification model, and a hybrid convolutional neural network model. Results: Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data. Conclusions: Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models. UR - https://www.jmir.org/2022/1/e28749 UR - http://dx.doi.org/10.2196/28749 UR - http://www.ncbi.nlm.nih.gov/pubmed/35040794 ID - info:doi/10.2196/28749 ER - TY - JOUR AU - Singh, Kumar Asmit AU - Mehan, Paras AU - Sharma, Divyanshu AU - Pandey, Rohan AU - Sethi, Tavpritesh AU - Kumaraguru, Ponnurangam PY - 2022/1/18 TI - COVID-19 Mask Usage and Social Distancing in Social Media Images: Large-scale Deep Learning Analysis JO - JMIR Public Health Surveill SP - e26868 VL - 8 IS - 1 KW - COVID-19 KW - mask detection KW - deep learning KW - classification KW - segmentation KW - social media analysis N2 - Background: The adoption of nonpharmaceutical interventions and their surveillance are critical for detecting and stopping possible transmission routes of COVID-19. A study of the effects of these interventions can help shape public health decisions. The efficacy of nonpharmaceutical interventions can be affected by public behaviors in events, such as protests. We examined mask use and mask fit in the United States, from social media images, especially during the Black Lives Matter (BLM) protests, representing the first large-scale public gatherings in the pandemic. Objective: This study assessed the use and fit of face masks and social distancing in the United States and events of large physical gatherings through public social media images from 6 cities and BLM protests. Methods: We collected and analyzed 2.04 million public social media images from New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis between February 1, 2020, and May 31, 2020. We evaluated correlations between online mask usage trends and COVID-19 cases. We looked for significant changes in mask use patterns and group posting around important policy decisions. For BLM protests, we analyzed 195,452 posts from New York and Minneapolis from May 25, 2020, to July 15, 2020. We looked at differences in adopting the preventive measures in the BLM protests through the mask fit score. Results: The average percentage of group pictures dropped from 8.05% to 4.65% after the lockdown week. New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis observed increases of 5.0%, 7.4%, 7.4%, 6.5%, 5.6%, and 7.1%, respectively, in mask use between February 2020 and May 2020. Boston and Minneapolis observed significant increases of 3.0% and 7.4%, respectively, in mask use after the mask mandates. Differences of 6.2% and 8.3% were found in group pictures between BLM posts and non-BLM posts for New York City and Minneapolis, respectively. In contrast, the differences in the percentage of masked faces in group pictures between BLM and non-BLM posts were 29.0% and 20.1% for New York City and Minneapolis, respectively. Across protests, 35% of individuals wore a mask with a fit score greater than 80%. Conclusions: The study found a significant drop in group posting when the stay-at-home laws were applied and a significant increase in mask use for 2 of 3 cities where masks were mandated. Although a positive trend toward mask use and social distancing was observed, a high percentage of posts showed disregard for the guidelines. BLM-related posts captured the lack of seriousness to safety measures, with a high percentage of group pictures and low mask fit scores. Thus, the methodology provides a directional indication of how government policies can be indirectly monitored through social media. UR - https://publichealth.jmir.org/2022/1/e26868 UR - http://dx.doi.org/10.2196/26868 UR - http://www.ncbi.nlm.nih.gov/pubmed/34479183 ID - info:doi/10.2196/26868 ER - TY - JOUR AU - Schmälzle, Ralf AU - Wilcox, Shelby PY - 2022/1/18 TI - Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine JO - J Med Internet Res SP - e28858 VL - 24 IS - 1 KW - human-centered AI KW - campaigns KW - health communication KW - NLP KW - health promotion N2 - Background: Communication campaigns using social media can raise public awareness; however, they are difficult to sustain. A barrier is the need to generate and constantly post novel but on-topic messages, which creates a resource-intensive bottleneck. Objective: In this study, we aim to harness the latest advances in artificial intelligence (AI) to build a pilot system that can generate many candidate messages, which could be used for a campaign to suggest novel, on-topic candidate messages. The issue of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example; however, the system can work with other issues that could benefit from higher levels of public awareness. Methods: We used the Generative Pretrained Transformer-2 architecture, a machine learning model trained on a large natural language corpus, and fine-tuned it using a data set of autodownloaded tweets about #folicacid. The fine-tuned model was then used as a message engine, that is, to create new messages about this topic. We conducted a web-based study to gauge how human raters evaluate AI-generated tweet messages compared with original, human-crafted messages. Results: We found that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Web-based raters evaluated the clarity and quality of a human-curated sample of AI-generated messages as on par with human-generated ones. Overall, these results showed that it is feasible to use such a message engine to suggest messages for web-based campaigns that focus on promoting awareness. Conclusions: The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for the quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage. UR - https://www.jmir.org/2022/1/e28858 UR - http://dx.doi.org/10.2196/28858 UR - http://www.ncbi.nlm.nih.gov/pubmed/35040800 ID - info:doi/10.2196/28858 ER - TY - JOUR AU - Schluter, J. Philip AU - Généreux, Mélissa AU - Hung, KC Kevin AU - Landaverde, Elsa AU - Law, P. Ronald AU - Mok, Yin Catherine Pui AU - Murray, Virginia AU - O'Sullivan, Tracey AU - Qadar, Zeeshan AU - Roy, Mathieu PY - 2022/1/17 TI - Patterns of Suicide Ideation Across Eight Countries in Four Continents During the COVID-19 Pandemic Era: Repeated Cross-sectional Study JO - JMIR Public Health Surveill SP - e32140 VL - 8 IS - 1 KW - pandemic KW - infodemic KW - psychosocial impacts KW - sense of coherence KW - suicide ideation KW - epidemiology KW - suicide KW - pattern KW - COVID-19 KW - cross-sectional KW - mental health KW - misinformation KW - risk KW - prevalence KW - gender KW - age KW - sociodemographic N2 - Background: The COVID-19 pandemic and countries? response measures have had a globally significant mental health impact. This mental health burden has also been fueled by an infodemic: an information overload that includes misinformation and disinformation. Suicide, the worst mental health outcome, is a serious public health problem that can be prevented with timely, evidence-based, and often low-cost interventions. Suicide ideation, one important risk factor for suicide, is thus important to measure and monitor, as are the factors that may impact on it. Objective: This investigation had 2 primary aims: (1) to estimate and compare country-specific prevalence of suicide ideation at 2 different time points, overall and by gender and age groups, and (2) to investigate the influence of sociodemographic and infodemic variables on suicide ideation. Methods: A repeated, online, 8-country (Canada, the United States, England, Switzerland, Belgium, Hong Kong, Philippines, and New Zealand), cross-sectional study was undertaken with adults aged ?18 years, with measurement wave 1 conducted from May 29, 2020 to June 12, 2020 and measurement wave 2 conducted November 6-18, 2021. Self-reported suicide ideation was derived from item 9 of the Patient Health Questionnaire-9 (PHQ-9). Age-standardized suicide ideation rates were reported, a binomial regression model was used to estimate suicide ideation indication rates for each country and measurement wave, and logistic regression models were then employed to relate sociodemographic, pandemic, and infodemic variables to suicide ideation. Results: The final sample totaled 17,833 adults: 8806 (49.4%) from measurement wave 1 and 9027 (50.6%) from wave 2. Overall, 24.2% (2131/8806) and 27.5% (2486/9027) of participants reported suicide ideation at measurement waves 1 and 2, respectively, a difference that was significant (P<.001). Considerable variability was observed in suicide ideation age-standardized rates between countries, ranging from 15.6% in Belgium (wave 1) to 42.9% in Hong Kong (wave 2). Frequent social media usage was associated with increased suicide ideation at wave 2 (adjusted odds ratio [AOR] 1.47, 95% CI 1.25-1.72; P<.001) but not wave 1 (AOR 1.11, 95% CI 0.96-1.23; P=.16). However, having a weaker sense of coherence (SOC; AOR 3.80, 95% CI 3.18-4.55 at wave 1 and AOR 4.39, 95% CI 3.66-5.27 at wave 2; both P<.001) had the largest overall effect size. Conclusions: Suicide ideation is prevalent and significantly increasing over time in this COVID-19 pandemic era, with considerable variability between countries. Younger adults and those residing in Hong Kong carried disproportionately higher rates. Social media appears to have an increasingly detrimental association with suicide ideation, although having a stronger SOC had a larger protective effect. Policies and promotion of SOC, together with disseminating health information that explicitly tackles the infodemic?s misinformation and disinformation, may importantly reduce the rising mental health morbidity and mortality triggered by this pandemic. UR - https://publichealth.jmir.org/2022/1/e32140 UR - http://dx.doi.org/10.2196/32140 UR - http://www.ncbi.nlm.nih.gov/pubmed/34727524 ID - info:doi/10.2196/32140 ER - TY - JOUR AU - Cheng, Cecilia AU - Ebrahimi, V. Omid AU - Luk, W. Jeremy PY - 2022/1/10 TI - Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis JO - J Med Internet Res SP - e27000 VL - 24 IS - 1 KW - behavioral addiction KW - compulsive social media use KW - information technology addiction KW - mental health KW - psychological assessment KW - sensitivity KW - social network site KW - social networking KW - well-being N2 - Background: As social media is a major channel of interpersonal communication in the digital age, social media addiction has emerged as a novel mental health issue that has raised considerable concerns among researchers, health professionals, policy makers, mass media, and the general public. Objective: The aim of this study is to examine the prevalence of social media addiction derived from 4 major classification schemes (strict monothetic, strict polythetic, monothetic, and polythetic), with latent profiles embedded in the empirical data adopted as the benchmark for comparison. The extent of matching between the classification of each scheme and the actual data pattern was evaluated using sensitivity and specificity analyses. The associations between social media addiction and 2 comorbid mental health conditions?depression and anxiety?were investigated. Methods: A cross-sectional web-based survey was conducted, and the replicability of findings was assessed in 2 independent samples comprising 573 adults from the United Kingdom (261/573, 45.6% men; mean age 43.62 years, SD 12.24 years) and 474 adults from the United States (224/474, 47.4% men; mean age 44.67 years, SD 12.99 years). The demographic characteristics of both samples were similar to those of their respective populations. Results: The prevalence estimates of social media addiction varied across the classification schemes, ranging from 1% to 15% for the UK sample and 0% to 11% for the US sample. The latent profile analysis identified 3 latent groups for both samples: low-risk, at-risk, and high-risk. The sensitivity, specificity, and negative predictive values were high (83%-100%) for all classification schemes, except for the relatively lower sensitivity (73%-74%) for the polythetic scheme. However, the polythetic scheme had high positive predictive values (88%-94%), whereas such values were low (2%-43%) for the other 3 classification schemes. The group membership yielded by the polythetic scheme was largely consistent (95%-96%) with that of the benchmark. Conclusions: Among the classification schemes, the polythetic scheme is more well-balanced across all 4 indices. UR - https://www.jmir.org/2022/1/e27000 UR - http://dx.doi.org/10.2196/27000 UR - http://www.ncbi.nlm.nih.gov/pubmed/35006084 ID - info:doi/10.2196/27000 ER - TY - JOUR AU - Klein, Z. Ari AU - O'Connor, Karen AU - Gonzalez-Hernandez, Graciela PY - 2022/1/6 TI - Toward Using Twitter Data to Monitor COVID-19 Vaccine Safety in Pregnancy: Proof-of-Concept Study of Cohort Identification JO - JMIR Form Res SP - e33792 VL - 6 IS - 1 KW - natural language processing KW - social media KW - COVID-19 KW - data mining KW - COVID-19 vaccine KW - pregnancy outcomes N2 - Background: COVID-19 during pregnancy is associated with an increased risk of maternal death, intensive care unit admission, and preterm birth; however, many people who are pregnant refuse to receive COVID-19 vaccination because of a lack of safety data. Objective: The objective of this preliminary study was to assess whether Twitter data could be used to identify a cohort for epidemiologic studies of COVID-19 vaccination in pregnancy. Specifically, we examined whether it is possible to identify users who have reported (1) that they received COVID-19 vaccination during pregnancy or the periconception period, and (2) their pregnancy outcomes. Methods: We developed regular expressions to search for reports of COVID-19 vaccination in a large collection of tweets posted through the beginning of July 2021 by users who have announced their pregnancy on Twitter. To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that reported their pregnancy outcomes. Results: We manually verified the content of tweets detected automatically, identifying 150 users who reported on Twitter that they received at least one dose of COVID-19 vaccination during pregnancy or the periconception period. We manually verified at least one reported outcome for 45 of the 60 (75%) completed pregnancies. Conclusions: Given the limited availability of data on COVID-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of COVID-19 vaccination in pregnant populations. The results of this preliminary study justify the development of scalable methods to identify a larger cohort for epidemiologic studies. UR - https://formative.jmir.org/2022/1/e33792 UR - http://dx.doi.org/10.2196/33792 UR - http://www.ncbi.nlm.nih.gov/pubmed/34870607 ID - info:doi/10.2196/33792 ER - TY - JOUR AU - Park, Susan AU - Choi, Hyun So AU - Song, Yun-Kyoung AU - Kwon, Jin-Won PY - 2022/1/4 TI - Comparison of Online Patient Reviews and National Pharmacovigilance Data for Tramadol-Related Adverse Events: Comparative Observational Study JO - JMIR Public Health Surveill SP - e33311 VL - 8 IS - 1 KW - drug safety KW - pharmacovigilance KW - tramadol KW - social media KW - adverse effect N2 - Background: Tramadol is known to cause fewer adverse events (AEs) than other opioids. However, recent research has raised concerns about various safety issues. Objective: We aimed to explore these new AEs related to tramadol using social media and conventional pharmacovigilance data. Methods: This study used 2 data sets, 1 from patients? drug reviews on WebMD (January 2007 to January 2021) and 1 from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS; January 2016 to December 2020). We analyzed 2062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities. To analyze AEs from FAERS, a disproportionality analysis was performed with 3 measures: proportional reporting ratio, reporting odds ratio, and information component. Results: From the 869 AEs reported, we identified 125 new signals related to tramadol use not listed on the drug label that satis?ed all 3 signal detection criteria. In addition, 20 serious AEs were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients? symptom descriptions, tramadol-induced pain might also be an unexpected AE. Conclusions: This study detected several novel signals related to tramadol use, suggesting newly identified possible AEs. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data. UR - https://publichealth.jmir.org/2022/1/e33311 UR - http://dx.doi.org/10.2196/33311 UR - http://www.ncbi.nlm.nih.gov/pubmed/34982723 ID - info:doi/10.2196/33311 ER - TY - JOUR AU - Kahanek, Alexander AU - Yu, Xinchen AU - Hong, Lingzi AU - Cleveland, Ana AU - Philbrick, Jodi PY - 2021/12/30 TI - Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets JO - JMIR Infodemiology SP - e31671 VL - 1 IS - 1 KW - COVID-19 KW - preventive practices KW - temporal variations KW - spatial disparities KW - Twitter KW - public sentiment KW - socioeconomic factors N2 - Background: During the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices. Objective: The purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors. Methods: The authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19?related tweets from January 21, 2020 to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment toward both general COVID-19 topics and preventive practices in the United States. Results: In the temporal analysis, we found a 4-stage pattern from high negative sentiment in the initial stage to decreasing and low negative sentiment in the second and third stages, to the rebound and increase in negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues. Conclusions: The differences between public sentiment toward COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and rebound stages in future pandemics. The urban and rural differences should be considered in terms of the communication strategies and decision making during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state levels, which could guide local and state governments and regional communities in making decisions and developing policies in crises. UR - https://infodemiology.jmir.org/2021/1/e31671 UR - http://dx.doi.org/10.2196/31671 UR - http://www.ncbi.nlm.nih.gov/pubmed/35013722 ID - info:doi/10.2196/31671 ER - TY - JOUR AU - Lu, Jiahui AU - Lee, J. Edmund W. PY - 2021/12/29 TI - Examining Twitter Discourse on Electronic Cigarette and Tobacco Consumption During National Cancer Prevention Month in 2018: Topic Modeling and Geospatial Analysis JO - J Med Internet Res SP - e28042 VL - 23 IS - 12 KW - electronic cigarette KW - smoking KW - lung cancer KW - Twitter KW - national cancer prevention month KW - policy KW - topic modeling KW - cessation KW - e-cigarette KW - cancer KW - social media KW - eHealth KW - cancer prevention KW - tweets KW - public health N2 - Background: Examining public perception of tobacco products is critical for effective tobacco policy making and public education outreach. While the link between traditional tobacco products and lung cancer is well established, it is not known how the public perceives the association between electronic cigarettes (e-cigarettes) and lung cancer. In addition, it is unclear how members of the public interact with official messages during cancer campaigns on tobacco consumption and lung cancer. Objective: In this study, we aimed to analyze e-cigarette and smoking tweets in the context of lung cancer during National Cancer Prevention Month in 2018 and examine how e-cigarette and traditional tobacco product discussions relate to implementation of tobacco control policies across different states in the United States. Methods: We mined tweets that contained the term ?lung cancer? on Twitter from February to March 2018. The data set contained 13,946 publicly available tweets that occurred during National Cancer Prevention Month (February 2018), and 10,153 tweets that occurred during March 2018. E-cigarette?related and smoking-related tweets were retrieved, using topic modeling and geospatial analysis. Results: Debates on harmfulness (454/915, 49.7%), personal experiences (316/915, 34.5%), and e-cigarette risks (145/915, 15.8%) were the major themes of e-cigarette tweets related to lung cancer. Policy discussions (2251/3870, 58.1%), smoking risks (843/3870, 21.8%), and personal experiences (776/3870, 20.1%) were the major themes of smoking tweets related to lung cancer. Geospatial analysis showed that discussion on e-cigarette risks was positively correlated with the number of state-level smoke-free policies enacted for e-cigarettes. In particular, the number of indoor and on campus smoke-free policies was related to the number of tweets on e-cigarette risks (smoke-free indoor, r49=0.33, P=.02; smoke-free campus, r49=0.32, P=.02). The total number of e-cigarette policies was also positively related to the number of tweets on e-cigarette risks (r49=0.32, P=.02). In contrast, the number of smoking policies was not significantly associated with any of the smoking themes in the lung cancer discourse (P>.13). Conclusions: Though people recognized the importance of traditional tobacco control policies in reducing lung cancer incidences, their views on e-cigarette risks were divided, and discussions on the importance of e-cigarette policy control were missing from public discourse. Findings suggest the need for health organizations to continuously engage the public in discussions on the potential health risks of e-cigarettes and raise awareness of the insidious lobbying efforts from the tobacco industry. UR - https://www.jmir.org/2021/12/e28042 UR - http://dx.doi.org/10.2196/28042 UR - http://www.ncbi.nlm.nih.gov/pubmed/34964716 ID - info:doi/10.2196/28042 ER - TY - JOUR AU - Beliga, Slobodan AU - Martin?i?-Ip?i?, Sanda AU - Mate?i?, Mihaela AU - Petrijev?anin Vuksanovi?, Irena AU - Me?trovi?, Ana PY - 2021/12/24 TI - Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing JO - JMIR Public Health Surveill SP - e31540 VL - 7 IS - 12 KW - COVID-19 KW - pandemic KW - online media KW - news coverage KW - infoveillance KW - infodemic KW - infodemiology KW - natural language processing KW - name entity recognition KW - longitudinal study N2 - Background: Online media play an important role in public health emergencies and serve as essential communication platforms. Infoveillance of online media during the COVID-19 pandemic is an important step toward gaining a better understanding of crisis communication. Objective: The goal of this study was to perform a longitudinal analysis of the COVID-19?related content on online media based on natural language processing. Methods: We collected a data set of news articles published by Croatian online media during the first 13 months of the pandemic. First, we tested the correlations between the number of articles and the number of new daily COVID-19 cases. Second, we analyzed the content by extracting the most frequent terms and applied the Jaccard similarity coefficient. Third, we compared the occurrence of the pandemic-related terms during the two waves of the pandemic. Finally, we applied named entity recognition to extract the most frequent entities and tracked the dynamics of changes during the observation period. Results: The results showed no significant correlation between the number of articles and the number of new daily COVID-19 cases. Furthermore, there were high overlaps in the terminology used in all articles published during the pandemic with a slight shift in the pandemic-related terms between the first and the second waves. Finally, the findings indicate that the most influential entities have lower overlaps for the identified people and higher overlaps for locations and institutions. Conclusions: Our study shows that online media have a prompt response to the pandemic with a large number of COVID-19?related articles. There was a high overlap in the frequently used terms across the first 13 months, which may indicate the narrow focus of reporting in certain periods. However, the pandemic-related terminology is well-covered. UR - https://publichealth.jmir.org/2021/12/e31540 UR - http://dx.doi.org/10.2196/31540 UR - http://www.ncbi.nlm.nih.gov/pubmed/34739388 ID - info:doi/10.2196/31540 ER - TY - JOUR AU - Yang, S. Joshua AU - Cuomo, E. Raphael AU - Purushothaman, Vidya AU - Nali, Matthew AU - Shah, Neal AU - Bardier, Cortni AU - Obradovich, Nick AU - Mackey, Tim PY - 2021/12/24 TI - Campus Smoking Policies and Smoking-Related Twitter Posts Originating From California Public Universities: Retrospective Study JO - JMIR Form Res SP - e33331 VL - 5 IS - 12 KW - tobacco-free policies KW - social media KW - colleges and universities KW - smoking KW - smoking policy KW - campus policy KW - tobacco use KW - Twitter analysis KW - smoke-free KW - tobacco-free KW - Twitter KW - college students KW - students KW - campus KW - health policy N2 - Background: The number of colleges and universities with smoke- or tobacco-free campus policies has been increasing. The effects of campus smoking policies on overall sentiment, particularly among young adult populations, are more difficult to assess owing to the changing tobacco and e-cigarette product landscape and differential attitudes toward policy implementation and enforcement. Objective: The goal of the study was to retrospectively assess the campus climate toward tobacco use by comparing tweets from California universities with and those without smoke- or tobacco-free campus policies. Methods: Geolocated Twitter posts from 2015 were collected using the Twitter public application programming interface in combination with cloud computing services on Amazon Web Services. Posts were filtered for tobacco products and behavior-related keywords. A total of 42,877,339 posts were collected from 2015, with 2837 originating from a University of California or California State University system campus, and 758 of these manually verified as being about smoking. Chi-square tests were conducted to determine if there were significant differences in tweet user sentiments between campuses that were smoke- or tobacco-free (all University of California campuses and California State University, Fullerton) compared to those that were not. A separate content analysis of tweets included in chi-square tests was conducted to identify major themes by campus smoking policy status. Results: The percentage of positive sentiment tweets toward tobacco use was higher on campuses without a smoke- or tobacco-free campus policy than on campuses with a smoke- or tobacco-free campus policy (76.7% vs 66.4%, P=.03). Higher positive sentiment on campuses without a smoke- or tobacco-free campus policy may have been driven by general comments about one?s own smoking behavior and comments about smoking as a general behavior. Positive sentiment tweets originating from campuses without a smoke- or tobacco-free policy had greater variation in tweet type, which may have also contributed to differences in sentiment among universities. Conclusions: Our study introduces preliminary data suggesting that campus smoke- and tobacco-free policies are associated with a reduction in positive sentiment toward smoking. However, continued expressions and intentions to smoke and reports of one?s own smoking among Twitter users suggest a need for more research to better understand the dynamics between implementation of smoke- and tobacco-free policies and resulting tobacco behavioral sentiment. UR - https://formative.jmir.org/2021/12/e33331 UR - http://dx.doi.org/10.2196/33331 UR - http://www.ncbi.nlm.nih.gov/pubmed/34951597 ID - info:doi/10.2196/33331 ER - TY - JOUR AU - Ning, Peishan AU - Cheng, Peixia AU - Li, Jie AU - Zheng, Ming AU - Schwebel, C. David AU - Yang, Yang AU - Lu, Peng AU - Mengdi, Li AU - Zhang, Zhuo AU - Hu, Guoqing PY - 2021/12/23 TI - COVID-19?Related Rumor Content, Transmission, and Clarification Strategies in China: Descriptive Study JO - J Med Internet Res SP - e27339 VL - 23 IS - 12 KW - COVID-19 KW - rumor KW - strategy KW - China KW - social media N2 - Background: Given the permeation of social media throughout society, rumors spread faster than ever before, which significantly complicates government responses to public health emergencies such as the COVID-19 pandemic. Objective: We aimed to examine the characteristics and propagation of rumors during the early months of the COVID-19 pandemic in China and evaluated the effectiveness of health authorities? release of correction announcements. Methods: We retrieved rumors widely circulating on social media in China during the early stages of the COVID-19 pandemic and assessed the effectiveness of official government clarifications and popular science articles refuting those rumors. Results: We show that the number of rumors related to the COVID-19 pandemic fluctuated widely in China between December 1, 2019 and April 15, 2020. Rumors mainly occurred in 3 provinces: Hubei, Zhejiang, and Guangxi. Personal social media accounts constituted the major source of media reports of the 4 most widely distributed rumors (the novel coronavirus can be prevented with ?Shuanghuanglian?: 7648/10,664, 71.7%; the novel coronavirus is the SARS coronavirus: 14,696/15,902, 92.4%; medical supplies intended for assisting Hubei were detained by the local government: 3911/3943, 99.2%; asymptomatically infected persons were regarded as diagnosed COVID-19 patients with symptoms in official counts: 322/323, 99.7%). The number of rumors circulating was positively associated with the severity of the COVID-19 epidemic (?=0.88, 95% CI 0.81-0.93). The release of correction articles was associated with a substantial decrease in the proportion of rumor reports compared to accurate reports. The proportions of negative sentiments appearing among comments by citizens in response to media articles disseminating rumors and disseminating correct information differ insignificantly (both correct reports: ?12=0.315, P=.58; both rumors: ?12=0.025, P=.88; first rumor and last correct report: ?12=1.287, P=.26; first correct report and last rumor: ?12=0.033, P=.86). Conclusions: Our results highlight the importance and urgency of monitoring and correcting false or misleading reports on websites and personal social media accounts. The circulation of rumors can influence public health, and government bodies should establish guidelines to monitor and mitigate the negative impact of such rumors. UR - https://www.jmir.org/2021/12/e27339 UR - http://dx.doi.org/10.2196/27339 UR - http://www.ncbi.nlm.nih.gov/pubmed/34806992 ID - info:doi/10.2196/27339 ER - TY - JOUR AU - AU - Delir Haghighi, Pari AU - Burstein, Frada AU - Urquhart, Donna AU - Cicuttini, Flavia PY - 2021/12/23 TI - Investigating Individuals? Perceptions Regarding the Context Around the Low Back Pain Experience: Topic Modeling Analysis of Twitter Data JO - J Med Internet Res SP - e26093 VL - 23 IS - 12 KW - low back pain KW - Twitter KW - content analysis KW - social media KW - topic modeling KW - patient-centered approach KW - pain experience KW - context of pain N2 - Background: Low back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact. Objective: The objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals? beliefs and perceptions. Methods: We analyzed 896,867 cleaned tweets about LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels. Results: LDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. ?Emotion and beliefs? had the largest proportion of total tweets (157,563/896,867, 17.6%), followed by ?physical activity? (124,251/896,867, 13.85%) and ?daily life? (80,730/896,867, 9%), while ?food and drink,? ?weather,? and ?not being understood? had the smallest proportions (11,551/896,867, 1.29%; 10,109/896,867, 1.13%; and 9180/896,867, 1.02%, respectively). Of the 11 topics within ?emotion and beliefs,? 113,562/157,563 (72%) had negative sentiment. Conclusions: The content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals? perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP. UR - https://www.jmir.org/2021/12/e26093 UR - http://dx.doi.org/10.2196/26093 UR - http://www.ncbi.nlm.nih.gov/pubmed/36260398 ID - info:doi/10.2196/26093 ER - TY - JOUR AU - Tan, YQ Edina AU - Wee, RE Russell AU - Saw, Ern Young AU - Heng, JQ Kylie AU - Chin, WE Joseph AU - Tong, MW Eddie AU - Liu, CJ Jean PY - 2021/12/23 TI - Tracking Private WhatsApp Discourse About COVID-19 in Singapore: Longitudinal Infodemiology Study JO - J Med Internet Res SP - e34218 VL - 23 IS - 12 KW - social media KW - WhatsApp KW - infodemiology KW - misinformation KW - COVID-19 KW - tracking KW - surveillance KW - app KW - longitudinal KW - Singapore KW - characteristic KW - usage KW - pattern KW - well-being KW - communication KW - risk N2 - Background: Worldwide, social media traffic increased following the onset of the COVID-19 pandemic. Although the spread of COVID-19 content has been described for several social media platforms (eg, Twitter and Facebook), little is known about how such content is spread via private messaging platforms, such as WhatsApp (WhatsApp LLC). Objective: In this study, we documented (1) how WhatsApp is used to transmit COVID-19 content, (2) the characteristics of WhatsApp users based on their usage patterns, and (3) how usage patterns link to COVID-19 concerns. Methods: We used the experience sampling method to track day-to-day WhatsApp usage during the COVID-19 pandemic. For 1 week, participants reported each day the extent to which they had received, forwarded, or discussed COVID-19 content. The final data set comprised 924 data points, which were collected from 151 participants. Results: During the weeklong monitoring process, most participants (143/151, 94.7%) reported at least 1 COVID-19?related use of WhatsApp. When a taxonomy was generated based on usage patterns, around 1 in 10 participants (21/151, 13.9%) were found to have received and shared a high volume of forwarded COVID-19 content, akin to super-spreaders identified on other social media platforms. Finally, those who engaged with more COVID-19 content in their personal chats were more likely to report having COVID-19?related thoughts throughout the day. Conclusions: Our findings provide a rare window into discourse on private messaging platforms. Such data can be used to inform risk communication strategies during the pandemic. Trial Registration: ClinicalTrials.gov NCT04367363; https://clinicaltrials.gov/ct2/show/NCT04367363 UR - https://www.jmir.org/2021/12/e34218 UR - http://dx.doi.org/10.2196/34218 UR - http://www.ncbi.nlm.nih.gov/pubmed/34881720 ID - info:doi/10.2196/34218 ER - TY - JOUR AU - ElSherief, Mai AU - Sumner, A. Steven AU - Jones, M. Christopher AU - Law, K. Royal AU - Kacha-Ochana, Akadia AU - Shieber, Lyna AU - Cordier, LeShaundra AU - Holton, Kelly AU - De Choudhury, Munmun PY - 2021/12/22 TI - Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach JO - J Med Internet Res SP - e30753 VL - 23 IS - 12 KW - opioid use disorder KW - substance use KW - addiction treatment KW - misinformation KW - social media KW - machine learning KW - natural language processing N2 - Background: Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. Objective: By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods: The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder?related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post?s language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results: Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. Conclusions: This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment. UR - https://www.jmir.org/2021/12/e30753 UR - http://dx.doi.org/10.2196/30753 UR - http://www.ncbi.nlm.nih.gov/pubmed/34941555 ID - info:doi/10.2196/30753 ER - TY - JOUR AU - Husnayain, Atina AU - Shim, Eunha AU - Fuad, Anis AU - Su, Chia-Yu Emily PY - 2021/12/22 TI - Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study JO - J Med Internet Res SP - e34178 VL - 23 IS - 12 KW - prediction KW - internet search KW - COVID-19 KW - South Korea KW - infodemiology N2 - Background: Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19?s disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction. Objective: The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods. Methods: We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics. Results: GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for ?thermometer? and ?mask strap,? showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models. Conclusions: NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions. UR - https://www.jmir.org/2021/12/e34178 UR - http://dx.doi.org/10.2196/34178 UR - http://www.ncbi.nlm.nih.gov/pubmed/34762064 ID - info:doi/10.2196/34178 ER - TY - JOUR AU - Drescher, S. Larissa AU - Roosen, Jutta AU - Aue, Katja AU - Dressel, Kerstin AU - Schär, Wiebke AU - Götz, Anne PY - 2021/12/22 TI - The Spread of COVID-19 Crisis Communication by German Public Authorities and Experts on Twitter: Quantitative Content Analysis JO - JMIR Public Health Surveill SP - e31834 VL - 7 IS - 12 KW - COVID-19 KW - crisis communication KW - content analysis KW - Twitter KW - experts KW - authorities KW - Germany KW - negative binomial regression KW - social media KW - communication KW - crisis KW - information KW - development N2 - Background: The COVID-19 pandemic led to the necessity of immediate crisis communication by public health authorities. In Germany, as in many other countries, people choose social media, including Twitter, to obtain real-time information and understanding of the pandemic and its consequences. Next to authorities, experts such as virologists and science communicators were very prominent at the beginning of German Twitter COVID-19 crisis communication. Objective: The aim of this study was to detect similarities and differences between public authorities and individual experts in COVID-19 crisis communication on Twitter during the first year of the pandemic. Methods: Descriptive analysis and quantitative content analysis were carried out on 8251 original tweets posted from January 1, 2020, to January 15, 2021. COVID-19?related tweets of 21 authorities and 18 experts were categorized into structural, content, and style components. Negative binomial regressions were performed to evaluate tweet spread measured by the retweet and like counts of COVID-19?related tweets. Results: Descriptive statistics revealed that authorities and experts increasingly tweeted about COVID-19 over the period under study. Two experts and one authority were responsible for 70.26% (544,418/774,865) of all retweets, thus representing COVID-19 influencers. Altogether, COVID-19 tweets by experts reached a 7-fold higher rate of retweeting (t8,249=26.94, P<.001) and 13.9 times the like rate (t8,249=31.27, P<.001) compared with those of authorities. Tweets by authorities were much more designed than those by experts, with more structural and content components; for example, 91.99% (4997/5432) of tweets by authorities used hashtags in contrast to only 19.01% (536/2819) of experts? COVID-19 tweets. Multivariate analysis revealed that such structural elements reduce the spread of the tweets, and the incidence rate of retweets for authorities? tweets using hashtags was approximately 0.64 that of tweets without hashtags (Z=?6.92, P<.001). For experts, the effect of hashtags on retweets was insignificant (Z=1.56, P=.12). Conclusions: Twitter data are a powerful information source and suitable for crisis communication in Germany. COVID-19 tweet activity mirrors the development of COVID-19 cases in Germany. Twitter users retweet and like communications regarding COVID-19 by experts more than those delivered by authorities. Tweets have higher coverage for both authorities and experts when they are plain and for authorities when they directly address people. For authorities, it appears that it was difficult to win recognition during COVID-19. For all stakeholders studied, the association between number of followers and number of retweets was highly significantly positive (authorities Z=28.74, P<.001; experts Z=25.99, P<.001). Updated standards might be required for successful crisis communication by authorities. UR - https://publichealth.jmir.org/2021/12/e31834 UR - http://dx.doi.org/10.2196/31834 UR - http://www.ncbi.nlm.nih.gov/pubmed/34710054 ID - info:doi/10.2196/31834 ER - TY - JOUR AU - Ming, Wai-kit AU - Huang, Fengqiu AU - Chen, Qiuyi AU - Liang, Beiting AU - Jiao, Aoao AU - Liu, Taoran AU - Wu, Huailiang AU - Akinwunmi, Babatunde AU - Li, Jia AU - Liu, Guan AU - Zhang, P. Casper J. AU - Huang, Jian AU - Liu, Qian PY - 2021/12/21 TI - Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries JO - JMIR Public Health Surveill SP - e26644 VL - 7 IS - 12 KW - COVID-19 KW - Google Trends KW - search peaks KW - news coverage KW - public concerns N2 - Background: Due to the COVID-19 pandemic, health information related to COVID-19 has spread across news media worldwide. Google is among the most used internet search engines, and the Google Trends tool can reflect how the public seeks COVID-19?related health information during the pandemic. Objective: The aim of this study was to understand health communication through Google Trends and news coverage and to explore their relationship with prevention and control of COVID-19 at the early epidemic stage. Methods: To achieve the study objectives, we analyzed the public?s information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries from eight countries (ie, the United States, the United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1 and April 29, 2020. We depicted the characteristics of the COVID-19 news coverage trends over time, as well as the search query trends for the topics of COVID-19?related ?diseases,? ?treatments and medical resources,? ?symptoms and signs,? and ?public measures.? The search query trends provided the relative search volume (RSV) as an indicator to represent the popularity of a specific search term in a specific geographic area over time. Also, time-lag correlation analysis was used to further explore the relationship between search terms trends and the number of new daily cases, as well as the relationship between search terms trends and news coverage. Results: Across all search trends in eight countries, almost all search peaks appeared between March and April 2020, and declined in April 2020. Regarding COVID-19?related ?diseases,? in most countries, the RSV of the term ?coronavirus? increased earlier than that of ?covid-19?; however, around April 2020, the search volume of the term ?covid-19? surpassed that of ?coronavirus.? Regarding the topic ?treatments and medical resources,? the most and least searched terms were ?mask? and ?ventilator,? respectively. Regarding the topic ?symptoms and signs,? ?fever? and ?cough? were the most searched terms. The RSV for the term ?lockdown? was significantly higher than that for ?social distancing? under the topic ?public health measures.? In addition, when combining search trends with news coverage, there were three main patterns: (1) the pattern for Singapore, (2) the pattern for the United States, and (3) the pattern for the other countries. In the time-lag correlation analysis between the RSV for the topic ?treatments and medical resources? and the number of new daily cases, the RSV for all countries except Singapore was positively correlated with new daily cases, with a maximum correlation of 0.8 for the United States. In addition, in the time-lag correlation analysis between the overall RSV for the topic ?diseases? and the number of daily news items, the overall RSV was positively correlated with the number of daily news items, the maximum correlation coefficient was more than 0.8, and the search behavior occurred 0 to 17 days earlier than the news coverage. Conclusions: Our findings revealed public interest in masks, disease control, and public measures, and revealed the potential value of Google Trends in the face of the emergence of new infectious diseases. Also, Google Trends combined with news media can achieve more efficient health communication. Therefore, both news media and Google Trends can contribute to the early prevention and control of epidemics. UR - https://publichealth.jmir.org/2021/12/e26644 UR - http://dx.doi.org/10.2196/26644 UR - http://www.ncbi.nlm.nih.gov/pubmed/34591781 ID - info:doi/10.2196/26644 ER - TY - JOUR AU - Liu, Jessica AU - Wright, Caroline AU - Williams, Philippa AU - Elizarova, Olga AU - Dahne, Jennifer AU - Bian, Jiang AU - Zhao, Yunpeng AU - Tan, L. Andy S. PY - 2021/12/21 TI - Smokers? Likelihood to Engage With Information and Misinformation on Twitter About the Relative Harms of e-Cigarette Use: Results From a Randomized Controlled Trial JO - JMIR Public Health Surveill SP - e27183 VL - 7 IS - 12 KW - e-cigarettes KW - misinformation KW - Twitter KW - social media N2 - Background: Information and misinformation on the internet about e-cigarette harms may increase smokers? misperceptions of e-cigarettes. There is limited research on smokers? engagement with information and misinformation about e-cigarettes on social media. Objective: This study assessed smokers? likelihood to engage with?defined as replying, retweeting, liking, and sharing?tweets that contain information and misinformation and uncertainty about the harms of e-cigarettes. Methods: We conducted a web-based randomized controlled trial among 2400 UK and US adult smokers who did not vape in the past 30 days. Participants were randomly assigned to view four tweets in one of four conditions: (1) e-cigarettes are as harmful or more harmful than smoking, (2) e-cigarettes are completely harmless, (3) uncertainty about e-cigarette harms, or (4) control (physical activity). The outcome measure was participants? likelihood of engaging with tweets, which comprised the sum of whether they would reply, retweet, like, and share each tweet. We fitted Poisson regression models to predict the likelihood of engagement with tweets among 974 Twitter users and 1287 non-Twitter social media users, adjusting for covariates and stratified by UK and US participants. Results: Among Twitter users, participants were more likely to engage with tweets in condition 1 (e-cigarettes are as harmful or more harmful than smoking) than in condition 2 (e-cigarettes are completely harmless). Among other social media users, participants were more likely to likely to engage with tweets in condition 1 than in conditions 2 and 3 (e-cigarettes are completely harmless and uncertainty about e-cigarette harms). Conclusions: Tweets stating information and misinformation that e-cigarettes were as harmful or more harmful than smoking regular cigarettes may receive higher engagement than tweets indicating e-cigarettes were completely harmless. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 16082420; https://doi.org/10.1186/ISRCTN16082420 UR - https://publichealth.jmir.org/2021/12/e27183 UR - http://dx.doi.org/10.2196/27183 UR - http://www.ncbi.nlm.nih.gov/pubmed/34931999 ID - info:doi/10.2196/27183 ER - TY - JOUR AU - Fedoruk, Benjamin AU - Nelson, Harrison AU - Frost, Russell AU - Fucile Ladouceur, Kai PY - 2021/12/21 TI - The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation JO - JMIR Form Res SP - e32427 VL - 5 IS - 12 KW - infodemiology KW - misinformation KW - algorithm KW - social media KW - plebeian KW - natural language processing KW - sentiment analysis KW - sentiment KW - trust KW - decision-making KW - COVID-19 N2 - Background: The infodemic created by the COVID-19 pandemic has created several societal issues, including a rise in distrust between the public and health experts, and even a refusal of some to accept vaccination; some sources suggest that 1 in 4 Americans will refuse the vaccine. This social concern can be traced to the level of digitization today, particularly in the form of social media. Objective: The goal of the research is to determine an optimal social media algorithm, one which is able to reduce the number of cases of misinformation and which also ensures that certain individual freedoms (eg, the freedom of expression) are maintained. After performing the analysis described herein, an algorithm was abstracted. The discovery of a set of abstract aspects of an optimal social media algorithm was the purpose of the study. Methods: As social media was the most significant contributing factor to the spread of misinformation, the team decided to examine infodemiology across various text-based platforms (Twitter, 4chan, Reddit, Parler, Facebook, and YouTube). This was done by using sentiment analysis to compare general posts with key terms flagged as misinformation (all of which concern COVID-19) to determine their verity. In gathering the data sets, both application programming interfaces (installed using Python?s pip) and pre-existing data compiled by standard scientific third parties were used. Results: The sentiment can be described using bimodal distributions for each platform, with a positive and negative peak, as well as a skewness. It was found that in some cases, misinforming posts can have up to 92.5% more negative sentiment skew compared to accurate posts. Conclusions: From this, the novel Plebeian Algorithm is proposed, which uses sentiment analysis and post popularity as metrics to flag a post as misinformation. This algorithm diverges from that of the status quo, as the Plebeian Algorithm uses a democratic process to detect and remove misinformation. A method was constructed in which content deemed as misinformation to be removed from the platform is determined by a randomly selected jury of anonymous users. This not only prevents these types of infodemics but also guarantees a more democratic way of using social media that is beneficial for repairing social trust and encouraging the public?s evidence-informed decision-making. UR - https://formative.jmir.org/2021/12/e32427 UR - http://dx.doi.org/10.2196/32427 UR - http://www.ncbi.nlm.nih.gov/pubmed/34854812 ID - info:doi/10.2196/32427 ER - TY - JOUR AU - Black, Joshua AU - Margolin, R. Zachary AU - Bau, Gabrielle AU - Olson, Richard AU - Iwanicki, L. Janetta AU - Dart, C. Richard PY - 2021/12/20 TI - Web-Based Discussion and Illicit Street Sales of Tapentadol and Oxycodone in Australia: Epidemiological Surveillance Study JO - JMIR Public Health Surveill SP - e29187 VL - 7 IS - 12 KW - Australia KW - opioids KW - web-based discussion KW - diversion N2 - Background: Opioid use disorder and its consequences are a persistent public health concern for Australians. Web activity has been used to understand the perception of drug safety and diversion of drugs in contexts outside of Australia. The anonymity of the internet offers several advantages for surveilling and inquiring about specific covert behaviors, such as diversion or discussion of sensitive subjects where traditional surveillance approaches might be limited. Objective: This study aims to characterize the content of web posts and compare reports of illicit sales of tapentadol and oxycodone from sources originating in Australia. First, post content is evaluated to determine whether internet discussion encourages or discourages proper therapeutic use of the drugs. Second, we hypothesize that tapentadol would have lower street price and fewer illicit sales than oxycodone. Methods: Web posts originating in Australia between 2017 and 2019 were collected using the Researched Abuse, Diversion, and Addiction-Related Surveillance System Web Monitoring Program. Using a manual coding process, unstructured post content from social media, blogs, and forums was categorized into topics of discussion related to the harms and behaviors that could lead to harm. Illicit sales data in a structured format were collected through a crowdsourcing website between 2016 and 2019 using the Researched Abuse, Diversion, and Addiction-Related Surveillance System StreetRx Program. In total, 2 multivariable regression models assessed the differences in illicit price and number of sales. Results: A total of 4.7% (28/600) of tapentadol posts discussed an adverse event, whereas 10.27% (95% CI 9.32-11.21) of oxycodone posts discussed this topic. A total of 10% (60/600) of tapentadol posts discussed unsafe use or side effects, whereas 20.17% (95% CI 18.92-21.41) of oxycodone posts discussed unsafe use or side effects. There were 31 illicit sales reports for tapentadol (geometric mean price per milligram: Aus $0.12 [US $0.09]) and 756 illicit sales reports for oxycodone (Aus $1.28 [US $0.91]). Models detected no differences in the street price or number of sales between the drugs when covariates were included, although the potency of the pill significantly predicted the street price (P<.001) and availability predicted the number of sales (P=.03). Conclusions: Australians searching the web for opinions could judge tapentadol as safer than oxycodone because of the web post content. The illicit sales market for tapentadol was smaller than that of oxycodone, and drug potency and licit availability are likely important factors influencing the illicit market. UR - https://publichealth.jmir.org/2021/12/e29187 UR - http://dx.doi.org/10.2196/29187 UR - http://www.ncbi.nlm.nih.gov/pubmed/34932012 ID - info:doi/10.2196/29187 ER - TY - JOUR AU - Turner, Jason AU - Kantardzic, Mehmed AU - Vickers-Smith, Rachel PY - 2021/12/20 TI - Infodemiological Examination of Personal and Commercial Tweets About Cannabidiol: Term and Sentiment Analysis JO - J Med Internet Res SP - e27307 VL - 23 IS - 12 KW - social media KW - social networks KW - text mining KW - CBD KW - cannabidiol KW - cannabis KW - public health KW - drug regulation KW - Twitter KW - sentiment analysis KW - unregulated substances N2 - Background: In the absence of official clinical trial information, data from social networks can be used by public health and medical researchers to assess public claims about loosely regulated substances such as cannabidiol (CBD). For example, this can be achieved by comparing the medical conditions targeted by those selling CBD against the medical conditions patients commonly treat with CBD. Objective: The objective of this study was to provide a framework for public health and medical researchers to use for identifying and analyzing the consumption and marketing of unregulated substances. Specifically, we examined CBD, which is a substance that is often presented to the public as medication despite complete evidence of efficacy and safety. Methods: We collected 567,850 tweets by searching Twitter with the Tweepy Python package using the terms ?CBD? and ?cannabidiol.? We trained two binary text classifiers to create two corpora of 167,755 personal use and 143,322 commercial/sales tweets. Using medical, standard, and slang dictionaries, we identified and compared the most frequently occurring medical conditions, symptoms, side effects, body parts, and other substances referenced in both corpora. In addition, to assess popular claims about the efficacy of CBD as a medical treatment circulating on Twitter, we performed sentiment analysis via the VADER (Valence Aware Dictionary for Sentiment Reasoning) model on the personal CBD tweets. Results: We found references to medically relevant terms that were unique to either personal or commercial CBD tweet classes, as well as medically relevant terms that were common to both classes. When we calculated the average sentiment scores for both personal and commercial CBD tweets referencing at least one of 17 medical conditions/symptoms terms, an overall positive sentiment was observed in both personal and commercial CBD tweets. We observed instances of negative sentiment conveyed in personal CBD tweets referencing autism, whereas CBD was also marketed multiple times as a treatment for autism within commercial tweets. Conclusions: Our proposed framework provides a tool for public health and medical researchers to analyze the consumption and marketing of unregulated substances on social networks. Our analysis showed that most users of CBD are satisfied with it in regard to the condition that it is being advertised for, with the exception of autism. UR - https://www.jmir.org/2021/12/e27307 UR - http://dx.doi.org/10.2196/27307 UR - http://www.ncbi.nlm.nih.gov/pubmed/34932014 ID - info:doi/10.2196/27307 ER - TY - JOUR AU - Song, Shijie AU - Xue, Xiang AU - Zhao, Chris Yuxiang AU - Li, Jinhao AU - Zhu, Qinghua AU - Zhao, Mingming PY - 2021/12/20 TI - Short-Video Apps as a Health Information Source for Chronic Obstructive Pulmonary Disease: Information Quality Assessment of TikTok Videos JO - J Med Internet Res SP - e28318 VL - 23 IS - 12 KW - COPD KW - information quality KW - social media KW - short-video apps KW - TikTok N2 - Background: Chronic obstructive pulmonary disease (COPD) has become one of the most critical public health problems worldwide. Because many COPD patients are using video-based social media to search for health information, there is an urgent need to assess the information quality of COPD videos on social media. Recently, the short-video app TikTok has demonstrated huge potential in disseminating health information and there are currently many COPD videos available on TikTok; however, the information quality of these videos remains unknown. Objective: The aim of this study was to investigate the information quality of COPD videos on TikTok. Methods: In December 2020, we retrieved and screened 300 videos from TikTok and collected a sample of 199 COPD-related videos in Chinese for data extraction. We extracted the basic video information, coded the content, and identified the video sources. Two independent raters assessed the information quality of each video using the DISCERN instrument. Results: COPD videos on TikTok came mainly from two types of sources: individual users (n=168) and organizational users (n=31). The individual users included health professionals, individual science communicators, and general TikTok users, whereas the organizational users consisted of for-profit organizations, nonprofit organizations, and news agencies. For the 199 videos, the mean scores of the DISCERN items ranged from 3.42 to 4.46, with a total mean score of 3.75. Publication reliability (P=.04) and overall quality (P=.02) showed significant differences across the six types of sources, whereas the quality of treatment choices showed only a marginally significant difference (P=.053) across the different sources. Conclusions: The overall information quality of COPD videos on TikTok is satisfactory, although the quality varies across different sources and according to specific quality dimensions. Patients should be selective and cautious when watching COPD videos on TikTok. UR - https://www.jmir.org/2021/12/e28318 UR - http://dx.doi.org/10.2196/28318 UR - http://www.ncbi.nlm.nih.gov/pubmed/34931996 ID - info:doi/10.2196/28318 ER - TY - JOUR AU - Parker, K. Jane AU - Kelly, E. Christine AU - Smith, C. Barry AU - Kirkwood, F. Aidan AU - Hopkins, Claire AU - Gane, Simon PY - 2021/12/14 TI - Patients? Perspectives on Qualitative Olfactory Dysfunction: Thematic Analysis of Social Media Posts JO - JMIR Form Res SP - e29086 VL - 5 IS - 12 KW - olfactory dysfunction KW - parosmia KW - phantosmia KW - olfactory perseveration KW - trigger foods KW - mental health KW - COVID-19 KW - patients? perspective KW - thematic analysis KW - social media KW - perspective KW - smell KW - nose KW - symptom KW - concern KW - support N2 - Background: The impact of qualitative olfactory disorders is underestimated. Parosmia, the distorted perception of familiar odors, and phantosmia, the experience of odors in the absence of a stimulus, can arise following postinfectious anosmia, and the incidences of both have increased substantially since the outbreak of COVID-19. Objective: The aims of this study are to explore the symptoms and sequalae of postinfectious olfactory dysfunction syndrome using unstructured and unsolicited threads from social media, and to articulate the perspectives and concerns of patients affected by these debilitating olfactory disorders. Methods: A thematic analysis and content analysis of posts in the AbScent Parosmia and Phantosmia Support group on Facebook was conducted between June and December 2020. Results: In this paper, we identify a novel symptom, olfactory perseveration, which is a triggered, identifiable, and usually unpleasant olfactory percept that persists in the absence of an ongoing stimulus. We also observe fluctuations in the intensity and duration of symptoms of parosmia, phantosmia, and olfactory perseveration. In addition, we identify a group of the most common items (coffee, meat, onion, and toothpaste) that trigger distortions; however, people have difficulty describing these distortions, using words associated with disgust and revulsion. The emotional aspect of living with qualitative olfactory dysfunction was evident and highlighted the detrimental impact on mental health. Conclusions: Qualitative and unsolicited data acquired from social media has provided useful insights into the patient experience of parosmia and phantosmia, which can inform rehabilitation strategies and ongoing research into understanding the molecular triggers associated with parosmic distortions and research into patient benefit. UR - https://formative.jmir.org/2021/12/e29086 UR - http://dx.doi.org/10.2196/29086 UR - http://www.ncbi.nlm.nih.gov/pubmed/34904953 ID - info:doi/10.2196/29086 ER - TY - JOUR AU - Link, Elena AU - Baumann, Eva AU - Klimmt, Christoph PY - 2021/12/10 TI - Explaining Online Information Seeking Behaviors in People With Different Health Statuses: German Representative Cross-sectional Survey JO - J Med Internet Res SP - e25963 VL - 23 IS - 12 KW - online health information seeking behavior KW - Planned Risk Information Seeking Model KW - health status KW - theory building KW - personal survey N2 - Background: Worldwide, the internet is an increasingly important channel for health information. Many theories have been applied in research on online health information seeking behaviors (HISBs), with each model integrating a different set of predictors; thus, a common understanding of the predictors of (online) HISB is still missing. Another shortcoming of the theories explaining (online) HISB is that most existing models, so far, focus on very specific health contexts such as cancer. Therefore, the assumptions of the Planned Risk Information Seeking Model (PRISM) as the latest integrative model are applied to study online HISB, because this model identifies the general cognitive and sociopsychological factors that explain health information seeking intention. We shift away from single diseases and explore cross-thematic patterns of online HISB intention and compare predictors concerning different health statuses as it can be assumed that groups of people perceiving themselves as ill or healthy will differ concerning their drivers of online HISB. Considering the specifics of online HISB and variation in individual context factors is key for the development of generalizable theories. Objective: The objective of our study was to contribute to the development of the concept of online HISB in 2 areas. First, this study aimed to explore individual-level predictors of individuals? online HISB intention by applying the postulates of PRISM. Second, we compared relevant predictors of online HISB in groups of people with different health statuses to identify cross-thematic central patterns of online HISB. Methods: Data from a representative sample of German internet users (n=822) served to explain online HISB intentions and influencing patterns in different groups of people. The applicability of the PRISM to online HISB intention was tested by structural equation modeling and multigroup comparison. Results: Our results revealed PRISM to be an effective framework for explaining online HISB intention. For online HISB, attitudes toward seeking health information online provided the most important explanatory power followed by risk perceptions and affective risk responses. The multigroup comparison revealed differences both regarding the explanatory power of the model and the relevance of predictors of online HISB. The online HISB intention could be better explained for people facing a health threat, suggesting that the predictors adopted from PRISM were more suitable to explain a problem-driven type of information-seeking behavior. Conclusions: Our findings indicate that attitudes toward seeking health information online and risk perceptions are of central importance for online HISB across different health-conditional contexts. Predictors such as self-efficacy and perceived knowledge insufficiency play a context-dependent role?they are more influential when individuals are facing health threats and the search for health information is of higher personal relevance and urgency. These findings can be understood as the first step to develop a generalized theory of online HISB. UR - https://www.jmir.org/2021/12/e25963 UR - http://dx.doi.org/10.2196/25963 UR - http://www.ncbi.nlm.nih.gov/pubmed/34890348 ID - info:doi/10.2196/25963 ER - TY - JOUR AU - Koren, Ainat AU - Alam, Ul Mohammad Arif AU - Koneru, Sravani AU - DeVito, Alexa AU - Abdallah, Lisa AU - Liu, Benyuan PY - 2021/12/10 TI - Nursing Perspectives on the Impacts of COVID-19: Social Media Content Analysis JO - JMIR Form Res SP - e31358 VL - 5 IS - 12 KW - mental health KW - information retrieval KW - coronavirus KW - COVID-19 KW - nursing KW - nurses KW - health care workers KW - pandemic KW - impact KW - social media analytics N2 - Background: Nurses are at the forefront of the COVID-19 pandemic. During the pandemic, nurses have faced an elevated risk of exposure and have experienced the hazards related to a novel virus. While being heralded as lifesaving heroes on the front lines of the pandemic, nurses have experienced more physical, mental, and psychosocial problems as a consequence of the COVID-19 outbreak. Social media discussions by nursing professionals participating in publicly formed Facebook groups constitute a valuable resource that offers longitudinal insights. Objective: This study aimed to explore how COVID-19 impacted nurses through capturing public sentiments expressed by nurses on a social media discussion platform and how these sentiments changed over time. Methods: We collected over 110,993 Facebook discussion posts and comments in an open COVID-19 group for nurses from March 2020 until the end of November 2020. Scraping of deidentified offline HTML tags on social media posts and comments was performed. Using subject-matter expert opinions and social media analytics (ie, topic modeling, information retrieval, and sentiment analysis), we performed a human-in-a-loop analysis of nursing professionals? key perspectives to identify trends of the COVID-19 impact among at-risk nursing communities. We further investigated the key insights of the trends of the nursing professionals? perspectives by detecting temporal changes of comments related to emotional effects, feelings of frustration, impacts of isolation, shortage of safety equipment, and frequency of safety equipment uses. Anonymous quotes were highlighted to add context to the data. Results: We determined that COVID-19 impacted nurses? physical, mental, and psychosocial health as expressed in the form of emotional distress, anger, anxiety, frustration, loneliness, and isolation. Major topics discussed by nurses were related to work during a pandemic, misinformation spread by the media, improper personal protective equipment (PPE), PPE side effects, the effects of testing positive for COVID-19, and lost days of work related to illness. Conclusions: Public Facebook nursing groups are venues for nurses to express their experiences, opinions, and concerns and can offer researchers an important insight into understanding the COVID-19 impact on health care workers. UR - https://formative.jmir.org/2021/12/e31358 UR - http://dx.doi.org/10.2196/31358 UR - http://www.ncbi.nlm.nih.gov/pubmed/34623957 ID - info:doi/10.2196/31358 ER - TY - JOUR AU - Keselman, Alla AU - Arnott Smith, Catherine AU - Leroy, Gondy AU - Kaufman, R. David PY - 2021/12/9 TI - Factors Influencing Willingness to Share Health Misinformation Videos on the Internet: Web-Based Survey JO - J Med Internet Res SP - e30323 VL - 23 IS - 12 KW - misinformation KW - information literacy KW - science literacy KW - webcasts as topic KW - YouTube N2 - Background: The rapidly evolving digital environment of the social media era has increased the reach of both quality health information and misinformation. Platforms such as YouTube enable easy sharing of attractive, if not always evidence-based, videos with large personal networks and the public. Although much research has focused on characterizing health misinformation on the internet, it has not sufficiently focused on describing and measuring individuals? information competencies that build resilience. Objective: This study aims to assess individuals? willingness to share a non?evidence-based YouTube video about strengthening the immune system; to describe types of evidence that individuals view as supportive of the claim by the video; and to relate information-sharing behavior to several information competencies, namely, information literacy, science literacy, knowledge of the immune system, interpersonal trust, and trust in health authority. Methods: A web-based survey methodology with 150 individuals across the United States was used. Participants were asked to watch a YouTube excerpt from a morning TV show featuring a wellness pharmacy representative promoting an immunity-boosting dietary supplement produced by his company; answer questions about the video and report whether they would share it with a cousin who was frequently sick; and complete instruments pertaining to the information competencies outlined in the objectives. Results: Most participants (105/150, 70%) said that they would share the video with their cousins. Their confidence in the supplement would be further boosted by a friend?s recommendations, positive reviews on a crowdsourcing website, and statements of uncited effectiveness studies on the producer?s website. Although all information literacy competencies analyzed in this study had a statistically significant relationship with the outcome, each competency was also highly correlated with the others. Information literacy and interpersonal trust independently predicted the largest amount of variance in the intention to share the video (17% and 16%, respectively). Interpersonal trust was negatively related to the willingness to share the video. Science literacy explained 7% of the variance. Conclusions: People are vulnerable to web-based misinformation and are likely to propagate it on the internet. Information literacy and science literacy are associated with less vulnerability to misinformation and a lower propensity to spread it. Of the two, information literacy holds a greater promise as an intervention target. Understanding the role of different kinds of trust in information sharing merits further research. UR - https://www.jmir.org/2021/12/e30323 UR - http://dx.doi.org/10.2196/30323 UR - http://www.ncbi.nlm.nih.gov/pubmed/34889750 ID - info:doi/10.2196/30323 ER - TY - JOUR AU - Ng, Reuben PY - 2021/12/8 TI - Anti-Asian Sentiments During the COVID-19 Pandemic Across 20 Countries: Analysis of a 12-Billion-Word News Media Database JO - J Med Internet Res SP - e28305 VL - 23 IS - 12 KW - racism KW - COVID-19 KW - anti-Asian sentiments KW - psychomics KW - quantitative social science KW - culture KW - text as data KW - xenophobia KW - digital humanities N2 - Background: US president Joe Biden signed an executive action directing federal agencies to combat hate crimes and racism against Asians, which have percolated during the COVID-19 pandemic. This is one of the first known empirical studies to dynamically test whether global societal sentiments toward Asians have become more negative during the COVID-19 pandemic. Objective: This study aimed to investigate whether global societal sentiments toward Asians across 20 countries have become more negative, month by month, from before the pandemic (October 2019) to May 2020, along with the pandemic (incidence and mortality rates) and cultural (Hofstede?s cultural dimensions) predictors of this trend. Methods: We leveraged a 12-billion-word web-based media database, with over 30 million newspaper and magazine articles taken from over 7000 sites across 20 countries, and identified 6 synonyms of ?Asian? that are related to the coronavirus. We compiled their most frequently used descriptors (collocates) from October 2019 to May 2020 across 20 countries, culminating in 85,827 collocates that were rated by 2 independent researchers to provide a Cumulative Asian Sentiment Score (CASS) per month. This allowed us to track significant shifts in societal sentiments toward Asians from a baseline period (October to December 2019) to the onset of the pandemic (January to May 2020). We tested the competing predictors of this trend: pandemic variables of incidence and mortality rates measured monthly for all 20 countries taken from the Oxford COVID-19 Government Response Tracker, and Hofstede?s Cultural Dimensions of Individualism, Power Distance, Uncertainty Avoidance, and Masculinity for the 20 countries. Results: Before the pandemic in December 2019, Jamaica and New Zealand evidenced the most negative societal sentiments toward Asians; when news about the coronavirus was released in January 2020, the United States and Nigeria evidenced the most negative sentiments toward Asians among 20 countries. Globally, sentiments of Asians became more negative?a significant linear decline during the COVID-19 pandemic. CASS trended neutral before the pandemic during the baseline period of October to November 2019 and then plummeted in February 2020. CASS were, ironically, not predicted by COVID-19?s incidence and mortality rates, but rather by Hofstede?s cultural dimensions: individualism, power distance, and uncertainty avoidance?as shown by mixed models (N=28,494). Specifically, higher power distance, individualism, and uncertainty avoidance were associated with negative societal sentiments toward Asians. Conclusions: Racism, in the form of Anti-Asian sentiments, are deep-seated, and predicated on structural undercurrents of culture. The COVID-19 pandemic may have indirectly and inadvertently exacerbated societal tendencies for racism. Our study lays the important groundwork to design interventions and policy communications to ameliorate Anti-Asian racism, which are culturally nuanced and contextually appropriate. UR - https://www.jmir.org/2021/12/e28305 UR - http://dx.doi.org/10.2196/28305 UR - http://www.ncbi.nlm.nih.gov/pubmed/34678754 ID - info:doi/10.2196/28305 ER - TY - JOUR AU - Syed Abdul, Shabbir AU - Ramaswamy, Meghna AU - Fernandez-Luque, Luis AU - John, Oommen AU - Pitti, Thejkiran AU - Parashar, Babita PY - 2021/12/8 TI - The Pandemic, Infodemic, and People?s Resilience in India: Viewpoint JO - JMIR Public Health Surveill SP - e31645 VL - 7 IS - 12 KW - pandemic KW - COVID-19 KW - India KW - digital health KW - infodemics KW - Sustainable Development Goals KW - SDGs UR - https://publichealth.jmir.org/2021/12/e31645 UR - http://dx.doi.org/10.2196/31645 UR - http://www.ncbi.nlm.nih.gov/pubmed/34787574 ID - info:doi/10.2196/31645 ER - TY - JOUR AU - Brauer, Eden AU - Choi, Kristen AU - Chang, John AU - Luo, Yi AU - Lewin, Bruno AU - Munoz-Plaza, Corrine AU - Bronstein, David AU - Bruxvoort, Katia PY - 2021/12/8 TI - Health Care Providers? Trusted Sources for Information About COVID-19 Vaccines: Mixed Methods Study JO - JMIR Infodemiology SP - e33330 VL - 1 IS - 1 KW - health information KW - trust KW - health care provider KW - COVID-19 KW - vaccine KW - mixed method KW - communication N2 - Background: Information and opinions shared by health care providers can affect patient vaccination decisions, but little is known about who health care providers themselves trust for information in the context of new COVID-19 vaccines. Objective: The purpose of this study is to investigate which sources of information about COVID-19 vaccines are trusted by health care providers and how they communicate this information to patients. Methods: This mixed methods study involved a one-time, web-based survey of health care providers and qualitative interviews with a subset of survey respondents. Health care providers (physicians, advanced practice providers, pharmacists, nurses) were recruited from an integrated health system in Southern California using voluntary response sampling, with follow-up interviews with providers who either accepted or declined a COVID-19 vaccine. The outcome was the type of information sources that respondents reported trusting for information about COVID-19 vaccines. Bivariate tests were used to compare trusted information sources by provider type; thematic analysis was used to explore perspectives about vaccine information and communicating with patients about vaccines. Results: The survey was completed by 2948 providers, of whom 91% (n=2683) responded that they had received ?1 dose of a COVID-19 vaccine. The most frequently trusted source of COVID-19 vaccine information was government agencies (n=2513, 84.2%); the least frequently trusted source was social media (n=691, 9.5%). More physicians trusted government agencies (n=1226, 93%) than nurses (n=927, 78%) or pharmacists (n=203, 78%; P<.001), and more physicians trusted their employer (n=1115, 84%) than advanced practice providers (n=95, 67%) and nurses (n=759, 64%; P=.002). Qualitative themes (n=32 participants) about trusted sources of COVID-19 vaccine information were identified: processing new COVID-19 information in a health care work context likened to a ?war zone? during the pandemic and communicating information to patients. Some providers were hesitant to recommend vaccines to pregnant people and groups they perceived to be at low risk for COVID-19. Conclusions: Physicians have stronger trust in government sources and their employers for information about COVID-19 vaccines compared with nurses, pharmacists, and advanced practice providers. Strategies such as role modeling, tailored messaging, or talking points with standard language may help providers to communicate accurate COVID-19 vaccine information to patients, and these strategies may also be used with providers with lower levels of trust in reputable information sources. UR - https://infodemiology.jmir.org/2021/1/e33330 UR - http://dx.doi.org/10.2196/33330 UR - http://www.ncbi.nlm.nih.gov/pubmed/34926995 ID - info:doi/10.2196/33330 ER - TY - JOUR AU - Mukka, Milla AU - Pesälä, Samuli AU - Hammer, Charlotte AU - Mustonen, Pekka AU - Jormanainen, Vesa AU - Pelttari, Hanna AU - Kaila, Minna AU - Helve, Otto PY - 2021/12/7 TI - Analyzing Citizens? and Health Care Professionals? Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs JO - JMIR Public Health Surveill SP - e31961 VL - 7 IS - 12 KW - COVID-19 KW - SARS-CoV-2 KW - smell disorders KW - taste disorders KW - information-seeking behavior KW - health personnel KW - statistical models KW - medical informatics N2 - Background: The COVID-19 pandemic has prevailed over a year, and log and register data on coronavirus have been utilized to establish models for detecting the pandemic. However, many sources contain unreliable health information on COVID-19 and its symptoms, and platforms cannot characterize the users performing searches. Prior studies have assessed symptom searches from general search engines (Google/Google Trends). Little is known about how modeling log data on smell/taste disorders and coronavirus from the dedicated internet databases used by citizens and health care professionals (HCPs) could enhance disease surveillance. Our material and method provide a novel approach to analyze web-based information seeking to detect infectious disease outbreaks. Objective: The aim of this study was (1) to assess whether citizens? and professionals? searches for smell/taste disorders and coronavirus relate to epidemiological data on COVID-19 cases, and (2) to test our negative binomial regression modeling (ie, whether the inclusion of the case count could improve the model). Methods: We collected weekly log data on searches related to COVID-19 (smell/taste disorders, coronavirus) between December 30, 2019, and November 30, 2020 (49 weeks). Two major medical internet databases in Finland were used: Health Library (HL), a free portal aimed at citizens, and Physician?s Database (PD), a database widely used among HCPs. Log data from databases were combined with register data on the numbers of COVID-19 cases reported in the Finnish National Infectious Diseases Register. We used negative binomial regression modeling to assess whether the case numbers could explain some of the dynamics of searches when plotting database logs. Results: We found that coronavirus searches drastically increased in HL (0 to 744,113) and PD (4 to 5375) prior to the first wave of COVID-19 cases between December 2019 and March 2020. Searches for smell disorders in HL doubled from the end of December 2019 to the end of March 2020 (2148 to 4195), and searches for taste disorders in HL increased from mid-May to the end of November (0 to 1980). Case numbers were significantly associated with smell disorders (P<.001) and taste disorders (P<.001) in HL, and with coronavirus searches (P<.001) in PD. We could not identify any other associations between case numbers and searches in either database. Conclusions: Novel infodemiological approaches could be used in analyzing database logs. Modeling log data from web-based sources was seen to improve the model only occasionally. However, search behaviors among citizens and professionals could be used as a supplementary source of information for infectious disease surveillance. Further research is needed to apply statistical models to log data of the dedicated medical databases. UR - https://publichealth.jmir.org/2021/12/e31961 UR - http://dx.doi.org/10.2196/31961 UR - http://www.ncbi.nlm.nih.gov/pubmed/34727525 ID - info:doi/10.2196/31961 ER - TY - JOUR AU - Valdez, Danny AU - Unger, B. Jennifer PY - 2021/12/7 TI - Difficulty Regulating Social Media Content of Age-Restricted Products: Comparing JUUL?s Official Twitter Timeline and Social Media Content About JUUL JO - JMIR Infodemiology SP - e29011 VL - 1 IS - 1 KW - social media KW - JUUL KW - underage marketing KW - LDA KW - Latent Dirichlet Allocation KW - topic models N2 - Background: In 2018, JUUL Labs Inc, a popular e-cigarette manufacturer, announced it would substantially limit its social media presence in compliance with the Food and Drug Administration?s (FDA) call to curb underage e-cigarette use. However, shortly after the announcement, a series of JUUL-related hashtags emerged on various social media platforms, calling the effectiveness of the FDA?s regulations into question. Objective: The purpose of this study is to determine whether hashtags remain a common venue to market age-restricted products on social media. Methods: We used Twitter?s standard application programming interface to download the 3200 most-recent tweets originating from JUUL Labs Inc?s official Twitter Account (@JUULVapor), and a series of tweets (n=28,989) from other Twitter users containing either #JUUL or mentioned JUUL in the tweet text. We ran exploratory (10×10) and iterative Latent Dirichlet Allocation (LDA) topic models to compare @JUULVapor?s content versus our hashtag corpus. We qualitatively deliberated topic meanings and substantiated our interpretations with tweets from either corpus. Results: The topic models generated for @JUULVapor?s timeline seemingly alluded to compliance with the FDA?s call to prohibit marketing of age-restricted products on social media. However, the topic models generated for the hashtag corpus of tweets from other Twitter users contained several references to flavors, vaping paraphernalia, and illicit drugs, which may be appealing to younger audiences. Conclusions: Our findings underscore the complicated nature of social media regulation. Although JUUL Labs Inc seemingly complied with the FDA to limit its social media presence, JUUL and other e-cigarette manufacturers are still discussed openly in social media spaces. Much discourse about JUUL and e-cigarettes is spread via hashtags, which allow messages to reach a wide audience quickly. This suggests that social media regulations on manufacturers cannot prevent e-cigarette users, influencers, or marketers from spreading information about e-cigarette attributes that appeal to the youth, such as flavors. Stricter protocols are needed to regulate discourse about age-restricted products on social media. UR - https://infodemiology.jmir.org/2021/1/e29011 UR - http://dx.doi.org/10.2196/29011 UR - http://www.ncbi.nlm.nih.gov/pubmed/37114198 ID - info:doi/10.2196/29011 ER - TY - JOUR AU - Abdel-Razig, Sawsan AU - Anglade, Pascale AU - Ibrahim, Halah PY - 2021/12/7 TI - Impact of the COVID-19 Pandemic on a Physician Group?s WhatsApp Chat: Qualitative Content Analysis JO - JMIR Form Res SP - e31791 VL - 5 IS - 12 KW - WhatsApp KW - social media KW - physician KW - pandemic KW - COVID-19 KW - qualitative KW - communication KW - misinformation KW - information-seeking behavior KW - information seeking KW - information sharing KW - content analysis KW - community N2 - Background: Social media has emerged as an effective means of information sharing and community building among health professionals. The utility of these platforms is likely heightened during times of health system crises and global uncertainty. Studies have demonstrated that physicians? social media platforms serve to bridge the gap of information between on-the-ground experiences of health care workers and emerging knowledge. Objective: The primary aim of this study was to characterize the use of a physician WhatsApp (WhatsApp LLC) group chat during the early months of the COVID-19 pandemic. Methods: Through the lens of the social network theory, we performed a qualitative content analysis of the posts of a women physician WhatsApp group located in the United Arab Emirates between February 1, 2020, and May 31, 2020, that is, during the initial surge of COVID-19 cases. Results: There were 6101 posts during the study period, which reflected a 2.6-fold increase in platform use when compared with platform use in the year prior. A total of 8 themes and 9 subthemes were described. The top 3 uses of the platform were requests for information (posts: 2818/6101, 46.2%), member support and promotion (posts: 988/6101, 16.2%), and information sharing (posts: 896/6101, 14.7%). A substantial proportion of posts were related to COVID-19 (2653/6101, 43.5%), with the most popular theme being requests for logistical (nonmedical) information. Among posts containing COVID-19?related medical information, it was notable that two-thirds (571/868, 65.8%) of these posts were from public mass media or unverified sources. Conclusions: Health crises can potentiate the use of social media platforms among physicians. This reflects physicians? tendency to turn to these platforms for information sharing and community building purposes. However, important questions remain regarding the accuracy and credibility of the information shared. Our findings suggest that the training of physicians in social media practices and information dissemination may be needed. UR - https://formative.jmir.org/2021/12/e31791 UR - http://dx.doi.org/10.2196/31791 UR - http://www.ncbi.nlm.nih.gov/pubmed/34784291 ID - info:doi/10.2196/31791 ER - TY - JOUR AU - Zhang, Jueman AU - Wang, Yi AU - Shi, Molu AU - Wang, Xiuli PY - 2021/12/3 TI - Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study JO - JMIR Public Health Surveill SP - e32814 VL - 7 IS - 12 KW - COVID-19 KW - vaccine KW - topic modeling KW - LDA KW - valence KW - share KW - viral KW - Twitter KW - social media N2 - Background: COVID-19 vaccination is considered a critical prevention measure to help end the pandemic. Social media platforms such as Twitter have played an important role in the public discussion about COVID-19 vaccines. Objective: The aim of this study was to investigate message-level drivers of the popularity and virality of tweets about COVID-19 vaccines using machine-based text-mining techniques. We further aimed to examine the topic communities of the most liked and most retweeted tweets using network analysis and visualization. Methods: We collected US-based English-language public tweets about COVID-19 vaccines from January 1, 2020, to April 30, 2021 (N=501,531). Topic modeling and sentiment analysis were used to identify latent topics and valence, which together with autoextracted information about media presence, linguistic features, and account verification were used in regression models to predict likes and retweets. Among the 2500 most liked tweets and 2500 most retweeted tweets, network analysis and visualization were used to detect topic communities and present the relationship between the topics and the tweets. Results: Topic modeling yielded 12 topics. The regression analyses showed that 8 topics positively predicted likes and 7 topics positively predicted retweets, among which the topic of vaccine development and people?s views and that of vaccine efficacy and rollout had relatively larger effects. Network analysis and visualization revealed that the 2500 most liked and most retweeted retweets clustered around the topics of vaccine access, vaccine efficacy and rollout, vaccine development and people?s views, and vaccination status. The overall valence of the tweets was positive. Positive valence increased likes, but valence did not affect retweets. Media (photo, video, gif) presence and account verification increased likes and retweets. Linguistic features had mixed effects on likes and retweets. Conclusions: This study suggests the public interest in and demand for information about vaccine development and people?s views, and about vaccine efficacy and rollout. These topics, along with the use of media and verified accounts, have enhanced the popularity and virality of tweets. These topics could be addressed in vaccine campaigns to help the diffusion of content on Twitter. UR - https://publichealth.jmir.org/2021/12/e32814 UR - http://dx.doi.org/10.2196/32814 UR - http://www.ncbi.nlm.nih.gov/pubmed/34665761 ID - info:doi/10.2196/32814 ER - TY - JOUR AU - Taira, Kazuya AU - Hosokawa, Rikuya AU - Itatani, Tomoya AU - Fujita, Sumio PY - 2021/12/3 TI - Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model JO - JMIR Public Health Surveill SP - e34016 VL - 7 IS - 12 KW - suicide KW - internet search engine KW - infoveillance KW - query KW - time series analysis KW - vector autoregression model KW - COVID-19 KW - suicide-related terms KW - internet KW - information seeking KW - time series KW - model KW - loneliness KW - mental health KW - prediction KW - Japan KW - behavior KW - trend N2 - Background: The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is important to take timely preventive measures. Objective: This study aims to clarify whether the number of suicides can be predicted by suicide-related search queries used before searching for the keyword ?suicide.? Methods: This study uses the infoveillance approach for suicide in Japan by search trends in search engines. The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender with queries associated with ?suicide? on ?Yahoo! JAPAN Search? from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to suicide were used as search terms before searching for the keyword ?suicide? and extracted and used for analyses: ?abuse?; ?work, don?t want to go?; ?company, want to quit?; ?divorce?; and ?no money.? The augmented Dickey-Fuller and Johansen tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch-Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque-Bera (JB) test were used to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. Results: In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: ?9.24; max tau 3: ?5.38) and women (minimum tau 3: ?9.24; max tau 3: ?5.38) had no unit roots for all variables. In the Johansen test, a cointegration relationship was observed among several variables. The queries used in the converged models were ?divorce? for men (BG-LM test: P=.55; ARCH-LM test: P=.63; JB test: P=.66) and ?no money? for women (BG-LM test: P=.17; ARCH-LM test: P=.15; JB test: P=.10). In the Granger causality test for each variable, ?divorce? was significant for both men (F104=3.29; P=.04) and women (F104=3.23; P=.04). Conclusions: The number of suicides can be predicted by search queries related to the keyword ?suicide.? Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on ?no money? and ?divorce? predicted suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary. UR - https://publichealth.jmir.org/2021/12/e34016 UR - http://dx.doi.org/10.2196/34016 UR - http://www.ncbi.nlm.nih.gov/pubmed/34823225 ID - info:doi/10.2196/34016 ER - TY - JOUR AU - Dey, Vishal AU - Krasniak, Peter AU - Nguyen, Minh AU - Lee, Clara AU - Ning, Xia PY - 2021/11/29 TI - A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness JO - JMIR Med Inform SP - e29768 VL - 9 IS - 11 KW - breast implant illness KW - social media KW - natural language processing KW - topic modeling N2 - Background: A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. Objective: The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. Methods: We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. Results: Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. Conclusions: Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. UR - https://medinform.jmir.org/2021/11/e29768 UR - http://dx.doi.org/10.2196/29768 UR - http://www.ncbi.nlm.nih.gov/pubmed/34847064 ID - info:doi/10.2196/29768 ER - TY - JOUR AU - Jarynowski, Andrzej AU - Semenov, Alexander AU - Kami?ski, Miko?aj AU - Belik, Vitaly PY - 2021/11/29 TI - Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning JO - J Med Internet Res SP - e30529 VL - 23 IS - 11 KW - adverse events KW - Sputnik V KW - Gam-COVID-Vac KW - social media KW - Telegram KW - COVID-19 KW - Sars-CoV-2 KW - deep learning KW - vaccine safety N2 - Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) ?DeepPavlov,? which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (?=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines. UR - https://www.jmir.org/2021/11/e30529 UR - http://dx.doi.org/10.2196/30529 UR - http://www.ncbi.nlm.nih.gov/pubmed/34662291 ID - info:doi/10.2196/30529 ER - TY - JOUR AU - Card, G. Kiffer AU - Lachowsky, J. Nathan AU - Hogg, S. Robert PY - 2021/11/29 TI - Using Google Trends to Inform the Population Size Estimation and Spatial Distribution of Gay, Bisexual, and Other Men Who Have Sex With Men: Proof-of-concept Study JO - JMIR Public Health Surveill SP - e27385 VL - 7 IS - 11 KW - gay, bisexual, and other men who have sex with men KW - spatial distribution KW - population size estimation KW - pornography KW - technology-aided surveillance N2 - Background: We must triangulate data sources to understand best the spatial distribution and population size of marginalized populations to empower public health leaders to address population-specific needs. Existing population size estimation techniques are difficult and limited. Objective: We sought to identify a passive surveillance strategy that utilizes internet and social media to enhance, validate, and triangulate population size estimates of gay, bisexual, and other men who have sex with men (gbMSM). Methods: We explored the Google Trends platform to approximate an estimate of the spatial heterogeneity of the population distribution of gbMSM. This was done by comparing the prevalence of the search term ?gay porn? with that of the search term ?porn.? Results: Our results suggested that most cities have a gbMSM population size between 2% and 4% of their total population, with large urban centers having higher estimates relative to rural or suburban areas. This represents nearly a double up of population size estimates compared to that found by other methods, which typically find that between 1% and 2% of the total population are gbMSM. We noted that our method was limited by unequal coverage in internet usage across Canada and differences in the frequency of porn use by gender and sexual orientation. Conclusions: We argue that Google Trends estimates may provide, for many public health planning purposes, adequate city-level estimates of gbMSM population size in regions with a high prevalence of internet access and for purposes in which a precise or narrow estimate of the population size is not required. Furthermore, the Google Trends platform does so in less than a minute at no cost, making it extremely timely and cost-effective relative to more precise (and complex) estimates. We also discuss future steps for further validation of this approach. UR - https://publichealth.jmir.org/2021/11/e27385 UR - http://dx.doi.org/10.2196/27385 UR - http://www.ncbi.nlm.nih.gov/pubmed/34618679 ID - info:doi/10.2196/27385 ER - TY - JOUR AU - Reuter, Katja AU - Angyan, Praveen AU - Le, NamQuyen AU - Buchanan, A. Thomas PY - 2021/11/26 TI - Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study JO - JMIR Form Res SP - e29958 VL - 5 IS - 11 KW - breast cancer KW - cancer KW - clinical research KW - clinical trial KW - colon cancer KW - infoveillance KW - kidney cancer KW - lung cancer KW - lymphoma KW - patient engagement KW - prostate cancer KW - recruitment KW - Twitter KW - social media N2 - Background: Failure to find and attract clinical trial participants remains a persistent barrier to clinical research. Researchers increasingly complement recruitment methods with social media?based methods. We hypothesized that user-generated data from cancer survivors and their family members and friends on the social network Twitter could be used to identify, engage, and recruit cancer survivors for cancer trials. Objective: This pilot study aims to examine the feasibility of using user-reported health data from cancer survivors and family members and friends on Twitter in Los Angeles (LA) County to enhance clinical trial recruitment. We focus on 6 cancer conditions (breast cancer, colon cancer, kidney cancer, lymphoma, lung cancer, and prostate cancer). Methods: The social media intervention involved monitoring cancer-specific posts about the 6 cancer conditions by Twitter users in LA County to identify cancer survivors and their family members and friends and contacting eligible Twitter users with information about open cancer trials at the University of Southern California (USC) Norris Comprehensive Cancer Center. We reviewed both retrospective and prospective data published by Twitter users in LA County between July 28, 2017, and November 29, 2018. The study enrolled 124 open clinical trials at USC Norris. We used descriptive statistics to report the proportion of Twitter users who were identified, engaged, and enrolled. Results: We analyzed 107,424 Twitter posts in English by 25,032 unique Twitter users in LA County for the 6 cancer conditions. We identified and contacted 1.73% (434/25,032) of eligible Twitter users (127/434, 29.3% cancer survivors; 305/434, 70.3% family members and friends; and 2/434, 0.5% Twitter users were excluded). Of them, 51.4% (223/434) were female and approximately one-third were male. About one-fifth were people of color, whereas most of them were White. Approximately one-fifth (85/434, 19.6%) engaged with the outreach messages (cancer survivors: 33/85, 38% and family members and friends: 52/85, 61%). Of those who engaged with the messages, one-fourth were male, the majority were female, and approximately one-fifth were people of color, whereas the majority were White. Approximately 12% (10/85) of the contacted users requested more information and 40% (4/10) set up a prescreening. Two eligible candidates were transferred to USC Norris for further screening, but neither was enrolled. Conclusions: Our findings demonstrate the potential of identifying and engaging cancer survivors and their family members and friends on Twitter. Optimization of downstream recruitment efforts such as screening for digital populations on social media may be required. Future research could test the feasibility of the approach for other diseases, locations, languages, social media platforms, and types of research involvement (eg, survey research). Computer science methods could help to scale up the analysis of larger data sets to support more rigorous testing of the intervention. Trial Registration: ClinicalTrials.gov NCT03408561; https://clinicaltrials.gov/ct2/show/NCT03408561 UR - https://formative.jmir.org/2021/11/e29958 UR - http://dx.doi.org/10.2196/29958 UR - http://www.ncbi.nlm.nih.gov/pubmed/34842538 ID - info:doi/10.2196/29958 ER - TY - JOUR AU - Gao, Yankun AU - Xie, Zidian AU - Sun, Li AU - Xu, Chenliang AU - Li, Dongmei PY - 2021/11/25 TI - Characteristics of and User Engagement With Antivaping Posts on Instagram: Observational Study JO - JMIR Public Health Surveill SP - e29600 VL - 7 IS - 11 KW - anti-vaping KW - Instagram KW - user engagement KW - e-cigarettes KW - vaping KW - social media KW - content analysis KW - public health KW - lung health N2 - Background: Although government agencies acknowledge that messages about the adverse health effects of e-cigarette use should be promoted on social media, effectively delivering those health messages is challenging. Instagram is one of the most popular social media platforms among US youth and young adults, and it has been used to educate the public about the potential harm of vaping through antivaping posts. Objective: We aim to analyze the characteristics of and user engagement with antivaping posts on Instagram to inform future message development and information delivery. Methods: A total of 11,322 Instagram posts were collected from November 18, 2019, to January 2, 2020, by using antivaping hashtags including #novape, #novaping, #stopvaping, #dontvape, #antivaping, #quitvaping, #antivape, #stopjuuling, #dontvapeonthepizza, and #escapethevape. Among those posts, 1025 posts were randomly selected and 500 antivaping posts were further identified by hand coding. The image type, image content, and account type of antivaping posts were hand coded, the text information in the caption was explored by topic modeling, and the user engagement of each category was compared. Results: Analyses found that antivaping images of the educational/warning type were the most common (253/500; 50.6%). The average likes of the educational/warning type (15 likes/post) were significantly lower than the catchphrase image type (these emphasized a slogan such as ?athletesdontvape? in the image; 32.5 likes/post; P<.001). The majority of the antivaping posts contained the image content element text (n=332, 66.4%), followed by the image content element people/person (n=110, 22%). The images containing people/person elements (32.8 likes/post) had more likes than the images containing other elements (13.8-21.1 likes/post). The captions of the antivaping Instagram posts covered topics including ?lung health,? ?teen vaping,? ?stop vaping,? and ?vaping death cases.? Among the 500 antivaping Instagram posts, while most posts were from the antivaping community (n=177, 35.4%) and personal account types (n=182, 36.4%), the antivaping community account type had the highest average number of posts (1.69 posts/account). However, there was no difference in the number of likes among different account types. Conclusions: Multiple features of antivaping Instagram posts may be related to user engagement and perception. This study identified the critical elements associated with high user engagement, which could be used to design antivaping posts to deliver health-related information more efficiently. UR - https://publichealth.jmir.org/2021/11/e29600 UR - http://dx.doi.org/10.2196/29600 UR - http://www.ncbi.nlm.nih.gov/pubmed/34842553 ID - info:doi/10.2196/29600 ER - TY - JOUR AU - Tan, Yi Ming AU - Goh, Enhui Charlene AU - Tan, Hon Hee PY - 2021/11/25 TI - Contemporary English Pain Descriptors as Detected on Social Media Using Artificial Intelligence and Emotion Analytics Algorithms: Cross-sectional Study JO - JMIR Form Res SP - e31366 VL - 5 IS - 11 KW - pain descriptors KW - social media KW - artificial intelligence KW - emotion analytics KW - McGill Pain Questionnaire N2 - Background: Pain description is fundamental to health care. The McGill Pain Questionnaire (MPQ) has been validated as a tool for the multidimensional measurement of pain; however, its use relies heavily on language proficiency. Although the MPQ has remained unchanged since its inception, the English language has evolved significantly since then. The advent of the internet and social media has allowed for the generation of a staggering amount of publicly available data, allowing linguistic analysis at a scale never seen before. Objective: The aim of this study is to use social media data to examine the relevance of pain descriptors from the existing MPQ, identify novel contemporary English descriptors for pain among users of social media, and suggest a modification for a new MPQ for future validation and testing. Methods: All posts from social media platforms from January 1, 2019, to December 31, 2019, were extracted. Artificial intelligence and emotion analytics algorithms (Crystalace and CrystalFeel) were used to measure the emotional properties of the text, including sarcasm, anger, fear, sadness, joy, and valence. Word2Vec was used to identify new pain descriptors associated with the original descriptors from the MPQ. Analysis of count and pain intensity formed the basis for proposing new pain descriptors and determining the order of pain descriptors within each subclass. Results: A total of 118 new associated words were found via Word2Vec. Of these 118 words, 49 (41.5%) words had a count of at least 110, which corresponded to the count of the bottom 10% (8/78) of the original MPQ pain descriptors. The count and intensity of pain descriptors were used to formulate the inclusion criteria for a new pain questionnaire. For the suggested new pain questionnaire, 11 existing pain descriptors were removed, 13 new descriptors were added to existing subclasses, and a new Psychological subclass comprising 9 descriptors was added. Conclusions: This study presents a novel methodology using social media data to identify new pain descriptors and can be repeated at regular intervals to ensure the relevance of pain questionnaires. The original MPQ contains several potentially outdated pain descriptors and is inadequate for reporting the psychological aspects of pain. Further research is needed to examine the reliability and validity of the revised MPQ. UR - https://formative.jmir.org/2021/11/e31366 UR - http://dx.doi.org/10.2196/31366 UR - http://www.ncbi.nlm.nih.gov/pubmed/34842554 ID - info:doi/10.2196/31366 ER - TY - JOUR AU - Assaf, Elias AU - Bond, M. Robert AU - Cranmer, J. Skyler AU - Kaizar, E. Eloise AU - Ratliff Santoro, Lauren AU - Shikano, Susumu AU - Sivakoff, J. David PY - 2021/11/23 TI - Understanding the Relationship Between Official and Social Information About Infectious Disease: Experimental Analysis JO - J Med Internet Res SP - e25287 VL - 23 IS - 11 KW - disease KW - social information KW - official information KW - network experiments N2 - Background: Communicating official public health information about infectious diseases is complicated by the fact that individuals receive much of their information from their social contacts, either via interpersonal interaction or social media, which can be prone to bias and misconception. Objective: This study aims to evaluate the effect of public health campaigns and the effect of socially communicated health information on learning about diseases simultaneously. Although extant literature addresses the effect of one source of information (official or social) or the other, it has not addressed the simultaneous interaction of official information (OI) and social information (SI) in an experimental setting. Methods: We used a series of experiments that exposed participants to both OI and structured SI about the symptoms and spread of hepatitis C over a series of 10 rounds of computer-based interactions. Participants were randomly assigned to receive a high, low, or control intensity of OI and to receive accurate or inaccurate SI about the disease. Results: A total of 195 participants consented to participate in the study. Of these respondents, 186 had complete responses across all ten experimental rounds, which corresponds to a 4.6% (9/195) nonresponse rate. The OI high intensity treatment increases learning over the control condition for all symptom and contagion questions when individuals have lower levels of baseline knowledge (all P values ?.04). The accurate SI condition increased learning across experimental rounds over the inaccurate condition (all P values ?.01). We find limited evidence of an interaction between official and SI about infectious diseases. Conclusions: This project demonstrates that exposure to official public health information increases individuals? knowledge of the spread and symptoms of a disease. Socially shared information also facilitates the learning of accurate and inaccurate information, though to a lesser extent than exposure to OI. Although the effect of OI persists, preliminary results suggest that it can be degraded by persistent contradictory SI over time. UR - https://www.jmir.org/2021/11/e25287 UR - http://dx.doi.org/10.2196/25287 UR - http://www.ncbi.nlm.nih.gov/pubmed/34817389 ID - info:doi/10.2196/25287 ER - TY - JOUR AU - Wang, W. Andrea AU - Lan, Jo-Yu AU - Wang, Ming-Hung AU - Yu, Chihhao PY - 2021/11/23 TI - The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study JO - JMIR Med Inform SP - e30467 VL - 9 IS - 11 KW - COVID-19 KW - rumors KW - rumor diffusion KW - rumor propagation KW - social listening KW - infodemic KW - social media KW - closed platform KW - natural language processing KW - machine learning KW - unsupervised learning KW - computers and society N2 - Background: In 2020, the COVID-19 pandemic put the world in a crisis regarding both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020. Objective: The aim of this study was to examine the propagation patterns and textual transformation of COVID-19?related rumors on a closed social media platform. Methods: We obtained a data set of suspicious text messages collected on Taiwan?s most popular instant messaging platform, LINE, between January and July 2020. We proposed a classification-based clustering algorithm that could efficiently cluster messages into groups, with each group representing a rumor. For ease of understanding, a group is referred to as a ?rumor group.? Messages in a rumor group could be identical or could have limited textual differences between them. Therefore, each message in a rumor group is a form of the rumor. Results: A total of 936 rumor groups with at least 10 messages each were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3%) were related to COVID-19. Of the 396 COVID-19?related rumors, 134 (33.8%) had been fact-checked by the International Fact-Checking Network?certified agencies in Taiwan and determined to be false or misleading. By studying the prevalence of simplified Chinese characters or phrases in the messages that originated in China, we found that COVID-19?related messages, compared to non?COVID-19?related messages, were more likely to have been written by non-Taiwanese users. The association was statistically significant, with P<.001, as determined by the chi-square independence test. The qualitative investigations of the three most popular COVID-19 rumors revealed that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or textual transformations. Conclusions: To fight the infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media has given rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study the propagation of rumors and their interactions with society. Therefore, we must put more effort into these areas. UR - https://medinform.jmir.org/2021/11/e30467 UR - http://dx.doi.org/10.2196/30467 UR - http://www.ncbi.nlm.nih.gov/pubmed/34623954 ID - info:doi/10.2196/30467 ER - TY - JOUR AU - Chang, Angela AU - Schulz, Johannes Peter AU - Jiao, Wen AU - Liu, Tingchi Matthew PY - 2021/11/23 TI - Obesity-Related Communication in Digital Chinese News From Mainland China, Hong Kong, and Taiwan: Automated Content Analysis JO - JMIR Public Health Surveill SP - e26660 VL - 7 IS - 11 KW - public health KW - computational content KW - digital research methods KW - obesity discourse KW - gene disorders KW - noncommunicable disease N2 - Background: The fact that the number of individuals with obesity has increased worldwide calls into question media efforts for informing the public. This study attempts to determine the ways in which the mainstream digital news covers the etiology of obesity and diseases associated with the burden of obesity. Objective: The dual objectives of this study are to obtain an understanding of what the news reports on obesity and to explore meaning in data by extending the preconceived grounded theory. Methods: The 10 years of news text from 2010 to 2019 compared the development of obesity-related coverage and its potential impact on its perception in Mainland China, Hong Kong, and Taiwan. Digital news stories on obesity along with affliction and inferences in 9 Chinese mainstream newspapers were sampled. An automatic content analysis tool, DiVoMiner was proposed. This computer-aided platform is designed to organize and filter large sets of data on the basis of the patterns of word occurrence and term discovery. Another programming language, Python 3, was used to explore connections and patterns created by the aggregated interactions. Results: A total of 30,968 news stories were identified with increasing attention since 2016. The highest intensity of newspaper coverage of obesity communication was observed in Taiwan. Overall, a stronger focus on 2 shared causative attributes of obesity is on stress (n=4483, 33.0%) and tobacco use (n=3148, 23.2%). The burdens of obesity and cardiovascular diseases are implied to be the most, despite the aggregated interaction of edge centrality showing the highest link between the ?cancer? and obesity. This study goes beyond traditional journalism studies by extending the framework of computational and customizable web-based text analysis. This could set a norm for researchers and practitioners who work on data projects largely for an innovative attempt. Conclusions: Similar to previous studies, the discourse between the obesity epidemic and personal afflictions is the most emphasized approach. Our study also indicates that the inclination of blaming personal attributes for health afflictions potentially limits social and governmental responsibility for addressing this issue. UR - https://publichealth.jmir.org/2021/11/e26660 UR - http://dx.doi.org/10.2196/26660 UR - http://www.ncbi.nlm.nih.gov/pubmed/34817383 ID - info:doi/10.2196/26660 ER - TY - JOUR AU - Muric, Goran AU - Wu, Yusong AU - Ferrara, Emilio PY - 2021/11/17 TI - COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content, Vaccine Misinformation, and Conspiracies JO - JMIR Public Health Surveill SP - e30642 VL - 7 IS - 11 KW - vaccine hesitancy KW - COVID-19 vaccines KW - dataset KW - COVID-19 KW - SARS-CoV-2 KW - social media KW - network analysis KW - hesitancy KW - vaccine KW - Twitter KW - misinformation KW - conspiracy KW - trust KW - public health KW - utilization N2 - Background: False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, posing a threat to global public health. Misinformation originating from various sources has been spreading on the web since the beginning of the COVID-19 pandemic. Antivaccine activists have also begun to use platforms such as Twitter to promote their views. To properly understand the phenomenon of vaccine hesitancy through the lens of social media, it is of great importance to gather the relevant data. Objective: In this paper, we describe a data set of Twitter posts and Twitter accounts that publicly exhibit a strong antivaccine stance. The data set is made available to the research community via our AvaxTweets data set GitHub repository. We characterize the collected accounts in terms of prominent hashtags, shared news sources, and most likely political leaning. Methods: We started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific antivaccine-related keywords. Then, we collected the historical tweets of the set of accounts that engaged in spreading antivaccination narratives between October 2020 and December 2020, leveraging the Academic Track Twitter API. The political leaning of the accounts was estimated by measuring the political bias of the media outlets they shared. Results: We gathered two curated Twitter data collections and made them publicly available: (1) a streaming keyword?centered data collection with more than 1.8 million tweets, and (2) a historical account?level data collection with more than 135 million tweets. The accounts engaged in the antivaccination narratives lean to the right (conservative) direction of the political spectrum. The vaccine hesitancy is fueled by misinformation originating from websites with already questionable credibility. Conclusions: The vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering progress toward vaccine-induced herd immunity, and could potentially increase the number of infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Because data access is the first obstacle to attain this goal, we published a data set that can be used in studying antivaccine misinformation on social media and enable a better understanding of vaccine hesitancy. UR - https://publichealth.jmir.org/2021/11/e30642 UR - http://dx.doi.org/10.2196/30642 UR - http://www.ncbi.nlm.nih.gov/pubmed/34653016 ID - info:doi/10.2196/30642 ER - TY - JOUR AU - Freeman, Benjamin Tobe Che AU - Rodriguez-Esteban, Raul AU - Gottowik, Juergen AU - Yang, Xing AU - Erpenbeck, Johannes Veit AU - Leddin, Mathias PY - 2021/11/11 TI - A Neural Network Approach for Understanding Patient Experiences of Chronic Obstructive Pulmonary Disease (COPD): Retrospective, Cross-sectional Study of Social Media Content JO - JMIR Med Inform SP - e26272 VL - 9 IS - 11 KW - outcomes research KW - natural language processing KW - neural networks (computer) KW - social media KW - exercise KW - sleep deprivation KW - social media listening KW - drug development N2 - Background: The abundance of online content contributed by patients is a rich source of insight about the lived experience of disease. Patients share disease experiences with other members of the patient and caregiver community and do so using their own lexicon of words and phrases. This lexicon and the topics that are communicated using words and phrases belonging to the lexicon help us better understand disease burden. Insights from social media may ultimately guide clinical development in ways that ensure that future treatments are fit for purpose from the patient?s perspective. Objective: We sought insights into the patient experience of chronic obstructive pulmonary disease (COPD) by analyzing a substantial corpus of social media content. The corpus was sufficiently large to make manual review and manual coding all but impossible to perform in a consistent and systematic fashion. Advanced analytics were applied to the corpus content in the search for associations between symptoms and impacts across the entire text corpus. Methods: We conducted a retrospective, cross-sectional study of 5663 posts sourced from open blogs and online forum posts published by COPD patients between February 2016 and August 2019. We applied a novel neural network approach to identify a lexicon of community words and phrases used by patients to describe their symptoms. We used this lexicon to explore the relationship between COPD symptoms and disease-related impacts. Results: We identified a diverse lexicon of community words and phrases for COPD symptoms, including gasping, wheezy, mucus-y, and muck. These symptoms were mentioned in association with specific words and phrases for disease impact such as frightening, breathing discomfort, and difficulty exercising. Furthermore, we found an association between mucus hypersecretion and moderate disease severity, which distinguished mucus from the other main COPD symptoms, namely breathlessness and cough. Conclusions: We demonstrated the potential of neural networks and advanced analytics to gain patient-focused insights about how each distinct COPD symptom contributes to the burden of chronic and acute respiratory illness. Using a neural network approach, we identified words and phrases for COPD symptoms that were specific to the patient community. Identifying patterns in the association between symptoms and impacts deepened our understanding of the patient experience of COPD. This approach can be readily applied to other disease areas. UR - https://medinform.jmir.org/2021/11/e26272 UR - http://dx.doi.org/10.2196/26272 UR - http://www.ncbi.nlm.nih.gov/pubmed/34762056 ID - info:doi/10.2196/26272 ER - TY - JOUR AU - Chen, Liang AU - Wang, Pianpian AU - Ma, Xin AU - Wang, Xiaohui PY - 2021/11/10 TI - Cancer Communication and User Engagement on Chinese Social Media: Content Analysis and Topic Modeling Study JO - J Med Internet Res SP - e26310 VL - 23 IS - 11 KW - cancer-related information KW - social media KW - topic modeling KW - user engagement KW - Weibo KW - cancer N2 - Background: Cancer ranks among the most serious public health challenges worldwide. In China?the world?s most populous country?about one-quarter of the population consists of people with cancer. Social media has become an important platform that the Chinese public uses to express opinions. Objective: We investigated cancer-related discussions on the Chinese social media platform Weibo (Sina Corporation) to identify cancer topics that generate the highest levels of user engagement. Methods: We conducted topic modeling and regression analyses to analyze and visualize cancer-related messages on Weibo and to examine the relationships between different cancer topics and user engagement (ie, the number of retweets, comments, and likes). Results: Our results revealed that cancer communication on Weibo has generally focused on the following six topics: social support, cancer treatment, cancer prevention, women?s cancers, smoking and skin cancer, and other topics. Discussions about social support and cancer treatment attracted the highest number of users and received the greatest numbers of retweets, comments, and likes. Conclusions: Our investigation of cancer-related communication on Weibo provides valuable insights into public concerns about cancer and can help guide the development of health campaigns in social media. UR - https://www.jmir.org/2021/11/e26310 UR - http://dx.doi.org/10.2196/26310 UR - http://www.ncbi.nlm.nih.gov/pubmed/34757320 ID - info:doi/10.2196/26310 ER - TY - JOUR AU - Zhang, Zizheng AU - Feng, Guanrui AU - Xu, Jiahong AU - Zhang, Yimin AU - Li, Jinhui AU - Huang, Jian AU - Akinwunmi, Babatunde AU - Zhang, P. Casper J. AU - Ming, Wai-kit PY - 2021/11/9 TI - The Impact of Public Health Events on COVID-19 Vaccine Hesitancy on Chinese Social Media: National Infoveillance Study JO - JMIR Public Health Surveill SP - e32936 VL - 7 IS - 11 KW - COVID-19 KW - vaccine KW - hesitancy KW - social media KW - China KW - sentiment analysis KW - infoveillance KW - public health KW - surveillance KW - Weibo KW - data mining KW - sentiment KW - attitude N2 - Background: The ongoing COVID-19 pandemic has brought unprecedented challenges to every country worldwide. A call for global vaccination for COVID-19 plays a pivotal role in the fight against this virus. With the development of COVID-19 vaccines, public willingness to get vaccinated has become an important public health concern, considering the vaccine hesitancy observed worldwide. Social media is powerful in monitoring public attitudes and assess the dissemination, which would provide valuable information for policy makers. Objective: This study aimed to investigate the responses of vaccine positivity on social media when major public events (major outbreaks) or major adverse events related to vaccination (COVID-19 or other similar vaccines) were reported. Methods: A total of 340,783 vaccine-related posts were captured with the poster?s information on Weibo, the largest social platform in China. After data cleaning, 156,223 posts were included in the subsequent analysis. Using pandas and SnowNLP Python libraries, posts were classified into 2 categories, positive and negative. After model training and sentiment analysis, the proportion of positive posts was computed to measure the public positivity toward the COVID-19 vaccine. Results: The positivity toward COVID-19 vaccines in China tends to fluctuate over time in the range of 45.7% to 77.0% and is intuitively correlated with public health events. In terms of gender, males were more positive (70.0% of the time) than females. In terms of region, when regional epidemics arose, not only the region with the epidemic and surrounding regions but also the whole country showed more positive attitudes to varying degrees. When the epidemic subsided temporarily, positivity decreased with varying degrees in each region. Conclusions: In China, public positivity toward COVID-19 vaccines fluctuates over time and a regional epidemic or news on social media may cause significant variations in willingness to accept a vaccine. Furthermore, public attitudes toward COVID-19 vaccination vary from gender and region. It is crucial for policy makers to adjust their policies through the use of positive incentives with prompt responses to pandemic-related news to promote vaccination acceptance. UR - https://publichealth.jmir.org/2021/11/e32936 UR - http://dx.doi.org/10.2196/32936 UR - http://www.ncbi.nlm.nih.gov/pubmed/34591782 ID - info:doi/10.2196/32936 ER - TY - JOUR AU - Wawrzuta, Dominik AU - Jaworski, Mariusz AU - Gotlib, Joanna AU - Panczyk, Mariusz PY - 2021/11/8 TI - Social Media Sharing of Articles About Measles in a European Context: Text Analysis Study JO - J Med Internet Res SP - e30150 VL - 23 IS - 11 KW - measles KW - Facebook KW - Twitter KW - Pinterest KW - social media KW - vaccine KW - infodemiology KW - public health N2 - Background: Despite the existence of an effective vaccine, measles still threatens the health and lives of many Europeans. Notably, during the COVID-19 pandemic, measles vaccine uptake declined; as a result, after the pandemic, European countries will have to increase vaccination rates to restore the extent of vaccination coverage among the population. Because information obtained from social media are one of the main causes of vaccine hesitancy, knowledge of the nature of information pertaining to measles that is shared on social media may help create educational campaigns. Objective: In this study, we aim to define the characteristics of European news about measles shared on social media platforms (ie, Facebook, Twitter, and Pinterest) from 2017 to 2019. Methods: We downloaded and translated (into English) 10,305 articles on measles published in European Union countries. Using latent Dirichlet allocation, we identified main topics and estimated the sentiments expressed in these articles. Furthermore, we used linear regression to determine factors related to the number of times a given article was shared on social media. Results: We found that, in most European social media posts, measles is only discussed in the context of local European events. Articles containing educational information and describing world outbreaks appeared less frequently. The most common emotions identified from the study?s news data set were fear and trust. Yet, it was found that readers were more likely to share information on educational topics and the situation in Germany, Ukraine, Italy, and Samoa. A high amount of anger, joy, and sadness expressed within the text was also associated with a higher number of shares. Conclusions: We identified which features of news articles were related to increased social media shares. We found that social media users prefer sharing educational news to sharing informational news. Appropriate emotional content can also increase the willingness of social media users to share an article. Effective media content that promotes measles vaccinations should contain educational or scientific information, as well as specific emotions (such as anger, joy, or sadness). Articles with this type of content may offer the best chance of disseminating vital messages to a broad social media audience. UR - https://www.jmir.org/2021/11/e30150 UR - http://dx.doi.org/10.2196/30150 UR - http://www.ncbi.nlm.nih.gov/pubmed/34570715 ID - info:doi/10.2196/30150 ER - TY - JOUR AU - Cawley, Caoimhe AU - Bergey, François AU - Mehl, Alicia AU - Finckh, Ashlee AU - Gilsdorf, Andreas PY - 2021/11/4 TI - Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study JO - JMIR Public Health Surveill SP - e26523 VL - 7 IS - 11 KW - ILI KW - influenza KW - syndromic surveillance KW - participatory surveillance KW - digital surveillance KW - mobile phone N2 - Background: Participatory epidemiology is an emerging field harnessing consumer data entries of symptoms. The free app Ada allows users to enter the symptoms they are experiencing and applies a probabilistic reasoning model to provide a list of possible causes for these symptoms. Objective: The objective of our study is to explore the potential contribution of Ada data to syndromic surveillance by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb. Methods: We extracted data for all assessments performed by Ada users in Germany over 3 seasons (2017/18, 2018/19, and 2019/20) and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age group), standardized for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson correlation coefficient. Results: In total, 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson correlation?2017-18: r=0.86, 95% CI 0.76-0.92; P<.001; 2018-19: r=0.90, 95% CI 0.84-0.94; P<.001; 2019-20: r=0.64, 95% CI 0.44-0.78; P<.001). However, there were differences in the exact timing and nature of the epidemic curves between years. Conclusions: With careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems or to the syndromic surveillance of symptoms not covered by existing systems. UR - https://publichealth.jmir.org/2021/11/e26523 UR - http://dx.doi.org/10.2196/26523 UR - http://www.ncbi.nlm.nih.gov/pubmed/34734836 ID - info:doi/10.2196/26523 ER - TY - JOUR AU - Liew, Ming Tau AU - Lee, Sin Cia PY - 2021/11/3 TI - Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts JO - JMIR Public Health Surveill SP - e29789 VL - 7 IS - 11 KW - social media KW - COVID-19 KW - vaccine hesitancy KW - natural language processing KW - machine learning KW - infodemiology N2 - Background: Although COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy. Objective: The aim of this study was to use social media data to capture close-to-real-time public perspectives and sentiments regarding COVID-19 vaccines, with the intention to understand the key issues that have captured public attention, as well as the barriers and facilitators to successful COVID-19 vaccination. Methods: Twitter was searched for tweets related to ?COVID-19? and ?vaccine? over an 11-week period after November 18, 2020, following a press release regarding the first effective vaccine. An unsupervised machine learning approach (ie, structural topic modeling) was used to identify topics from tweets, with each topic further grouped into themes using manually conducted thematic analysis as well as guided by the theoretical framework of the COM-B (capability, opportunity, and motivation components of behavior) model. Sentiment analysis of the tweets was also performed using the rule-based machine learning model VADER (Valence Aware Dictionary and Sentiment Reasoner). Results: Tweets related to COVID-19 vaccines were posted by individuals around the world (N=672,133). Six overarching themes were identified: (1) emotional reactions related to COVID-19 vaccines (19.3%), (2) public concerns related to COVID-19 vaccines (19.6%), (3) discussions about news items related to COVID-19 vaccines (13.3%), (4) public health communications about COVID-19 vaccines (10.3%), (5) discussions about approaches to COVID-19 vaccination drives (17.1%), and (6) discussions about the distribution of COVID-19 vaccines (20.3%). Tweets with negative sentiments largely fell within the themes of emotional reactions and public concerns related to COVID-19 vaccines. Tweets related to facilitators of vaccination showed temporal variations over time, while tweets related to barriers remained largely constant throughout the study period. Conclusions: The findings from this study may facilitate the formulation of comprehensive strategies to improve COVID-19 vaccine uptake; they highlight the key processes that require attention in the planning of COVID-19 vaccination and provide feedback on evolving barriers and facilitators in ongoing vaccination drives to allow for further policy tweaks. The findings also illustrate three key roles of social media in COVID-19 vaccination, as follows: surveillance and monitoring, a communication platform, and evaluation of government responses. UR - https://publichealth.jmir.org/2021/11/e29789 UR - http://dx.doi.org/10.2196/29789 UR - http://www.ncbi.nlm.nih.gov/pubmed/34583316 ID - info:doi/10.2196/29789 ER - TY - JOUR AU - Haupt, Robert Michael AU - Xu, Qing AU - Yang, Joshua AU - Cai, Mingxiang AU - Mackey, K. Tim PY - 2021/10/29 TI - Characterizing Vaping Industry Political Influence and Mobilization on Facebook: Social Network Analysis JO - J Med Internet Res SP - e28069 VL - 23 IS - 10 KW - vaping KW - alternative tobacco industry KW - e-cigarettes KW - Facebook KW - social network analysis KW - social networks KW - ehealth KW - health policy N2 - Background: In response to recent policy efforts to regulate tobacco and vaping products, the vaping industry has been aggressive in mobilizing opposition by using a network of manufacturers, trade associations, and tobacco user communities, and by appealing to the general public. One strategy the alternative tobacco industry uses to mobilize political action is coordinating on social media platforms, such as the social networking site Facebook. However, few studies have specifically assessed how platforms such as Facebook are used to influence public sentiment and attitudes towards tobacco control policy. Objective: This study used social network analysis to examine how the alternative tobacco industry uses Facebook to mobilize online users to influence tobacco control policy outcomes with a focus on the state of California. Methods: Data were collected from local and national alternative tobacco Facebook groups that had affiliations with activities in the state of California. Network ties were constructed based on users? reactions to posts (eg, ?like? and ?love?) and comments to characterize political mobilization networks. Results: Findings show that alternative tobacco industry employees were more likely to engage within these networks and that these employees were also more likely to be influential members (ie, be more active) in the network. Comparisons between subnetworks show that communication within the local alternative tobacco advocacy group network was less dense and more centralized in contrast to a national advocacy group that had overall higher levels of engagement among members. A timeline analysis found that a higher number of influential posts that disseminated widely across networks occurred during e-cigarette?related legislative events, suggesting strategic online engagement and increased mobilization of online activity for the purposes of influencing policy outcomes. Conclusions: Results from this study provide important insights into how tobacco industry?related advocacy groups leverage the Facebook platform to mobilize their online constituents in an effort to influence public perceptions and coordinate to defeat tobacco control efforts at the local, state, and federal level. Study results reveal one part of a vast network of socially enabled alternative tobacco industry actors and constituents that use Facebook as a mobilization point to support goals of the alternative tobacco industry. UR - https://www.jmir.org/2021/10/e28069 UR - http://dx.doi.org/10.2196/28069 UR - http://www.ncbi.nlm.nih.gov/pubmed/34714245 ID - info:doi/10.2196/28069 ER - TY - JOUR AU - Silangcruz, Krixie AU - Nishimura, Yoshito AU - Czech, Torrey AU - Kimura, Nobuhiko AU - Hagiya, Hideharu AU - Koyama, Toshihiro AU - Otsuka, Fumio PY - 2021/10/28 TI - Impact of the World Inflammatory Bowel Disease Day and Crohn?s and Colitis Awareness Week on Population Interest Between 2016 and 2020: Google Trends Analysis JO - JMIR Infodemiology SP - e32856 VL - 1 IS - 1 KW - inflammatory bowel disease KW - ulcerative colitis KW - Crohn disease KW - google trends KW - trend analysis KW - online health information KW - awareness KW - chronic disease KW - gastrointestinal KW - trend KW - impact KW - public health KW - United States N2 - Background: More than 6 million people are affected by inflammatory bowel disease (IBD) globally. The World IBD Day (WID, May 19) and Crohn?s and Colitis Awareness Week (CCAW, December 1-7) occur yearly as national health observances to raise public awareness of IBD, but their effects are unclear. Objective: The aim of this study was to analyze the relationship between WID or CCAW and the public health awareness on IBD represented by the Google search engine query data. Methods: This study evaluates the impact of WID and CCAW on the public awareness of IBD in the United States and worldwide from 2016 to 2020 by using the relative search volume of ?IBD,? ?ulcerative colitis,? and ?Crohn?s disease? in Google Trends. To identify significant time points of trend changes (joinpoints), we performed joinpoint regression analysis. Results: No joinpoints were noted around the time of WID or CCAW during the study period in the search results of the United States. Worldwide, joinpoints were noted around WID in 2020 with the search for ?IBD? and around CCAW in 2017 and 2019 with the search for ?ulcerative colitis.? However, the extents of trend changes were modest without statistically significant increases. Conclusions: These results posed a question that WID and CCAW might not have worked as expected to raise public awareness of IBD. Additional studies are needed to precisely estimate the impact of health observances to raise the awareness of IBD. UR - https://infodemiology.jmir.org/2021/1/e32856 UR - http://dx.doi.org/10.2196/32856 UR - http://www.ncbi.nlm.nih.gov/pubmed/37114197 ID - info:doi/10.2196/32856 ER - TY - JOUR AU - Monzani, Dario AU - Vergani, Laura AU - Pizzoli, Maria Silvia Francesca AU - Marton, Giulia AU - Pravettoni, Gabriella PY - 2021/10/27 TI - Emotional Tone, Analytical Thinking, and Somatosensory Processes of a Sample of Italian Tweets During the First Phases of the COVID-19 Pandemic: Observational Study JO - J Med Internet Res SP - e29820 VL - 23 IS - 10 KW - internet KW - mHealth KW - infodemiology KW - infoveillance KW - pandemic KW - public health KW - COVID-19 KW - Twitter KW - psycholinguistic analysis KW - trauma N2 - Background: The COVID-19 pandemic is a traumatic individual and collective chronic experience, with tremendous consequences on mental and psychological health that can also be reflected in people?s use of words. Psycholinguistic analysis of tweets from Twitter allows obtaining information about people?s emotional expression, analytical thinking, and somatosensory processes, which are particularly important in traumatic events contexts. Objective: We aimed to analyze the influence of official Italian COVID-19 daily data (new cases, deaths, and hospital discharges) and the phase of managing the pandemic on how people expressed emotions and their analytical thinking and somatosensory processes in Italian tweets written during the first phases of the COVID-19 pandemic in Italy. Methods: We retrieved 1,697,490 Italian COVID-19?related tweets written from February 24, 2020 to June 14, 2020 and analyzed them using LIWC2015 to calculate 3 summary psycholinguistic variables: emotional tone, analytical thinking, and somatosensory processes. Official daily data about new COVID-19 cases, deaths, and hospital discharges were retrieved from the Italian Prime Minister's Office and Civil Protection Department GitHub page. We considered 3 phases of managing the COVID-19 pandemic in Italy. We performed 3 general models, 1 for each summary variable as the dependent variable and with daily data and phase of managing the pandemic as independent variables. Results: General linear models to assess differences in daily scores of emotional tone, analytical thinking, and somatosensory processes were significant (F6,104=21.53, P<.001, R2= .55; F5,105=9.20, P<.001, R2= .30; F6,104=6.15, P<.001, R2=.26, respectively). Conclusions: The COVID-19 pandemic affects how people express emotions, analytical thinking, and somatosensory processes in tweets. Our study contributes to the investigation of pandemic psychological consequences through psycholinguistic analysis of social media textual data. UR - https://www.jmir.org/2021/10/e29820 UR - http://dx.doi.org/10.2196/29820 UR - http://www.ncbi.nlm.nih.gov/pubmed/34516386 ID - info:doi/10.2196/29820 ER - TY - JOUR AU - Alvarez-Mon, Angel Miguel AU - Llavero-Valero, Maria AU - Asunsolo del Barco, Angel AU - Zaragozá, Cristina AU - Ortega, A. Miguel AU - Lahera, Guillermo AU - Quintero, Javier AU - Alvarez-Mon, Melchor PY - 2021/10/26 TI - Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter JO - J Med Internet Res SP - e24336 VL - 23 IS - 10 KW - obesity KW - social media KW - Twitter KW - drug therapy KW - pharmacotherapy KW - attitude KW - thematic analysis KW - quantitative analysis KW - drug N2 - Background: Antiobesity drugs are prescribed for the treatment of obesity in conjunction with healthy eating, physical activity, and behavior modification. However, poor adherence rates have been reported. Attitudes or beliefs toward medications are important to ascertain because they may be associated with patient behavior. The analysis of tweets has become a tool for health research. Objective: The aim of this study is to investigate the content and key metrics of tweets referring to antiobesity drugs. Methods: In this observational quantitative and qualitative study, we focused on tweets containing hashtags related to antiobesity drugs between September 20, 2019, and October 31, 2019. Tweets were first classified according to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. We additionally rated it as positive or negative. Furthermore, we classified any links included within a tweet as either scientific or nonscientific. Finally, the number of retweets generated as well as the dissemination and sentiment score obtained by the antiobesity drugs analyzed were also measured. Results: We analyzed a total of 2045 tweets, 945 of which were excluded according to the criteria of the study. Finally, 320 out of the 1,100 remaining tweets were also excluded because their content, although related to drugs for obesity treatment, did not address the efficacy, side effects, or adherence to medication. Liraglutide and semaglutide accumulated the majority of tweets (682/780, 87.4%). Notably, the content that generated the highest frequency of tweets was related to treatment efficacy, with liraglutide-, semaglutide-, and lorcaserin-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to liraglutide and semaglutide. Semaglutide-related tweets obtained the highest probability of likes and were the most disseminated within the Twitter community. Conclusions: This analysis of posted tweets related to antiobesity drugs shows that the interest, beliefs, and experiences regarding these pharmacological treatments are heterogeneous. The efficacy of the treatment accounts for the majority of interest among Twitter users. UR - https://www.jmir.org/2021/10/e24336 UR - http://dx.doi.org/10.2196/24336 UR - http://www.ncbi.nlm.nih.gov/pubmed/34698653 ID - info:doi/10.2196/24336 ER - TY - JOUR AU - Ainley, Esther AU - Witwicki, Cara AU - Tallett, Amy AU - Graham, Chris PY - 2021/10/25 TI - Using Twitter Comments to Understand People?s Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis JO - J Med Internet Res SP - e31101 VL - 23 IS - 10 KW - patient experience KW - COVID-19 KW - remote health care KW - phone consultation KW - video consultation KW - Twitter KW - sentiment analysis KW - social media KW - digital health KW - public health KW - public opinion N2 - Background: The COVID-19 pandemic has led to changes in health service utilization patterns and a rapid rise in care being delivered remotely. However, there has been little published research examining patients? experiences of accessing remote consultations since COVID-19. Such research is important as remote methods for delivering some care may be maintained in the future. Objective: The aim of this study was to use content from Twitter to understand discourse around health and care delivery in the United Kingdom as a result of COVID-19, focusing on Twitter users? views on and attitudes toward care being delivered remotely. Methods: Tweets posted from the United Kingdom between January 2018 and October 2020 were extracted using the Twitter application programming interface. A total of 1408 tweets across three search terms were extracted into Excel; 161 tweets were removed following deduplication and 610 were identified as irrelevant to the research question. The remaining relevant tweets (N=637) were coded into categories using NVivo software, and assigned a positive, neutral, or negative sentiment. To examine views of remote care over time, the coded data were imported back into Excel so that each tweet was associated with both a theme and sentiment. Results: The volume of tweets on remote care delivery increased markedly following the COVID-19 outbreak. Five main themes were identified in the tweets: access to remote care (n=267), quality of remote care (n=130), anticipation of remote care (n=39), online booking and asynchronous communication (n=85), and publicizing changes to services or care delivery (n=160). Mixed public attitudes and experiences to the changes in service delivery were found. The proportion of positive tweets regarding access to, and quality of, remote care was higher in the immediate period following the COVID-19 outbreak (March-May 2020) when compared to the time before COVID-19 onset and the time when restrictions from the first lockdown eased (June-October 2020). Conclusions: Using Twitter data to address our research questions proved beneficial for providing rapid access to Twitter users? attitudes to remote care delivery at a time when it would have been difficult to conduct primary research due to COVID-19. This approach allowed us to examine the discourse on remote care over a relatively long period and to explore shifting attitudes of Twitter users at a time of rapid changes in care delivery. The mixed attitudes toward remote care highlight the importance for patients to have a choice over the type of consultation that best suits their needs, and to ensure that the increased use of technology for delivering care does not become a barrier for some. The finding that overall sentiment about remote care was more positive in the early stages of the pandemic but has since declined emphasizes the need for a continued examination of people?s preference, particularly if remote appointments are likely to remain central to health care delivery. UR - https://www.jmir.org/2021/10/e31101 UR - http://dx.doi.org/10.2196/31101 UR - http://www.ncbi.nlm.nih.gov/pubmed/34469327 ID - info:doi/10.2196/31101 ER - TY - JOUR AU - Elyashar, Aviad AU - Plochotnikov, Ilia AU - Cohen, Idan-Chaim AU - Puzis, Rami AU - Cohen, Odeya PY - 2021/10/22 TI - The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses JO - J Med Internet Res SP - e30217 VL - 23 IS - 10 KW - health care professionals KW - Twitter KW - COVID-19 KW - topic analysis KW - emotion analysis KW - sentiment analysis KW - social media KW - machine learning KW - active learning N2 - Background: The COVID-19 pandemic has affected populations worldwide, with extreme health, economic, social, and political implications. Health care professionals (HCPs) are at the core of pandemic response and are among the most crucial factors in maintaining coping capacities. Yet, they are also vulnerable to mental health effects caused by managing a long-lasting emergency with a lack of resources and under complicated personal concerns. However, there are a lack of longitudinal studies that investigate the HCP population. Objective: The aim of this study was to analyze the state of mind of HCPs as expressed in online discussions published on Twitter in light of the COVID-19 pandemic, from the onset of the pandemic until the end of 2020. Methods: The population for this study was selected from followers of a few hundred Twitter accounts of health care organizations and common HCP points of interest. We used active learning, a process that iteratively uses machine learning and manual data labeling, to select the large-scale population of Twitter accounts maintained by English-speaking HCPs, focusing on individuals rather than official organizations. We analyzed the topics and emotions in their discourses during 2020. The topic distributions were obtained using the latent Dirichlet allocation algorithm. We defined a measure of topic cohesion and described the most cohesive topics. The emotions expressed in tweets during 2020 were compared to those in 2019. Finally, the emotion intensities were cross-correlated with the pandemic waves to explore possible associations between the pandemic development and emotional response. Results: We analyzed the timelines of 53,063 Twitter profiles, 90% of which were maintained by individual HCPs. Professional topics accounted for 44.5% of tweets by HCPs from January 1, 2019, to December 6, 2020. Events such as the pandemic waves, US elections, or the George Floyd case affected the HCPs? discourse. The levels of joy and sadness exceeded their minimal and maximal values from 2019, respectively, 80% of the time (P=.001). Most interestingly, fear preceded the pandemic waves, in terms of the differences in confirmed cases, by 2 weeks with a Spearman correlation coefficient of ?(47 pairs)=0.340 (P=.03). Conclusions: Analyses of longitudinal data over the year 2020 revealed that a large fraction of HCP discourse is directly related to professional content, including the increase in the volume of discussions following the pandemic waves. The changes in emotional patterns (ie, decrease in joy and increase in sadness, fear, and disgust) during the year 2020 may indicate the utmost importance in providing emotional support for HCPs to prevent fatigue, burnout, and mental health disorders during the postpandemic period. The increase in fear 2 weeks in advance of pandemic waves indicates that HCPs are in a position, and with adequate qualifications, to anticipate pandemic development, and could serve as a bottom-up pathway for expressing morbidity and clinical situations to health agencies. UR - https://www.jmir.org/2021/10/e30217 UR - http://dx.doi.org/10.2196/30217 UR - http://www.ncbi.nlm.nih.gov/pubmed/34550899 ID - info:doi/10.2196/30217 ER - TY - JOUR AU - Monselise, Michal AU - Chang, Chia-Hsuan AU - Ferreira, Gustavo AU - Yang, Rita AU - Yang, C. Christopher PY - 2021/10/21 TI - Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis JO - J Med Internet Res SP - e30765 VL - 23 IS - 10 KW - health care informatics KW - topic detection KW - unsupervised sentiment analysis KW - COVID-19 KW - vaccine hesitancy KW - sentiment KW - concern KW - vaccine KW - social media KW - trend KW - trust KW - health information KW - Twitter KW - discussion KW - communication KW - hesitancy KW - emotion KW - fear N2 - Background: As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective: The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. Methods: To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. Results: After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. Conclusions: This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust. UR - https://www.jmir.org/2021/10/e30765 UR - http://dx.doi.org/10.2196/30765 UR - http://www.ncbi.nlm.nih.gov/pubmed/34581682 ID - info:doi/10.2196/30765 ER - TY - JOUR AU - Lavertu, Adam AU - Hamamsy, Tymor AU - Altman, B. Russ PY - 2021/10/21 TI - Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis JO - J Med Internet Res SP - e27714 VL - 23 IS - 10 KW - social media for health KW - pharmacovigilance KW - adverse drug reactions KW - machine learning KW - network analysis KW - word embeddings KW - drug safety KW - social media N2 - Background: Adverse drug reactions (ADRs) affect the health of hundreds of thousands of individuals annually in the United States, with associated costs of hundreds of billions of dollars. The monitoring and analysis of the severity of ADRs is limited by the current qualitative and categorical systems of severity classification. Previous efforts have generated quantitative estimates for a subset of ADRs but were limited in scope because of the time and costs associated with the efforts. Objective: The aim of this study is to increase the number of ADRs for which there are quantitative severity estimates while improving the quality of these severity estimates. Methods: We present a semisupervised approach that estimates ADR severity by using social media word embeddings to construct a lexical network of ADRs and perform label propagation. We used this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from the Medical Dictionary for Regulatory Activities. Results: Our Severity of Adverse Events Derived from Reddit (SAEDR) scores have good correlations with real-world outcomes. The SAEDR scores had Spearman correlations of 0.595, 0.633, and ?0.748 for death, serious outcome, and no outcome, respectively, with ADR case outcomes in the Food and Drug Administration Adverse Event Reporting System. We investigated different methods for defining initial seed term sets and evaluated their impact on the severity estimates. We analyzed severity distributions for ADRs based on their appearance in boxed warning drug label sections, as well as for ADRs with sex-specific associations. We found that ADRs discovered in the postmarketing period had significantly greater severity than those discovered during the clinical trial (P<.001). We created quantitative drug-risk profile (DRIP) scores for 968 drugs that had a Spearman correlation of 0.377 with drugs ranked by the Food and Drug Administration Adverse Event Reporting System cases resulting in death, where the given drug was the primary suspect. Conclusions: Our SAEDR and DRIP scores are well correlated with the real-world outcomes of the entities they represent and have demonstrated utility in pharmacovigilance research. We make the SAEDR scores for 12,198 ADRs and the DRIP scores for 968 drugs publicly available to enable more quantitative analysis of pharmacovigilance data. UR - https://www.jmir.org/2021/10/e27714 UR - http://dx.doi.org/10.2196/27714 UR - http://www.ncbi.nlm.nih.gov/pubmed/34673524 ID - info:doi/10.2196/27714 ER - TY - JOUR AU - Liu, Qian AU - Zheng, Zequan AU - Chen, Jingsen AU - Tsang, Winghei AU - Jin, Shan AU - Zhang, Yimin AU - Akinwunmi, Babatunde AU - Zhang, JP Casper AU - Ming, Wai-kit PY - 2021/10/21 TI - Health Communication About Hospice Care in Chinese Media: Digital Topic Modeling Study JO - JMIR Public Health Surveill SP - e29375 VL - 7 IS - 10 KW - health communication KW - hospice care KW - mass media KW - China KW - topic modeling KW - communication KW - media KW - model KW - hospice KW - end-of-life KW - misconception KW - health information KW - news N2 - Background: Hospice care, a type of end-of-life care provided for dying patients and their families, has been rooted in China since the 1980s. It can improve receivers? quality of life as well as ease their economic burden. The Chinese mass media have continued to actively dispel misconceptions surrounding hospice care and deliver the latest information to citizens. Objective: This study aims to retrieve and analyze news reports on hospice care in order to gain insight into whether any differences existed in heath information delivered over time and to evaluate the role of mass media in health communication in recent years. Methods: We searched the Huike (WiseSearch) news database for relevant news reports from Chinese mass media released between 2014 and 2019. We defined two time periods for this study: (1) January 1, 2014, to December 31, 2016, and (2) January 1, 2017, to December 31, 2019. The data cleaning process was completed using Python. We determined appropriate topic numbers for these two periods based on the coherence score and applied latent Dirichlet allocation topic modeling. Keywords for each topic and corresponding topics? names were then generated. The topics were plotted into different circles, and their distances on the 2D plane was represented by multidimensional scaling. Results: After removing duplicated and irrelevant news articles, we obtained a total of 2227 articles. We chose 8 as the suitable topic number for both study periods and generated topic names and associated keywords. The top 3 most reported topics in the first period were patient treatment, hospice care stories, and development of health care services and health insurance, accounting for 18.68% (178/953), 16.58% (158/953), and 14.17% (135/953) of the collected reports, respectively. The top 3 most reported topics in the second period were hospice care stories, patient treatment, and development of health care services, accounting for 15.62% (199/953), 15.38% (15.38/953), and 14.27% (182/953), respectively. Conclusions: Topic modeling of news reports gives us a better understanding of the patterns of health communication about hospice care by mass media. Chinese mass media frequently reported on hospice care in April of every year on account of a traditional Chinese festival. Moreover, an increase in coverage was observed in the second period. The two periods shared 6 similar topics, of which patient treatment outstrips hospice care stories was the most reported topic in the second period, implying the humanistic spirit behind the reports. Based on the findings of this study, we suggest stakeholders cooperate with the mass media when planning to update policies. UR - https://publichealth.jmir.org/2021/10/e29375 UR - http://dx.doi.org/10.2196/29375 UR - http://www.ncbi.nlm.nih.gov/pubmed/34673530 ID - info:doi/10.2196/29375 ER - TY - JOUR AU - Zhao, Y. Ivy AU - Ma, Xuan Ye AU - Yu, Cecilia Man Wai AU - Liu, Jia AU - Dong, Nan Wei AU - Pang, Qin AU - Lu, Qin Xiao AU - Molassiotis, Alex AU - Holroyd, Eleanor AU - Wong, William Chi Wai PY - 2021/10/20 TI - Ethics, Integrity, and Retributions of Digital Detection Surveillance Systems for Infectious Diseases: Systematic Literature Review JO - J Med Internet Res SP - e32328 VL - 23 IS - 10 KW - artificial intelligence KW - electronic medical records KW - ethics KW - infectious diseases KW - machine learning N2 - Background: The COVID-19 pandemic has increased the importance of the deployment of digital detection surveillance systems to support early warning and monitoring of infectious diseases. These opportunities create a ?double-edge sword,? as the ethical governance of such approaches often lags behind technological achievements. Objective: The aim was to investigate ethical issues identified from utilizing artificial intelligence?augmented surveillance or early warning systems to monitor and detect common or novel infectious disease outbreaks. Methods: In a number of databases, we searched relevant articles that addressed ethical issues of using artificial intelligence, digital surveillance systems, early warning systems, and/or big data analytics technology for detecting, monitoring, or tracing infectious diseases according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and further identified and analyzed them with a theoretical framework. Results: This systematic review identified 29 articles presented in 6 major themes clustered under individual, organizational, and societal levels, including awareness of implementing digital surveillance, digital integrity, trust, privacy and confidentiality, civil rights, and governance. While these measures were understandable during a pandemic, the public had concerns about receiving inadequate information; unclear governance frameworks; and lack of privacy protection, data integrity, and autonomy when utilizing infectious disease digital surveillance. The barriers to engagement could widen existing health care disparities or digital divides by underrepresenting vulnerable and at-risk populations, and patients? highly sensitive data, such as their movements and contacts, could be exposed to outside sources, impinging significantly upon basic human and civil rights. Conclusions: Our findings inform ethical considerations for service delivery models for medical practitioners and policymakers involved in the use of digital surveillance for infectious disease spread, and provide a basis for a global governance structure. Trial Registration: PROSPERO CRD42021259180; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259180 UR - https://www.jmir.org/2021/10/e32328 UR - http://dx.doi.org/10.2196/32328 UR - http://www.ncbi.nlm.nih.gov/pubmed/34543228 ID - info:doi/10.2196/32328 ER - TY - JOUR AU - Fang, Yang AU - Shepherd, A. Thomas AU - Smith, E. Helen PY - 2021/10/18 TI - Examining the Trends in Online Health Information?Seeking Behavior About Chronic Obstructive Pulmonary Disease in Singapore: Analysis of Data From Google Trends and the Global Burden of Disease Study JO - J Med Internet Res SP - e19307 VL - 23 IS - 10 KW - online health information seeking KW - infodemiology KW - Google Trends KW - Global Burden of Disease study KW - chronic obstructive pulmonary disease KW - respiratory health N2 - Background: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally, and timely health care seeking is imperative for its prevention, early detection, and management. While online health information?seeking behavior (OHISB) is increasingly popular due to widespread internet connectivity, little is known about how OHISB for COPD has changed in comparison with the COPD disease burden, particularly at a country-specific level. Objective: This study aimed to examine the trends in OHISB for COPD and how that compared with the estimates of COPD disease burden in Singapore, a highly wired country with a steadily increasing COPD disease burden. Methods: To examine the trends in OHISB for COPD, we performed Prais-Winsten regression analyses on monthly search volume data for COPD from January 2004 to June 2020 downloaded from Google Trends. We then conducted cross-correlational analyses to examine the relationship between annualized search volume on COPD topics and estimates of COPD morbidity and mortality reported in the Global Burden of Disease study from 2004 to 2017. Results: From 2004 to 2020, the trend in COPD search volume was curvilinear (?=1.69, t194=6.64, P<.001), with a slope change around the end of 2006. There was a negative linear trend (?=?0.53, t33=?3.57, P=.001) from 2004 to 2006 and a positive linear trend (?=0.51, t159=7.43, P<.001) from 2007 to 2020. Cross-correlation analyses revealed positive associations between COPD search volume and COPD disease burden indicators: positive correlations between search volume and prevalence, incidence, years living with disability (YLD) at lag 0, and positive correlations between search volume and prevalence, YLD at lag 1. Conclusions: Google search volume on COPD increased from 2007 to 2020; this trend correlated with the upward trajectory of several COPD morbidity estimates, suggesting increasing engagement in OHISB for COPD in Singapore. These findings underscore the importance of making high-quality, web-based information accessible to the public, particularly COPD patients and their carers. UR - https://www.jmir.org/2021/10/e19307 UR - http://dx.doi.org/10.2196/19307 UR - http://www.ncbi.nlm.nih.gov/pubmed/34661539 ID - info:doi/10.2196/19307 ER - TY - JOUR AU - Rovetta, Alessandro AU - Castaldo, Lucia PY - 2021/10/18 TI - Influence of Mass Media on Italian Web Users During the COVID-19 Pandemic: Infodemiological Analysis JO - JMIRx Med SP - e32233 VL - 2 IS - 4 KW - COVID-19 KW - Google Trends KW - infodemiology KW - infoveillance KW - infodemic KW - media coverage KW - mass media influence KW - mass media KW - social media N2 - Background: Concurrently with the COVID-19 pandemic, the world has been facing a growing infodemic, which has caused severe damage to economic and health systems and has often compromised the effectiveness of infection containment regulations. Although this infodemic has spread mainly through social media, there are numerous occasions on which mass media outlets have shared dangerous information, giving resonance to statements without a scientific basis. For these reasons, infoveillance and infodemiology methods are increasingly exploited to monitor information traffic on the web and make epidemiological predictions. Objective: The purpose of this paper is to estimate the impact of Italian mass media on users? web searches to understand the role of press and television channels in both the infodemic and the interest of Italian netizens in COVID-19. Methods: We collected the headlines published from January 2020 to March 2021 containing specific COVID-19?related keywords published on PubMed, Google, the Italian Ministry of Health website, and the most-read newspapers in Italy. We evaluated the percentages of infodemic terms on these platforms. Through Google Trends, we searched for cross-correlations between newspaper headlines and COVID-19?related web searches. Finally, we analyzed the web interest in infodemic content posted on YouTube. Results: During the first wave of COVID-19, the Italian press preferred to draw on infodemic terms (rate of adoption: 1.6%-6.3%) and moderately infodemic terms (rate of adoption: 88%-94%), while scientific sources favored the correct names (rate of adoption: 65%-88%). The correlational analysis showed that the press heavily influenced users in adopting terms to identify the novel coronavirus (cross-correlations of ?0.74 to ?0.89, P value <.001; maximum lag=1 day). The use of scientific denominations by the press reached acceptable values only during the third wave (approximately 80%, except for the television services Rai and Mediaset). Web queries about COVID-19 symptoms also appeared to be influenced by the press (best average correlation=0.92, P<.007). Furthermore, web users showed pronounced interest in YouTube videos of an infodemic nature. Finally, the press gave resonance to serious ?fake news? on COVID-19, which caused pronounced spikes of interest from web users. Conclusions: Our results suggest that the Italian mass media have played a decisive role in spreading the COVID-19 infodemic and addressing netizens? web interest, thus favoring the adoption of terms that are unsuitable for identifying COVID-19. Therefore, the directors of news channels and newspapers should be more cautious, and government dissemination agencies should exert more control over such news stories. UR - https://med.jmirx.org/2021/4/e32233 UR - http://dx.doi.org/10.2196/32233 UR - http://www.ncbi.nlm.nih.gov/pubmed/34842858 ID - info:doi/10.2196/32233 ER - TY - JOUR AU - Bedford-Petersen, Cianna AU - Weston, J. Sara PY - 2021/10/15 TI - Mapping Individual Differences on the Internet: Case Study of the Type 1 Diabetes Community JO - JMIR Diabetes SP - e30756 VL - 6 IS - 4 KW - type 1 diabetes KW - diabetes community KW - social media KW - Twitter KW - natural language processing KW - social network analysis KW - Latent Dirichlet Allocation KW - diabetes KW - data scraping KW - sentiment analysis N2 - Background: Social media platforms, such as Twitter, are increasingly popular among communities of people with chronic conditions, including those with type 1 diabetes (T1D). There is some evidence that social media confers emotional and health-related benefits to people with T1D, including emotional support and practical information regarding health maintenance. Research on social media has primarily relied on self-reports of web-based behavior and qualitative assessment of web-based content, which can be expensive and time-consuming. Meanwhile, recent advances in natural language processing have allowed for large-scale assessment of social media behavior. Objective: This study attempts to document the major themes of Twitter posts using a natural language processing method to identify topics of interest in the T1D web-based community. We also seek to map social relations on Twitter as they relate to these topics of interest, to determine whether Twitter users in the T1D community post in ?echo chambers,? which reflect their own topics back to them, or whether users typically see a mix of topics on the internet. Methods: Through Twitter scraping, we gathered a data set of 691,691 tweets from 8557 accounts, spanning a date range from 2008 to 2020, which includes people with T1D, their caregivers, health practitioners, and advocates. Tweet content was analyzed for sentiment and topic, using Latent Dirichlet Allocation. We used social network analysis to examine the degree to which identified topics are siloed within specific groups or disseminated through the broader T1D web-based community. Results: Tweets were, on average, positive in sentiment. Through topic modeling, we identified 6 broad-bandwidth topics, ranging from clinical to advocacy to daily management to emotional health, which can inform researchers and practitioners interested in the needs of people with T1D. These analyses also replicate prior work using machine learning methods to map social behavior on the internet. We extend these results through social network analysis, indicating that users are likely to see a mix of these topics discussed by the accounts they follow. Conclusions: Twitter communities are sources of information for people with T1D and members related to that community. Topics identified reveal key concerns of the T1D community and may be useful to practitioners and researchers alike. The methods used are efficient (low cost) while providing researchers with enormous amounts of data. We provide code to facilitate the use of these methods with other populations. UR - https://diabetes.jmir.org/2021/4/e30756 UR - http://dx.doi.org/10.2196/30756 UR - http://www.ncbi.nlm.nih.gov/pubmed/34652277 ID - info:doi/10.2196/30756 ER - TY - JOUR AU - Bos, C. Véronique L. L. AU - Jansen, Tessa AU - Klazinga, S. Niek AU - Kringos, S. Dionne PY - 2021/10/12 TI - Development and Actionability of the Dutch COVID-19 Dashboard: Descriptive Assessment and Expert Appraisal Study JO - JMIR Public Health Surveill SP - e31161 VL - 7 IS - 10 KW - COVID-19 KW - dashboard KW - performance intelligence KW - Netherlands KW - actionability KW - communication KW - government KW - pandemic KW - public health N2 - Background: Web-based public reporting by means of dashboards has become an essential tool for governments worldwide to monitor COVID-19 information and communicate it to the public. The actionability of such dashboards is determined by their fitness for purpose?meeting a specific information need?and fitness for use?placing the right information into the right hands at the right time and in a manner that can be understood. Objective: The aim of this study was to identify specific areas where the actionability of the Dutch government?s COVID-19 dashboard could be improved, with the ultimate goal of enhancing public understanding of the pandemic. Methods: The study was conducted from February 2020 to April 2021. A mixed methods approach was carried out, using (1) a descriptive checklist over time to monitor changes made to the dashboard, (2) an actionability scoring of the dashboard to pinpoint areas for improvement, and (3) a reflection meeting with the dashboard development team to contextualize findings and discuss areas for improvement. Results: The dashboard predominantly showed epidemiological information on COVID-19. It had been developed and adapted by adding more in-depth indicators, more geographic disaggregation options, and new indicator themes. It also changed in target audience from policy makers to the general public; thus, a homepage was added with the most important information, using news-like items to explain the provided indicators and conducting research to enhance public understanding of the dashboard. However, disaggregation options such as sex, socioeconomic status, and ethnicity and indicators on dual-track health system management and social and economic impact that have proven to give important insights in other countries are missing from the Dutch COVID-19 dashboard, limiting its actionability. Conclusions: The Dutch COVID-19 dashboard developed over time its fitness for purpose and use in terms of providing epidemiological information to the general public as a target audience. However, to strengthen the Dutch health system?s ability to cope with upcoming phases of the COVID-19 pandemic or future public health emergencies, we advise (1) establishing timely indicators relating to health system capacity, (2) including relevant data disaggregation options (eg, sex, socioeconomic status), and (3) enabling interoperability between social, health, and economic data sources. UR - https://publichealth.jmir.org/2021/10/e31161 UR - http://dx.doi.org/10.2196/31161 UR - http://www.ncbi.nlm.nih.gov/pubmed/34543229 ID - info:doi/10.2196/31161 ER - TY - JOUR AU - Kummervold, E. Per AU - Martin, Sam AU - Dada, Sara AU - Kilich, Eliz AU - Denny, Chermain AU - Paterson, Pauline AU - Larson, J. Heidi PY - 2021/10/8 TI - Categorizing Vaccine Confidence With a Transformer-Based Machine Learning Model: Analysis of Nuances of Vaccine Sentiment in Twitter Discourse JO - JMIR Med Inform SP - e29584 VL - 9 IS - 10 KW - computer science KW - information technology KW - public health KW - health humanities KW - vaccines KW - machine learning N2 - Background: Social media has become an established platform for individuals to discuss and debate various subjects, including vaccination. With growing conversations on the web and less than desired maternal vaccination uptake rates, these conversations could provide useful insights to inform future interventions. However, owing to the volume of web-based posts, manual annotation and analysis are difficult and time consuming. Automated processes for this type of analysis, such as natural language processing, have faced challenges in extracting complex stances such as attitudes toward vaccination from large amounts of text. Objective: The aim of this study is to build upon recent advances in transposer-based machine learning methods and test whether transformer-based machine learning could be used as a tool to assess the stance expressed in social media posts toward vaccination during pregnancy. Methods: A total of 16,604 tweets posted between November 1, 2018, and April 30, 2019, were selected using keyword searches related to maternal vaccination. After excluding irrelevant tweets, the remaining tweets were coded by 3 individual researchers into the categories Promotional, Discouraging, Ambiguous, and Neutral or No Stance. After creating a final data set of 2722 unique tweets, multiple machine learning techniques were trained on a part of this data set and then tested and compared with the human annotators. Results: We found the accuracy of the machine learning techniques to be 81.8% (F score=0.78) compared with the agreed score among the 3 annotators. For comparison, the accuracies of the individual annotators compared with the final score were 83.3%, 77.9%, and 77.5%. Conclusions: This study demonstrates that we are able to achieve close to the same accuracy in categorizing tweets using our machine learning models as could be expected from a single human coder. The potential to use this automated process, which is reliable and accurate, could free valuable time and resources for conducting this analysis, in addition to informing potentially effective and necessary interventions. UR - https://medinform.jmir.org/2021/10/e29584 UR - http://dx.doi.org/10.2196/29584 UR - http://www.ncbi.nlm.nih.gov/pubmed/34623312 ID - info:doi/10.2196/29584 ER - TY - JOUR AU - Heyerdahl, W. Leonardo AU - Lana, Benedetta AU - Giles-Vernick, Tamara PY - 2021/10/6 TI - The Impact of the Online COVID-19 Infodemic on French Red Cross Actors? Field Engagement and Protective Behaviors: Mixed Methods Study JO - JMIR Infodemiology SP - e27472 VL - 1 IS - 1 KW - COVID-19 KW - infodemics KW - social listening KW - epidemics KW - medical anthropology KW - nongovernmental organizations N2 - Background: The COVID-19 pandemic has been widely described as an infodemic, an excess of rapidly circulating information in social and traditional media in which some information may be erroneous, contradictory, or inaccurate. One key theme cutting across many infodemic analyses is that it stymies users? capacities to identify appropriate information and guidelines, encourages them to take inappropriate or even harmful actions, and should be managed through multiple transdisciplinary approaches. Yet, investigations demonstrating how the COVID-19 information ecosystem influences complex public decision making and behavior offline are relatively few. Objective: The aim of this study was to investigate whether information reported through the social media channel Twitter, linked articles and websites, and selected traditional media affected the risk perception, engagement in field activities, and protective behaviors of French Red Cross (FRC) volunteers and health workers in the Paris region of France from June to October 2020. Methods: We used a hybrid approach that blended online and offline data. We tracked daily Twitter discussions and selected traditional media in France for 7 months, qualitatively evaluating COVID-19 claims and debates about nonpharmaceutical protective measures. We conducted 24 semistructured interviews with FRC workers and volunteers. Results: Social and traditional media debates about viral risks and nonpharmaceutical interventions fanned anxieties among FRC volunteers and workers. Decisions to continue conducting FRC field activities and daily protective practices were also influenced by other factors unrelated to the infodemic: familial and social obligations, gender expectations, financial pressures, FRC rules and communications, state regulations, and relationships with coworkers. Some respondents developed strategies for ?tuning out? social and traditional media. Conclusions: This study suggests that during the COVID-19 pandemic, the information ecosystem may be just one among multiple influences on one group?s offline perceptions and behavior. Measures to address users who have disengaged from online sources of health information and who rely on social relationships to obtain information are needed. Tuning out can potentially lead to less informed decision making, leading to worse health outcomes. UR - https://infodemiology.jmir.org/2021/1/e27472 UR - http://dx.doi.org/10.2196/27472 UR - http://www.ncbi.nlm.nih.gov/pubmed/34661065 ID - info:doi/10.2196/27472 ER - TY - JOUR AU - Sharp, J. Kendall AU - Vitagliano, A. Julia AU - Weitzman, R. Elissa AU - Fitzgerald, Susan AU - Dahlberg, E. Suzanne AU - Austin, Bryn S. PY - 2021/10/4 TI - Peer-to-Peer Social Media Communication About Dietary Supplements Used for Weight Loss and Sports Performance Among Military Personnel: Pilot Content Analysis of 11 Years of Posts on Reddit JO - JMIR Form Res SP - e28957 VL - 5 IS - 10 KW - dietary supplements KW - social media KW - Reddit KW - OPSS N2 - Background: Over 60% of military personnel in the United States currently use dietary supplements. Two types of dietary supplements, weight loss and sports performance (WLSP) supplements, are commonly used by military personnel despite the associated serious adverse effects such as dehydration and stroke. Objective: To understand peer-to-peer communication about WLSP supplements among military personnel, we conducted a pilot study using the social media website, Reddit. Methods: A total of 64 relevant posts and 243 comments from 2009 to 2019 were collected from 6 military subreddits. The posts were coded for year of posting, subreddit, and content consistent with the following themes: resources about supplement safety and regulation, discernability of supplement use through drug testing, serious adverse effects, brand names or identifiers, and reasons for supplement use. Results: A primary concern posted by personnel who used supplements was uncertainty about the supplements that were not detectable on a drug test. Supplements to improve workout performance were the most frequently used. Conclusions: Our pilot study suggests that military personnel may seek out peer advice about WLSP supplements on Reddit and spread misinformation about the safety and effectiveness of these products through this platform. Future directions for the monitoring of WLSP supplement use in military personnel are discussed. UR - https://formative.jmir.org/2021/10/e28957 UR - http://dx.doi.org/10.2196/28957 UR - http://www.ncbi.nlm.nih.gov/pubmed/34605769 ID - info:doi/10.2196/28957 ER - TY - JOUR AU - Usher, Kim AU - Durkin, Joanne AU - Martin, Sam AU - Vanderslott, Samantha AU - Vindrola-Padros, Cecilia AU - Usher, Luke AU - Jackson, Debra PY - 2021/10/1 TI - Public Sentiment and Discourse on Domestic Violence During the COVID-19 Pandemic in Australia: Analysis of Social Media Posts JO - J Med Internet Res SP - e29025 VL - 23 IS - 10 KW - COVID-19 KW - domestic violence KW - social media KW - Twitter KW - sentiment analysis KW - discourse analysis KW - keyword analysis KW - pandemic KW - sentiment KW - public health KW - public expression N2 - Background: Measuring public response during COVID-19 is an important way of ensuring the suitability and effectiveness of epidemic response efforts. An analysis of social media provides an approximation of public sentiment during an emergency like the current pandemic. The measures introduced across the globe to help curtail the spread of the coronavirus have led to the development of a situation labeled as a ?perfect storm,? triggering a wave of domestic violence. As people use social media to communicate their experiences, analyzing public discourse and sentiment on social platforms offers a way to understand concerns and issues related to domestic violence during the COVID-19 pandemic. Objective: This study was based on an analysis of public discourse and sentiment related to domestic violence during the stay-at-home periods of the COVID-19 pandemic in Australia in 2020. It aimed to understand the more personal self-reported experiences, emotions, and reactions toward domestic violence that were not always classified or collected by official public bodies during the pandemic. Methods: We searched social media and news posts in Australia using key terms related to domestic violence and COVID-19 during 2020 via digital analytics tools to determine sentiments related to domestic violence during this period. Results: The study showed that the use of sentiment and discourse analysis to assess social media data is useful in measuring the public expression of feelings and sharing of resources in relation to the otherwise personal experience of domestic violence. There were a total of 63,800 posts across social media and news media. Within these posts, our analysis found that domestic violence was mentioned an average of 179 times a day. There were 30,100 tweets, 31,700 news reports, 1500 blog posts, 548 forum posts, and 7 comments (posted on news and blog websites). Negative or neutral sentiment centered on the sharp rise in domestic violence during different lockdown periods of the 2020 pandemic, and neutral and positive sentiments centered on praise for efforts that raised awareness of domestic violence as well as the positive actions of domestic violence charities and support groups in their campaigns. There were calls for a positive and proactive handling (rather than a mishandling) of the pandemic, and results indicated a high level of public discontent related to the rising rates of domestic violence and the lack of services during the pandemic. Conclusions: This study provided a timely understanding of public sentiment related to domestic violence during the COVID-19 lockdown periods in Australia using social media analysis. Social media represents an important avenue for the dissemination of information; posts can be widely dispersed and easily accessed by a range of different communities who are often difficult to reach. An improved understanding of these issues is important for future policy direction. Heightened awareness of this could help agencies tailor and target messaging to maximize impact. UR - https://www.jmir.org/2021/10/e29025 UR - http://dx.doi.org/10.2196/29025 UR - http://www.ncbi.nlm.nih.gov/pubmed/34519659 ID - info:doi/10.2196/29025 ER - TY - JOUR AU - Sager, A. Monique AU - Kashyap, M. Aditya AU - Tamminga, Mila AU - Ravoori, Sadhana AU - Callison-Burch, Christopher AU - Lipoff, B. Jules PY - 2021/9/30 TI - Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study JO - JMIR Dermatol SP - e20975 VL - 4 IS - 2 KW - bots KW - natural language processing KW - artificial intelligence KW - Reddit, medical misinformation KW - health misinformation KW - detecting misinformation KW - dermatology KW - misinformation N2 - Background: Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to the provision of inappropriate care. Initial testing has revealed that artificially intelligent bots can detect misinformation regarding tanning and essential oils on Reddit dermatology forums and may be able to produce responses to posts containing misinformation. Objective: To analyze the ability of bots to find and respond to tanning and essential oil?related health misinformation on Reddit?s dermatology forums in a controlled test environment. Methods: Using natural language processing techniques, we trained bots to target misinformation, using relevant keywords and to post prefabricated responses. By evaluating different model architectures across a held-out test set, we compared performances. Results: Our models yielded data test accuracies ranging 95%-100%, with a Bidirectional Encoder Representations from Transformers (BERT) fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective prefabricated responses to misinformation in a test environment. Conclusions: Using a limited data set, bots accurately detected examples of health misinformation within Reddit dermatology forums. Given that these bots can then post prefabricated responses, this technique may allow for interception of misinformation. Providing correct information does not mean that users will be receptive or find such interventions persuasive. Further studies should investigate this strategy?s effectiveness to inform future deployment of bots as a technique in combating health misinformation. UR - https://derma.jmir.org/2021/2/e20975 UR - http://dx.doi.org/10.2196/20975 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632809 ID - info:doi/10.2196/20975 ER - TY - JOUR AU - Yuan, Kai AU - Huang, Guangrui AU - Wang, Lepeng AU - Wang, Ting AU - Liu, Wenbin AU - Jiang, Haixu AU - Yang, C. Albert PY - 2021/9/29 TI - Predicting Norovirus in the United States Using Google Trends: Infodemiology Study JO - J Med Internet Res SP - e24554 VL - 23 IS - 9 KW - norovirus KW - Google Trends KW - correlation KW - outbreak KW - predictors N2 - Background: Norovirus is a contagious disease. The transmission of norovirus spreads quickly and easily in various ways. Because effective methods to prevent or treat norovirus have not been discovered, it is important to rapidly recognize and report norovirus outbreaks in the early phase. Internet search has been a useful method for people to access information immediately. With the precise record of internet search trends, internet search has been a useful tool to manifest infectious disease outbreaks. Objective: In this study, we tried to discover the correlation between internet search terms and norovirus infection. Methods: The internet search trend data of norovirus were obtained from Google Trends. We used cross-correlation analysis to discover the temporal correlation between norovirus and other terms. We also used multiple linear regression with the stepwise method to recognize the most important predictors of internet search trends and norovirus. In addition, we evaluated the temporal correlation between actual norovirus cases and internet search terms in New York, California, and the United States as a whole. Results: Some Google search terms such as gastroenteritis, watery diarrhea, and stomach bug coincided with norovirus Google Trends. Some Google search terms such as contagious, travel, and party presented earlier than norovirus Google Trends. Some Google search terms such as dehydration, bar, and coronavirus presented several months later than norovirus Google Trends. We found that fever, gastroenteritis, poison, cruise, wedding, and watery diarrhea were important factors correlated with norovirus Google Trends. In actual norovirus cases from New York, California, and the United States as a whole, some Google search terms presented with, earlier, or later than actual norovirus cases. Conclusions: Our study provides novel strategy-based internet search evidence regarding the epidemiology of norovirus. UR - https://www.jmir.org/2021/9/e24554 UR - http://dx.doi.org/10.2196/24554 UR - http://www.ncbi.nlm.nih.gov/pubmed/34586079 ID - info:doi/10.2196/24554 ER - TY - JOUR AU - Geronikolou, Styliani AU - Drosatos, George AU - Chrousos, George PY - 2021/9/29 TI - Emotional Analysis of Twitter Posts During the First Phase of the COVID-19 Pandemic in Greece: Infoveillance Study JO - JMIR Form Res SP - e27741 VL - 5 IS - 9 KW - emotional analysis KW - COVID-19 KW - Twitter KW - Greece KW - infodemics KW - emotional contagion KW - epidemiology KW - pandemic KW - mental health N2 - Background: The effectiveness of public health measures depends upon a community?s compliance as well as on its positive or negative emotions. Objective: The purpose of this study was to perform an analysis of the expressed emotions in English tweets by Greek Twitter users during the first phase of the COVID-19 pandemic in Greece. Methods: The period of this study was from January 25, 2020 to June 30, 2020. Data collection was performed by using appropriate search words with the filter-streaming application programming interface of Twitter. The emotional analysis of the tweets that satisfied the inclusion criteria was achieved using a deep learning approach that performs better by utilizing recurrent neural networks on sequences of characters. Emotional epidemiology tools such as the 6 basic emotions, that is, joy, sadness, disgust, fear, surprise, and anger based on the Paul Ekman classification were adopted. Results: The most frequent emotion that was detected in the tweets was ?surprise? at the emerging contagion, while the imposed isolation resulted mostly in ?anger? (odds ratio 2.108, 95% CI 0.986-4.506). Although the Greeks felt rather safe during the first phase of the COVID-19 pandemic, their positive and negative emotions reflected a masked ?flight or fight? or ?fear versus anger? response to the contagion. Conclusions: The findings of our study show that emotional analysis emerges as a valid tool for epidemiology evaluations, design, and public health strategy and surveillance. UR - https://formative.jmir.org/2021/9/e27741 UR - http://dx.doi.org/10.2196/27741 UR - http://www.ncbi.nlm.nih.gov/pubmed/34469328 ID - info:doi/10.2196/27741 ER - TY - JOUR AU - Li, Jinhui AU - Zheng, Han AU - Duan, Xu PY - 2021/9/28 TI - Factors Influencing the Popularity of a Health-Related Answer on a Chinese Question-and-Answer Website: Case Study JO - J Med Internet Res SP - e29885 VL - 23 IS - 9 KW - answer-response behaviors KW - Zhihu KW - HPV vaccine information KW - content features KW - context features KW - contributor features N2 - Background: Social question-and-answer (Q&A) sites have become an important venue for individuals to obtain and share human papillomavirus (HPV) vaccine knowledge. Objective: This study aims to examine how different features of an HPV vaccine?related answer are associated with users? response behaviors on social Q&A websites. Methods: A total of 2953 answers and 270 corresponding questions regarding the HPV vaccine were collected from a leading Chinese social Q&A platform, Zhihu. Three types of key features, including content, context, and contributor, were extracted and coded. Negative binomial regression models were used to examine their impact on the vote and comment count of an HPV vaccine?related answer. Results: The findings showed that both content length and vividness were positively related to the response behaviors of HPV vaccine?related answers. In addition, compared with answers under the question theme benefits and risks, answers under the question theme vaccination experience received fewer votes and answers under the theme news opinions received more votes but fewer comments. The effects of characteristics of contributors were also supported, suggesting that answers from a male contributor with more followers and no professional identity would attract more votes and comments from community members. The significant interaction effect between content and context features further showed that long and vivid answers about HPV vaccination experience were more likely to receive votes and comments of users than those about benefits and risks. Conclusions: The study provides a complete picture of the underlying mechanism behind response behaviors of users toward HPV vaccine?related answers on social Q&A websites. The results help health community organizers develop better strategies for building and maintaining a vibrant web-based community for communicating HPV vaccine knowledge. UR - https://www.jmir.org/2021/9/e29885 UR - http://dx.doi.org/10.2196/29885 UR - http://www.ncbi.nlm.nih.gov/pubmed/34581675 ID - info:doi/10.2196/29885 ER - TY - JOUR AU - Ding, Qinglan AU - Massey, Daisy AU - Huang, Chenxi AU - Grady, B. Connor AU - Lu, Yuan AU - Cohen, Alina AU - Matzner, Pini AU - Mahajan, Shiwani AU - Caraballo, César AU - Kumar, Navin AU - Xue, Yuchen AU - Dreyer, Rachel AU - Roy, Brita AU - Krumholz, M. Harlan PY - 2021/9/28 TI - Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study JO - JMIR Public Health Surveill SP - e29413 VL - 7 IS - 9 KW - health conditions KW - symptoms KW - mental health KW - social media KW - infoveillance KW - public health surveillance KW - COVID-19 KW - pandemic KW - natural language processing N2 - Background: Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. Objective: This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States. Methods: We used natural language processing (NLP) algorithms to identify symptom- and medical condition?related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics. Results: Within a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition?related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05). Conclusions: COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population?s mental health status and enhance public health surveillance for infectious disease. UR - https://publichealth.jmir.org/2021/9/e29413 UR - http://dx.doi.org/10.2196/29413 UR - http://www.ncbi.nlm.nih.gov/pubmed/34517338 ID - info:doi/10.2196/29413 ER - TY - JOUR AU - Yan, Cathy AU - Law, Melanie AU - Nguyen, Stephanie AU - Cheung, Janelle AU - Kong, Jude PY - 2021/9/24 TI - Comparing Public Sentiment Toward COVID-19 Vaccines Across Canadian Cities: Analysis of Comments on Reddit JO - J Med Internet Res SP - e32685 VL - 23 IS - 9 KW - COVID-19 KW - public sentiment KW - social media KW - Reddit KW - Canada KW - communication KW - sentiment KW - opinion KW - emotion KW - concern KW - pandemic KW - vaccine KW - hesitancy N2 - Background: Social media enables the rapid consumption of news related to COVID-19 and serves as a platform for discussions. Its richness in text-based data in the form of posts and comments allows researchers to identify popular topics and assess public sentiment. Nonetheless, the vast majority of topic extraction and sentiment analysis based on social media is performed on the platform or country level and does not account for local culture and policies. Objective: The aim of this study is to use location-based subreddits on Reddit to study city-level variations in sentiments toward vaccine-related topics. Methods: Comments on posts providing regular updates on COVID-19 statistics in the Vancouver (r/vancouver, n=49,291), Toronto (r/toronto, n=20,764), and Calgary (r/calgary, n=21,277) subreddits between July 13, 2020, and June 14, 2021, were extracted. Latent Dirichlet allocation was used to identify frequently discussed topics. Sentiment (joy, sadness, fear, and anger) scores were assigned to comments through random forest regression. Results: The number of comments on the 250 posts from the Vancouver subreddit positively correlated with the number of new daily COVID-19 cases in British Columbia (R=0.51, 95% CI for slope 0.18-0.29; P<.001). From the comments, 13 topics were identified. Two were related to vaccines, 1 regarding vaccine uptake and the other about vaccine supply. The levels of discussion for both topics were linked to the total number of vaccines administered (Granger test for causality, P<.001). Comments pertaining to either topic displayed higher scores for joy than for other topics (P<.001). Calgary and Toronto also discussed vaccine uptake. Sentiment scores for this topic differed across the 3 cities (P<.001). Conclusions: Our work demonstrates that data from city-specific subreddits can be used to better understand concerns and sentiments around COVID-19 vaccines at the local level. This can potentially lead to more targeted and publicly acceptable policies based on content on social media. UR - https://www.jmir.org/2021/9/e32685 UR - http://dx.doi.org/10.2196/32685 UR - http://www.ncbi.nlm.nih.gov/pubmed/34519654 ID - info:doi/10.2196/32685 ER - TY - JOUR AU - Huemer, Matthias AU - Jahn-Kuch, Daniela AU - Hofmann, Guenter AU - Andritsch, Elisabeth AU - Farkas, Clemens AU - Schaupp, Walter AU - Masel, Katharina Eva AU - Jost, J. Philipp AU - Pichler, Martin PY - 2021/9/20 TI - Trends and Patterns in the Public Awareness of Palliative Care, Euthanasia, and End-of-Life Decisions in 3 Central European Countries Using Big Data Analysis From Google: Retrospective Analysis JO - J Med Internet Res SP - e28635 VL - 23 IS - 9 KW - Google Trends KW - end-of-life decisions KW - assisted suicide KW - euthanasia KW - palliative care KW - health care policy N2 - Background: End-of-life decisions, specifically the provision of euthanasia and assisted suicide services, challenge traditional medical and ethical principles. Austria and Germany have decided to liberalize their laws restricting assisted suicide, thus reigniting the debate about a meaningful framework in which the practice should be embedded. Evidence of the relevance of assisted suicide and euthanasia for the general population in Germany and Austria is limited. Objective: The aim of this study is to examine whether the public awareness documented by search activities in the most frequently used search engine, Google, on the topics of palliative care, euthanasia, and advance health care directives changed with the implementation of palliative care services and new governmental regulations concerning end-of-life decisions. Methods: We searched for policies, laws, and regulations promulgated or amended in Austria, Germany, and Switzerland between 2004 and 2020 and extracted data on the search volume for each search term topic from Google Trends as a surrogate of public awareness and interest. Annual averages were analyzed using the Joinpoint Regression Program. Results: Important policy changes yielded significant changes in search trends for the investigated topics. The enactment of laws regulating advance health care directives coincided with a significant drop in the volume of searches for the topic of euthanasia in all 3 countries (Austria: ?24.48%, P=.02; Germany: ?14.95%, P<.001; Switzerland: ?11.75%, P=.049). Interest in palliative care increased with the availability of care services and the implementation of laws and policies to promote palliative care (Austria: 22.69%, P=.01; Germany: 14.39, P<.001; Switzerland: 17.59%, P<.001). The search trends for advance health care directives showed mixed results. While interest remained steady in Austria within the study period, it increased by 3.66% (P<.001) in Switzerland and decreased by 2.85% (P<.001) in Germany. Conclusions: Our results demonstrate that legal measures securing patients? autonomy at the end of life may lower the search activities for topics related to euthanasia and assisted suicide. Palliative care may be a meaningful way to raise awareness of the different options for end-of-life care and to guide patients in their decision-making process regarding the same. UR - https://www.jmir.org/2021/9/e28635 UR - http://dx.doi.org/10.2196/28635 UR - http://www.ncbi.nlm.nih.gov/pubmed/34542419 ID - info:doi/10.2196/28635 ER - TY - JOUR AU - Teodoro, Douglas AU - Ferdowsi, Sohrab AU - Borissov, Nikolay AU - Kashani, Elham AU - Vicente Alvarez, David AU - Copara, Jenny AU - Gouareb, Racha AU - Naderi, Nona AU - Amini, Poorya PY - 2021/9/17 TI - Information Retrieval in an Infodemic: The Case of COVID-19 Publications JO - J Med Internet Res SP - e30161 VL - 23 IS - 9 KW - information retrieval KW - multistage retrieval KW - neural search KW - deep learning KW - COVID-19 KW - coronavirus KW - infodemic KW - infodemiology KW - literature KW - online information N2 - Background: The COVID-19 global health crisis has led to an exponential surge in published scientific literature. In an attempt to tackle the pandemic, extremely large COVID-19?related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses. Objective: In the context of searching for scientific evidence in the deluge of COVID-19?related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language. Methods: Our multistage retrieval methodology combines probabilistic weighting models and reranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries is compared to documents, and a series of postprocessing methods is applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents. Results: The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed an Okapi Best Match 25?based baseline, retrieving on average, 83% of relevant documents in the top 20. Conclusions: These results indicate that multistage retrieval supported by deep learning could enhance identification of literature for COVID-19?related questions posed using natural language. UR - https://www.jmir.org/2021/9/e30161 UR - http://dx.doi.org/10.2196/30161 UR - http://www.ncbi.nlm.nih.gov/pubmed/34375298 ID - info:doi/10.2196/30161 ER - TY - JOUR AU - Alsudias, Lama AU - Rayson, Paul PY - 2021/9/17 TI - Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study JO - JMIR Med Inform SP - e27670 VL - 9 IS - 9 KW - Arabic KW - COVID-19 KW - infectious disease KW - influenza KW - infodemiology KW - infoveillance KW - social listening KW - informal language KW - multilabel classification KW - natural language processing KW - named entity recognition KW - Twitter N2 - Background: Twitter is a real-time messaging platform widely used by people and organizations to share information on many topics. Systematic monitoring of social media posts (infodemiology or infoveillance) could be useful to detect misinformation outbreaks as well as to reduce reporting lag time and to provide an independent complementary source of data compared with traditional surveillance approaches. However, such an analysis is currently not possible in the Arabic-speaking world owing to a lack of basic building blocks for research and dialectal variation. Objective: We collected around 4000 Arabic tweets related to COVID-19 and influenza. We cleaned and labeled the tweets relative to the Arabic Infectious Diseases Ontology, which includes nonstandard terminology, as well as 11 core concepts and 21 relations. The aim of this study was to analyze Arabic tweets to estimate their usefulness for health surveillance, understand the impact of the informal terms in the analysis, show the effect of deep learning methods in the classification process, and identify the locations where the infection is spreading. Methods: We applied the following multilabel classification techniques: binary relevance, classifier chains, label power set, adapted algorithm (multilabel adapted k-nearest neighbors [MLKNN]), support vector machine with naive Bayes features (NBSVM), bidirectional encoder representations from transformers (BERT), and AraBERT (transformer-based model for Arabic language understanding) to identify tweets appearing to be from infected individuals. We also used named entity recognition to predict the place names mentioned in the tweets. Results: We achieved an F1 score of up to 88% in the influenza case study and 94% in the COVID-19 one. Adapting for nonstandard terminology and informal language helped to improve accuracy by as much as 15%, with an average improvement of 8%. Deep learning methods achieved an F1 score of up to 94% during the classifying process. Our geolocation detection algorithm had an average accuracy of 54% for predicting the location of users according to tweet content. Conclusions: This study identified two Arabic social media data sets for monitoring tweets related to influenza and COVID-19. It demonstrated the importance of including informal terms, which are regularly used by social media users, in the analysis. It also proved that BERT achieves good results when used with new terms in COVID-19 tweets. Finally, the tweet content may contain useful information to determine the location of disease spread. UR - https://medinform.jmir.org/2021/9/e27670 UR - http://dx.doi.org/10.2196/27670 UR - http://www.ncbi.nlm.nih.gov/pubmed/34346892 ID - info:doi/10.2196/27670 ER - TY - JOUR AU - Ricard, Joseph Benjamin AU - Hassanpour, Saeed PY - 2021/9/15 TI - Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes JO - J Med Internet Res SP - e27314 VL - 23 IS - 9 KW - social media KW - natural language processing KW - alcohol abuse KW - machine learning N2 - Background: Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth in the United States. Objective: This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System. Methods: The Bidirectional Encoder Representations From Transformers neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled data set of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey. Results: Significant associations were observed: between alcohol-hashtagged tweets and alcohol consumption (P=.01) and heavy drinking (P=.005) but not binge drinking (P=.37), self-reported at the metropolitan-micropolitan statistical area level; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); between alcohol-hashtagged tweets and the number of breweries (P<.001), wineries (P<.001), and beer, wine, and liquor stores (P<.001) but not drinking places (P=.23), per capita at the US county and county-equivalent level; and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P<.001), as well as ethanol consumed from wine (P<.001) and liquor (P=.01) sources but not beer (P=.63), at the US state level. Conclusions: Here, we present a novel natural language processing pipeline developed using Reddit?s alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes. UR - https://www.jmir.org/2021/9/e27314 UR - http://dx.doi.org/10.2196/27314 UR - http://www.ncbi.nlm.nih.gov/pubmed/34524095 ID - info:doi/10.2196/27314 ER - TY - JOUR AU - Jiang, Crystal Li AU - Chu, Hang Tsz AU - Sun, Mengru PY - 2021/9/14 TI - Characterization of Vaccine Tweets During the Early Stage of the COVID-19 Outbreak in the United States: Topic Modeling Analysis JO - JMIR Infodemiology SP - e25636 VL - 1 IS - 1 KW - topic modeling KW - social media KW - infoveillance KW - vaccine KW - coronavirus KW - COVID-19 N2 - Background: During the early stages of the COVID-19 pandemic, developing safe and effective coronavirus vaccines was considered critical to arresting the spread of the disease. News and social media discussions have extensively covered the issue of coronavirus vaccines, with a mixture of vaccine advocacies, concerns, and oppositions. Objective: This study aimed to uncover the emerging themes in Twitter users? perceptions and attitudes toward vaccines during the early stages of the COVID-19 outbreak. Methods: This study employed topic modeling to analyze tweets related to coronavirus vaccines at the start of the COVID-19 outbreak in the United States (February 21 to March 20, 2020). We created a predefined query (eg, ?COVID? AND ?vaccine?) to extract the tweet text and metadata (number of followers of the Twitter account and engagement metrics based on likes, comments, and retweeting) from the Meltwater database. After preprocessing the data, we tested Latent Dirichlet Allocation models to identify topics associated with these tweets. The model specifying 20 topics provided the best overall coherence, and each topic was interpreted based on its top associated terms. Results: In total, we analyzed 100,209 tweets containing keywords related to coronavirus and vaccines. The 20 topics were further collapsed based on shared similarities, thereby generating 7 major themes. Our analysis characterized 26.3% (26,234/100,209) of the tweets as News Related to Coronavirus and Vaccine Development, 25.4% (25,425/100,209) as General Discussion and Seeking of Information on Coronavirus, 12.9% (12,882/100,209) as Financial Concerns, 12.7% (12,696/100,209) as Venting Negative Emotions, 9.9% (9908/100,209) as Prayers and Calls for Positivity, 8.1% (8155/100,209) as Efficacy of Vaccine and Treatment, and 4.9% (4909/100,209) as Conspiracies about Coronavirus and Its Vaccines. Different themes demonstrated some changes over time, mostly in close association with news or events related to vaccine developments. Twitter users who discussed conspiracy theories, the efficacy of vaccines and treatments, and financial concerns had more followers than those focused on other vaccine themes. The engagement level?the extent to which a tweet being retweeted, quoted, liked, or replied by other users?was similar among different themes, but tweets venting negative emotions yielded the lowest engagement. Conclusions: This study enriches our understanding of public concerns over new vaccines or vaccine development at early stages of the outbreak, bearing implications for influencing vaccine attitudes and guiding public health efforts to cope with infectious disease outbreaks in the future. This study concluded that public concerns centered on general policy issues related to coronavirus vaccines and that the discussions were considerably mixed with political views when vaccines were not made available. Only a small proportion of tweets focused on conspiracy theories, but these tweets demonstrated high engagement levels and were often contributed by Twitter users with more influence. UR - https://infodemiology.jmir.org/2021/1/e25636 UR - http://dx.doi.org/10.2196/25636 UR - http://www.ncbi.nlm.nih.gov/pubmed/34604707 ID - info:doi/10.2196/25636 ER - TY - JOUR AU - Al Tamime, Reham AU - Weber, Ingmar PY - 2021/9/14 TI - Tracking Exposure to Ads Amid the COVID-19 Pandemic: Development of a Public Google Ads Data Set JO - JMIR Data SP - e22446 VL - 2 IS - 1 KW - COVID-19 KW - coronavirus KW - SARS-CoV-2 KW - panic buying KW - Google Ads KW - data KW - database KW - tracking KW - research KW - public availability KW - online behaviors N2 - Background: The COVID-19 pandemic has had a substantial impact on economies, governments, businesses, and most importantly, people?s health. To bring the spread of COVID-19 under control, strict lockdown measures have been implemented across the globe. These lockdown measures resulted in a spate of panic buying and increase in demand for hygiene products and other grocery items. Objective: In this paper, we describe a data set from Google Ads that looks at the presentation of ads to people while they browse the web during the COVID-19 pandemic. We are making the data set available to the research community. Methods: We started this ongoing data collection on March 28, 2020, leveraging Developer Tools? network requests to retrieve Google Ads data. We identified a list of items related and unrelated to panic buying. We then captured these items as targeting criteria under what people are actively researching or planning on Google Ads. Google Ads data has been filtered using additional targeting criteria such as country, gender, and parental status. Results: Since the inception of our collection, we have actively maintained and updated our repository on a monthly basis. In total, we have published over 4116 data points. This paper also presents basic statistics that reveal variations in Google Ads data across countries, gender, and parental status. Conclusions: We hope that this Google Ads data set can increase our understanding of ad exposure during the COVID-19 outbreak. In particular, this data set can lead to further studies that look at the relationship between exposure to ads, time spent web browsing, and health outcomes. UR - https://data.jmir.org/2021/1/e22446 UR - http://dx.doi.org/10.2196/22446 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/22446 ER - TY - JOUR AU - Hu, Tao AU - Wang, Siqin AU - Luo, Wei AU - Zhang, Mengxi AU - Huang, Xiao AU - Yan, Yingwei AU - Liu, Regina AU - Ly, Kelly AU - Kacker, Viraj AU - She, Bing AU - Li, Zhenlong PY - 2021/9/10 TI - Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective JO - J Med Internet Res SP - e30854 VL - 23 IS - 9 KW - Twitter KW - public opinion KW - COVID-19 vaccines KW - sentiment analysis KW - emotion analysis KW - topic modeling KW - COVID-19 N2 - Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective: The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. Methods: We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. Results: An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines. UR - https://www.jmir.org/2021/9/e30854 UR - http://dx.doi.org/10.2196/30854 UR - http://www.ncbi.nlm.nih.gov/pubmed/34346888 ID - info:doi/10.2196/30854 ER - TY - JOUR AU - Tahamtan, Iman AU - Potnis, Devendra AU - Mohammadi, Ehsan AU - Miller, E. Laura AU - Singh, Vandana PY - 2021/9/10 TI - Framing of and Attention to COVID-19 on Twitter: Thematic Analysis of Hashtags JO - J Med Internet Res SP - e30800 VL - 23 IS - 9 KW - COVID-19 KW - framing KW - Twitter KW - social media KW - public opinion KW - engagement KW - public attention KW - thematic analysis KW - public health N2 - Background: Although past research has focused on COVID-19?related frames in the news media, such research may not accurately capture and represent the perspectives of people from diverse backgrounds. Additionally, research on the public attention to COVID-19 as reflected through frames on social media is scarce. Objective: This study identified the frames about the COVID-19 pandemic in the public discourse on Twitter, which voices diverse opinions. This study also investigated the amount of public attention to those frames on Twitter. Methods: We collected 22 trending hashtags related to COVID-19 in the United States and 694,582 tweets written in English containing these hashtags in March 2020 and analyzed them via thematic analysis. Public attention to these frames was measured by evaluating the amount of public engagement with frames and public adoption of those frames. Results: We identified 9 frames including ?public health guidelines,? ?quarantine life,? ?solidarity,? ?evidence and facts,? ?call for action,? ?politics,? ?post-pandemic life,? ?shortage panic,? and ?conflict.? Results showed that some frames such as ?call for action? are more appealing than others during a global pandemic, receiving greater public adoption and engagement. The ?call for action? frame had the highest engagement score, followed by ?conflict? and ?evidence and facts.? Additionally, ?post-pandemic life? had the highest adoption score, followed by ?call for action? and ?shortage panic.? The findings indicated that the frequency of a frame on social media does not necessarily mean greater public adoption of or engagement with the frame. Conclusions: This study contributes to framing theory and research by demonstrating how trending hashtags can be used as new user-generated data to identify frames on social media. This study concludes that the identified frames such as ?quarantine life? and ?conflict? and themes such as ?isolation? and ?toilet paper panic? represent the consequences of the COVID-19 pandemic. The consequences could be (1) exclusively related to COVID-19, such as hand hygiene or isolation; (2) related to any health crisis such as social support of vulnerable groups; and (3) generic that are irrespective of COVID-19, such as homeschooling or remote working. UR - https://www.jmir.org/2021/9/e30800 UR - http://dx.doi.org/10.2196/30800 UR - http://www.ncbi.nlm.nih.gov/pubmed/34406961 ID - info:doi/10.2196/30800 ER - TY - JOUR AU - Tomaszewski, Tre AU - Morales, Alex AU - Lourentzou, Ismini AU - Caskey, Rachel AU - Liu, Bing AU - Schwartz, Alan AU - Chin, Jessie PY - 2021/9/9 TI - Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models JO - J Med Internet Res SP - e30451 VL - 23 IS - 9 KW - misinformation KW - disinformation KW - social media KW - HPV KW - human papillomavirus vaccination KW - vaccination KW - causality mining KW - cause KW - effect KW - risk perceptions KW - vaccine KW - perception KW - risk KW - Twitter KW - machine learning KW - natural language processing KW - cervical cancer N2 - Background: The vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy. Objective: Given the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media. Methods: This study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine?related information on Twitter. Results: We found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine?related information (F score=91.95). We also developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV vaccines. Furthermore, we found that false information contained mostly loss-framed messages focusing on the potential risk of vaccines covering a variety of topics using more diverse vocabulary, while true information contained both gain- and loss-framed messages focusing on the effectiveness of vaccines covering fewer topics using relatively limited vocabulary. Conclusions: Our research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media. UR - https://www.jmir.org/2021/9/e30451 UR - http://dx.doi.org/10.2196/30451 UR - http://www.ncbi.nlm.nih.gov/pubmed/34499043 ID - info:doi/10.2196/30451 ER - TY - JOUR AU - Lee, Hocheol AU - Noh, Bi Eun AU - Park, Jong Sung AU - Nam, Kweun Hae AU - Lee, Ho Tae AU - Lee, Ram Ga AU - Nam, Woo Eun PY - 2021/9/8 TI - COVID-19 Vaccine Perception in South Korea: Web Crawling Approach JO - JMIR Public Health Surveill SP - e31409 VL - 7 IS - 9 KW - COVID-19 vaccine KW - COVID-19 KW - instagram KW - social media KW - infodemiology KW - sentiment analysis KW - vaccine perception KW - South Korea KW - web crawling KW - AstraZeneca KW - Pfizer N2 - Background: The US Centers for Disease Control and Prevention and the World Health Organization emphasized vaccination against COVID-19 because physical distancing proved inadequate to mitigate death, illness, and massive economic loss. Objective: This study aimed to investigate Korean citizens? perceptions of vaccines by examining their views on COVID-19 vaccines, their positive and negative perceptions of each vaccine, and ways to enhance policies to increase vaccine acceptance. Methods: This cross-sectional study analyzed posts on NAVER and Instagram to examine Korean citizens? perception of COVID-19 vaccines. The keywords searched were ?vaccine,? ?AstraZeneca,? and ?Pfizer.? In total 8100 posts in NAVER and 5291 posts in Instagram were sampled through web crawling. Morphology analysis was performed, overlapping or meaningless words were removed, sentiment analysis was implemented, and 3 public health professionals reviewed the results. Results: The findings revealed a negative perception of COVID-19 vaccines; of the words crawled, the proportion of negative words for AstraZeneca was 71.0% (476/670) and for Pfizer was 56.3% (498/885). Among words crawled with ?vaccine,? ?good? ranked first, with a frequency of 13.43% (312/2323). Meanwhile, ?side effect? ranked highest, with a frequency of 29.2% (163/559) for ?AstraZeneca,? but 0.6% (4/673) for ?Pfizer.? With ?vaccine,? positive words were more frequently used, whereas with ?AstraZeneca? and ?Pfizer? negative words were prevalent. Conclusions: There is a negative perception of AstraZeneca and Pfizer vaccines in Korea, with 1 in 4 people refusing vaccination. To address this, accurate information needs to be shared about vaccines including AstraZeneca, and the experiences of those vaccinated. Furthermore, government communication about risk management is required to increase the AstraZeneca vaccination rate for herd immunity before the vaccine expires. UR - https://publichealth.jmir.org/2021/9/e31409 UR - http://dx.doi.org/10.2196/31409 UR - http://www.ncbi.nlm.nih.gov/pubmed/34348890 ID - info:doi/10.2196/31409 ER - TY - JOUR AU - Akpan, Justice Ikpe AU - Aguolu, Genevieve Obianuju AU - Kobara, Mamoua Yawo AU - Razavi, Rouzbeh AU - Akpan, A. Asuama AU - Shanker, Murali PY - 2021/9/2 TI - Association Between What People Learned About COVID-19 Using Web Searches and Their Behavior Toward Public Health Guidelines: Empirical Infodemiology Study JO - J Med Internet Res SP - e28975 VL - 23 IS - 9 KW - internet KW - novel coronavirus KW - SARS-CoV-2 KW - COVID-19 KW - infodemiology KW - misinformation KW - conspiracy theories KW - public health N2 - Background: The use of the internet and web-based platforms to obtain public health information and manage health-related issues has become widespread in this digital age. The practice is so pervasive that the first reaction to obtaining health information is to ?Google it.? As SARS-CoV-2 broke out in Wuhan, China, in December 2019 and quickly spread worldwide, people flocked to the internet to learn about the novel coronavirus and the disease, COVID-19. Lagging responses by governments and public health agencies to prioritize the dissemination of information about the coronavirus outbreak through the internet and the World Wide Web and to build trust gave room for others to quickly populate social media, online blogs, news outlets, and websites with misinformation and conspiracy theories about the COVID-19 pandemic, resulting in people?s deviant behaviors toward public health safety measures. Objective: The goals of this study were to determine what people learned about the COVID-19 pandemic through web searches, examine any association between what people learned about COVID-19 and behavior toward public health guidelines, and analyze the impact of misinformation and conspiracy theories about the COVID-19 pandemic on people?s behavior toward public health measures. Methods: This infodemiology study used Google Trends? worldwide search index, covering the first 6 months after the SARS-CoV-2 outbreak (January 1 to June 30, 2020) when the public scrambled for information about the pandemic. Data analysis employed statistical trends, correlation and regression, principal component analysis (PCA), and predictive models. Results: The PCA identified two latent variables comprising past coronavirus epidemics (pastCoVepidemics: keywords that address previous epidemics) and the ongoing COVID-19 pandemic (presCoVpandemic: keywords that explain the ongoing pandemic). Both principal components were used significantly to learn about SARS-CoV-2 and COVID-19 and explained 88.78% of the variability. Three principal components fuelled misinformation about COVID-19: misinformation (keywords ?biological weapon,? ?virus hoax,? ?common cold,? ?COVID-19 hoax,? and ?China virus?), conspiracy theory 1 (ConspTheory1; keyword ?5G? or ?@5G?), and conspiracy theory 2 (ConspTheory2; keyword ?ingest bleach?). These principal components explained 84.85% of the variability. The principal components represent two measurements of public health safety guidelines?public health measures 1 (PubHealthMes1; keywords ?social distancing,? ?wash hands,? ?isolation,? and ?quarantine?) and public health measures 2 (PubHealthMes2; keyword ?wear mask?)?which explained 84.7% of the variability. Based on the PCA results and the log-linear and predictive models, ConspTheory1 (keyword ?@5G?) was identified as a predictor of people?s behavior toward public health measures (PubHealthMes2). Although correlations of misinformation (keywords ?COVID-19,? ?hoax,? ?virus hoax,? ?common cold,? and more) and ConspTheory2 (keyword ?ingest bleach?) with PubHealthMes1 (keywords ?social distancing,? ?hand wash,? ?isolation,? and more) were r=0.83 and r=?0.11, respectively, neither was statistically significant (P=.27 and P=.13, respectively). Conclusions: Several studies focused on the impacts of social media and related platforms on the spreading of misinformation and conspiracy theories. This study provides the first empirical evidence to the mainly anecdotal discourse on the use of web searches to learn about SARS-CoV-2 and COVID-19. UR - https://www.jmir.org/2021/9/e28975 UR - http://dx.doi.org/10.2196/28975 UR - http://www.ncbi.nlm.nih.gov/pubmed/34280117 ID - info:doi/10.2196/28975 ER - TY - JOUR AU - Park, Myung-Bae AU - Park, Young Eun AU - Lee, Sic Tae AU - Lee, Jinhee PY - 2021/9/1 TI - Effect of the Period From COVID-19 Symptom Onset to Confirmation on Disease Duration: Quantitative Analysis of Publicly Available Patient Data JO - J Med Internet Res SP - e29576 VL - 23 IS - 9 KW - COVID-19 KW - SARS-CoV-2 KW - symptoms onset KW - duration of prevalence KW - confirmation KW - South Korea KW - data crawling KW - social media KW - Internet KW - dataset KW - symptom KW - duration KW - outcome KW - diagnosis KW - prevalence N2 - Background: In general, early intervention in disease based on early diagnosis is considered to be very important for improving health outcomes. However, there is still insufficient evidence regarding how medical care that is based on the early diagnosis of confirmed cases can affect the outcome of COVID-19 treatment. Objective: We aimed to investigate the effect of the duration from the onset of clinical symptoms to confirmation of COVID-19 on the duration from the onset of symptoms to the resolution of COVID-19 (release from quarantine). Methods: For preliminary data collection, we performed data crawling to extract data from social networks, blogs, and official websites operated by local governments. We collected data from the 4002 confirmed cases in 33 cities reported up to May 31, 2020, for whom sex and age information could be verified. Subsequently, 2494 patients with unclear symptom onset dates and 1349 patients who had not been released or had no data about their release dates were excluded. Thus, 159 patients were finally included in this study. To investigate whether rapid confirmation reduces the prevalence period, we divided the duration from symptom onset to confirmation into quartiles of ?1, ?3, ?6, and ?7 days, respectively. We investigated the duration from symptom onset to release and that from confirmation to release according to these quartiles. Furthermore, we performed multiple regression analysis to investigate the effects of rapid confirmation after symptom onset on the treatment period, duration of prevalence, and duration until release from isolation. Results: We performed multiple regression analysis to investigate the association between rapid confirmation after symptom onset and the total prevalence period (faster release from isolation). The time from symptom onset to confirmation showed a negative association with the time from confirmation to release (t1=?3.58; P<.001) and a positive association with the time from symptom onset to release (t1=5.86; P<.001); these associations were statistically significant. Conclusions: The duration from COVID-19 symptom onset to confirmation date is an important variable for predicting disease prevalence, and these results support the hypothesis that a short duration of symptom onset to confirmation can reduce the time from symptom onset to release. UR - https://www.jmir.org/2021/9/e29576 UR - http://dx.doi.org/10.2196/29576 UR - http://www.ncbi.nlm.nih.gov/pubmed/34280114 ID - info:doi/10.2196/29576 ER - TY - JOUR AU - Bautista, Robert John AU - Zhang, Yan AU - Gwizdka, Jacek PY - 2021/9/1 TI - US Physicians? and Nurses? Motivations, Barriers, and Recommendations for Correcting Health Misinformation on Social Media: Qualitative Interview Study JO - JMIR Public Health Surveill SP - e27715 VL - 7 IS - 9 KW - correction KW - COVID-19 KW - physicians KW - misinformation KW - infodemic KW - infodemiology KW - nurses KW - social media N2 - Background: Health misinformation is a public health concern. Various stakeholders have called on health care professionals, such as nurses and physicians, to be more proactive in correcting health misinformation on social media. Objective: This study aims to identify US physicians? and nurses? motivations for correcting health misinformation on social media, the barriers they face in doing so, and their recommendations for overcoming such barriers. Methods: In-depth interviews were conducted with 30 participants, which comprised 15 (50%) registered nurses and 15 (50%) physicians. Qualitative data were analyzed by using thematic analysis. Results: Participants were personally (eg, personal choice) and professionally (eg, to fulfill the responsibility of a health care professional) motivated to correct health misinformation on social media. However, they also faced intrapersonal (eg, a lack of positive outcomes and time), interpersonal (eg, harassment and bullying), and institutional (eg, a lack of institutional support and social media training) barriers to correcting health misinformation on social media. To overcome these barriers, participants recommended that health care professionals should receive misinformation and social media training, including building their social media presence. Conclusions: US physicians and nurses are willing to correct health misinformation on social media despite several barriers. Nonetheless, this study provides recommendations that can be used to overcome such barriers. Overall, the findings can be used by health authorities and organizations to guide policies and activities aimed at encouraging more health care professionals to be present on social media to counteract health misinformation. UR - https://publichealth.jmir.org/2021/9/e27715 UR - http://dx.doi.org/10.2196/27715 UR - http://www.ncbi.nlm.nih.gov/pubmed/34468331 ID - info:doi/10.2196/27715 ER - TY - JOUR AU - Worrall, P. Amy AU - Kelly, Claire AU - O'Neill, Aine AU - O'Doherty, Murray AU - Kelleher, Eoin AU - Cushen, Marie Anne AU - McNally, Cora AU - McConkey, Samuel AU - Glavey, Siobhan AU - Lavin, Michelle AU - de Barra, Eoghan PY - 2021/8/31 TI - Online Search Trends Influencing Anticoagulation in Patients With COVID-19: Observational Study JO - JMIR Form Res SP - e21817 VL - 5 IS - 8 KW - COVID-19 KW - coronavirus KW - online search engines KW - anticoagulation KW - thrombosis KW - online influence KW - health information dissemination N2 - Background: Early evidence of COVID-19?associated coagulopathy disseminated rapidly online during the first months of 2020, followed by clinical debate about how best to manage thrombotic risks in these patients. The rapid online spread of case reports was followed by online interim guidelines, discussions, and worldwide online searches for further information. The impact of global online search trends and online discussion on local approaches to coagulopathy in patients with COVID-19 has not been studied. Objective: The goal of this study was to investigate the relationship between online search trends using Google Trends and the rate of appropriate venous thromboembolism (VTE) prophylaxis and anticoagulation therapy in a cohort of patients with COVID-19 admitted to a tertiary hospital in Ireland. Methods: A retrospective audit of anticoagulation therapy and VTE prophylaxis among patients with COVID-19 who were admitted to a tertiary hospital was conducted between February 29 and May 31, 2020. Worldwide Google search trends of the term ?COVID-19? and anticoagulation synonyms during this time period were determined and correlated against one another using a Spearman correlation. A P value of <.05 was considered significant, and analysis was completed using Prism, version 8 (GraphPad). Results: A statistically significant Spearman correlation (P<.001, r=0.71) was found between the two data sets, showing an increase in VTE prophylaxis in patients with COVID-19 with increasing online searches worldwide. This represents a proxy for online searches and discussion, dissemination of information, and Google search trends relating to COVID-19 and clotting risk, in particular, which correlated with an increasing trend of providing thromboprophylaxis and anticoagulation therapy to patients with COVID-19 in our tertiary center. Conclusions: We described a correlation of local change in clinical practice with worldwide online dialogue and digital search trends that influenced individual clinicians, prior to the publication of formal guidelines or a local quality-improvement intervention. UR - https://formative.jmir.org/2021/8/e21817 UR - http://dx.doi.org/10.2196/21817 UR - http://www.ncbi.nlm.nih.gov/pubmed/34292865 ID - info:doi/10.2196/21817 ER - TY - JOUR AU - Luo, Chen AU - Ji, Kaiyuan AU - Tang, Yulong AU - Du, Zhiyuan PY - 2021/8/27 TI - Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach JO - J Med Internet Res SP - e30715 VL - 23 IS - 8 KW - COVID-19 KW - vaccine KW - Zhihu KW - structural topic modeling KW - medical professional KW - laypeople KW - adverse reactions KW - vaccination KW - vaccine effectiveness KW - vaccine development N2 - Background: COVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between medical professionals and laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines. Objective: This study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context. Methods: We collected 3196 answers under 65 filtered questions concerning the COVID-19 vaccine from the China-based question and answer forum Zhihu. The questions were classified into 5 categories depending on their contents and description: adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the 2 groups, including answer length, attitude distribution, and high-frequency words. Furthermore, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was used to extract topics under each question category, and thematic disparities were evaluated between the 2 groups. Results: Laypeople are more prevailing in the COVID-19 vaccine?related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance toward vaccine effectiveness than did laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. Statistical analysis revealed that laypeople preferred the ?safety of Chinese-made vaccine? topic and other vaccine-related issues in other countries. However, the professionals paid more attention to medical principles and professional standards underlying the COVID-19 vaccine. With respect to topics associated with the social implications of vaccines, the 2 groups showed no significant difference. Conclusions: Our findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. These incongruities can be summarized as ?qualitatively different? in perspective rather than ?quantitatively different? in scientific knowledge. Among those questions closely associated with medical expertise, the ?qualitatively different? characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine, in a more nuanced way. Web-based question and answer forums are a bonanza for examining perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of diverse groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, and pay special attention to the topics with significant inconsistency across groups to build consensus and promote vaccination effectively. UR - https://www.jmir.org/2021/8/e30715 UR - http://dx.doi.org/10.2196/30715 UR - http://www.ncbi.nlm.nih.gov/pubmed/34346885 ID - info:doi/10.2196/30715 ER - TY - JOUR AU - Lin, Leesa AU - Song, Yi AU - Wang, Qian AU - Pu, Jialu AU - Sun, Yueqian Fiona AU - Zhang, Yixuan AU - Zhou, Xinyu AU - Larson, J. Heidi AU - Hou, Zhiyuan PY - 2021/8/27 TI - Public Attitudes and Factors of COVID-19 Testing Hesitancy in the United Kingdom and China: Comparative Infodemiology Study JO - JMIR Infodemiology SP - e26895 VL - 1 IS - 1 KW - COVID-19 KW - test KW - public response KW - sentiment KW - social listening KW - United Kingdom KW - China N2 - Background: Massive community-wide testing has become the cornerstone of management strategies for the COVID-19 pandemic. Objective: This study was a comparative analysis between the United Kingdom and China, which aimed to assess public attitudes and uptake regarding COVID-19 testing, with a focus on factors of COVID-19 testing hesitancy, including effectiveness, access, risk perception, and communication. Methods: We collected and manually coded 3856 UK tweets and 9299 Chinese Sina Weibo posts mentioning COVID-19 testing from June 1 to July 15, 2020. Adapted from the World Health Organization?s 3C Model of Vaccine Hesitancy, we employed social listening analysis examining key factors of COVID-19 testing hesitancy (confidence, complacency, convenience, and communication). Descriptive analysis, time trends, geographical mapping, and chi-squared tests were performed to assess the temporal, spatial, and sociodemographic characteristics that determine the difference in attitudes or uptake of COVID-19 tests. Results: The UK tweets demonstrated a higher percentage of support toward COVID-19 testing than the posts from China. There were much wider reports of public uptake of COVID-19 tests in mainland China than in the United Kingdom; however, uncomfortable experiences and logistical barriers to testing were more expressed in China. The driving forces for undergoing COVID-19 testing were personal health needs, community-wide testing, and mandatory testing policies for travel, with major differences in the ranking order between the two countries. Rumors and information inquiries about COVID-19 testing were also identified. Conclusions: Public attitudes and acceptance toward COVID-19 testing constantly evolve with local epidemic situations. Policies and information campaigns that emphasize the importance of timely testing and rapid communication responses to inquiries and rumors, and provide a supportive environment for accessing tests are key to tackling COVID-19 testing hesitancy and increasing uptake. UR - https://infodemiology.jmir.org/2021/1/e26895 UR - http://dx.doi.org/10.2196/26895 UR - http://www.ncbi.nlm.nih.gov/pubmed/34541460 ID - info:doi/10.2196/26895 ER - TY - JOUR AU - Wei, Shanzun AU - Ma, Ming AU - Wen, Xi AU - Wu, Changjing AU - Zhu, Guonian AU - Zhou, Xiangfu PY - 2021/8/26 TI - Online Public Attention Toward Premature Ejaculation in Mainland China: Infodemiology Study Using the Baidu Index JO - J Med Internet Res SP - e30271 VL - 23 IS - 8 KW - premature ejaculation KW - Baidu Index KW - infodemiology KW - public interest KW - patients? concern KW - sexuality KW - sexual dysfunction N2 - Background: Premature ejaculation (PE) is one of the most described psychosocial stress and sexual complaints worldwide. Previous investigations have focused predominantly on the prospective identification of cases that meet researchers? specific criteria. The genuine demand from patients with regard to information on PE and related issues may thus be neglected. Objective: This study aims to examine the online search trend and user demand related to PE on a national and regional scale using the dominant major search engine in mainland China. Methods: The Baidu Index was queried using the PE-related terms for the period of January 2011 to December 2020. The search volume for each term was recorded to analyze the search trend and demographic distributions. For user interest, the demand and trend data were collected and analyzed. Results: Of the 36 available PE search keywords, 4 PE searching topics were identified. The Baidu Search Index for each PE topic varied from 46.30% (86,840,487/187,558,154) to 6.40% (12,009,307/187,558,154). The annual percent change (APC) for the complaint topic was 48.80% (P<.001) for 2011 to 2014 and ?16.82% (P<.001) for 2014 to 2020. The APC for the inquiry topic was 16.21% (P=.41) for 2011 to 2014 and ?11.00% (P<.001) for 2014 to 2020. For the prognosis topic, the annual APC was 11.18% (P<.001) for 2011 to 2017 and ?19.86% (P<.001) for 2017 to 2020. For the treatment topic, the annual APC was 14.04% (P<.001) for 2011 to 2016 and ?38.83% (P<.001) for 2016 to 2020. The age distribution of those searching for topics related to PE showed that the population aged 20 to 40 years comprised nearly 70% of the total search inquiries (second was 17.95% in the age group younger than 19 years). People from East China made over 50% of the total search queries. Conclusions: The fluctuating online popularity of PE searches reflects the real-time population demands. It may help medical professionals better understand population interest, population concerns, regional variations, and gender differences on a nationwide scale and make disease-specific health care policies. The internet search data could be more reliable when the insufficient and lagging registry data are completed. UR - https://www.jmir.org/2021/8/e30271 UR - http://dx.doi.org/10.2196/30271 UR - http://www.ncbi.nlm.nih.gov/pubmed/34435970 ID - info:doi/10.2196/30271 ER - TY - JOUR AU - Zhu, Peng Yu AU - Park, Woo Han PY - 2021/8/26 TI - Development of a COVID-19 Web Information Transmission Structure Based on a Quadruple Helix Model: Webometric Network Approach Using Bing JO - J Med Internet Res SP - e27681 VL - 23 IS - 8 KW - quadruple helix model KW - COVID-19 KW - structural analysis KW - content analysis KW - network analysis KW - public health KW - webometrics KW - infodemiology KW - infoveillance KW - development KW - internet KW - online health information KW - structure KW - communication KW - big data N2 - Background: Developing an understanding of the social structure and phenomenon of pandemic information sources worldwide is immensely significant. Objective: Based on the quadruple helix model, the aim of this study was to construct and analyze the structure and content of the internet information sources regarding the COVID-19 pandemic, considering time and space. The broader goal was to determine the status and limitations of web information transmission and online communication structure during public health emergencies. Methods: By sorting the second top-level domain, we divided the structure of network information sources into four levels: government, educational organizations, companies, and nonprofit organizations. We analyzed the structure of information sources and the evolution of information content at each stage using quadruple helix and network analysis methods. Results: The results of the structural analysis indicated that the online sources of information in Asia were more diverse than those in other regions in February 2020. As the pandemic spread in April, the information sources in non-Asian regions began to diversify, and the information source structure diversified further in July. With the spread of the pandemic, for an increasing number of countries, not only the government authorities of high concern but also commercial and educational organizations began to produce and provide significant amounts of information and advice. Nonprofit organizations also produced information, but to a lesser extent. The impact of the virus spread from the initial public level of the government to many levels within society. After April, the government?s role in the COVID-19 network information was central. The results of the content analysis showed that there was an increased focus on discussion regarding public health?related campaign materials at all stages. The information content changed with the changing stages. In the early stages, the basic situation regarding the virus and its impact on health attracted most of the attention. Later, the content was more focused on prevention. The business and policy environment also changed from the beginning of the pandemic, and the social changes caused by the pandemic became a popular discussion topic. Conclusions: For public health emergencies, some online and offline information sources may not be sufficient. Diversified institutions must pay attention to public health emergencies and actively respond to multihelical information sources. In terms of published messages, the educational sector plays an important role in public health events. However, educational institutions release less information than governments and businesses. This study proposes that the quadruple helix not only has research significance in the field of scientific cooperation but could also be used to perform effective research regarding web information during crises. This is significant for further development of the quadruple helix model in the medical internet research area. UR - https://www.jmir.org/2021/8/e27681 UR - http://dx.doi.org/10.2196/27681 UR - http://www.ncbi.nlm.nih.gov/pubmed/34280119 ID - info:doi/10.2196/27681 ER - TY - JOUR AU - Fu, Guanghui AU - Song, Changwei AU - Li, Jianqiang AU - Ma, Yue AU - Chen, Pan AU - Wang, Ruiqian AU - Yang, Xiang Bing AU - Huang, Zhisheng PY - 2021/8/26 TI - Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study JO - J Med Internet Res SP - e26119 VL - 23 IS - 8 KW - deep learning KW - distant supervision KW - mental health KW - crisis prevention N2 - Background: Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. Objective: We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. Methods: To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). Results: Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. Conclusions: In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide. UR - https://www.jmir.org/2021/8/e26119 UR - http://dx.doi.org/10.2196/26119 UR - http://www.ncbi.nlm.nih.gov/pubmed/34435964 ID - info:doi/10.2196/26119 ER - TY - JOUR AU - Sajjadi, B. Nicholas AU - Feldman, Kaylea AU - Shepard, Samuel AU - Reddy, K. Arjun AU - Torgerson, Trevor AU - Hartwell, Micah AU - Vassar, Matt PY - 2021/8/26 TI - Public Interest and Behavior Change in the United States Regarding Colorectal Cancer Following the Death of Chadwick Boseman: Infodemiology Investigation of Internet Search Trends Nationally and in At-Risk Areas JO - JMIR Infodemiology SP - e29387 VL - 1 IS - 1 KW - Google Trends KW - colerectal cancer KW - search analytics KW - public health KW - data analytics KW - Chadwick Boseman KW - Twitter KW - infodemiology N2 - Background: Colorectal cancer (CRC) has the third highest cancer mortality rate in the United States. Enhanced screening has reduced mortality rates; however, certain populations remain at high risk, notably African Americans. Raising awareness among at-risk populations may lead to improved CRC outcomes. The influence of celebrity death and illness is an important driver of public awareness. As such, the death of actor Chadwick Boseman from CRC may have influenced CRC awareness. Objective: We sought to assess the influence of Chadwick Boseman?s death on public interest in CRC in the United States, evidenced by internet searches, website traffic, and donations to prominent cancer organizations. Methods: We used an auto-regressive integrated moving average model to forecast Google searching trends for the topic ?Colorectal cancer? in the United States. We performed bivariate and multivariable regressions on state-wise CRC incidence rateand percent Black population. We obtained data from the American Cancer Society (ACS) and the Colon Cancer Foundation (CCF) for information regarding changes in website traffic and donations. Results: The expected national relative search volume (RSV) for colorectal cancer was 2.71 (95% CI 1.76-3.66), reflecting a 3590% (95% CI 2632%-5582%) increase compared to the expected values. With multivariable regression, the statewise RSV increased for each percent Black population by 1.09 (SE 0.18, P<.001), with 42% of the variance explained (P<.001). The American Cancer Society reported a 58,000% increase in CRC-related website traffic the weekend following Chadwick Boseman?s death compared to the weekend before. The Colon Cancer Foundation reported a 331% increase in donations and a 144% increase in revenue in the month following Boseman?s death compared to the month prior. Conclusions: Our results suggest that Chadwick Boseman?s death was associated with substantial increases in awareness of CRC. Increased awareness of CRC may support earlier detection and better prognoses. UR - https://infodemiology.jmir.org/2021/1/e29387 UR - http://dx.doi.org/10.2196/29387 UR - http://www.ncbi.nlm.nih.gov/pubmed/37114199 ID - info:doi/10.2196/29387 ER - TY - JOUR AU - Chum, Antony AU - Nielsen, Andrew AU - Bellows, Zachary AU - Farrell, Eddie AU - Durette, Pierre-Nicolas AU - Banda, M. Juan AU - Cupchik, Gerald PY - 2021/8/25 TI - Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data JO - J Med Internet Res SP - e28716 VL - 23 IS - 8 KW - COVID-19 KW - public opinion KW - social media KW - sentiment analysis KW - public health restrictions KW - infodemiology KW - infoveillance KW - coronavirus KW - evaluation N2 - Background: News media coverage of antimask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views but has done little to represent views of the general public. Investigating the public?s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policy makers to craft better public health messages in anticipation of poor reactions to controversial restrictions. Objective: Using data from social media, this infoveillance study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (eg, business and school closures, regional lockdown differences, and additional public health restrictions, such as social distancing and masking). Methods: COVID-19?related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 and October 31, 2020. Sentiment scores were calculated using the VADER (Valence Aware Dictionary and Sentiment Reasoner) algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites. Dynamic regression models with autoregressive integrated moving average errors were used to examine the association between public health restrictions and changes in public opinion over time (ie, collective attention, aggregate positive sentiment, and level of disagreement), controlling for the effects of confounders (ie, daily COVID-19 case counts, holidays, and COVID-19?related official updates). Results: In addition to expected direct effects (eg, business closures led to decreased positive sentiment and increased disagreements), the impact of restrictions on public opinion was contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closures and other restrictions (eg, masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (ie, sentiment polarization). Partial (ie, region-targeted) lockdowns were associated with better public response (ie, higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. Conclusions: Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policy makers anticipate public response to future pandemic restrictions and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions. UR - https://www.jmir.org/2021/8/e28716 UR - http://dx.doi.org/10.2196/28716 UR - http://www.ncbi.nlm.nih.gov/pubmed/34227996 ID - info:doi/10.2196/28716 ER - TY - JOUR AU - Mohd Hanim, Faiz Muhammad AU - Md Sabri, Aslinie Budi AU - Yusof, Norashikin PY - 2021/8/18 TI - Online News Coverage of the Sugar-Sweetened Beverages Tax in Malaysia: Content Analysis JO - JMIR Public Health Surveill SP - e24523 VL - 7 IS - 8 KW - sugar-sweetened beverages KW - obesity KW - taxes KW - media content analysis KW - public health policy KW - media content KW - public health KW - netnography KW - malaysia KW - budget N2 - Background: In Malaysia, the Sugar-Sweetened Beverages (SSBs) tax was announced during the parliament's 2019 Budget Speech. The tax was slated to be enforced by April 2019 but was later postponed to July 2019. The announcement has since generated significant media coverage and public feedback. Objective: This study presents a qualitative and quantitative cross-sectional study using netnography to examine how Malaysian online news articles responded to the SSBs tax after the announcement and postimplementation. Methods: Online news articles published on popular online news platforms from November 2018 to August 2019 were downloaded using NCapture and imported into NVivo for analysis using the inductive approach and thematic content analysis following the initial SSBs implementation announcement. Results: A total of 62 news articles were analyzed. Most of the articles positively portrayed the SSBs tax (46.8%) and highlighted its health impacts (76%). There were 7 key framing arguments identified in the articles. The positive arguments revolved around incentivizing manufacturers to introduce healthier products voluntarily, positive health consequences, the tax?s impact on government revenue, and the use of the generated revenue toward beneficial social programs. The opposing arguments included increased operating costs to the manufacturer, the increased retail price of drinks, and how the SSBs tax is not a robust solution to obesity. The top priority sector considered in introducing the tax was the health perspective, followed by economic purposes and creating policies such as regulating the food and drinks industry. Conclusions: The majority of online news articles positively reported the implementation of the SSBs tax in Malaysia. This suggests media played a role in garnering support for the health policy. As such, relevant bodies can use negative findings to anticipate and reframe counteracting arguments opposing the SSBs tax. UR - https://publichealth.jmir.org/2021/8/e24523 UR - http://dx.doi.org/10.2196/24523 UR - http://www.ncbi.nlm.nih.gov/pubmed/34406125 ID - info:doi/10.2196/24523 ER - TY - JOUR AU - Effenberger, Maria AU - Kronbichler, Andreas AU - Bettac, Erica AU - Grabherr, Felix AU - Grander, Christoph AU - Adolph, Erik Timon AU - Mayer, Gert AU - Zoller, Heinz AU - Perco, Paul AU - Tilg, Herbert PY - 2021/8/17 TI - Using Infodemiology Metrics to Assess Public Interest in Liver Transplantation: Google Trends Analysis JO - J Med Internet Res SP - e21656 VL - 23 IS - 8 KW - digital medicine KW - search trends KW - public awareness KW - infodemiology KW - eHealth N2 - Background: Liver transplantation (LT) is the only curative treatment for end-stage liver disease. Less than 10% of global transplantation needs are met worldwide, and the need for LT is still increasing. The death rates on the waiting list remain too high. Objective: It is, therefore, critical to raise awareness among the public and health care providers and in turn increasingly acquire donors. Methods: We performed a Google Trends search using the search terms liver transplantation and liver transplant on October 15, 2020. On the basis of the resulting monthly data, the annual average Google Trends indices were calculated for the years 2004 to 2018. We not only investigated the trend worldwide but also used data from the United Network for Organ Sharing (UNOS), Spain, and Eurotransplant. Using pairwise Spearman correlations, Google Trends indices were examined over time and compared with the total number of liver transplants retrieved from the respective official websites of UNOS, the Organización Nacional de Trasplantes, and Eurotransplant. Results: From 2004 to 2018, there was a significant decrease in the worldwide Google Trends index from 78.2 in 2004 to 20.5 in 2018 (?71.2%). This trend was more evident in UNOS than in the Eurotransplant group. In the same period, the number of transplanted livers increased worldwide. The waiting list mortality rate was 31% for Eurotransplant and 29% for UNOS. However, in Spain, where there are excellent awareness programs, the Google Trends index remained stable over the years with comparable, increasing LT numbers but a significantly lower waiting list mortality (15%). Conclusions: Public awareness in LT has decreased significantly over the past two decades. Therefore, novel awareness programs should be initialized. UR - https://www.jmir.org/2021/8/e21656 UR - http://dx.doi.org/10.2196/21656 UR - http://www.ncbi.nlm.nih.gov/pubmed/34402801 ID - info:doi/10.2196/21656 ER - TY - JOUR AU - Tan, Hao AU - Peng, Sheng-Lan AU - Zhu, Chun-Peng AU - You, Zuo AU - Miao, Ming-Cheng AU - Kuai, Shu-Guang PY - 2021/8/12 TI - Long-term Effects of the COVID-19 Pandemic on Public Sentiments in Mainland China: Sentiment Analysis of Social Media Posts JO - J Med Internet Res SP - e29150 VL - 23 IS - 8 KW - COVID-19 KW - emotional trauma KW - public sentiment on social media KW - long-term effect N2 - Background: The COVID-19 outbreak has induced negative emotions among people. These emotions are expressed by the public on social media and are rapidly spread across the internet, which could cause high levels of panic among the public. Understanding the changes in public sentiment on social media during the pandemic can provide valuable information for developing appropriate policies to reduce the negative impact of the pandemic on the public. Previous studies have consistently shown that the COVID-19 outbreak has had a devastating negative impact on public sentiment. However, it remains unclear whether there has been a variation in the public sentiment during the recovery phase of the pandemic. Objective: In this study, we aim to determine the impact of the COVID-19 pandemic in mainland China by continuously tracking public sentiment on social media throughout 2020. Methods: We collected 64,723,242 posts from Sina Weibo, China?s largest social media platform, and conducted a sentiment analysis based on natural language processing to analyze the emotions reflected in these posts. Results: We found that the COVID-19 pandemic not only affected public sentiment on social media during the initial outbreak but also induced long-term negative effects even in the recovery period. These long-term negative effects were no longer correlated with the number of new confirmed COVID-19 cases both locally and nationwide during the recovery period, and they were not attributed to the postpandemic economic recession. Conclusions: The COVID-19 pandemic induced long-term negative effects on public sentiment in mainland China even as the country recovered from the pandemic. Our study findings remind public health and government administrators of the need to pay attention to public mental health even once the pandemic has concluded. UR - https://www.jmir.org/2021/8/e29150 UR - http://dx.doi.org/10.2196/29150 UR - http://www.ncbi.nlm.nih.gov/pubmed/34280118 ID - info:doi/10.2196/29150 ER - TY - JOUR AU - Boucher, Jean-Christophe AU - Cornelson, Kirsten AU - Benham, L. Jamie AU - Fullerton, M. Madison AU - Tang, Theresa AU - Constantinescu, Cora AU - Mourali, Mehdi AU - Oxoby, J. Robert AU - Marshall, A. Deborah AU - Hemmati, Hadi AU - Badami, Abbas AU - Hu, Jia AU - Lang, Raynell PY - 2021/8/12 TI - Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study JO - JMIR Infodemiology SP - e28800 VL - 1 IS - 1 KW - coronavirus KW - COVID-19 KW - public health KW - social media KW - Twitter KW - behavior KW - risk reduction KW - attitudes KW - social network analysis KW - machine learning N2 - Background: The rollout of COVID-19 vaccines has brought vaccine hesitancy to the forefront in managing this pandemic. COVID-19 vaccine hesitancy is fundamentally different from that of other vaccines due to the new technologies being used, rapid development, and widespread global distribution. Attitudes on vaccines are largely driven by online information, particularly information on social media. The first step toward influencing attitudes about immunization is understanding the current patterns of communication that characterize the immunization debate on social media platforms. Objective: We aimed to evaluate societal attitudes, communication trends, and barriers to COVID-19 vaccine uptake through social media content analysis to inform communication strategies promoting vaccine acceptance. Methods: Social network analysis (SNA) and unsupervised machine learning were used to characterize COVID-19 vaccine content on Twitter globally. Tweets published in English and French were collected through the Twitter application programming interface between November 19 and 26, 2020, just following the announcement of initial COVID-19 vaccine trials. SNA was used to identify social media clusters expressing mistrustful opinions on COVID-19 vaccination. Based on the SNA results, an unsupervised machine learning approach to natural language processing using a sentence-level algorithm transfer function to detect semantic textual similarity was performed in order to identify the main themes of vaccine hesitancy. Results: The tweets (n=636,516) identified that the main themes driving the vaccine hesitancy conversation were concerns of safety, efficacy, and freedom, and mistrust in institutions (either the government or multinational corporations). A main theme was the safety and efficacy of mRNA technology and side effects. The conversation around efficacy was that vaccines were unlikely to completely rid the population of COVID-19, polymerase chain reaction testing is flawed, and there is no indication of long-term T-cell immunity for COVID-19. Nearly one-third (45,628/146,191, 31.2%) of the conversations on COVID-19 vaccine hesitancy clusters expressed concerns for freedom or mistrust of institutions (either the government or multinational corporations) and nearly a quarter (34,756/146,191, 23.8%) expressed criticism toward the government?s handling of the pandemic. Conclusions: Social media content analysis combined with social network analysis provides insights into the themes of the vaccination conversation on Twitter. The themes of safety, efficacy, and trust in institutions will need to be considered, as targeted outreach programs and intervention strategies are deployed on Twitter to improve the uptake of COVID-19 vaccination. UR - https://infodemiology.jmir.org/2021/1/e28800 UR - http://dx.doi.org/10.2196/28800 UR - http://www.ncbi.nlm.nih.gov/pubmed/34447924 ID - info:doi/10.2196/28800 ER - TY - JOUR AU - Liu, Siru AU - Li, Jili AU - Liu, Jialin PY - 2021/8/10 TI - Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis JO - J Med Internet Res SP - e30251 VL - 23 IS - 8 KW - vaccine KW - COVID-19 KW - leveraging transfer learning KW - pandemic KW - infodemiology KW - infoveillance KW - public health KW - social media KW - content analysis KW - machine learning KW - online health N2 - Background: The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines. Objective: Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets. Methods: We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine?related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and P values from the Augmented Dickey-Fuller test were used to assess whether users? perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis. Results: We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category. Conclusions: Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines. UR - https://www.jmir.org/2021/8/e30251 UR - http://dx.doi.org/10.2196/30251 UR - http://www.ncbi.nlm.nih.gov/pubmed/34254942 ID - info:doi/10.2196/30251 ER - TY - JOUR AU - Tri Sakti, Muhammad Andi AU - Mohamad, Emma AU - Azlan, Anis Arina PY - 2021/8/9 TI - Mining of Opinions on COVID-19 Large-Scale Social Restrictions in Indonesia: Public Sentiment and Emotion Analysis on Online Media JO - J Med Internet Res SP - e28249 VL - 23 IS - 8 KW - large-scale social restrictions KW - social media KW - public sentiment KW - Twitter KW - COVID-19 KW - infodemiology KW - infoveillance N2 - Background: One of the successful measures to curb COVID-19 spread in large populations is the implementation of a movement restriction order. Globally, it was observed that countries implementing strict movement control were more successful in controlling the spread of the virus as compared with those with less stringent measures. Society?s adherence to the movement control order has helped expedite the process to flatten the pandemic curve as seen in countries such as China and Malaysia. At the same time, there are countries facing challenges with society?s nonconformity toward movement restriction orders due to various claims such as human rights violations as well as sociocultural and economic issues. In Indonesia, society?s adherence to its large-scale social restrictions (LSSRs) order is also a challenge to achieve. Indonesia is regarded as among the worst in Southeast Asian countries in terms of managing the spread of COVID-19. It is proven by the increased number of daily confirmed cases and the total number of deaths, which was more than 6.21% (1351/21,745) of total active cases as of May 2020. Objective: The aim of this study was to explore public sentiments and emotions toward the LSSR and identify issues, fear, and reluctance to observe this restriction among the Indonesian public. Methods: This study adopts a sentiment analysis method with a supervised machine learning approach on COVID-19-related posts on selected media platforms (Twitter, Facebook, Instagram, and YouTube). The analysis was also performed on COVID-19-related news contained in more than 500 online news platforms recognized by the Indonesian Press Council. Social media posts and news originating from Indonesian online media between March 31 and May 31, 2020, were analyzed. Emotion analysis on Twitter platform was also performed to identify collective public emotions toward the LSSR. Results: The study found that positive sentiment surpasses other sentiment categories by 51.84% (n=1,002,947) of the total data (N=1,934,596) collected via the search engine. Negative sentiment was recorded at 35.51% (686,892/1,934,596) and neutral sentiment at 12.65% (244,757/1,934,596). The analysis of Twitter posts also showed that the majority of public have the emotion of ?trust? toward the LSSR. Conclusions: Public sentiment toward the LSSR appeared to be positive despite doubts on government consistency in executing the LSSR. The emotion analysis also concluded that the majority of people believe in LSSR as the best method to break the chain of COVID-19 transmission. Overall, Indonesians showed trust and expressed hope toward the government?s ability to manage this current global health crisis and win against COVID-19. UR - https://www.jmir.org/2021/8/e28249 UR - http://dx.doi.org/10.2196/28249 UR - http://www.ncbi.nlm.nih.gov/pubmed/34280116 ID - info:doi/10.2196/28249 ER - TY - JOUR AU - Rovetta, Alessandro PY - 2021/8/6 TI - The Impact of COVID-19 on Conspiracy Hypotheses and Risk Perception in Italy: Infodemiological Survey Study Using Google Trends JO - JMIR Infodemiology SP - e29929 VL - 1 IS - 1 KW - COVID-19 KW - fake news KW - Google Trends KW - infodemiology KW - Italy KW - risk perception N2 - Background: COVID-19 has caused the worst international crisis since World War II. Italy was one of the countries most affected by both the pandemic and the related infodemic. The success of anti?COVID-19 strategies and future public health policies in Italy cannot separate itself from the containment of fake news and the divulgation of correct information. Objective: The aim of this paper was to analyze the impact of COVID-19 on web interest in conspiracy hypotheses and risk perception of Italian web users. Methods: Google Trends was used to monitor users? web interest in specific topics, such as conspiracy hypotheses, vaccine side effects, and pollution and climate change. The keywords adopted to represent these topics were mined from Bufale.net?an Italian website specializing in detecting online hoaxes?and Google Trends suggestions (ie, related topics and related queries). Relative search volumes (RSVs) of the time-lapse periods of 2016-2020 (pre?COVID-19) and 2020-2021 (post?COVID-19) were compared through percentage difference (?%) and the Welch t test (t). When data series were not stationary, other ad hoc criteria were used. The trend slopes were assessed through Sen slope (SS). The significance thresholds have been indicatively set at P=.05 and t=1.9. Results: The COVID-19 pandemic drastically increased Italian netizens? interest in conspiracies (?% ? [60, 288], t ? [6, 12]). Web interest in conspiracy-related queries across Italian regions increased and became more homogeneous compared to the pre?COVID-19 period (average RSV=80±2.8, tmin=1.8, ?min%=+12.4, min?SD%=?25.8). In addition, a growing trend in web interest in the infodemic YouTube channel ByoBlu has been highlighted. Web interest in hoaxes has increased more than interest in antihoax services (t1=11.3 vs t2=4.5; ?1%=+157.6 vs ?2%=+84.7). Equivalently, web interest in vaccine side effects exceeded interest in pollution and climate change (SSvaccines=0.22, P<.001 vs SSpollution=0.05, P<.001; ?%=+296.4). To date, a significant amount of fake news related to COVID-19 vaccines, unproven remedies, and origin has continued to circulate. In particular, the creation of SARS-CoV-2 in a Chinese laboratory constituted about 0.04% of the entire web interest in the pandemic. Conclusions: COVID-19 has given a significant boost to web interest in conspiracy hypotheses and has made it more uniform across regions in Italy. The pandemic accelerated an already-growing trend in users? interest toward some fake news sources, including the 500,000-subscriber YouTube channel ByoBlu, which was removed from the platform by YouTube for disinformation in March 2021. The risk perception related to COVID-19 vaccines has been so distorted that vaccine side effect?related queries outweighed those relating to pollution and climate change, which are much more urgent issues. Moreover, a large amount of fake news has circulated about COVID-19 vaccines, remedies, and origin. Based on these findings, it is recommended that the Italian authorities implement more effective infoveillance systems, and that communication by the mass media be less sensationalistic and more consistent with the available scientific evidence. In this context, Google Trends can be used to monitor users? response to specific infodemiological countermeasures. Further research is needed to understand the psychological mechanisms that regulate risk perception. UR - https://infodemiology.jmir.org/2021/1/e29929 UR - http://dx.doi.org/10.2196/29929 UR - http://www.ncbi.nlm.nih.gov/pubmed/34447925 ID - info:doi/10.2196/29929 ER - TY - JOUR AU - Du, Jingcheng AU - Preston, Sharice AU - Sun, Hanxiao AU - Shegog, Ross AU - Cunningham, Rachel AU - Boom, Julie AU - Savas, Lara AU - Amith, Muhammad AU - Tao, Cui PY - 2021/8/5 TI - Using Machine Learning?Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions JO - J Med Internet Res SP - e26478 VL - 23 IS - 8 KW - HPV vaccine KW - social media KW - misinformation KW - infodemiology KW - infoveillance KW - deep learning KW - Reddit KW - machine learning N2 - Background: The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. Objective: The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)?based methods. Methods: Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. Results: A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). Conclusions: ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion. UR - https://www.jmir.org/2021/8/e26478 UR - http://dx.doi.org/10.2196/26478 UR - http://www.ncbi.nlm.nih.gov/pubmed/34383667 ID - info:doi/10.2196/26478 ER - TY - JOUR AU - Jiang, Julie AU - Ren, Xiang AU - Ferrara, Emilio PY - 2021/8/5 TI - Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study JO - JMIRx Med SP - e29570 VL - 2 IS - 3 KW - social media KW - opinion KW - infodemiology KW - infoveillance KW - COVID-19 KW - case study KW - polarization KW - communication KW - Twitter KW - echo chamber N2 - Background: Social media chatter in 2020 has been largely dominated by the COVID-19 pandemic. Existing research shows that COVID-19 discourse is highly politicized, with political preferences linked to beliefs and disbeliefs about the virus. As it happens with topics that become politicized, people may fall into echo chambers, which is the idea that one is only presented with information they already agree with, thereby reinforcing one?s confirmation bias. Understanding the relationship between information dissemination and political preference is crucial for effective public health communication. Objective: We aimed to study the extent of polarization and examine the structure of echo chambers related to COVID-19 discourse on Twitter in the United States. Methods: First, we presented Retweet-BERT, a scalable and highly accurate model for estimating user polarity by leveraging language features and network structures. Then, by analyzing the user polarity predicted by Retweet-BERT, we provided new insights into the characterization of partisan users. Results: We observed that right-leaning users were noticeably more vocal and active in the production and consumption of COVID-19 information. We also found that most of the highly influential users were partisan, which may contribute to further polarization. Importantly, while echo chambers exist in both the right- and left-leaning communities, the right-leaning community was by far more densely connected within their echo chamber and isolated from the rest. Conclusions: We provided empirical evidence that political echo chambers are prevalent, especially in the right-leaning community, which can exacerbate the exposure to information in line with pre-existing users? views. Our findings have broader implications in developing effective public health campaigns and promoting the circulation of factual information online. UR - https://med.jmirx.org/2021/3/e29570 UR - http://dx.doi.org/10.2196/29570 UR - http://www.ncbi.nlm.nih.gov/pubmed/34459833 ID - info:doi/10.2196/29570 ER - TY - JOUR AU - Masngut, Nasaai AU - Mohamad, Emma PY - 2021/8/4 TI - Association Between Public Opinion and Malaysian Government Communication Strategies About the COVID-19 Crisis: Content Analysis of Image Repair Strategies in Social Media JO - J Med Internet Res SP - e28074 VL - 23 IS - 8 KW - COVID-19 KW - crisis KW - health communication KW - image repair KW - Malaysian government KW - sentiment KW - communication KW - content analysis KW - public opinion KW - social media KW - strategy N2 - Background: The COVID-19 health crisis has posed an unprecedented challenge for governments worldwide to manage and communicate about the pandemic effectively, while maintaining public trust. Good leadership image in times of a health emergency is paramount to ensure public confidence in governments? abilities to manage the crisis. Objective: The aim of this study was to identify types of image repair strategies utilized by the Malaysian government in their communication about COVID-19 in the media and analyze public responses to these messages on social media. Methods: Content analysis was employed to analyze 120 media statements and 382 comments retrieved from Facebook pages of 2 mainstream newspapers?Berita Harian and The Star. These media statements and comments were collected within a span of 6 weeks prior to and during the first implementation of Movement Control Order by the Malaysian Government. The media statements were analyzed according to Image Repair Theory to categorize strategies employed in government communications related to COVID-19 crisis. Public opinion responses were measured using modified lexicon-based sentiment analysis to categorize positive, negative, and neutral statements. Results: The Malaysian government employed all 5 Image Repair Theory strategies in their communications in both newspapers. The strategy most utilized was reducing offensiveness (75/120, 62.5%), followed by corrective action (30/120, 25.0%), evading responsibilities (10/120, 8.3%), denial (4/120, 3.3%), and mortification (1/120, 0.8%). This study also found multiple substrategies in government media statements including denial, shifting blame, provocation, defeasibility, accident, good intention, bolstering, minimization, differentiation, transcendence, attacking accuser, resolve problem, prevent recurrence, admit wrongdoing, and apologize. This study also found that 64.7% of public opinion was positive in response to media statements made by the Malaysian government and also revealed a significant positive association (P=.04) between image repair strategies utilized by the Malaysian government and public opinion. Conclusions: Communication in the media may assist the government in fostering positive support from the public. Suitable image repair strategies could garner positive public responses and help build trust in times of crisis. UR - https://www.jmir.org/2021/8/e28074 UR - http://dx.doi.org/10.2196/28074 UR - http://www.ncbi.nlm.nih.gov/pubmed/34156967 ID - info:doi/10.2196/28074 ER - TY - JOUR AU - Alshareef, Noor AU - Yunusa, Ismaeel AU - Al-Hanawi, Khaled Mohammed PY - 2021/7/30 TI - The Influence of COVID-19 Information Sources on the Attitudes and Practices Toward COVID-19 Among the General Public of Saudi Arabia: Cross-sectional Online Survey Study JO - JMIR Public Health Surveill SP - e28888 VL - 7 IS - 7 KW - attitudes KW - communications media KW - COVID-19 KW - information-seeking behavior KW - pandemics KW - practices KW - Saudi Arabia KW - social media KW - sources N2 - Background: The COVID-19 pandemic has resulted in panic among the general public, leading many people to seek out information related to COVID-19 through various sources, including social media and traditional media. Identifying public preferences for obtaining such information may help health authorities to effectively plan successful health preventive and educational intervention strategies. Objective: The aim of this study was to examine the impact of the types of sources used for obtaining COVID-19 information on the attitudes and practices of the general public in Saudi Arabia during the pandemic, and to identify the socioeconomic and demographic factors associated with the use of different sources of information. Methods: This study used data from a cross-sectional online survey conducted on residents of Saudi Arabia from March 20 to 24, 2020. Data were analyzed using descriptive, bivariate, and multivariable logistic regression analyses. Bivariate analysis of categorical variables was performed to determine the associations between information sources and socioeconomic and demographic factors. Multivariable logistic regression analyses were employed to examine whether socioeconomic and demographic variables were associated with the source of information used to obtain information about COVID-19. Moreover, univariable and multivariable logistic regression analyses were conducted to examine how sources of information influence attitudes and practices of adhering to preventive measures. Results: In this analysis of cross-sectional survey data, 3358 participants were included. Most participants reported using social media, followed by the Ministry of Health (MOH) of the Kingdom of Saudi Arabia, as their primary source of information. Seeking information via social media was significantly associated with lower odds of having an optimistic attitude (adjusted odds ratio [aOR] 0.845, 95% CI 0.733-0.974; P=.02) and adhering to preventive measures (aOR 0.725, 95% CI 0.630-0.835; P<.001) compared to other sources of information. Participants who obtained their COVID-19 information via the MOH had greater odds of having an optimistic attitude (aOR 1.437, 95% CI 1.234-1.673; P<.001) and adhering to preventive measures (aOR 1.393, 95% CI 1.201-1.615; P<.001) than those who obtained information via other sources. Conclusions: This study provides evidence that different sources of information influence attitudes and preventive actions differently within a pandemic crisis context. Health authorities in Saudi Arabia should pay attention to the use of appropriate social media channels and sources to allow for more effective dissemination of critical information to the public. UR - https://publichealth.jmir.org/2021/7/e28888 UR - http://dx.doi.org/10.2196/28888 UR - http://www.ncbi.nlm.nih.gov/pubmed/34081610 ID - info:doi/10.2196/28888 ER - TY - JOUR AU - Purnat, D. Tina AU - Vacca, Paolo AU - Czerniak, Christine AU - Ball, Sarah AU - Burzo, Stefano AU - Zecchin, Tim AU - Wright, Amy AU - Bezbaruah, Supriya AU - Tanggol, Faizza AU - Dubé, Čve AU - Labbé, Fabienne AU - Dionne, Maude AU - Lamichhane, Jaya AU - Mahajan, Avichal AU - Briand, Sylvie AU - Nguyen, Tim PY - 2021/7/28 TI - Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations JO - JMIR Infodemiology SP - e30971 VL - 1 IS - 1 KW - infodemic KW - COVID-19 KW - infodemic management KW - social listening KW - social monitoring KW - social media KW - pandemic preparedness KW - pandemic response KW - risk communication KW - information voids KW - data deficits KW - information overload N2 - Background: The COVID-19 pandemic has been accompanied by an infodemic: excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities. Objective: In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. Methods: We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19?related topics. Results: A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health?related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication. Conclusions: This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence?based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning. UR - https://infodemiology.jmir.org/2021/1/e30971 UR - http://dx.doi.org/10.2196/30971 UR - http://www.ncbi.nlm.nih.gov/pubmed/34447926 ID - info:doi/10.2196/30971 ER - TY - JOUR AU - Vandormael, Alain AU - Adam, Maya AU - Greuel, Merlin AU - Gates, Jennifer AU - Favaretti, Caterina AU - Hachaturyan, Violetta AU - Bärnighausen, Till PY - 2021/7/27 TI - The Effect of a Wordless, Animated, Social Media Video Intervention on COVID-19 Prevention: Online Randomized Controlled Trial JO - JMIR Public Health Surveill SP - e29060 VL - 7 IS - 7 KW - social media KW - cultural and social implications KW - randomized controlled trial KW - list experiment KW - information literacy KW - COVID-19 KW - pandemic KW - digital health KW - infodemiology KW - global health KW - public health N2 - Background: Innovative approaches to the dissemination of evidence-based COVID-19 health messages are urgently needed to counter social media misinformation about the pandemic. To this end, we designed a short, wordless, animated global health communication video (the CoVideo), which was rapidly distributed through social media channels to an international audience. Objective: The objectives of this study were to (1) establish the CoVideo?s effectiveness in improving COVID-19 prevention knowledge, and (2) establish the CoVideo?s effectiveness in increasing behavioral intent toward COVID-19 prevention. Methods: In May and June 2020, we enrolled 15,163 online participants from the United States, Mexico, the United Kingdom, Germany, and Spain. We randomized participants to (1) the CoVideo arm, (2) an attention placebo control (APC) arm, and (3) a do-nothing arm, and presented 18 knowledge questions about preventive COVID-19 behaviors, which was our first primary endpoint. To measure behavioral intent, our second primary endpoint, we randomized participants in each arm to five list experiments. Results: Globally, the video intervention was viewed 1.2 million times within the first 10 days of its release and more than 15 million times within the first 4 months. Knowledge in the CoVideo arm was significantly higher (mean 16.95, 95% CI 16.91-16.99) than in the do-nothing (mean 16.86, 95% CI 16.83-16.90; P<.001) arm. We observed high baseline levels of behavioral intent to perform many of the preventive behaviors featured in the video intervention. We were only able to detect a statistically significant impact of the CoVideo on one of the five preventive behaviors. Conclusions: Despite high baseline levels, the intervention was effective at boosting knowledge of COVID-19 prevention. We were only able to capture a measurable change in behavioral intent toward one of the five COVID-19 preventive behaviors examined in this study. The global reach of this health communication intervention and the high voluntary engagement of trial participants highlight several innovative features that could inform the design and dissemination of public health messages. Short, wordless, animated videos, distributed by health authorities via social media, may be an effective pathway for rapid global health communication during health crises. Trial Registration: German Clinical Trials Register DRKS00021582; https://tinyurl.com/6r4zkbbn International Registered Report Identifier (IRRID): RR2-10.1186/s13063-020-04942-7 UR - https://publichealth.jmir.org/2021/7/e29060 UR - http://dx.doi.org/10.2196/29060 UR - http://www.ncbi.nlm.nih.gov/pubmed/34174778 ID - info:doi/10.2196/29060 ER - TY - JOUR AU - Walsh-Buhi, Eric AU - Houghton, Fagen Rebecca AU - Lange, Claire AU - Hockensmith, Ryli AU - Ferrand, John AU - Martinez, Lourdes PY - 2021/7/27 TI - Pre-exposure Prophylaxis (PrEP) Information on Instagram: Content Analysis JO - JMIR Public Health Surveill SP - e23876 VL - 7 IS - 7 KW - digital health KW - social media KW - HIV KW - pre-exposure prophylaxis KW - Instagram KW - content analysis KW - communication N2 - Background: There is still an HIV epidemic in the United States, which is a substantial issue for populations bearing a disproportionate burden of HIV infections. Daily oral pre-exposure prophylaxis (PrEP) has proven to be safe and effective in reducing HIV acquisition risk. However, studies document that PrEP awareness/usage is low. There is also limited understanding of social media platforms, such as Instagram, as PrEP information sources. Objective: Given the paucity of research on PrEP-related Instagram posts and popularity of this social media platform, the purpose of this research is to describe the source characteristics, image types, and textual contents of PrEP-related posts on Instagram. Methods: Using Crowdtangle Search, a public insights tool owned/operated by Facebook, we retrieved publicly accessible and English-language-only Instagram posts for the 12-month period preceding April 22, 2020, using the following terms: Truvada or ?pre-exposure prophylaxis? or #truvada or #truvadaprep or #truvadawhore or #truvadaforprep. We employed a qualitative coding methodology to manually extract information from posts. Using a pretested codebook, we performed content analysis on 250 posts, examining message and source characteristics (ie, organization type [eg, government, news] and individual type [eg, physician]), including information about PrEP (eg, how it works, cost), and indicated users. Frequencies and percentages were calculated for all categorical variables. A Chi-square test was conducted to determine differences between source types on a variety of message characteristics. Results: Three-quarters of the posts (193/250, 77.2%) were posted by organizations. Of the 250 posts reviewed, approximately two-thirds (174/250, 69.6%) included a photograph, more than half (142/250, 56.8%) included an infographic, and approximately one-tenth (30/250, 12%) included a video. More than half defined PrEP (137/250, 54.8%), but fewer posts promoted PrEP use, explained how PrEP works, and included information on the effectiveness of PrEP or who can use it. The most commonly hashtagged populations among posts were men who have sex with men (MSM), but not necessarily bisexual men. Few posts contained race-/ethnicity-related hashtags (11/250, 4.4%). Fewer posts contained transgender-associated tags (eg, #transgirl; 5/250, 2%). No posts contained tags related to heterosexuals or injection drug users. We found statistical differences between source types (ie, individual versus organization). Specifically, posts from organizations more frequently contained information about who can use PrEP, whereas posts from individuals more frequently contained information describing adverse effects. Conclusions: This study is among the first to review Instagram for PrEP-related content, and it answers the National AIDS Strategy?s call for a clearer articulation of the science surrounding HIV risk/prevention through better understanding of the current public information environment. This study offers a snapshot of how PrEP is being discussed (and by whom) on one of the most popular social media platforms and provides a foundation for developing and implementing PrEP promotion interventions on Instagram. UR - https://publichealth.jmir.org/2021/7/e23876 UR - http://dx.doi.org/10.2196/23876 UR - http://www.ncbi.nlm.nih.gov/pubmed/34061759 ID - info:doi/10.2196/23876 ER - TY - JOUR AU - Gao, Zhiwei AU - Fujita, Sumio AU - Shimizu, Nobuyuki AU - Liew, Kongmeng AU - Murayama, Taichi AU - Yada, Shuntaro AU - Wakamiya, Shoko AU - Aramaki, Eiji PY - 2021/7/20 TI - Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study JO - JMIR Public Health Surveill SP - e29865 VL - 7 IS - 7 KW - COVID-19 KW - search query KW - infodemiology KW - quantitative analysis KW - concern KW - rural KW - urban KW - Internet KW - information-seeking behavior KW - attitude KW - Japan N2 - Background: COVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas. Objective: This infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries. Methods: We measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19?related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices?the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)?to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people's concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization. Results: Our results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures? level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas). Conclusions: We quantitatively defined Japanese Yahoo users? concerns about COVID-19 by using the search counts of COVID-19?related search queries. Our results also showed that the LCI and LCIPP have external validity. UR - https://publichealth.jmir.org/2021/7/e29865 UR - http://dx.doi.org/10.2196/29865 UR - http://www.ncbi.nlm.nih.gov/pubmed/34174781 ID - info:doi/10.2196/29865 ER - TY - JOUR AU - Andy, Anietie PY - 2021/7/20 TI - Studying How Individuals Who Express the Feeling of Loneliness in an Online Loneliness Forum Communicate in a Nonloneliness Forum: Observational Study JO - JMIR Form Res SP - e28738 VL - 5 IS - 7 KW - loneliness KW - Reddit KW - nonloneliness KW - mental health KW - eHealth KW - forum KW - online forum KW - communication KW - natural language processing KW - language KW - linguistics N2 - Background: Loneliness is a public health concern, and increasingly, individuals experiencing loneliness are seeking support on online forums, some of which focus on discussions around loneliness (loneliness forums). Some of these individuals may also seek support around loneliness on online forums not related to loneliness or well-being (nonloneliness forums). Hence, to design and implement appropriate and efficient online loneliness interventions, it is important to understand how individuals who express and seek support around loneliness on online loneliness forums communicate in nonloneliness forums; this could provide further insights into the support needs and concerns of these users. Objective: This study aims to explore how users who express the feeling of loneliness and seek support around loneliness on an online loneliness forum communicate in an online nonloneliness forum. Methods: A total of 2401 users who expressed loneliness in posts published on a loneliness forum on Reddit and had published posts in a nonloneliness forum were identified. Using latent Dirichlet allocation (a natural language processing algorithm); Linguistic Inquiry and Word Count (a psycholinguistic dictionary); and the word score?based language features valence, arousal, and dominance, the language use differences in posts published in the nonloneliness forum by these users compared to a control group of users who did not belong to any loneliness forum on Reddit were determined. Results: It was found that in posts published in the nonloneliness forum, users who expressed loneliness tend to use more words associated with the Linguistic Inquiry and Word Count categories on sadness (Cohen d=0.10) and seeking to socialize (Cohen d=0.114), and use words associated with valence (Cohen d=0.364) and dominance (Cohen d=0.117). In addition, they tend to publish posts related to latent Dirichlet allocation topics such as relationships (Cohen d=0.105) and family and friends and mental health (Cohen d=0.10). Conclusions: There are clear distinctions in language use in nonloneliness forum posts by users who express loneliness compared to a control group of users. These findings can help with the design and implementation of online interventions around loneliness. UR - https://formative.jmir.org/2021/7/e28738 UR - http://dx.doi.org/10.2196/28738 UR - http://www.ncbi.nlm.nih.gov/pubmed/34283026 ID - info:doi/10.2196/28738 ER - TY - JOUR AU - Allem, Jon-Patrick AU - Dormanesh, Allison AU - Majmundar, Anuja AU - Rivera, Vanessa AU - Chu, Maya AU - Unger, B. Jennifer AU - Cruz, Boley Tess PY - 2021/7/19 TI - Leading Topics in Twitter Discourse on JUUL and Puff Bar Products: Content Analysis JO - J Med Internet Res SP - e26510 VL - 23 IS - 7 KW - electronic cigarettes KW - JUUL KW - public health KW - Puff Bar KW - social media KW - Twitter KW - infodemiology N2 - Background: In response to the recent government restrictions, flavored JUUL products, which are rechargeable closed-system electronic cigarettes (e-cigarettes), are no longer available for sale. However, disposable closed-system products such as the flavored Puff Bar e-cigarette continues to be available. If e-cigarette consumers simply switch between products during the current government restrictions limited to 1 type of product over another, then such restrictions would be less effective. A step forward in this line of research is to understand how the public discusses these products by examining discourse referencing both Puff Bar and JUUL in the same conversation. Twitter data provide ample opportunity to capture such early trends that could be used to help public health researchers stay abreast of the rapidly changing e-cigarette marketplace. Objective: The goal of this study was to examine public discourse referencing both Puff Bar and JUUL products in the same conversation on Twitter. Methods: We collected data from Twitter?s streaming application programming interface between July 16, 2019, and August 29, 2020, which included both ?Puff Bar? and ?JUUL? (n=2632). We then used an inductive approach to become familiar with the data and generate a codebook to identify common themes. Saturation was determined to be reached with 10 themes. Results: Posts often mentioned flavors, dual use, design features, youth use, health risks, switching 1 product for the other, price, confusion over the differences between products, longevity of the products, and nicotine concentration. Conclusions: On examining the public?s conversations about Puff Bar and JUUL products on Twitter, having described themes in posts, this study aimed to help the tobacco control community stay informed about 2 popular e-cigarette products with different device features, which can be potentially substituted for one another. Future health communication campaigns may consider targeting the health consequences of using multiple e-cigarette products or dual use to reduce exposure to high levels of nicotine among younger populations. UR - https://www.jmir.org/2021/7/e26510 UR - http://dx.doi.org/10.2196/26510 UR - http://www.ncbi.nlm.nih.gov/pubmed/34279236 ID - info:doi/10.2196/26510 ER - TY - JOUR AU - Fazel, S. Sajjad AU - Quinn, K. Emma AU - Ford-Sahibzada, A. Chelsea AU - Szarka, Steven AU - Peters, E. Cheryl PY - 2021/7/19 TI - Sunscreen Posts on Twitter in the United States and Canada, 2019: Content Analysis JO - JMIR Dermatol SP - e29723 VL - 4 IS - 2 KW - sunscreen KW - skin cancer KW - Twitter KW - misinformation KW - prevention KW - skin KW - social media KW - health promotion KW - melanoma UR - https://derma.jmir.org/2021/2/e29723 UR - http://dx.doi.org/10.2196/29723 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632814 ID - info:doi/10.2196/29723 ER - TY - JOUR AU - Zhang, Yipeng AU - Lyu, Hanjia AU - Liu, Yubao AU - Zhang, Xiyang AU - Wang, Yu AU - Luo, Jiebo PY - 2021/7/18 TI - Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study JO - JMIR Infodemiology SP - e26769 VL - 1 IS - 1 KW - mental health KW - depression KW - social media KW - Twitter KW - data mining KW - natural language processing KW - transformers KW - COVID-19 N2 - Background: The COVID-19 pandemic has affected people?s daily lives and has caused economic loss worldwide. Anecdotal evidence suggests that the pandemic has increased depression levels among the population. However, systematic studies of depression detection and monitoring during the pandemic are lacking. Objective: This study aims to develop a method to create a large-scale depression user data set in an automatic fashion so that the method is scalable and can be adapted to future events; verify the effectiveness of transformer-based deep learning language models in identifying depression users from their everyday language; examine psychological text features? importance when used in depression classification; and, finally, use the model for monitoring the fluctuation of depression levels of different groups as the disease propagates. Methods: To study this subject, we designed an effective regular expression-based search method and created the largest English Twitter depression data set containing 2575 distinct identified users with depression and their past tweets. To examine the effect of depression on people?s Twitter language, we trained three transformer-based depression classification models on the data set, evaluated their performance with progressively increased training sizes, and compared the model?s tweet chunk-level and user-level performances. Furthermore, inspired by psychological studies, we created a fusion classifier that combines deep learning model scores with psychological text features and users? demographic information, and investigated these features? relations to depression signals. Finally, we demonstrated our model?s capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. Results: Our fusion model demonstrated an accuracy of 78.9% on a test set containing 446 people, half of which were identified as having depression. Conscientiousness, neuroticism, appearance of first person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness were shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model showed that depressive users, in general, responded to the pandemic later than the control group based on their tweets (n=500). It was also shown that three US states?New York, California, and Florida?shared a similar depression trend as the whole US population (n=9050). When compared to New York and California, people in Florida demonstrated a substantially lower level of depression. Conclusions: This study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the public of COVID-19?s impact on people?s mental health. The noninvasive monitoring system can also be readily adapted to other big events besides COVID-19 and can be useful during future outbreaks. UR - https://infodemiology.jmir.org/2021/1/e26769 UR - http://dx.doi.org/10.2196/26769 UR - http://www.ncbi.nlm.nih.gov/pubmed/34458682 ID - info:doi/10.2196/26769 ER - TY - JOUR AU - Tran, Thanh Huyen Thi AU - Lu, Shih-Hao AU - Tran, Thu Ha Thi AU - Nguyen, Van Bien PY - 2021/7/16 TI - Social Media Insights During the COVID-19 Pandemic: Infodemiology Study Using Big Data JO - JMIR Med Inform SP - e27116 VL - 9 IS - 7 KW - COVID-19 KW - Vietnam KW - public attention KW - social media KW - infodemic KW - issue-attention cycle KW - media framing KW - big data KW - health crisis management KW - insight KW - infodemiology KW - infoveillance KW - social listening N2 - Background: The COVID-19 pandemic is still undergoing complicated developments in Vietnam and around the world. There is a lot of information about the COVID-19 pandemic, especially on the internet where people can create and share information quickly. This can lead to an infodemic, which is a challenge every government might face in the fight against pandemics. Objective: This study aims to understand public attention toward the pandemic (from December 2019 to November 2020) through 7 types of sources: Facebook, Instagram, YouTube, blogs, news sites, forums, and e-commerce sites. Methods: We collected and analyzed nearly 38 million pieces of text data from the aforementioned sources via SocialHeat, a social listening (infoveillance) platform developed by YouNet Group. We described not only public attention volume trends, discussion sentiments, top sources, top posts that gained the most public attention, and hot keyword frequency but also hot keywords? co-occurrence as visualized by the VOSviewer software tool. Results: In this study, we reached four main conclusions. First, based on changing discussion trends regarding the COVID-19 subject, 7 periods were identified based on events that can be aggregated into two pandemic waves in Vietnam. Second, community pages on Facebook were the source of the most engagement from the public. However, the sources with the highest average interaction efficiency per article were government sources. Third, people?s attitudes when discussing the pandemic have changed from negative to positive emotions. Fourth, the type of content that attracts the most interactions from people varies from time to time. Besides that, the issue-attention cycle theory occurred not only once but four times during the COVID-19 pandemic in Vietnam. Conclusions: Our study shows that online resources can help the government quickly identify public attention to public health messages during times of crisis. We also determined the hot spots that most interested the public and public attention communication patterns, which can help the government get practical information to make more effective policy reactions to help prevent the spread of the pandemic. UR - https://medinform.jmir.org/2021/7/e27116 UR - http://dx.doi.org/10.2196/27116 UR - http://www.ncbi.nlm.nih.gov/pubmed/34152994 ID - info:doi/10.2196/27116 ER - TY - JOUR AU - Fan, Zina AU - Yin, Wenqiang AU - Zhang, Han AU - Wang, Dandan AU - Fan, Chengxin AU - Chen, Zhongming AU - Hu, Jinwei AU - Ma, Dongping AU - Guo, Hongwei PY - 2021/7/16 TI - COVID-19 Information Dissemination Using the WeChat Communication Index: Retrospective Analysis Study JO - J Med Internet Res SP - e28563 VL - 23 IS - 7 KW - COVID-19 KW - information dissemination KW - People?s Daily KW - Chinese news KW - public health and communication KW - media salience KW - WeChat N2 - Background: The COVID-19 outbreak has tremendously impacted the world. The number of confirmed cases has continued to increase, causing damage to society and the economy worldwide. The public pays close attention to information on the pandemic and learns about the disease through various media outlets. The dissemination of comprehensive and accurate COVID-19 information that the public needs helps to educate people so they can take preventive measures. Objective: This study aimed to examine the dissemination of COVID-19 information by analyzing the information released by the official WeChat account of the People?s Daily during the pandemic. The most-read COVID-19 information in China was summarized, and the factors that influence information dissemination were studied to understand the characteristics that affect its dissemination. Moreover, this was conducted in order to identify how to effectively disseminate COVID-19 information and to provide suggestions on how to manage public opinion and information governance during a pandemic. Methods: This was a retrospective study based on a WeChat official account. We collected all COVID-19?related information, starting with the first report about COVID-19 from the People?s Daily and ending with the last piece of information about lifting the first-level emergency response in 34 Chinese provinces. A descriptive analysis was then conducted on this information, as well as on Qingbo Big Data?s dissemination index. Multiple linear regression was utilized to study the factors that affected information dissemination based on various characteristics and the dissemination index. Results: From January 19 to May 2, 2020, the People?s Daily released 1984 pieces of information; 1621 were related to COVID-19, which mainly included headline news items, items with emotional content, and issues related to the pandemic?s development. By analyzing the dissemination index, seven information dissemination peaks were discerned. Among the three dimensions of COVID-19 information?media salience, content, and format?eight factors affected the spread of COVID-19 information. Conclusions: Different types of pandemic-related information have varying dissemination power. To effectively disseminate information and prevent the spread of COVID-19, we should identify the factors that affect this dissemination. We should then disseminate the types of information the public is most concerned about, use information to educate people to improve their health literacy, and improve public opinion and information governance. UR - https://www.jmir.org/2021/7/e28563 UR - http://dx.doi.org/10.2196/28563 UR - http://www.ncbi.nlm.nih.gov/pubmed/34129515 ID - info:doi/10.2196/28563 ER - TY - JOUR AU - Margus, Colton AU - Brown, Natasha AU - Hertelendy, J. Attila AU - Safferman, R. Michelle AU - Hart, Alexander AU - Ciottone, R. Gregory PY - 2021/7/14 TI - Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study JO - J Med Internet Res SP - e28615 VL - 23 IS - 7 KW - COVID-19 pandemic KW - emergency medicine KW - disaster medicine KW - crisis standards of care KW - latent Dirichlet allocation KW - topic modeling KW - Twitter KW - sentiment analysis KW - surge capacity KW - physician wellness KW - social media KW - internet KW - infodemiology KW - COVID-19 N2 - Background: The early conversations on social media by emergency physicians offer a window into the ongoing response to the COVID-19 pandemic. Objective: This retrospective observational study of emergency physician Twitter use details how the health care crisis has influenced emergency physician discourse online and how this discourse may have use as a harbinger of ensuing surge. Methods: Followers of the three main emergency physician professional organizations were identified using Twitter?s application programming interface. They and their followers were included in the study if they identified explicitly as US-based emergency physicians. Statuses, or tweets, were obtained between January 4, 2020, when the new disease was first reported, and December 14, 2020, when vaccination first began. Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and Sentiment Reasoner (VADER) tool as well as topic modeling using latent Dirichlet allocation unsupervised machine learning. Sentiment and topic trends were then correlated with daily change in new COVID-19 cases and inpatient bed utilization. Results: A total of 3463 emergency physicians produced 334,747 unique English-language tweets during the study period. Out of 3463 participants, 910 (26.3%) stated that they were in training, and 466 of 902 (51.7%) participants who provided their gender identified as men. Overall tweet volume went from a pre-March 2020 mean of 481.9 (SD 72.7) daily tweets to a mean of 1065.5 (SD 257.3) daily tweets thereafter. Parameter and topic number tuning led to 20 tweet topics, with a topic coherence of 0.49. Except for a week in June and 4 days in November, discourse was dominated by the health care system (45,570/334,747, 13.6%). Discussion of pandemic response, epidemiology, and clinical care were jointly found to moderately correlate with COVID-19 hospital bed utilization (Pearson r=0.41), as was the occurrence of ?covid,? ?coronavirus,? or ?pandemic? in tweet texts (r=0.47). Momentum in COVID-19 tweets, as demonstrated by a sustained crossing of 7- and 28-day moving averages, was found to have occurred on an average of 45.0 (SD 12.7) days before peak COVID-19 hospital bed utilization across the country and in the four most contributory states. Conclusions: COVID-19 Twitter discussion among emergency physicians correlates with and may precede the rising of hospital burden. This study, therefore, begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online and suggests a potential avenue for understanding predictors of surge. UR - https://www.jmir.org/2021/7/e28615 UR - http://dx.doi.org/10.2196/28615 UR - http://www.ncbi.nlm.nih.gov/pubmed/34081612 ID - info:doi/10.2196/28615 ER - TY - JOUR AU - Riem, E. Madelon M. AU - De Carli, Pietro AU - Guo, Jing AU - Bakermans-Kranenburg, J. Marian AU - van IJzendoorn, H. Marinus AU - Lodder, Paul PY - 2021/7/13 TI - Internet Searches for Terms Related to Child Maltreatment During COVID-19: Infodemiology Approach JO - JMIR Pediatr Parent SP - e27974 VL - 4 IS - 3 KW - child KW - maltreatment KW - COVID-19 KW - pandemic KW - internet searches KW - information-seeking KW - internet KW - abuse KW - trend KW - Google trends KW - infodemiology UR - https://pediatrics.jmir.org/2021/3/e27974 UR - http://dx.doi.org/10.2196/27974 UR - http://www.ncbi.nlm.nih.gov/pubmed/34174779 ID - info:doi/10.2196/27974 ER - TY - JOUR AU - Prata, Ndola AU - Weidert, Karen AU - Zepecki, Anne AU - Yon, Elina AU - Pleasants, Elizabeth AU - Sams-Abiodun, Petrice AU - Guendelman, Sylvia PY - 2021/7/12 TI - Using Application Programming Interfaces (APIs) to Access Google Data and Gain Insights Into Searches on Birth Control in Louisiana and Mississippi, 2014-2018: Infoveillance Study JO - J Med Internet Res SP - e25923 VL - 23 IS - 7 KW - birth control KW - search data KW - Google Trends KW - infoveillance KW - infodemiology KW - Louisiana KW - Mississippi N2 - Background: It is now common to search for health information online. A 2013 Pew Research Center survey found that 77% of online health seekers began their query at a search engine. The widespread use of online health information seeking also applies to women?s reproductive health. Despite online interest in birth control, not much is known about related interests and concerns reflected in the search terms in the United States. Objective: In this study, we identify the top search terms on Google related to birth control in Louisiana and Mississippi and compare those results to the broader United States, examining how Google searches on birth control have evolved over time and identifying regional variation within states. Methods: We accessed search data on birth control from 2014-2018 from 2 Google application programming interfaces (APIs), Google Trends and Google Health Trends. We selected Google as it is the most commonly used search engine. We focused our analysis on data from 2017 and compared with 2018 data as appropriate. To assess trends, we analyzed data from 2014 through 2018. To compare the relative search frequencies of the top queries across Louisiana, Mississippi, and the United States, we used the Google Health Trends API. Relative search volume by designated marketing area (DMA) gave us the rankings of search volume for each birth control method in each DMA as compared to one another. Results: Results showed that when people searched for ?birth control? in Louisiana and the broader United States, they were searching for information on a diverse spectrum of methods. This differs from Mississippi, where the data indicated people were mainly searching for information related to birth control pills. Across all locations, searches for birth control pills were significantly higher than any other queries related to birth control in the United States, Louisiana, and Mississippi, and this trend remained constant from 2014 to 2018. Regional level analysis showed variations in search traffic for birth control across each state. Conclusions: The internet is a growing source of health information for many users, including information on birth control. Understanding popular Google search queries on birth control can inform in-person discussions initiated by family planning practitioners and broader birth control messaging campaigns. International Registered Report Identifier (IRRID): RR2-10.2196/16543 UR - https://www.jmir.org/2021/7/e25923 UR - http://dx.doi.org/10.2196/25923 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255662 ID - info:doi/10.2196/25923 ER - TY - JOUR AU - Alhassan, Mohammed Fatimah AU - AlDossary, Abdullah Sharifah PY - 2021/7/12 TI - The Saudi Ministry of Health?s Twitter Communication Strategies and Public Engagement During the COVID-19 Pandemic: Content Analysis Study JO - JMIR Public Health Surveill SP - e27942 VL - 7 IS - 7 KW - COVID-19 KW - Crisis and Emergency Risk Communication KW - effective communication KW - health authorities KW - outbreak KW - pandemic KW - public engagement KW - public health KW - social media KW - Twitter N2 - Background: During a public health crisis such as the current COVID-19 pandemic, governments and health authorities need quick and accurate methods of communicating with the public. While social media can serve as a useful tool for effective communication during disease outbreaks, few studies have elucidated how these platforms are used by the Ministry of Health (MOH) during disease outbreaks in Saudi Arabia. Objective: Guided by the Crisis and Emergency Risk Communication model, this study aimed to explore the MOH?s use of Twitter and the public?s engagement during different stages of the COVID-19 pandemic in Saudi Arabia. Methods: Tweets and corresponding likes and retweets were extracted from the official Twitter account of the MOH in Saudi Arabia for the period of January 1 through August 31, 2020. Tweets related to COVID-19 were identified; subsequently, content analysis was performed, in which tweets were coded for the following message types: risk messages, warnings, preparations, uncertainty reduction, efficacy, reassurance, and digital health responses. Public engagement was measured by examining the numbers of likes and retweets. The association between outbreak stages and types of messages was assessed, as well as the effect of these messages on public engagement. Results: The MOH posted a total of 1393 original tweets during the study period. Of the total tweets, 1293 (92.82%) were related to COVID-19, and 1217 were ultimately included in the analysis. The MOH posted the majority of its tweets (65.89%) during the initial stage of the outbreak. Accordingly, the public showed the highest level of engagement (as indicated by numbers of likes and retweets) during the initial stage. The types of messages sent by the MOH significantly differed across outbreak stages, with messages related to uncertainty reduction, reassurance, and efficacy being prevalent among all stages. Tweet content, media type, and crisis stage influenced the level of public engagement. Engagement was negatively associated with the inclusion of hyperlinks and multimedia files, while higher level of public engagement was associated with the use of hashtags. Tweets related to warnings, uncertainty reduction, and reassurance received high levels of public engagement. Conclusions: This study provides insights into the Saudi MOH?s communication strategy during the COVID-19 pandemic. Our results have implications for researchers, governments, health organizations, and practitioners with regard to their communication practices during outbreaks. To increase public engagement, governments and health authorities should consider the public?s need for information. This, in turn, could raise public awareness regarding disease outbreaks. UR - https://publichealth.jmir.org/2021/7/e27942 UR - http://dx.doi.org/10.2196/27942 UR - http://www.ncbi.nlm.nih.gov/pubmed/34117860 ID - info:doi/10.2196/27942 ER - TY - JOUR AU - Chan, Calvin AU - Sounderajah, Viknesh AU - Daniels, Elisabeth AU - Acharya, Amish AU - Clarke, Jonathan AU - Yalamanchili, Seema AU - Normahani, Pasha AU - Markar, Sheraz AU - Ashrafian, Hutan AU - Darzi, Ara PY - 2021/7/8 TI - The Reliability and Quality of YouTube Videos as a Source of Public Health Information Regarding COVID-19 Vaccination: Cross-sectional Study JO - JMIR Public Health Surveill SP - e29942 VL - 7 IS - 7 KW - COVID-19 KW - infodemiology KW - public health KW - quality KW - reliability KW - social media KW - vaccination KW - vaccine KW - video KW - web-based health information KW - YouTube N2 - Background: Recent emergency authorization and rollout of COVID-19 vaccines by regulatory bodies has generated global attention. As the most popular video-sharing platform globally, YouTube is a potent medium for the dissemination of key public health information. Understanding the nature of available content regarding COVID-19 vaccination on this widely used platform is of substantial public health interest. Objective: This study aimed to evaluate the reliability and quality of information on COVID-19 vaccination in YouTube videos. Methods: In this cross-sectional study, the phrases ?coronavirus vaccine? and ?COVID-19 vaccine? were searched on the UK version of YouTube on December 10, 2020. The 200 most viewed videos of each search were extracted and screened for relevance and English language. Video content and characteristics were extracted and independently rated against Health on the Net Foundation Code of Conduct and DISCERN quality criteria for consumer health information by 2 authors. Results: Forty-eight videos, with a combined total view count of 30,100,561, were included in the analysis. Topics addressed comprised the following: vaccine science (n=18, 58%), vaccine trials (n=28, 58%), side effects (n=23, 48%), efficacy (n=17, 35%), and manufacturing (n=8, 17%). Ten (21%) videos encouraged continued public health measures. Only 2 (4.2%) videos made nonfactual claims. The content of 47 (98%) videos was scored to have low (n=27, 56%) or moderate (n=20, 42%) adherence to Health on the Net Foundation Code of Conduct principles. Median overall DISCERN score per channel type ranged from 40.3 (IQR 34.8-47.0) to 64.3 (IQR 58.5-66.3). Educational channels produced by both medical and nonmedical professionals achieved significantly higher DISCERN scores than those of other categories. The highest median DISCERN scores were achieved by educational videos produced by medical professionals (64.3, IQR 58.5-66.3) and the lowest median scores by independent users (18, IQR 18-20). Conclusions: The overall quality and reliability of information on COVID-19 vaccines on YouTube remains poor. Videos produced by educational channels, especially by medical professionals, were higher in quality and reliability than those produced by other sources, including health-related organizations. Collaboration between health-related organizations and established medical and educational YouTube content producers provides an opportunity for the dissemination of high-quality information on COVID-19 vaccination. Such collaboration holds potential as a rapidly implementable public health intervention aiming to engage a wide audience and increase public vaccination awareness and knowledge. UR - https://publichealth.jmir.org/2021/7/e29942 UR - http://dx.doi.org/10.2196/29942 UR - http://www.ncbi.nlm.nih.gov/pubmed/34081599 ID - info:doi/10.2196/29942 ER - TY - JOUR AU - Sousa-Pinto, Bernardo AU - Halonen, I. Jaana AU - Antó, Aram AU - Jormanainen, Vesa AU - Czarlewski, Wienczyslawa AU - Bedbrook, Anna AU - Papadopoulos, G. Nikolaos AU - Freitas, Alberto AU - Haahtela, Tari AU - Antó, M. Josep AU - Fonseca, Almeida Joăo AU - Bousquet, Jean PY - 2021/7/6 TI - Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends: Infodemiology Study JO - J Med Internet Res SP - e27044 VL - 23 IS - 7 KW - asthma KW - common cold KW - Google Trends KW - hospitalizations KW - time series analysis KW - mobile phone N2 - Background: In contrast to air pollution and pollen exposure, data on the occurrence of the common cold are difficult to incorporate in models predicting asthma hospitalizations. Objective: This study aims to assess whether web-based searches on common cold would correlate with and help to predict asthma hospitalizations. Methods: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 5 different countries (Portugal, Spain, Finland, Norway, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold were retrieved from Google Trends (GT) using the pseudo-influenza syndrome topic and local language search terms for common cold for the same countries and periods. We applied time series analysis methods to estimate the correlation between GT and hospitalization data. In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Results: In time series analyses, GT data on common cold displayed strong correlations with asthma hospitalizations occurring in Portugal (correlation coefficients ranging from 0.63 to 0.73), Spain (?=0.82-0.84), and Brazil (?=0.77-0.83) and moderate correlations with those occurring in Norway (?=0.32-0.35) and Finland (?=0.44-0.47). Similar patterns were observed in the correlation between forecasted and observed asthma hospitalizations from June 2015 to June 2016, with the number of forecasted hospitalizations differing on average between 12% (Spain) and 33% (Norway) from observed hospitalizations. Conclusions: Common cold?related web-based searches display moderate-to-strong correlations with asthma hospitalizations and may be useful in forecasting them. UR - https://www.jmir.org/2021/7/e27044 UR - http://dx.doi.org/10.2196/27044 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255692 ID - info:doi/10.2196/27044 ER - TY - JOUR AU - Wei, Shanzun AU - Ma, Ming AU - Wu, Changjing AU - Yu, Botao AU - Jiang, Lisha AU - Wen, Xi AU - Fu, Fudong AU - Shi, Ming PY - 2021/7/6 TI - Using Search Trends to Analyze Web-Based Interest in Lower Urinary Tract Symptoms-Related Inquiries, Diagnoses, and Treatments in Mainland China: Infodemiology Study of Baidu Index Data JO - J Med Internet Res SP - e27029 VL - 23 IS - 7 KW - lower urinary tract symptoms KW - patient education KW - Baidu Index KW - infodemiology KW - public interest KW - urinary tract disorders KW - infoveillance KW - web-based search KW - search engines KW - health care policy KW - digital health N2 - Background: Lower urinary tract symptoms (LUTS) are one of the most commonly described urination disorders worldwide. Previous investigations have focused predominantly on the prospective identification of cases that meet the researchers? criteria; thus, the genuine demands regarding LUTS from patients and related issues may be neglected. Objective: We aimed to examine web-based search trends and behaviors related to LUTS on a national and regional scale by using the dominant, major search engine in mainland China. Methods: Baidu Index was queried by using LUTS-related terms for the period of January 2011 to September 2020. The search volume for each term was recorded to analyze search trends and demographic distributions. For user interest, user demand graph data and trend data were collected and analyzed. Results: Of the 13 LUTS domains, 11 domains are available in the Baidu Index database. The Baidu search index for each LUTS domain varied from 37.78% to 1.47%. The search trends for urinary frequency (2011-2018: annual percent change APC=7.82%; P<.001), incomplete emptying (2011-2014: APC=17.74%; P<.001), nocturia (2011-2018: APC=11.54%; P<.001), dysuria (2017-2020: APC=20.77%; P<.001), and incontinence (2011-2016: APC=13.39%; P<.001) exhibited fluctuations over time. The search index trends for weak stream (2011-2017: APC=?4.68%; P<.001; 2017-2020: APC=9.32%; P=.23), split stream (2011-2013: APC=9.50%; P=.44; 2013-2020: APC=2.05%; P=.71), urgency (2011-2018: APC=?2.63%; P=.03; 2018-2020: APC=8.58%; P=.19), and nocturnal enuresis (2011-2018: APC=?3.20%; P=.001; 2018-2020: APC=?4.21%; P=.04) remained relatively stable and consistent. The age distribution of the population for all LUTS-related inquiries showed that individuals aged 20 to 40 years made 73.86% (49,218,123/66,635,247) of the total search inquiries. Further, individuals aged 40 to 49 years made 12.29% (8,193,922/66,635,247) of the total search inquiries for all LUTS-related terms. People from the east part of China made 67.79% (45,172,031/66,635,247) of the total search queries. Additionally, most of the searches for LUTS-related terms were related to those for urinary diseases to varying degrees. Conclusions: Web-based interest in LUTS-related terms fluctuated wildly and was reflected timely by Baidu Index in mainland China. The web-based search popularity of each LUTS-related term varied significantly and differed based on personal interests, the population?s concerns, regional variations, and gender. These data can be used by care providers to track the prevalence of LUTS and the population?s interests, guide the establishment of disease-specific health care policies, and optimize physician-patient health care sessions. UR - https://www.jmir.org/2021/7/e27029 UR - http://dx.doi.org/10.2196/27029 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255683 ID - info:doi/10.2196/27029 ER - TY - JOUR AU - Xie, Zidian AU - Wang, Xueting AU - Gu, Yu AU - Li, Dongmei PY - 2021/7/6 TI - Exploratory Analysis of Electronic Cigarette?Related Videos on YouTube: Observational Study JO - Interact J Med Res SP - e27302 VL - 10 IS - 3 KW - infodemiology KW - infoveillance KW - social listening KW - electronic cigarettes KW - e-cigarette KW - YouTube KW - user engagement KW - provaping KW - vaping-warning N2 - Background: Electronic cigarette (e-cigarette) use has become more popular than cigarette smoking, especially among youth. Social media platforms, including YouTube, are a popular means of sharing information about e-cigarette use (vaping). Objective: This study aimed to characterize the content and user engagement of e-cigarette?related YouTube videos. Methods: The top 400 YouTube search videos related to e-cigarettes were collected in January 2020. Among them, 340 valid videos were classified into provaping, vaping-warning, and neutral categories by hand coding. Additionally, the content of e-cigarette videos and their user engagement (including average views and likes) were analyzed and compared. Results: While provaping videos were dominant among e-cigarette?related YouTube videos from 2007 to 2017, vaping-warning videos started to emerge in 2013 and became dominant between 2018 and 2019. Compared to vaping-warning videos, provaping videos had higher average daily views (1077 vs 822) but lower average daily likes (12 vs 15). Among 161 provaping videos, videos on user demonstration (n=100, 62.11%) were dominant, and videos on comparison with smoking had the highest user engagement (2522 average daily views and 28 average daily likes). Conversely, among 141 vaping-warning videos, videos on potential health risks were the most popular topic (n=57, 40.42%) with the highest user engagement (1609 average daily views and 33 average daily likes). Conclusions: YouTube was dominated by provaping videos, with the majority of videos on user demonstrations before 2018. The vaping-warning videos became dominant between 2018 and 2019, with videos on potential health risks being the most popular topic. This study provides updated surveillance on e-cigarette?related YouTube videos and some important guidance on associated social media regulations. UR - https://www.i-jmr.org/2021/3/e27302 UR - http://dx.doi.org/10.2196/27302 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255663 ID - info:doi/10.2196/27302 ER - TY - JOUR AU - Wang, Peng AU - Xu, Qing AU - Cao, Rong-Rong AU - Deng, Fei-Yan AU - Lei, Shu-Feng PY - 2021/7/5 TI - Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study JO - J Med Internet Res SP - e25422 VL - 23 IS - 7 KW - global public interest KW - Google trends KW - osteoporosis KW - seasonality KW - trends KW - infodemiology KW - information seeking KW - web-based information N2 - Background: With the prolonging of human life expectancy and subsequent population aging, osteoporosis (OP) has become an important public health issue. Objective: This study aimed to understand the global public search interests and dynamic trends in ?osteoporosis? using the data derived from Google Trends. Methods: An online search was performed using the term ?osteoporosis? in Google Trends from January 1, 2004, to December 31, 2019, under the category ?Health.? Cosinor analysis was used to test the seasonality of relative search volume (RSV) for ?osteoporosis.? An analysis was conducted to investigate the public search topic rising in RSV for ?osteoporosis.? Results: There was a descending trend of global RSV for ?osteoporosis? from January 2004 to December 2014, and a slowly increasing trend from January 2015 to December 2019. Cosinor analysis showed significant seasonal variations in global RSV for ?osteoporosis? (P=.01), with a peak in March and a trough in September. In addition, similar decreasing trends of RSV for ?osteoporosis? were found in Australia, New Zealand, Ireland, and Canada from January 2004 to December 2019. Cosinor test revealed significant seasonal variations in RSV for ?osteoporosis? in Australia, New Zealand, Canada, Ireland, UK, and USA (all P<.001). Furthermore, public search rising topics related to ?osteoporosis? included denosumab, fracture risk assessment tool, bone density, osteopenia, osteoarthritis, and risk factor. Conclusions: Our study provided evidence about the public search interest and dynamic trends in OP using web-based data, which would be helpful for public health and policy making. UR - https://www.jmir.org/2021/7/e25422 UR - http://dx.doi.org/10.2196/25422 UR - http://www.ncbi.nlm.nih.gov/pubmed/36260400 ID - info:doi/10.2196/25422 ER - TY - JOUR AU - Matsuda, Shinichi AU - Ohtomo, Takumi AU - Tomizawa, Shiho AU - Miyano, Yuki AU - Mogi, Miwako AU - Kuriki, Hiroshi AU - Nakayama, Terumi AU - Watanabe, Shinichi PY - 2021/6/29 TI - Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus JO - JMIR Public Health Surveill SP - e29238 VL - 7 IS - 6 KW - social media KW - adverse drug reaction KW - pharmacovigilance KW - text mining KW - systemic lupus erythematosus KW - natural language processing KW - NLP KW - lupus KW - chronic disease KW - narrative KW - insurance KW - data KW - epidemiology KW - burden KW - Japan KW - patient-generated N2 - Background: Gaining insights that cannot be obtained from health care databases from patients has become an important topic in pharmacovigilance. Objective: Our objective was to demonstrate a use case, in which patient-generated data were incorporated in pharmacovigilance, to understand the epidemiology and burden of illness in Japanese patients with systemic lupus erythematosus. Methods: We used data on systemic lupus erythematosus, an autoimmune disease that substantially impairs quality of life, from 2 independent data sets. To understand the disease?s epidemiology, we analyzed a Japanese health insurance claims database. To understand the disease?s burden, we analyzed text data collected from Japanese disease blogs (t?by?ki) written by patients with systemic lupus erythematosus. Natural language processing was applied to these texts to identify frequent patient-level complaints, and term frequency?inverse document frequency was used to explore patient burden during treatment. We explored health-related quality of life based on patient descriptions. Results: We analyzed data from 4694 and 635 patients with systemic lupus erythematosus in the health insurance claims database and t?by?ki blogs, respectively. Based on health insurance claims data, the prevalence of systemic lupus erythematosus is 107.70 per 100,000 persons. T?by?ki text data analysis showed that pain-related words (eg, pain, severe pain, arthralgia) became more important after starting treatment. We also found an increase in patients? references to mobility and self-care over time, which indicated increased attention to physical disability due to disease progression. Conclusions: A classical medical database represents only a part of a patient's entire treatment experience, and analysis using solely such a database cannot represent patient-level symptoms or patient concerns about treatments. This study showed that analysis of t?by?ki blogs can provide added information on patient-level details, advancing patient-centric pharmacovigilance. UR - https://publichealth.jmir.org/2021/6/e29238 UR - http://dx.doi.org/10.2196/29238 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255719 ID - info:doi/10.2196/29238 ER - TY - JOUR AU - Lyu, Chen Joanne AU - Han, Le Eileen AU - Luli, K. Garving PY - 2021/6/29 TI - COVID-19 Vaccine?Related Discussion on Twitter: Topic Modeling and Sentiment Analysis JO - J Med Internet Res SP - e24435 VL - 23 IS - 6 KW - COVID-19 KW - vaccine KW - vaccination KW - Twitter KW - infodemiology KW - infoveillance KW - topic KW - sentiment KW - opinion KW - discussion KW - communication KW - social media KW - perception KW - concern KW - emotion N2 - Background: Vaccination is a cornerstone of the prevention of communicable infectious diseases; however, vaccines have traditionally met with public fear and hesitancy, and COVID-19 vaccines are no exception. Social media use has been demonstrated to play a role in the low acceptance of vaccines. Objective: The aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine?related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals. Methods: Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users. We used R to perform latent Dirichlet allocation for topic modeling as well as sentiment and emotion analysis using the National Research Council of Canada Emotion Lexicon. Results: Topic modeling of tweets related to COVID-19 vaccines yielded 16 topics, which were grouped into 5 overarching themes. Opinions about vaccination (227,840/1,499,421 tweets, 15.2%) was the most tweeted topic and remained a highly discussed topic during the majority of the period of our examination. Vaccine progress around the world became the most discussed topic around August 11, 2020, when Russia approved the world?s first COVID-19 vaccine. With the advancement of vaccine administration, the topic of instruction on getting vaccines gradually became more salient and became the most discussed topic after the first week of January 2021. Weekly mean sentiment scores showed that despite fluctuations, the sentiment was increasingly positive in general. Emotion analysis further showed that trust was the most predominant emotion, followed by anticipation, fear, sadness, etc. The trust emotion reached its peak on November 9, 2020, when Pfizer announced that its vaccine is 90% effective. Conclusions: Public COVID-19 vaccine?related discussion on Twitter was largely driven by major events about COVID-19 vaccines and mirrored the active news topics in mainstream media. The discussion also demonstrated a global perspective. The increasingly positive sentiment around COVID-19 vaccines and the dominant emotion of trust shown in the social media discussion may imply higher acceptance of COVID-19 vaccines compared with previous vaccines. UR - https://www.jmir.org/2021/6/e24435 UR - http://dx.doi.org/10.2196/24435 UR - http://www.ncbi.nlm.nih.gov/pubmed/34115608 ID - info:doi/10.2196/24435 ER - TY - JOUR AU - Lee, Jae-Young AU - Lee, Yae-Seul AU - Kim, Hyun Dong AU - Lee, Sol Han AU - Yang, Ram Bo AU - Kim, Gyu Myeong PY - 2021/6/28 TI - The Use of Social Media in Detecting Drug Safety?Related New Black Box Warnings, Labeling Changes, or Withdrawals: Scoping Review JO - JMIR Public Health Surveill SP - e30137 VL - 7 IS - 6 KW - adverse event KW - black box warning KW - detect KW - pharmacovigilance KW - real-world data KW - review KW - safety KW - social media KW - withdrawal of approval N2 - Background: Social media has become a new source for obtaining real-world data on adverse drug reactions. Many studies have investigated the use of social media to detect early signals of adverse drug reactions. However, the trustworthiness of signals derived from social media is questionable. To confirm this, a confirmatory study with a positive control (eg, new black box warnings, labeling changes, or withdrawals) is required. Objective: This study aimed to evaluate the use of social media in detecting new black box warnings, labeling changes, or withdrawals in advance. Methods: This scoping review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. A researcher searched PubMed and EMBASE in January 2021. Original studies analyzing black box warnings, labeling changes, or withdrawals from social media were selected, and the results of the studies were summarized. Results: A total of 14 studies were included in this scoping review. Most studies (8/14, 57.1%%) collected data from a single source, and 10 (71.4%) used specialized health care social networks and forums. The analytical methods used in these studies varied considerably. Three studies (21.4%) manually annotated posts, while 5 (35.7%) adopted machine learning algorithms. Nine studies (64.2%) concluded that social media could detect signals 3 months to 9 years before action from regulatory authorities. Most of these studies (8/9, 88.9%) were conducted on specialized health care social networks and forums. On the contrary, 5 (35.7%) studies yielded modest or negative results. Of these, 2 (40%) used generic social networking sites, 2 (40%) used specialized health care networks and forums, and 1 (20%) used both generic social networking sites and specialized health care social networks and forums. The most recently published study recommends not using social media for pharmacovigilance. Several challenges remain in using social media for pharmacovigilance regarding coverage, data quality, and analytic processing. Conclusions: Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized health care social networks and forums. Further studies are required to advance natural language processing and mine real-world data on social media. UR - https://publichealth.jmir.org/2021/6/e30137 UR - http://dx.doi.org/10.2196/30137 UR - http://www.ncbi.nlm.nih.gov/pubmed/34185021 ID - info:doi/10.2196/30137 ER - TY - JOUR AU - Brkic, F. Faris AU - Besser, Gerold AU - Schally, Martin AU - Schmid, M. Elisabeth AU - Parzefall, Thomas AU - Riss, Dominik AU - Liu, T. David PY - 2021/6/20 TI - Biannual Differences in Interest Peaks for Web Inquiries Into Ear Pain and Ear Drops: Infodemiology Study JO - J Med Internet Res SP - e28328 VL - 23 IS - 6 KW - otitis media KW - otitis externa KW - otalgia KW - Google Trends KW - infodemiology KW - infoveillance KW - infodemic KW - social listening N2 - Background: The data retrieved with the online search engine, Google Trends, can summarize internet inquiries into specified search terms. This engine may be used for analyzing inquiry peaks for different medical conditions and symptoms. Objective: The aim of this study was to analyze World Wide Web interest peaks for ?ear pain,? ?ear infection,? and ?ear drops.? Methods: We used Google Trends to assess the public online interest for search terms ?ear pain,? ?ear infection,? and ?ear drops? in 5 English and non?English-speaking countries from both hemispheres based on time series data. We performed our analysis for the time frame between January 1, 2004, and December 31, 2019. First, we assessed whether our search terms were most relevant to the topics of ear pain, ear infection, and ear drops. We then tested the reliability of Google Trends time series data using the intraclass correlation coefficient. In a second step, we computed univariate time series plots to depict peaks in web-based interest. In the last step, we used the cosinor analysis to test the statistical significance of seasonal interest peaks. Results: In the first part of the study, it was revealed that ?ear infection,? ?ear pain,? and ?ear drops? were the most relevant search terms in the noted time frame. Next, the intraclass correlation analysis showed a moderate to excellent reliability for all 5 countries? 3 primary search terms. The subsequent analysis revealed winter interest peaks for ?ear infection? and ?ear pain?. On the other hand, the World Wide Web search for ?ear drops? peaked annually during the summer months. All peaks were statistically significant as revealed by the cosinor model (all P values <.001). Conclusions: It can be concluded that individuals affected by otitis media or externa, possibly the majority, look for medical information online. Therefore, there is a need for accurate and easily accessible information on these conditions in the World Wide Web, particularly on differentiating signs and therapy options. Meeting this need may facilitate timely diagnosis, proper therapy, and eventual circumvention of potentially life-threatening complications. UR - https://www.jmir.org/2021/6/e28328/ UR - http://dx.doi.org/10.2196/28328 UR - http://www.ncbi.nlm.nih.gov/pubmed/34185016 ID - info:doi/10.2196/28328 ER - TY - JOUR AU - Argyris, Anna Young AU - Monu, Kafui AU - Tan, Pang-Ning AU - Aarts, Colton AU - Jiang, Fan AU - Wiseley, Anne Kaleigh PY - 2021/6/24 TI - Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study JO - JMIR Public Health Surveill SP - e23105 VL - 7 IS - 6 KW - antivaccination movement KW - Twitter messaging KW - public health informatics KW - supervised machine learning algorithm KW - unsupervised machine learning algorithm KW - qualitative content analysis KW - data visualization KW - infodemiology KW - infodemic KW - health misinformation KW - infoveillance KW - social listening N2 - Background: Despite numerous counteracting efforts, antivaccine content linked to delays and refusals to vaccinate has grown persistently on social media, while only a few provaccine campaigns have succeeded in engaging with or persuading the public to accept immunization. Many prior studies have associated the diversity of topics discussed by antivaccine advocates with the public?s higher engagement with such content. Nonetheless, a comprehensive comparison of discursive topics in pro- and antivaccine content in the engagement-persuasion spectrum remains unexplored. Objective: We aimed to compare discursive topics chosen by pro- and antivaccine advocates in their attempts to influence the public to accept or reject immunization in the engagement-persuasion spectrum. Our overall objective was pursued through three specific aims as follows: (1) we classified vaccine-related tweets into provaccine, antivaccine, and neutral categories; (2) we extracted and visualized discursive topics from these tweets to explain disparities in engagement between pro- and antivaccine content; and (3) we identified how those topics frame vaccines using Entman?s four framing dimensions. Methods: We adopted a multimethod approach to analyze discursive topics in the vaccine debate on public social media sites. Our approach combined (1) large-scale balanced data collection from a public social media site (ie, 39,962 tweets from Twitter); (2) the development of a supervised classification algorithm for categorizing tweets into provaccine, antivaccine, and neutral groups; (3) the application of an unsupervised clustering algorithm for identifying prominent topics discussed on both sides; and (4) a multistep qualitative content analysis for identifying the prominent discursive topics and how vaccines are framed in these topics. In so doing, we alleviated methodological challenges that have hindered previous analyses of pro- and antivaccine discursive topics. Results: Our results indicated that antivaccine topics have greater intertopic distinctiveness (ie, the degree to which discursive topics are distinct from one another) than their provaccine counterparts (t122=2.30, P=.02). In addition, while antivaccine advocates use all four message frames known to make narratives persuasive and influential, provaccine advocates have neglected having a clear problem statement. Conclusions: Based on our results, we attribute higher engagement among antivaccine advocates to the distinctiveness of the topics they discuss, and we ascribe the influence of the vaccine debate on uptake rates to the comprehensiveness of the message frames. These results show the urgency of developing clear problem statements for provaccine content to counteract the negative impact of antivaccine content on uptake rates. UR - https://publichealth.jmir.org/2021/6/e23105/ UR - http://dx.doi.org/10.2196/23105 UR - http://www.ncbi.nlm.nih.gov/pubmed/34185004 ID - info:doi/10.2196/23105 ER - TY - JOUR AU - Massey, Daisy AU - Huang, Chenxi AU - Lu, Yuan AU - Cohen, Alina AU - Oren, Yahel AU - Moed, Tali AU - Matzner, Pini AU - Mahajan, Shiwani AU - Caraballo, César AU - Kumar, Navin AU - Xue, Yuchen AU - Ding, Qinglan AU - Dreyer, Rachel AU - Roy, Brita AU - Krumholz, Harlan PY - 2021/6/21 TI - Engagement With COVID-19 Public Health Measures in the United States: A Cross-sectional Social Media Analysis from June to November 2020 JO - J Med Internet Res SP - e26655 VL - 23 IS - 6 KW - COVID-19 KW - public perception KW - social media KW - infodemiology KW - infoveillance KW - infodemic KW - social media research KW - social listening KW - social media analysis KW - natural language processing KW - Reddit data KW - Facebook data KW - COVID-19 public health measures KW - public health KW - surveillance KW - engagement KW - United States KW - cross-sectional KW - Reddit KW - Facebook KW - behavior KW - perception KW - NLP N2 - Background: COVID-19 has continued to spread in the United States and globally. Closely monitoring public engagement and perceptions of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. Objective: The aim of this study is to measure the public?s behaviors and perceptions regarding COVID-19 and its effects on daily life during 5 months of the pandemic. Methods: Natural language processing (NLP) algorithms were used to identify COVID-19?related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged by NetBase, a third-party data provider, and sensitivity and positive predictive value were both calculated to validate the classification of posts. Each post may have included discussion of multiple topics. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the United States. Results: The final sample size included 9,065,733 posts, 70% of which were sourced from the United States. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the United States beginning in October. Additionally, discussion was more focused on daily life topics (n=6,210,255, 69%), compared with COVID-19 in general (n=3,390,139, 37%) and COVID-19 public health measures (n=1,836,200, 20%). Conclusions: There was a decline in COVID-19?related social media discussion sourced mainly from the United States, even as COVID-19 cases in the United States increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures as global vaccination efforts continue. UR - https://www.jmir.org/2021/6/e26655 UR - http://dx.doi.org/10.2196/26655 UR - http://www.ncbi.nlm.nih.gov/pubmed/34086593 ID - info:doi/10.2196/26655 ER - TY - JOUR AU - Pollack, C. Catherine AU - Gilbert-Diamond, Diane AU - Alford-Teaster, A. Jennifer AU - Onega, Tracy PY - 2021/6/21 TI - Language and Sentiment Regarding Telemedicine and COVID-19 on Twitter: Longitudinal Infodemiology Study JO - J Med Internet Res SP - e28648 VL - 23 IS - 6 KW - telemedicine KW - telehealth KW - COVID-19 pandemic KW - social media KW - sentiment analysis KW - Twitter KW - COVID-19 KW - pandemic N2 - Background: The COVID-19 pandemic has necessitated a rapid shift in how individuals interact with and receive fundamental services, including health care. Although telemedicine is not a novel technology, previous studies have offered mixed opinions surrounding its utilization. However, there exists a dearth of research on how these opinions have evolved over the course of the current pandemic. Objective: This study aims to evaluate how the language and sentiment surrounding telemedicine has evolved throughout the COVID-19 pandemic. Methods: Tweets published between January 1, 2020, and April 24, 2021, containing at least one telemedicine-related and one COVID-19?related search term (?telemedicine-COVID?) were collected from the Twitter full archive search (N=351,718). A comparator sample containing only COVID-19 terms (?general-COVID?) was collected and sampled based on the daily distribution of telemedicine-COVID tweets. In addition to analyses of retweets and favorites, sentiment analysis was performed on both data sets in aggregate and within a subset of tweets receiving the top 100 most and least retweets. Results: Telemedicine gained prominence during the early stages of the pandemic (ie, March through May 2020) before leveling off and reaching a steady state from June 2020 onward. Telemedicine-COVID tweets had a 21% lower average number of retweets than general-COVID tweets (incidence rate ratio 0.79, 95% CI 0.63-0.99; P=.04), but there was no difference in favorites. A majority of telemedicine-COVID tweets (180,295/351,718, 51.3%) were characterized as ?positive,? compared to only 38.5% (135,434/351,401) of general-COVID tweets (P<.001). This trend was also true on a monthly level from March 2020 through April 2021. The most retweeted posts in both telemedicine-COVID and general-COVID data sets were authored by journalists and politicians. Whereas the majority of the most retweeted posts within the telemedicine-COVID data set were positive (55/101, 54.5%), a plurality of the most retweeted posts within the general-COVID data set were negative (44/89, 49.4%; P=.01). Conclusions: During the COVID-19 pandemic, opinions surrounding telemedicine evolved to become more positive, especially when compared to the larger pool of COVID-19?related tweets. Decision makers should capitalize on these shifting public opinions to invest in telemedicine infrastructure and ensure its accessibility and success in a postpandemic world. UR - https://www.jmir.org/2021/6/e28648 UR - http://dx.doi.org/10.2196/28648 UR - http://www.ncbi.nlm.nih.gov/pubmed/34086591 ID - info:doi/10.2196/28648 ER - TY - JOUR AU - Lee, Jinhee AU - Kwan, Yunna AU - Lee, Young Jun AU - Shin, Il Jae AU - Lee, Hwa Keum AU - Hong, Hwi Sung AU - Han, Joo Young AU - Kronbichler, Andreas AU - Smith, Lee AU - Koyanagi, Ai AU - Jacob, Louis AU - Choi, SungWon AU - Ghayda, Abou Ramy AU - Park, Myung-Bae PY - 2021/6/18 TI - Public Interest in Immunity and the Justification for Intervention in the Early Stages of the COVID-19 Pandemic: Analysis of Google Trends Data JO - J Med Internet Res SP - e26368 VL - 23 IS - 6 KW - COVID-19 KW - social big data KW - infodemiology KW - infoveillance KW - social listening KW - immune KW - vitamin KW - big data KW - public interest KW - intervention KW - immune system KW - immunity KW - trends KW - Google Trends KW - internet KW - digital health KW - web-based health information KW - correlation KW - social media KW - infectious disease N2 - Background: The use of social big data is an important emerging concern in public health. Internet search volumes are useful data that can sensitively detect trends of the public's attention during a pandemic outbreak situation. Objective: Our study aimed to analyze the public?s interest in COVID-19 proliferation, identify the correlation between the proliferation of COVID-19 and interest in immunity and products that have been reported to confer an enhancement of immunity, and suggest measures for interventions that should be implemented from a health and medical point of view. Methods: To assess the level of public interest in infectious diseases during the initial days of the COVID-19 outbreak, we extracted Google search data from January 20, 2020, onward and compared them to data from March 15, 2020, which was approximately 2 months after the COVID-19 outbreak began. In order to determine whether the public became interested in the immune system, we selected coronavirus, immune, and vitamin as our final search terms. Results: The increase in the cumulative number of confirmed COVID-19 cases that occurred after January 20, 2020, had a strong positive correlation with the search volumes for the terms coronavirus (R=0.786; P<.001), immune (R=0.745; P<.001), and vitamin (R=0.778; P<.001), and the correlations between variables were all mutually statistically significant. Moreover, these correlations were confirmed on a country basis when we restricted our analyses to the United States, the United Kingdom, Italy, and Korea. Our findings revealed that increases in search volumes for the terms coronavirus and immune preceded the actual occurrences of confirmed cases. Conclusions: Our study shows that during the initial phase of the COVID-19 crisis, the public?s desire and actions of strengthening their own immune systems were enhanced. Further, in the early stage of a pandemic, social media platforms have a high potential for informing the public about potentially helpful measures to prevent the spread of an infectious disease and provide relevant information about immunity, thereby increasing the public?s knowledge. UR - https://www.jmir.org/2021/6/e26368 UR - http://dx.doi.org/10.2196/26368 UR - http://www.ncbi.nlm.nih.gov/pubmed/34038375 ID - info:doi/10.2196/26368 ER - TY - JOUR AU - Miller, Michele AU - Romine, William AU - Oroszi, Terry PY - 2021/6/18 TI - Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events JO - JMIR Public Health Surveill SP - e27976 VL - 7 IS - 6 KW - anthrax KW - big data KW - internet KW - infodemiology KW - infoveillance KW - social listening KW - digital health KW - biological weapon KW - terrorism KW - Federal Bureau of Investigation KW - machine learning KW - public health threat KW - Twitter N2 - Background: Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events. Objective: The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of the tweets and topics of discussion over 12 months of data collection. Methods: This is an infoveillance study, using tweets in English containing the keyword ?Anthrax? and ?Bacillus anthracis?, collected from September 25, 2017, through August 15, 2018. Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was plotted to determine whether an event was detected (a 3-fold spike in tweets). A machine learning classifier was created to categorize tweets by relevance to anthrax. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how these events influence that discussion. Results: Over the 12 months of data collection, a total of 204,008 tweets were collected. Logistic regression analysis revealed the best performance for relevance (precision=0.81; recall=0.81; F1-score=0.80). In total, 26 topics were associated with anthrax-related events, tweets that were highly retweeted, natural outbreaks, and news stories. Conclusions: This study shows that tweets related to anthrax can be collected and analyzed over time to determine what people are discussing and to detect key anthrax-related events. Future studies are required to focus only on opinion tweets, use the methodology to study other terrorism events, or to monitor for terrorism threats. UR - https://publichealth.jmir.org/2021/6/e27976 UR - http://dx.doi.org/10.2196/27976 UR - http://www.ncbi.nlm.nih.gov/pubmed/34142975 ID - info:doi/10.2196/27976 ER - TY - JOUR AU - Tao, Chunliang AU - Diaz, Destiny AU - Xie, Zidian AU - Chen, Long AU - Li, Dongmei AU - O?Connor, Richard PY - 2021/6/15 TI - Potential Impact of a Paper About COVID-19 and Smoking on Twitter Users? Attitudes Toward Smoking: Observational Study JO - JMIR Form Res SP - e25010 VL - 5 IS - 6 KW - COVID-19 KW - smoking KW - Twitter KW - infodemiology KW - infodemic KW - infoveillance KW - impact KW - attitude KW - perception KW - observational KW - social media KW - cross-sectional KW - dissemination KW - research N2 - Background: A cross-sectional study (Miyara et al, 2020) conducted by French researchers showed that the rate of current daily smoking was significantly lower in patients with COVID-19 than in the French general population, implying a potentially protective effect of smoking. Objective: We aimed to examine the dissemination of the Miyara et al study among Twitter users and whether a shift in their attitudes toward smoking occurred after its publication as preprint on April 21, 2020. Methods: Twitter posts were crawled between April 14 and May 4, 2020, by the Tweepy stream application programming interface, using a COVID-19?related keyword query. After filtering, the final 1929 tweets were classified into three groups: (1) tweets that were not related to the Miyara et al study before it was published, (2) tweets that were not related to Miyara et al study after it was published, and (3) tweets that were related to Miyara et al study after it was published. The attitudes toward smoking, as expressed in the tweets, were compared among the above three groups using multinomial logistic regression models in the statistical analysis software R (The R Foundation). Results: Temporal analysis showed a peak in the number of tweets discussing the results from the Miyara et al study right after its publication. Multinomial logistic regression models on sentiment scores showed that the proportion of negative attitudes toward smoking in tweets related to the Miyara et al study after it was published (17.07%) was significantly lower than the proportion in tweets that were not related to the Miyara et al study, either before (44/126, 34.9%; P<.001) or after the Miyara et al study was published (68/198, 34.3%; P<.001). Conclusions: The public?s attitude toward smoking shifted in a positive direction after the Miyara et al study found a lower incidence of COVID-19 cases among daily smokers. UR - https://formative.jmir.org/2021/6/e25010 UR - http://dx.doi.org/10.2196/25010 UR - http://www.ncbi.nlm.nih.gov/pubmed/33939624 ID - info:doi/10.2196/25010 ER - TY - JOUR AU - Rao, Ashwin AU - Morstatter, Fred AU - Hu, Minda AU - Chen, Emily AU - Burghardt, Keith AU - Ferrara, Emilio AU - Lerman, Kristina PY - 2021/6/14 TI - Political Partisanship and Antiscience Attitudes in Online Discussions About COVID-19: Twitter Content Analysis JO - J Med Internet Res SP - e26692 VL - 23 IS - 6 KW - COVID-19 KW - Twitter KW - infodemiology KW - infodemic KW - infoveillance KW - multidimensional polarization KW - social media KW - social network N2 - Background: The novel coronavirus pandemic continues to ravage communities across the United States. Opinion surveys identified the importance of political ideology in shaping perceptions of the pandemic and compliance with preventive measures. Objective: The aim of this study was to measure political partisanship and antiscience attitudes in the discussions about the pandemic on social media, as well as their geographic and temporal distributions. Methods: We analyzed a large set of tweets from Twitter related to the pandemic, collected between January and May 2020, and developed methods to classify the ideological alignment of users along the moderacy (hardline vs moderate), political (liberal vs conservative), and science (antiscience vs proscience) dimensions. Results: We found a significant correlation in polarized views along the science and political dimensions. Moreover, politically moderate users were more aligned with proscience views, while hardline users were more aligned with antiscience views. Contrary to expectations, we did not find that polarization grew over time; instead, we saw increasing activity by moderate proscience users. We also show that antiscience conservatives in the United States tended to tweet from the southern and northwestern states, while antiscience moderates tended to tweet from the western states. The proportion of antiscience conservatives was found to correlate with COVID-19 cases. Conclusions: Our findings shed light on the multidimensional nature of polarization and the feasibility of tracking polarized opinions about the pandemic across time and space through social media data. UR - https://www.jmir.org/2021/6/e26692 UR - http://dx.doi.org/10.2196/26692 UR - http://www.ncbi.nlm.nih.gov/pubmed/34014831 ID - info:doi/10.2196/26692 ER - TY - JOUR AU - Neely, Stephen AU - Eldredge, Christina AU - Sanders, Ron PY - 2021/6/11 TI - Health Information Seeking Behaviors on Social Media During the COVID-19 Pandemic Among American Social Networking Site Users: Survey Study JO - J Med Internet Res SP - e29802 VL - 23 IS - 6 KW - social media KW - internet KW - communication KW - public health KW - COVID-19 KW - usage KW - United States KW - information seeking KW - web-based health information KW - survey KW - mistrust N2 - Background: In recent years, medical journals have emphasized the increasingly critical role that social media plays in the dissemination of public health information and disease prevention guidelines. However, platforms such as Facebook and Twitter continue to pose unique challenges for clinical health care providers and public health officials alike. In order to effectively communicate during public health emergencies, such as the COVID-19 pandemic, it is increasingly critical for health care providers and public health officials to understand how patients gather health-related information on the internet and adjudicate the merits of such information. Objective: With that goal in mind, we conducted a survey of 1003 US-based adults to better understand how health consumers have used social media to learn and stay informed about the COVID-19 pandemic, the extent to which they have relied on credible scientific information sources, and how they have gone about fact-checking pandemic-related information. Methods: A web-based survey was conducted with a sample that was purchased through an industry-leading market research provider. The results were reported with a 95% confidence level and a margin of error of 3. Participants included 1003 US-based adults (aged ?18 years). Participants were selected via a stratified quota sampling approach to ensure that the sample was representative of the US population. Balanced quotas were determined (by region of the country) for gender, age, race, and ethnicity. Results: The results showed a heavy reliance on social media during the COVID-19 pandemic; more than three-quarters of respondents (762/1003, 76%) reported that they have relied on social media at least ?a little,? and 59.2% (594/1003) of respondents indicated that they read information about COVID-19 on social media at least once per week. According to the findings, most social media users (638/1003, 63.6%) were unlikely to fact-check what they see on the internet with a health professional, despite the high levels of mistrust in the accuracy of COVID-19?related information on social media. We also found a greater likelihood of undergoing vaccination among those following more credible scientific sources on social media during the pandemic (?216=50.790; ?=0.258; P<.001). Conclusions: The findings suggest that health professionals will need to be both strategic and proactive when engaging with health consumers on social media if they hope to counteract the deleterious effects of misinformation and disinformation. Effective training, institutional support, and proactive collaboration can help health professionals adapt to the evolving patterns of health information seeking. UR - https://www.jmir.org/2021/6/e29802 UR - http://dx.doi.org/10.2196/29802 UR - http://www.ncbi.nlm.nih.gov/pubmed/34043526 ID - info:doi/10.2196/29802 ER - TY - JOUR AU - Hou, Zhiyuan AU - Tong, Yixin AU - Du, Fanxing AU - Lu, Linyao AU - Zhao, Sihong AU - Yu, Kexin AU - Piatek, J. Simon AU - Larson, J. Heidi AU - Lin, Leesa PY - 2021/6/11 TI - Assessing COVID-19 Vaccine Hesitancy, Confidence, and Public Engagement: A Global Social Listening Study JO - J Med Internet Res SP - e27632 VL - 23 IS - 6 KW - COVID-19 vaccine KW - hesitancy KW - infoveillance KW - infodemiology KW - confidence KW - acceptance KW - engagement KW - social media KW - COVID-19 N2 - Background: Monitoring public confidence and hesitancy is crucial for the COVID-19 vaccine rollout. Social media listening (infoveillance) can not only monitor public attitudes on COVID-19 vaccines but also assess the dissemination of and public engagement with these opinions. Objective: This study aims to assess global hesitancy, confidence, and public engagement toward COVID-19 vaccination. Methods: We collected posts mentioning the COVID-19 vaccine between June and July 2020 on Twitter from New York (United States), London (United Kingdom), Mumbai (India), and Sao Paulo (Brazil), and Sina Weibo posts from Beijing (China). In total, we manually coded 12,886 posts from the five global metropolises with high COVID-19 burdens, and after assessment, 7032 posts were included in the analysis. We manually double-coded these posts using a coding framework developed according to the World Health Organization?s Confidence, Complacency, and Convenience model of vaccine hesitancy, and conducted engagement analysis to investigate public communication about COVID-19 vaccines on social media. Results: Among social media users, 36.4% (571/1568) in New York, 51.3% (738/1440) in London, 67.3% (144/214) in Sao Paulo, 69.8% (726/1040) in Mumbai, and 76.8% (2128/2770) in Beijing indicated that they intended to accept a COVID-19 vaccination. With a high perceived risk of getting COVID-19, more tweeters in New York and London expressed a lack of confidence in vaccine safety, distrust in governments and experts, and widespread misinformation or rumors. Tweeters from Mumbai, Sao Paulo, and Beijing worried more about vaccine production and supply, whereas tweeters from New York and London had more concerns about vaccine distribution and inequity. Negative tweets expressing lack of vaccine confidence and misinformation or rumors had more followers and attracted more public engagement online. Conclusions: COVID-19 vaccine hesitancy is prevalent worldwide, and negative tweets attract higher engagement on social media. It is urgent to develop an effective vaccine campaign that boosts public confidence and addresses hesitancy for COVID-19 vaccine rollouts. UR - https://www.jmir.org/2021/6/e27632 UR - http://dx.doi.org/10.2196/27632 UR - http://www.ncbi.nlm.nih.gov/pubmed/34061757 ID - info:doi/10.2196/27632 ER - TY - JOUR AU - Basch, H. Corey AU - Mohlman, Jan AU - Fera, Joseph AU - Tang, Hao AU - Pellicane, Alessia AU - Basch, E. Charles PY - 2021/6/10 TI - Community Mitigation of COVID-19 and Portrayal of Testing on TikTok: Descriptive Study JO - JMIR Public Health Surveill SP - e29528 VL - 7 IS - 6 KW - TikTok KW - social media KW - COVID-19 KW - testing KW - disgust KW - anxiety KW - content analysis KW - communication KW - infodemiology KW - infoveillance KW - public health KW - digital public health KW - digital health KW - community mitigation N2 - Background: COVID-19 testing remains an essential element of a comprehensive strategy for community mitigation. Social media is a popular source of information about health, including COVID-19 and testing information. One of the most popular communication channels used by adolescents and young adults who search for health information is TikTok?an emerging social media platform. Objective: The purpose of this study was to describe TikTok videos related to COVID-19 testing. Methods: The hashtag #covidtesting was searched, and the first 100 videos were included in the study sample. At the time the sample was drawn, these 100 videos garnered more than 50% of the views for all videos cataloged under the hashtag #covidtesting. The content characteristics that were coded included mentions, displays, or suggestions of anxiety, COVID-19 symptoms, quarantine, types of tests, results of test, and disgust/unpleasantness. Additional data that were coded included the number and percentage of views, likes, and comments and the use of music, dance, and humor. Results: The 100 videos garnered more than 103 million views; 111,000 comments; and over 12.8 million likes. Even though only 44 videos mentioned or suggested disgust/unpleasantness and 44 mentioned or suggested anxiety, those that portrayed tests as disgusting/unpleasant garnered over 70% of the total cumulative number of views (73,479,400/103,071,900, 71.29%) and likes (9,354,691/12,872,505, 72.67%), and those that mentioned or suggested anxiety attracted about 60% of the total cumulative number of views (61,423,500/103,071,900, 59.59%) and more than 8 million likes (8,339,598/12,872,505, 64.79%). Independent one-tailed t tests (?=.05) revealed that videos that mentioned or suggested that COVID-19 testing was disgusting/unpleasant were associated with receiving a higher number of views and likes. Conclusions: Our finding of an association between TikTok videos that mentioned or suggested that COVID-19 tests were disgusting/unpleasant and these videos? propensity to garner views and likes is of concern. There is a need for public health agencies to recognize and address connotations of COVID-19 testing on social media. UR - https://publichealth.jmir.org/2021/6/e29528 UR - http://dx.doi.org/10.2196/29528 UR - http://www.ncbi.nlm.nih.gov/pubmed/34081591 ID - info:doi/10.2196/29528 ER - TY - JOUR AU - Guelmami, Noomen AU - Ben Khalifa, Maher AU - Chalghaf, Nasr AU - Kong, Dzevela Jude AU - Amayra, Tannoubi AU - Wu, Jianhong AU - Azaiez, Fairouz AU - Bragazzi, Luigi Nicola PY - 2021/6/9 TI - Development of the 12-Item Social Media Disinformation Scale and its Association With Social Media Addiction and Mental Health Related to COVID-19 in Tunisia: Survey-Based Pilot Case Study JO - JMIR Form Res SP - e27280 VL - 5 IS - 6 KW - COVID-19 pandemic KW - media disinformation KW - social media addiction KW - mental health KW - scale validation N2 - Background: In recent years, online disinformation has increased. Fake news has been spreading about the COVID-19 pandemic. Since January 2020, the culprits and antidotes to disinformation have been digital media and social media. Objective: Our study aimed to develop and test the psychometric properties of the 12-item Social Media Disinformation Scale (SMDS-12), which assesses the consumption, confidence, and sharing of information related to COVID-19 by social media users. Methods: A total of 874 subjects were recruited over two phases: the exploratory phase group had a mean age of 28.39 years (SD 9.32) and the confirmatory phase group had a mean age of 32.84 years (SD 12.72). Participants completed the SMDS-12, the Internet Addiction Test, the COVID-19 Fear Scale, and the 10-item Perceived Stress Scale. The SMDS-12 was initially tested by exploratory factor analysis and was subsequently tested by confirmatory factor analysis. Results: The test supported the three-factor structure. In addition, no items were removed from the measurement scale, with three factors explaining up to 73.72% of the total variance, and the items had a lambda factor loading ranging from 0.73 to 0.85. Subsequently, confirmatory factor analysis confirmed the robustness of the measure by referring to a wide range of goodness-of-fit indices that met the recommended standards. The construct validity of the scale was supported by its convergent and discriminant validity. The reliability of the instrument examined by means of three internal consistency indices, and the corrected item-total correlation, demonstrated that the three dimensions of the instrument were reliable: Cronbach ? values were .89, .88, and .88 for the consumption, confidence, and sharing subscales, respectively. The corrected item-total correlation ranged from 0.70 to 0.78. The correlation of the instrument?s dimensions with internet addiction and mental health factors showed positive associations. Conclusions: The SMDS-12 can be reliably utilized to measure the credibility of social media disinformation and can be adapted to measure the credibility of disinformation in other contexts. UR - https://formative.jmir.org/2021/6/e27280 UR - http://dx.doi.org/10.2196/27280 UR - http://www.ncbi.nlm.nih.gov/pubmed/34021742 ID - info:doi/10.2196/27280 ER - TY - JOUR AU - Garcia-Souto, Fernando AU - Pereyra-Rodriguez, Juan Jose PY - 2021/6/8 TI - Psoriasis Google Trends JO - JMIR Dermatol SP - e21709 VL - 4 IS - 1 KW - Google Trends KW - psoriasis KW - treatment UR - https://derma.jmir.org/2021/1/e21709 UR - http://dx.doi.org/10.2196/21709 UR - http://www.ncbi.nlm.nih.gov/pubmed/37625163 ID - info:doi/10.2196/21709 ER - TY - JOUR AU - Cui, Limeng AU - Chu, Lijuan PY - 2021/6/7 TI - YouTube Videos Related to the Fukushima Nuclear Disaster: Content Analysis JO - JMIR Public Health Surveill SP - e26481 VL - 7 IS - 6 KW - YouTube KW - Fukushima nuclear disaster KW - social media KW - risk communication KW - disaster KW - video platform KW - radiation KW - public safety KW - nuclear disaster N2 - Background: YouTube (Alphabet Incorporated) has become the most popular video-sharing platform in the world. The Fukushima Daiichi Nuclear Power Plant (FDNPP) disaster resulted in public anxiety toward nuclear power and radiation worldwide. YouTube is an important source of information about the FDNPP disaster for the world. Objective: This study's objectives were to examine the characteristics of YouTube videos related to the FDNPP disaster, analyze the content and comments of videos with a quantitative method, and determine which features contribute to making a video popular with audiences. This study is the first to examine FDNPP disaster?related videos on YouTube. Methods: We searched for the term ?Fukushima nuclear disaster? on YouTube on November 2, 2019. The first 60 eligible videos in the relevance, upload date, view count, and rating categories were recorded. ?Videos that were irrelevant, were non-English, had inappropriate words, were machine synthesized, and were <3 minutes long were excluded. In total, 111 videos met the inclusion criteria. Parameters of the videos, including the number of subscribers, length, the number of days since the video was uploaded, region, video popularity (views, views/day, likes, likes/day, dislikes, dislikes/day, comments, comments/day), the tone of the videos, the top ten comments, affiliation, whether Japanese people participated in the video, whether the video recorder visited Fukushima, whether the video contained theoretical knowledge, and whether the video contained information about the recent situation in Fukushima, were recorded. By using criteria for content and ?technical design, two evaluators scored videos and grouped them into the useful (score: 11-14), slightly useful (score: 6-10), and useless (score: 0-5) video categories. Results: Of the 111 videos, 43 (38.7%) videos were useful, 43 (38.7%) were slightly useful, and 25 (22.5%) were useless. Useful videos had good visual and aural effects, provided vivid information on the Fukushima disaster, and had a mean score of 12 (SD 0.9). Useful videos had more views per day (P<.001), likes per day (P<.001), and comments per day (P=.02) than useless and slightly useful videos. The popularity of videos had a significant correlation with clear sounds (likes/day: P=.001; comments/day: P=.02), vivid information (likes/day: P<.001; comments/day: P=.007), understanding content (likes/day: P=.001; comments/day: P=.04). There was no significant difference in likes per day (P=.72) and comments per day (P=.11) between negative and neutral- and mixed-tone videos. Videos about the recent situation in Fukushima had more likes and comments per day. Video recorders who personally visited Fukushima Prefecture had more subscribers and received more views and likes. Conclusions: The possible features that made videos popular to the public included ?video quality, videos made in Fukushima, and information on the recent situation in Fukushima. During risk communication on new forms of media, health institutes should increase publicity and be more approachable to resonate with international audiences. UR - https://publichealth.jmir.org/2021/6/e26481 UR - http://dx.doi.org/10.2196/26481 UR - http://www.ncbi.nlm.nih.gov/pubmed/34096880 ID - info:doi/10.2196/26481 ER - TY - JOUR AU - Guntuku, Chandra Sharath AU - Purtle, Jonathan AU - Meisel, F. Zachary AU - Merchant, M. Raina AU - Agarwal, Anish PY - 2021/6/3 TI - Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study JO - J Med Internet Res SP - e27300 VL - 23 IS - 6 KW - Twitter KW - COVID-19 KW - digital health KW - US legislators KW - natural language processing KW - policy makers KW - social media KW - policy KW - politics KW - language KW - cross-sectional KW - content KW - sentiment KW - infodemiology KW - infoveillance N2 - Background: As policy makers continue to shape the national and local responses to the COVID-19 pandemic, the information they choose to share and how they frame their content provide key insights into the public and health care systems. Objective: We examined the language used by the members of the US House and Senate during the first 10 months of the COVID-19 pandemic and measured content and sentiment based on the tweets that they shared. Methods: We used Quorum (Quorum Analytics Inc) to access more than 300,000 tweets posted by US legislators from January 1 to October 10, 2020. We used differential language analyses to compare the content and sentiment of tweets posted by legislators based on their party affiliation. Results: We found that health care?related themes in Democratic legislators? tweets focused on racial disparities in care (odds ratio [OR] 2.24, 95% CI 2.22-2.27; P<.001), health care and insurance (OR 1.74, 95% CI 1.7-1.77; P<.001), COVID-19 testing (OR 1.15, 95% CI 1.12-1.19; P<.001), and public health guidelines (OR 1.25, 95% CI 1.22-1.29; P<.001). The dominant themes in the Republican legislators? discourse included vaccine development (OR 1.51, 95% CI 1.47-1.55; P<.001) and hospital resources and equipment (OR 1.22, 95% CI 1.18-1.25). Nonhealth care?related topics associated with a Democratic affiliation included protections for essential workers (OR 1.55, 95% CI 1.52-1.59), the 2020 election and voting (OR 1.31, 95% CI 1.27-1.35), unemployment and housing (OR 1.27, 95% CI 1.24-1.31), crime and racism (OR 1.22, 95% CI 1.18-1.26), public town halls (OR 1.2, 95% CI 1.16-1.23), the Trump Administration (OR 1.22, 95% CI 1.19-1.26), immigration (OR 1.16, 95% CI 1.12-1.19), and the loss of life (OR 1.38, 95% CI 1.35-1.42). The themes associated with the Republican affiliation included China (OR 1.89, 95% CI 1.85-1.92), small business assistance (OR 1.27, 95% CI 1.23-1.3), congressional relief bills (OR 1.23, 95% CI 1.2-1.27), press briefings (OR 1.22, 95% CI 1.19-1.26), and economic recovery (OR 1.2, 95% CI 1.16-1.23). Conclusions: Divergent language use on social media corresponds to the partisan divide in the first several months of the course of the COVID-19 public health crisis. UR - https://www.jmir.org/2021/6/e27300 UR - http://dx.doi.org/10.2196/27300 UR - http://www.ncbi.nlm.nih.gov/pubmed/33939620 ID - info:doi/10.2196/27300 ER - TY - JOUR AU - Rotter, Dominik AU - Doebler, Philipp AU - Schmitz, Florian PY - 2021/6/1 TI - Interests, Motives, and Psychological Burdens in Times of Crisis and Lockdown: Google Trends Analysis to Inform Policy Makers JO - J Med Internet Res SP - e26385 VL - 23 IS - 6 KW - coronavirus KW - Google Trends KW - infodemiology KW - infoveillance KW - pandemic KW - information search KW - trend KW - COVID-19 KW - burden KW - mental health KW - policy KW - online health information N2 - Background: In the face of the COVID-19 pandemic, the German government and the 16 German federal states implemented a variety of nonpharmaceutical interventions (NPIs) to decelerate the spread of the SARS-CoV-2 virus and thus prevent a collapse of the health care system. These measures comprised, among others, social distancing, the temporary closure of shops and schools, and a ban of large public gatherings and meetings with people not living in the same household. Objective: It is fair to assume that the issued NPIs have heavily affected social life and psychological functioning. We therefore aimed to examine possible effects of this lockdown in conjunction with daily new infections and the state of the national economy on people?s interests, motives, and other psychological states. Methods: We derived 249 keywords from the Google Trends database, tapping into 27 empirically and rationally selected psychological domains. To overcome issues with reliability and specificity of individual indicator variables, broad factors were derived by means of time series factor analysis. All domains were subjected to a change point analysis and time series regression analysis with infection rates, NPIs, and the state of the economy as predictors. All keywords and analyses were preregistered prior to analysis. Results: With the pandemic arriving in Germany, significant increases in people?s search interests were observed in virtually all domains. Although most of the changes were short-lasting, each had a distinguishable onset during the lockdown period. Regression analysis of the Google Trends data confirmed pronounced autoregressive effects for the investigated variables, while forecasting by means of the tested predictors (ie, daily new infections, NPIs, and the state of economy) was moderate at best. Conclusions: Our findings indicate that people?s interests, motives, and psychological states are heavily affected in times of crisis and lockdown. Specifically, disease- and virus-related domains (eg, pandemic disease, symptoms) peaked early, whereas personal health strategies (eg, masks, homeschooling) peaked later during the lockdown. Domains addressing social life and psychosocial functioning showed long-term increases in public interest. Renovation was the only domain to show a decrease in search interest with the onset of the lockdown. As changes in search behavior are consistent over multiple domains, a Google Trends analysis may provide information for policy makers on how to adapt and develop intervention, information, and prevention strategies, especially when NPIs are in effect. UR - https://www.jmir.org/2021/6/e26385 UR - http://dx.doi.org/10.2196/26385 UR - http://www.ncbi.nlm.nih.gov/pubmed/33999837 ID - info:doi/10.2196/26385 ER - TY - JOUR AU - Kochan, Andrew AU - Ong, Shaun AU - Guler, Sabina AU - Johannson, A. Kerri AU - Ryerson, J. Christopher AU - Goobie, C. Gillian PY - 2021/5/31 TI - Social Media Content of Idiopathic Pulmonary Fibrosis Groups and Pages on Facebook: Cross-sectional Analysis JO - JMIR Public Health Surveill SP - e24199 VL - 7 IS - 5 KW - interstitial lung disease KW - idiopathic pulmonary fibrosis KW - patient education KW - social media KW - internet N2 - Background: Patients use Facebook as a resource for medical information. We analyzed posts on idiopathic pulmonary fibrosis (IPF)-related Facebook groups and pages for the presence of guideline content, user engagement, and usefulness. Objective: The objective of this study was to describe and analyze posts from Facebook groups and pages that primarily focus on IPF-related content. Methods: Cross-sectional analysis was performed on a single date, identifying Facebook groups and pages resulting from separately searching ?IPF? and ?idiopathic pulmonary fibrosis.? For inclusion, groups and pages needed to meet either search term and be in English, publicly available, and relevant to IPF. Every 10th post was assessed for general characteristics, source, focus, and user engagement metrics. Posts were analyzed for presence of IPF guideline content, useful scientific information (eg, scientific publications), useful support information (eg, information about support groups), and potentially harmful information. Results: Eligibility criteria were met by 12 groups and 27 pages, leading to analysis of 523 posts. Of these, 42% contained guideline content, 24% provided useful support, 20% provided useful scientific information, and 5% contained potentially harmful information. The most common post source was nonmedical users (85%). Posts most frequently focused on IPF-related news (29%). Posts containing any guideline content had fewer likes or comments and a higher likelihood of containing potentially harmful content. Posts containing useful supportive information had more likes, shares, and comments. Conclusions: Facebook contains useful information about IPF, but posts with misinformation and less guideline content have higher user engagement, making them more visible. Identifying ways to help patients with IPF discriminate between useful and harmful information on Facebook and other social media platforms is an important task for health care professionals. UR - https://publichealth.jmir.org/2021/5/e24199 UR - http://dx.doi.org/10.2196/24199 UR - http://www.ncbi.nlm.nih.gov/pubmed/34057425 ID - info:doi/10.2196/24199 ER - TY - JOUR AU - Moreno, A. Megan AU - Gaus, Quintin AU - Wilt, Megan AU - Arseniev-Koehler, Alina AU - Ton, Adrienne AU - Adrian, Molly AU - VanderStoep, Ann PY - 2021/5/31 TI - Displayed Depression Symptoms on Facebook at Two Time Points: Content Analysis JO - JMIR Form Res SP - e20179 VL - 5 IS - 5 KW - adolescents KW - content analysis KW - depression KW - Facebook KW - social media N2 - Background: Depression is a prevalent and problematic mental disorder that often has an onset in adolescence. Previous studies have illustrated that depression disclosures on social media are common and may be linked to an individual?s experiences of depression. However, most studies have examined depression displays on social media at a single time point. Objective: This study aims to investigate displayed depression symptoms on Facebook at 2 developmental time points based on symptom type and gender. Methods: Participants were recruited from an ongoing longitudinal cohort study. The content analysis of text-based Facebook data over 1 year was conducted at 2 time points: time 1 (adolescence; age 17-18 years) and time 2 (young adulthood; ages 20-22 years). Diagnostic criteria for depression were applied to each post to identify the displayed depression symptoms. Data were extracted verbatim. The analysis included nonparametric tests for comparisons. Results: A total of 78 participants? Facebook profiles were examined, of which 40 (51%) were male. At time 1, 62% (48/78) of the adolescents had a Facebook profile, and 54% (26/78) displayed depression symptom references with an average of 9.4 (SD 3.1) references and 3.3 (SD 2.3) symptom types. Of the 78 participants, 15 (19%) females and 12 (15%) males displayed depression symptom references; these prevalence estimates were not significantly different by gender (P=.59). At time 2, 35 young adults displayed symptoms of depression with an average of 4.6 (SD 2.3) references and 2.4 (SD 1.3) symptom types. There were no differences in the prevalence of symptoms of depression displayed between males (n=19) and females (n=16; P=.63). Conclusions: This content analysis study within an ongoing cohort study illustrates the differences in depression displays on Facebook by developmental stage and symptom. This study contributes to a growing body of literature by showing that using social media to observe and understand depression during the emerging adult developmental period may be a valuable approach. UR - https://formative.jmir.org/2021/5/e20179 UR - http://dx.doi.org/10.2196/20179 UR - http://www.ncbi.nlm.nih.gov/pubmed/34057422 ID - info:doi/10.2196/20179 ER - TY - JOUR AU - Daughton, R. Ashlynn AU - Shelley, D. Courtney AU - Barnard, Martha AU - Gerts, Dax AU - Watson Ross, Chrysm AU - Crooker, Isabel AU - Nadiga, Gopal AU - Mukundan, Nilesh AU - Vaquera Chavez, Yadira Nidia AU - Parikh, Nidhi AU - Pitts, Travis AU - Fairchild, Geoffrey PY - 2021/5/25 TI - Mining and Validating Social Media Data for COVID-19?Related Human Behaviors Between January and July 2020: Infodemiology Study JO - J Med Internet Res SP - e27059 VL - 23 IS - 5 KW - Twitter KW - social media KW - human behavior KW - infectious disease KW - COVID-19 KW - coronavirus KW - infodemiology KW - infoveillance KW - social distancing KW - shelter-in-place KW - mobility KW - COVID-19 intervention N2 - Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to ?0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors. UR - https://www.jmir.org/2021/5/e27059 UR - http://dx.doi.org/10.2196/27059 UR - http://www.ncbi.nlm.nih.gov/pubmed/33882015 ID - info:doi/10.2196/27059 ER - TY - JOUR AU - Chan, K. Alex AU - Wu, Constance AU - Cheung, Andrew AU - Succi, D. Marc PY - 2021/5/25 TI - Characterization of an Open-Access Medical News Platform?s Readership During the COVID-19 Pandemic: Retrospective Observational Study JO - J Med Internet Res SP - e26666 VL - 23 IS - 5 KW - COVID-19 KW - internet KW - medical news KW - text summaries KW - readership trends KW - news KW - media KW - open access KW - literature KW - web-based health information KW - survey KW - cross-sectional KW - trend N2 - Background: There are many alternatives to direct journal access, such as podcasts, blogs, and news sites, that allow physicians and the general public to stay up to date with medical literature. However, there is a scarcity of literature that investigates the readership characteristics of open-access medical news sites and how these characteristics may have shifted during the COVID-19 pandemic. Objective: This study aimed to assess readership and survey data to characterize open-access medical news readership trends related to the COVID-19 pandemic and overall readership trends regarding pandemic-related information delivery. Methods: Anonymous, aggregate readership data were obtained from 2 Minute Medicine, an open-access, physician-run medical news organization that has published over 8000 original, physician-written texts and visual summaries of new medical research since 2013. In this retrospective observational study, the average number of article views, number of actions (defined as the sum of the number of views, shares, and outbound link clicks), read times, and bounce rates (probability of leaving a page in <30 s) were compared between COVID-19 articles published from January 1 to May 31, 2020 (n=40) and non?COVID-19 articles (n=145) published in the same time period. A voluntary survey was also sent to subscribed 2 Minute Medicine readers to further characterize readership demographics and preferences, which were scored on a Likert scale. Results: COVID-19 articles had a significantly higher median number of views than non?COVID-19 articles (296 vs 110; U=748.5; P<.001). There were no significant differences in average read times (P=.12) or bounce rates (P=.12). Non?COVID-19 articles had a higher median number of actions than COVID-19 articles (2.9 vs 2.5; U=2070.5; P=.02). On a Likert scale of 1 (strongly disagree) to 5 (strongly agree), our survey data revealed that 65.5% (78/119) of readers agreed or strongly agreed that they preferred staying up to date with emerging literature about COVID-19 by using sources such as 2 Minute Medicine instead of journals. A greater proportion of survey respondents also indicated that open-access news sources were one of their primary sources for staying informed (86/120, 71.7%) compared to the proportion who preferred direct journal article access (61/120, 50.8%). The proportion of readers indicating they were reading one or less full-length medical studies a month were lower following introduction to 2 Minute Medicine compared to prior (21/120, 17.5% vs 38/120, 31.6%; P=.005). Conclusions: The readership significantly increased for one open-access medical literature platform during the pandemic. This reinforces the idea that open-access, physician-written sources of medical news represent an important alternative to direct journal access for readers who want to stay up to date with medical literature. UR - https://www.jmir.org/2021/5/e26666 UR - http://dx.doi.org/10.2196/26666 UR - http://www.ncbi.nlm.nih.gov/pubmed/33866307 ID - info:doi/10.2196/26666 ER - TY - JOUR AU - Yang, Xue AU - Yip, K. Benjamin H. AU - Mak, P. Arthur D. AU - Zhang, Dexing AU - Lee, P. Eric K. AU - Wong, S. Samuel Y. PY - 2021/5/25 TI - The Differential Effects of Social Media on Depressive Symptoms and Suicidal Ideation Among the Younger and Older Adult Population in Hong Kong During the COVID-19 Pandemic: Population-Based Cross-sectional Survey Study JO - JMIR Public Health Surveill SP - e24623 VL - 7 IS - 5 KW - social media KW - depression KW - suicidal ideation KW - social loneliness KW - posttraumatic stress KW - suicide KW - mental health KW - COVID-19 KW - loneliness KW - age KW - mediation N2 - Background: Social media has become a ubiquitous part of daily life during the COVID-19 pandemic isolation. However, the role of social media use in depression and suicidal ideation of the general public remains unclear. Related empirical studies were limited and reported inconsistent findings. Little is known about the potential underlying mechanisms that may illustrate the relationship between social media use and depression and suicidal ideation during the COVID-19 pandemic. Objective: This study tested the mediation effects of social loneliness and posttraumatic stress disorder (PTSD) symptoms on the relationship between social media use and depressive symptoms and suicidal ideation, as well as the moderation effect of age on the mediation models. Methods: We administered a population-based random telephone survey in May and June 2020, when infection control measures were being vigorously implemented in Hong Kong. A total of 1070 adults (658 social media users and 412 nonusers) completed the survey. Structural equation modeling (SEM) and multigroup SEM were conducted to test the mediation and moderation effects. Results: The weighted prevalence of probable depression was 11.6%; 1.6% had suicidal ideation in the past 2 weeks. Both moderated mediation models of depressive symptoms (?262=335.3; P<.05; comparative fit index [CFI]=0.94; nonnormed fit index [NNFI]=0.92; root mean square error of approximation [RMSEA]=0.06) and suicidal ideation (?234=50.8; P<.05; CFI=0.99; NNFI=0.99; RMSEA=0.02) showed acceptable model fit. There was a significantly negative direct effect of social media use on depressive symptoms among older people (?=?.07; P=.04) but not among younger people (?=.04; P=.55). The indirect effect via PTSD symptoms was significantly positive among both younger people (?=.09; P=.02) and older people (?=.10; P=.01). The indirect effect via social loneliness was significant among older people (?=?.01; P=.04) but not among younger people (?=.01; P=.31). The direct effect of social media use on suicidal ideation was not statistically significant in either age group (P>.05). The indirect effects via PTSD symptoms were statistically significant among younger people (?=.02; P=.04) and older people (?=.03; P=.01). Social loneliness was not a significant mediator between social media use and suicidal ideation among either age group (P>.05). Conclusions: Social media may be a ?double-edged sword? for psychosocial well-being during the COVID-19 pandemic, and its roles vary across age groups. The mediators identified in this study can be addressed by psychological interventions to prevent severe mental health problems during and after the COVID-19 pandemic. UR - https://publichealth.jmir.org/2021/5/e24623 UR - http://dx.doi.org/10.2196/24623 UR - http://www.ncbi.nlm.nih.gov/pubmed/33835937 ID - info:doi/10.2196/24623 ER - TY - JOUR AU - Sivesind, Elise Torunn AU - Szeto, D. Mindy AU - Kim, William AU - Dellavalle, Paul Robert PY - 2021/5/25 TI - Google Trends in Dermatology: Scoping Review of the Literature JO - JMIR Dermatol SP - e27712 VL - 4 IS - 1 KW - Google Trends KW - search trends KW - internet KW - infodemiology KW - infoveillance KW - search terms KW - dermatology KW - skin cancer KW - databases N2 - Background: Google Trends is a powerful online database and analytics tool of popular Google search queries over time and has the potential to inform medical practice and priorities. Objective: This review aimed to survey Google Trends literature in dermatology and elucidate its current roles and relationships with the field. Methods: A literature search was performed using PubMed to access and review relevant dermatology-related Google Trends studies published within the last 5 years. Results: Current research utilizing Google Trends data provides insight related to skin cancer, pruritus, cosmetic procedures, and COVID-19. We also found that dermatology is presently the highest-searched medical specialty?among 15 medical and surgical specialties as well as general practitioners. Google searches related to dermatology demonstrate a seasonal nature for various skin conditions and sun-related topics, depending on a region?s inherent climate and hemi-sphere. In addition, celebrity social media and other viral posts have been found to potentiate Google searches about dermatology and drive public interest. Conclusions: A limited number of relevant studies may have been omitted by the simplified search strategy of this study, as well as by restriction to English language articles and articles indexed in the PubMed database. This could be expanded upon in a secondary systematic review. Future re-search is warranted to better understand how Google Trends can be utilized to improve the quality of clinic visits, drive public health campaigns, and detect disease clusters in real time. UR - https://derma.jmir.org/2021/1/e27712 UR - http://dx.doi.org/10.2196/27712 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632813 ID - info:doi/10.2196/27712 ER - TY - JOUR AU - Jang, Beakcheol AU - Kim, Inhwan AU - Kim, Wook Jong PY - 2021/5/25 TI - Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study JO - JMIR Med Inform SP - e23305 VL - 9 IS - 5 KW - influenza KW - training data extraction KW - keyword KW - sorting KW - word embedding KW - Pearson correlation coefficient KW - long short-term memory KW - surveillance KW - infodemiology KW - infoveillance KW - model N2 - Background: Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data. Objective: In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction. Methods: Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques. Results: Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6%, although smaller amounts of training data were selected, with only 20.6 keywords on average. Conclusions: The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction. UR - https://medinform.jmir.org/2021/5/e23305 UR - http://dx.doi.org/10.2196/23305 UR - http://www.ncbi.nlm.nih.gov/pubmed/34032577 ID - info:doi/10.2196/23305 ER - TY - JOUR AU - Tao, Zhuo-Ying AU - Su, Yu-Xiong PY - 2021/5/21 TI - Authors? Reply to: Methodological Clarifications and Generalizing From Weibo Data. Comment on ?Nature and Diffusion of COVID-19?related Oral Health Information on Chinese Social Media: Analysis of Tweets on Weibo? JO - J Med Internet Res SP - e29145 VL - 23 IS - 5 KW - COVID-19 KW - dentistry KW - oral health KW - dental health KW - online health KW - social media KW - tweet KW - Weibo KW - China KW - health information UR - https://www.jmir.org/2021/5/e29145 UR - http://dx.doi.org/10.2196/29145 UR - http://www.ncbi.nlm.nih.gov/pubmed/33989166 ID - info:doi/10.2196/29145 ER - TY - JOUR AU - Yadav, Prakash Om PY - 2021/5/21 TI - Methodological Clarifications and Generalizing From Weibo Data. Comment on ?Nature and Diffusion of COVID-19?related Oral Health Information on Chinese Social Media: Analysis of Tweets on Weibo? JO - J Med Internet Res SP - e26255 VL - 23 IS - 5 KW - COVID-19 KW - dentistry KW - oral health KW - dental health KW - online health KW - social media KW - tweet KW - Weibo KW - China KW - health information UR - https://www.jmir.org/2021/5/e26255 UR - http://dx.doi.org/10.2196/26255 UR - http://www.ncbi.nlm.nih.gov/pubmed/33989161 ID - info:doi/10.2196/26255 ER - TY - JOUR AU - Himelein-Wachowiak, McKenzie AU - Giorgi, Salvatore AU - Devoto, Amanda AU - Rahman, Muhammad AU - Ungar, Lyle AU - Schwartz, Andrew H. AU - Epstein, H. David AU - Leggio, Lorenzo AU - Curtis, Brenda PY - 2021/5/20 TI - Bots and Misinformation Spread on Social Media: Implications for COVID-19 JO - J Med Internet Res SP - e26933 VL - 23 IS - 5 KW - COVID-19 KW - coronavirus KW - social media KW - bots KW - infodemiology KW - infoveillance KW - social listening KW - infodemic KW - spambots KW - misinformation KW - disinformation KW - fake news KW - online communities KW - Twitter KW - public health UR - https://www.jmir.org/2021/5/e26933 UR - http://dx.doi.org/10.2196/26933 UR - http://www.ncbi.nlm.nih.gov/pubmed/33882014 ID - info:doi/10.2196/26933 ER - TY - JOUR AU - Kwok, Hang Stephen Wai AU - Vadde, Kumar Sai AU - Wang, Guanjin PY - 2021/5/19 TI - Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis JO - J Med Internet Res SP - e26953 VL - 23 IS - 5 KW - COVID-19 KW - vaccination KW - public topics KW - public sentiments KW - Twitter KW - infodemiology KW - infoveillance KW - social listening KW - infodemic KW - social media KW - natural language processing KW - machine learning KW - latent Dirichlet allocation N2 - Background: COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. Objective: This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. Methods: We collected 31,100 English tweets containing COVID-19 vaccine?related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. Results: Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. Conclusions: Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines. UR - https://www.jmir.org/2021/5/e26953 UR - http://dx.doi.org/10.2196/26953 UR - http://www.ncbi.nlm.nih.gov/pubmed/33886492 ID - info:doi/10.2196/26953 ER - TY - JOUR AU - Moreno, Andreas Megan AU - Jenkins, C. Marina AU - Lazovich, DeAnn PY - 2021/5/19 TI - Tanning Misinformation Posted by Businesses on Social Media and Related Perceptions of Adolescent and Young Adult White Non-Hispanic Women: Mixed Methods Study JO - JMIR Dermatol SP - e25661 VL - 4 IS - 1 KW - adolescent KW - social media KW - tanning KW - technology KW - media N2 - Background: Indoor ultraviolet (UV) tanning is common and consequential, increasing the risk for cancers including melanoma and basal cell carcinoma. At-risk groups include adolescents and young adults, who often report beliefs about benefits of tanning. Adolescent and young adults are also among the most ubiquitous social media users. As previous studies support that content about tanning is common on social media, this may be a way that young women are exposed to influential content promoting tanning, including health misinformation. Objective: The purpose of this study was to evaluate health misinformation promoted by indoor tanning businesses via social media and to understand young women?s perceptions of this misinformation. Methods: This mixed methods study included (1) retrospective observational content analysis of indoor tanning salons? content on Facebook over 1 year and (2) qualitative interviews with a purposeful national sample of 46 White non-Hispanic women, age 16 to 23 years, who had recently tanned indoors. We assessed experiences with tanning businesses? posted content on social media through interviews. We used the constant comparative approach for qualitative analyses. Results: Content analysis findings included data from indoor tanning businesses (n=147) across 50 states, yielding 4956 total posts. Among 9 health misinformation topics identified, the most common was the promotion of UV tanning as a safe way to get Vitamin D (n=73, 1.5%). An example post was ?Stop by Body and Sol to get your daily dose of Vitamin D.? Another misinformation topic was promoting tanning for health benefits (n=31, 0.62%), an example post was ?the flu is not a season, it?s an inability to adapt due to decreased sun exposure?? A total of 46 participants completed interviews (age: mean 20 years, SD 2). Almost all participants (45/46, 98%) used Facebook, and 43.5% (20/46) followed an indoor tanning business on social media. Approximately half of participants reported seeing social media posts from tanning salons about Vitamin D, an example of a participant comment was ?I have [seen that] a few times...? Among the participants, approximately half believed it was safe to get Vitamin D from indoor UV tanning; a participant stated: ?I think it is a valid benefit to UV tanning.? Conclusions: Despite the low frequency (range 0.5%-1.5%) of social media posts promoting health misinformation, participants commonly reported viewing these posts, and their perceptions aligned with health misinformation. Health education campaigns, possibly using social media to target at-risk populations, may be an innovative approach for tanning prevention messages. UR - https://derma.jmir.org/2021/1/e25661 UR - http://dx.doi.org/10.2196/25661 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632797 ID - info:doi/10.2196/25661 ER - TY - JOUR AU - Cresswell, Kathrin AU - Tahir, Ahsen AU - Sheikh, Zakariya AU - Hussain, Zain AU - Domínguez Hernández, Andrés AU - Harrison, Ewen AU - Williams, Robin AU - Sheikh, Aziz AU - Hussain, Amir PY - 2021/5/17 TI - Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence?Enabled Social Media Analysis JO - J Med Internet Res SP - e26618 VL - 23 IS - 5 KW - artificial intelligence KW - sentiment analysis KW - COVID-19 KW - contact tracing KW - social media KW - perception KW - app KW - exploratory KW - suitability KW - AI KW - Facebook KW - Twitter KW - United Kingdom KW - sentiment KW - attitude KW - infodemiology KW - infoveillance N2 - Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2. Objective: In this study, we sought to explore the suitability of artificial intelligence (AI)?enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom. Methods: We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19?related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app?related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning?based approaches. Results: Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology. Conclusions: Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns. UR - https://www.jmir.org/2021/5/e26618 UR - http://dx.doi.org/10.2196/26618 UR - http://www.ncbi.nlm.nih.gov/pubmed/33939622 ID - info:doi/10.2196/26618 ER - TY - JOUR AU - Nguyen, Xuan-Lan Anne AU - Trinh, Xuan-Vi AU - Wang, Y. Sophia AU - Wu, Y. Albert PY - 2021/5/17 TI - Determination of Patient Sentiment and Emotion in Ophthalmology: Infoveillance Tutorial on Web-Based Health Forum Discussions JO - J Med Internet Res SP - e20803 VL - 23 IS - 5 KW - sentiment analysis KW - emotions analysis KW - natural language processing KW - online forums KW - social media KW - patient attitudes KW - medicine KW - infodemiology KW - infoveillance KW - digital health N2 - Background: Clinical data in social media are an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes, and preferences. Objective: This tutorial aims to describe a novel, robust, and modular method for the sentiment analysis and emotion detection of free text from web-based forums and the factors to consider during its application. Methods: We mined the discussion and user information of all posts containing search terms related to a medical subspecialty (oculoplastics) from MedHelp, the largest web-based platform for patient health forums. We used data cleaning and processing tools to define the relevant subset of results and prepare them for sentiment analysis. We executed sentiment and emotion analyses by using IBM Watson Natural Language Understanding to generate sentiment and emotion scores for the posts and their associated keywords. The keywords were aggregated using natural language processing tools. Results: Overall, 39 oculoplastic-related search terms resulted in 46,381 eligible posts within 14,329 threads. Posts were written by 18,319 users (117 doctors; 18,202 patients) and included 201,611 associated keywords. Keywords that occurred ?500 times in the corpus were used to identify the most prominent topics, including specific symptoms, medication, and complications. The sentiment and emotion scores of these keywords and eligible posts were analyzed to provide concrete examples of the potential of this methodology to allow for a better understanding of patients? attitudes. The overall sentiment score reflects a positive, neutral, or negative sentiment, whereas the emotion scores (anger, disgust, fear, joy, and sadness) represent the likelihood of the presence of the emotion. In keyword grouping analyses, medical signs, symptoms, and diseases had the lowest overall sentiment scores (?0.598). Complications were highly associated with sadness (0.485). Forum posts mentioning body parts were related to sadness (0.416) and fear (0.321). Administration was the category with the highest anger score (0.146). The top 6 forum subgroups had an overall negative sentiment score; the most negative one was the Neurology forum, with a score of ?0.438. The Undiagnosed Symptoms forum had the highest sadness score (0.448). The least likely fearful posts were those from the Eye Care forum, with a score of 0.260. The overall sentiment score was much more negative before the doctor replied. The anger, disgust, fear, and sadness emotion scores decreased in likelihood, whereas joy was slightly more likely to be expressed after doctors replied. Conclusions: This report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients? perspectives and promote patient-centric care. Important factors to be considered during its application include evaluating the scope of the search; selecting search terms and understanding their linguistic usages; and establishing selection, filtering, and processing criteria for posts and keywords tailored to the desired results. UR - https://www.jmir.org/2021/5/e20803 UR - http://dx.doi.org/10.2196/20803 UR - http://www.ncbi.nlm.nih.gov/pubmed/33999001 ID - info:doi/10.2196/20803 ER - TY - JOUR AU - Liu, Siru AU - Liu, Jialin PY - 2021/5/12 TI - Understanding Behavioral Intentions Toward COVID-19 Vaccines: Theory-Based Content Analysis of Tweets JO - J Med Internet Res SP - e28118 VL - 23 IS - 5 KW - vaccine KW - COVID-19 KW - behavior KW - tweet KW - intention KW - content analysis KW - Twitter KW - social media KW - acceptance KW - threshold KW - willing KW - theory KW - model KW - infodemiology KW - infoveillance N2 - Background: Acceptance rates of COVID-19 vaccines have still not reached the required threshold to achieve herd immunity. Understanding why some people are willing to be vaccinated and others are not is a critical step to develop efficient implementation strategies to promote COVID-19 vaccines. Objective: We conducted a theory-based content analysis based on the capability, opportunity, motivation?behavior (COM-B) model to characterize the factors influencing behavioral intentions toward COVID-19 vaccines mentioned on the Twitter platform. Methods: We collected tweets posted in English from November 1-22, 2020, using a combination of relevant keywords and hashtags. After excluding retweets, we randomly selected 5000 tweets for manual coding and content analysis. We performed a content analysis informed by the adapted COM-B model. Results: Of the 5000 COVID-19 vaccine?related tweets that were coded, 4796 (95.9%) were posted by unique users. A total of 97 tweets carried positive behavioral intent, while 182 tweets contained negative behavioral intent. Of these, 28 tweets were mapped to capability factors, 155 tweets were related to motivation, 23 tweets were related to opportunities, and 74 tweets did not contain any useful information about the reasons for their behavioral intentions (?=0.73). Some tweets mentioned two or more constructs at the same time. Tweets that were mapped to capability (P<.001), motivation (P<.001), and opportunity (P=.03) factors were more likely to indicate negative behavioral intentions. Conclusions: Most behavioral intentions regarding COVID-19 vaccines were related to the motivation construct. The themes identified in this study could be used to inform theory-based and evidence-based interventions to improve acceptance of COVID-19 vaccines. UR - https://www.jmir.org/2021/5/e28118 UR - http://dx.doi.org/10.2196/28118 UR - http://www.ncbi.nlm.nih.gov/pubmed/33939625 ID - info:doi/10.2196/28118 ER - TY - JOUR AU - Hswen, Yulin AU - Zhang, Amanda AU - Ventelou, Bruno PY - 2021/5/10 TI - Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis JO - JMIR Public Health Surveill SP - e18593 VL - 7 IS - 5 KW - digital epidemiology KW - Google queries KW - asthma KW - symptoms KW - health information seeking N2 - Background: Asthma affects over 330 million people worldwide. Timing of an asthma event is extremely important and lack of identification of asthma increases the risk of death. A major challenge for health systems is the length of time between symptom onset and care seeking, which could result in delayed treatment initiation and worsening of symptoms. Objective: This study evaluates the utility of the internet search query data for the identification of the onset of asthma symptoms. Methods: Pearson correlation coefficients between the time series of hospital admissions and Google searches were computed at lag times from 4 weeks before hospital admission to 4 weeks after hospital admission. An autoregressive integrated moving average (ARIMAX) model with an autoregressive process at lags of 1 and 2 and Google searches at weeks ?1 and ?2 as exogenous variables were conducted to validate our correlation results. Results: Google search volume for asthma had the highest correlation at 2 weeks before hospital admission. The ARIMAX model using an autoregressive process showed that the relative searches from Google about asthma were significant at lags 1 (P<.001) and 2 (P=.04). Conclusions: Our findings demonstrate that internet search queries may provide a real-time signal for asthma events and may be useful to measure the timing of symptom onset. UR - https://publichealth.jmir.org/2021/5/e18593 UR - http://dx.doi.org/10.2196/18593 UR - http://www.ncbi.nlm.nih.gov/pubmed/33970108 ID - info:doi/10.2196/18593 ER - TY - JOUR AU - Bunyan, Alden AU - Venuturupalli, Swamy AU - Reuter, Katja PY - 2021/5/6 TI - Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed Methods Study JO - JMIR Res Protoc SP - e15716 VL - 10 IS - 5 KW - health promotion KW - infodemiology KW - infoveillance KW - Internet KW - listening KW - lupus KW - systematic lupus erythematosus KW - surveillance KW - Twitter KW - survey KW - social media KW - social network N2 - Background: Lupus is a complex autoimmune disease that is difficult to diagnose and treat. It is estimated that at least 5 million Americans have lupus, with more than 16,000 new cases of lupus being reported annually in the United States. Social media provides a platform for patients to find rheumatologists and peers and build awareness of the condition. Researchers have suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues. However, there is a lack of research about the characteristics of lupus patients on Twitter and their attitudes toward using Twitter for engaging them with their health care. Objective: This study has two objectives: (1) to conduct a content analysis of Twitter data published by users (in English) in the United States between September 1, 2017 and October 31, 2018 to identify patients who publicly discuss their lupus condition and to assess their expressed health themes and (2) to conduct a cross-sectional survey among these lupus patients on Twitter to study their attitudes toward using Twitter for engaging them with their health care. Methods: This is a mixed methods study that analyzes retrospective Twitter data and conducts a cross-sectional survey among lupus patients on Twitter. We used Symplur Signals, a health care social media analytics platform, to access the Twitter data and analyze user-generated posts that include keywords related to lupus. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among lupus patients. We will further conduct self-report surveys via Twitter by inviting all identified lupus patients who discuss their lupus condition on Twitter. The goal of the survey is to collect data about the characteristics of lupus patients (eg, gender, race/ethnicity, educational level) and their attitudes toward using Twitter for engaging them with their health care. Results: This study has been funded by the National Center for Advancing Translational Science through a Clinical and Translational Science Award. The institutional review board at the University of Southern California (HS-19-00048) approved the study. Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to ?lupus? from users in the United States published in English between September 1, 2017 and October 31, 2018. We included 40,885 posts in the analysis. Data analysis was completed in Fall 2020. Conclusions: The data obtained in this pilot study will shed light on whether Twitter provides a promising data source for garnering health-related attitudes among lupus patients. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of lupus among patients and implementing related health education interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/15716 UR - https://www.researchprotocols.org/2021/5/e15716 UR - http://dx.doi.org/10.2196/15716 UR - http://www.ncbi.nlm.nih.gov/pubmed/33955845 ID - info:doi/10.2196/15716 ER - TY - JOUR AU - Basch, E. Charles AU - Basch, H. Corey AU - Hillyer, C. Grace AU - Meleo-Erwin, C. Zoe AU - Zagnit, A. Emily PY - 2021/5/6 TI - YouTube Videos and Informed Decision-Making About COVID-19 Vaccination: Successive Sampling Study JO - JMIR Public Health Surveill SP - e28352 VL - 7 IS - 5 KW - YouTube KW - vaccination KW - COVID-19 KW - social media KW - communication KW - misinformation KW - disinformation KW - adverse reactions N2 - Background: Social media platforms such as YouTube are used by many people to seek and share health-related information that may influence their decision-making about COVID-19 vaccination. Objective: The purpose of this study was to improve the understanding about the sources and content of widely viewed YouTube videos on COVID-19 vaccination. Methods: Using the keywords ?coronavirus vaccination,? we searched for relevant YouTube videos, sorted them by view count, and selected two successive samples (with replacement) of the 100 most widely viewed videos in July and December 2020, respectively. Content related to COVID-19 vaccines were coded by two observers, and inter-rater reliability was demonstrated. Results: The videos observed in this study were viewed over 55 million times cumulatively. The number of videos that addressed fear increased from 6 in July to 20 in December 2020, and the cumulative views correspondingly increased from 2.6% (1,449,915 views) to 16.6% (9,553,368 views). There was also a large increase in the number of videos and cumulative views with respect to concerns about vaccine effectiveness, from 6 videos with approximately 6 million views in July to 25 videos with over 12 million views in December 2020. The number of videos and total cumulative views covering adverse reactions almost tripled, from 11 videos with approximately 6.5 million (11.7% of cumulative views) in July to 31 videos with almost 15.7 million views (27.2% of cumulative views) in December 2020. Conclusions: Our data show the potentially inaccurate and negative influence social media can have on population-wide vaccine uptake, which should be urgently addressed by agencies of the United States Public Health Service as well as its global counterparts. UR - https://publichealth.jmir.org/2021/5/e28352 UR - http://dx.doi.org/10.2196/28352 UR - http://www.ncbi.nlm.nih.gov/pubmed/33886487 ID - info:doi/10.2196/28352 ER - TY - JOUR AU - Vaghela, Uddhav AU - Rabinowicz, Simon AU - Bratsos, Paris AU - Martin, Guy AU - Fritzilas, Epameinondas AU - Markar, Sheraz AU - Purkayastha, Sanjay AU - Stringer, Karl AU - Singh, Harshdeep AU - Llewellyn, Charlie AU - Dutta, Debabrata AU - Clarke, M. Jonathan AU - Howard, Matthew AU - AU - Serban, Ovidiu AU - Kinross, James PY - 2021/5/6 TI - Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study JO - J Med Internet Res SP - e25714 VL - 23 IS - 5 KW - structured data synthesis KW - data science KW - critical analysis KW - web crawl data KW - pipeline KW - database KW - literature KW - research KW - COVID-19 KW - infodemic KW - decision making KW - data KW - data synthesis KW - misinformation KW - infrastructure KW - methodology N2 - Background: The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented ?infodemic?; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis?related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query. Objective: The main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19?related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data. Methods: To create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources. Results: REDASA (Realtime Data Synthesis and Analysis) is now one of the world?s largest and most up-to-date sources of COVID-19?related evidence; it consists of 104,000 documents. By capturing curators? critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19?related information and represent around 10% of all papers about COVID-19. Conclusions: This data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA?s design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers? critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world?s largest COVID-19?related data corpora for searches and curation. UR - https://www.jmir.org/2021/5/e25714 UR - http://dx.doi.org/10.2196/25714 UR - http://www.ncbi.nlm.nih.gov/pubmed/33835932 ID - info:doi/10.2196/25714 ER - TY - JOUR AU - Mangono, Tichakunda AU - Smittenaar, Peter AU - Caplan, Yael AU - Huang, S. Vincent AU - Sutermaster, Staci AU - Kemp, Hannah AU - Sgaier, K. Sema PY - 2021/5/3 TI - Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data JO - J Med Internet Res SP - e22933 VL - 23 IS - 5 KW - Google Trends KW - coronavirus KW - COVID-19 KW - principal component analysis KW - information-seeking trends KW - information retrieval KW - trend KW - infodemiology KW - infoveillance KW - virus KW - public health KW - information seeking KW - online health information N2 - Background: The COVID-19 pandemic has impacted people?s lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. Objective: We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with?or precede?real-life events? Methods: We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. Results: The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor?s appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. Conclusions: COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool. UR - https://www.jmir.org/2021/5/e22933 UR - http://dx.doi.org/10.2196/22933 UR - http://www.ncbi.nlm.nih.gov/pubmed/33878015 ID - info:doi/10.2196/22933 ER - TY - JOUR AU - Yang, Yuan-Chi AU - Al-Garadi, Ali Mohammed AU - Bremer, Whitney AU - Zhu, M. Jane AU - Grande, David AU - Sarker, Abeed PY - 2021/5/3 TI - Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid JO - J Med Internet Res SP - e26616 VL - 23 IS - 5 KW - natural language processing KW - machine learning KW - Twitter KW - infodemiology KW - infoveillance KW - social media KW - Medicaid KW - consumer feedback N2 - Background: The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers? perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter. Objective: This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example. Methods: We collected data from Twitter in two ways: via the public streaming application programming interface using Medicaid-related keywords (Corpus 1) and by using the website?s search option for tweets mentioning agency-specific handles (Corpus 2). We manually labeled a sample of tweets in 5 predetermined categories or other and artificially increased the number of training posts from specific low-frequency categories. Using the manually labeled data, we trained and evaluated several supervised learning algorithms, including support vector machine, random forest (RF), naďve Bayes, shallow neural network (NN), k-nearest neighbor, bidirectional long short-term memory, and bidirectional encoder representations from transformers (BERT). We then applied the best-performing classifier to the collected tweets for postclassification analyses to assess the utility of our methods. Results: We manually annotated 11,379 tweets (Corpus 1: 9179; Corpus 2: 2200) and used 7930 (69.7%) for training, 1449 (12.7%) for validation, and 2000 (17.6%) for testing. A classifier based on BERT obtained the highest accuracies (81.7%, Corpus 1; 80.7%, Corpus 2) and F1 scores on consumer feedback (0.58, Corpus 1; 0.90, Corpus 2), outperforming the second best classifiers in terms of accuracy (74.6%, RF on Corpus 1; 69.4%, RF on Corpus 2) and F1 score on consumer feedback (0.44, NN on Corpus 1; 0.82, RF on Corpus 2). Postclassification analyses revealed differing intercorpora distributions of tweet categories, with political (400778/628411, 63.78%) and consumer feedback (15073/27337, 55.14%) tweets being the most frequent for Corpus 1 and Corpus 2, respectively. Conclusions: The broad and variable content of Medicaid-related tweets necessitates automatic categorization to identify topic-relevant posts. Our proposed system presents a feasible solution for automatic categorization and can be deployed and generalized for health service programs other than Medicaid. Annotated data and methods are available for future studies. UR - https://www.jmir.org/2021/5/e26616 UR - http://dx.doi.org/10.2196/26616 UR - http://www.ncbi.nlm.nih.gov/pubmed/33938807 ID - info:doi/10.2196/26616 ER - TY - JOUR AU - Adikari, Achini AU - Nawaratne, Rashmika AU - De Silva, Daswin AU - Ranasinghe, Sajani AU - Alahakoon, Oshadi AU - Alahakoon, Damminda PY - 2021/4/30 TI - Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence JO - J Med Internet Res SP - e27341 VL - 23 IS - 4 KW - COVID-19 KW - pandemic KW - lockdown KW - human emotions KW - affective computing KW - human-centric artificial intelligence KW - artificial intelligence KW - AI KW - machine learning KW - natural language processing KW - language modeling KW - infodemiology KW - infoveillance N2 - Background: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. Objective: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. Methods: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. Results: The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens? mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles. Conclusions: This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises. UR - https://www.jmir.org/2021/4/e27341 UR - http://dx.doi.org/10.2196/27341 UR - http://www.ncbi.nlm.nih.gov/pubmed/33819167 ID - info:doi/10.2196/27341 ER - TY - JOUR AU - Xun, Helen AU - He, Waverley AU - Chen, Jonlin AU - Sylvester, Scott AU - Lerman, F. Sheera AU - Caffrey, Julie PY - 2021/4/30 TI - Characterization and Comparison of the Utilization of Facebook Groups Between Public Medical Professionals and Technical Communities to Facilitate Idea Sharing and Crowdsourcing During the COVID-19 Pandemic: Cross-sectional Observational Study JO - JMIR Form Res SP - e22983 VL - 5 IS - 4 KW - cognitive intelligence KW - communication KW - COVID-19 KW - crowdsourcing KW - evidence-based KW - Facebook KW - Facebook groups KW - internet KW - social media KW - virtual communities N2 - Background: Strict social distancing measures owing to the COVID-19 pandemic have led people to rely more heavily on social media, such as Facebook groups, as a means of communication and information sharing. Multiple Facebook groups have been formed by medical professionals, laypeople, and engineering or technical groups to discuss current issues and possible solutions to the current medical crisis. Objective: This study aimed to characterize Facebook groups formed by laypersons, medical professionals, and technical professionals, with specific focus on information dissemination and requests for crowdsourcing. Methods: Facebook was queried for user-created groups with the keywords ?COVID,? ?Coronavirus,? and ?SARS-CoV-2? at a single time point on March 31, 2020. The characteristics of each group were recorded, including language, privacy settings, security requirements to attain membership, and membership type. For each membership type, the group with the greatest number of members was selected, and in each of these groups, the top 100 posts were identified using Facebook?s algorithm. Each post was categorized and characterized (evidence-based, crowd-sourced, and whether the poster self-identified). STATA (version 13 SE, Stata Corp) was used for statistical analysis. Results: Our search yielded 257 COVID-19?related Facebook groups. Majority of the groups (n=229, 89%) were for laypersons, 26 (10%) were for medical professionals, and only 2 (1%) were for technical professionals. The number of members was significantly greater in medical groups (21,215, SD 35,040) than in layperson groups (7623, SD 19,480) (P<.01). Medical groups were significantly more likely to require security checks to attain membership (81% vs 43%; P<.001) and less likely to be public (3 vs 123; P<.001) than layperson groups. Medical groups had the highest user engagement, averaging 502 (SD 633) reactions (P<.01) and 224 (SD 311) comments (P<.01) per post. Medical professionals were more likely to use the Facebook groups for education and information sharing, including academic posts (P<.001), idea sharing (P=.003), resource sharing (P=.02) and professional opinions (P<.001), and requesting for crowdsourcing (P=.003). Layperson groups were more likely to share news (P<.001), humor and motivation (P<.001), and layperson opinions (P<.001). There was no significant difference in the number of evidence-based posts among the groups (P=.10). Conclusions: Medical professionals utilize Facebook groups as a forum to facilitate collective intelligence (CI) and are more likely to use Facebook groups for education and information sharing, including academic posts, idea sharing, resource sharing, and professional opinions, which highlights the power of social media to facilitate CI across geographic distances. Layperson groups were more likely to share news, humor, and motivation, which suggests the utilization of Facebook groups to provide comedic relief as a coping mechanism. Further investigations are necessary to study Facebook groups? roles in facilitating CI, crowdsourcing, education, and community-building. UR - https://formative.jmir.org/2021/4/e22983 UR - http://dx.doi.org/10.2196/22983 UR - http://www.ncbi.nlm.nih.gov/pubmed/33878013 ID - info:doi/10.2196/22983 ER - TY - JOUR AU - Oladeji, Olubusola AU - Zhang, Chi AU - Moradi, Tiam AU - Tarapore, Dharmesh AU - Stokes, C. Andrew AU - Marivate, Vukosi AU - Sengeh, D. Moinina AU - Nsoesie, O. Elaine PY - 2021/4/29 TI - Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study JO - JMIR Public Health Surveill SP - e24348 VL - 7 IS - 4 KW - obesity KW - overweight KW - Africa KW - chronic diseases KW - hypertension KW - digital phenotype KW - infodemiology KW - infoveillance N2 - Background: The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. Objective: The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. Methods: We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. Results: The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. Conclusions: Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys. UR - https://publichealth.jmir.org/2021/4/e24348 UR - http://dx.doi.org/10.2196/24348 UR - http://www.ncbi.nlm.nih.gov/pubmed/33913815 ID - info:doi/10.2196/24348 ER - TY - JOUR AU - Romer, Daniel AU - Jamieson, Hall Kathleen PY - 2021/4/27 TI - Patterns of Media Use, Strength of Belief in COVID-19 Conspiracy Theories, and the Prevention of COVID-19 From March to July 2020 in the United States: Survey Study JO - J Med Internet Res SP - e25215 VL - 23 IS - 4 KW - COVID-19 KW - conspiracy beliefs KW - social media KW - print news media KW - broadcast news media KW - conservative media KW - vaccination KW - mask wearing KW - belief KW - misinformation KW - infodemic KW - United States KW - intention KW - prevention N2 - Background: Holding conspiracy beliefs regarding the COVID-19 pandemic in the United States has been associated with reductions in both actions to prevent the spread of the infection (eg, mask wearing) and intentions to accept a vaccine when one becomes available. Patterns of media use have also been associated with acceptance of COVID-19 conspiracy beliefs. Here we ask whether the type of media on which a person relies increased, decreased, or had no additional effect on that person?s COVID-19 conspiracy beliefs over a 4-month period. Objective: We used panel data to explore whether use of conservative and social media in the United States, which were previously found to be positively related to holding conspiracy beliefs about the origins and prevention of COVID-19, were associated with a net increase in the strength of those beliefs from March to July of 2020. We also asked whether mainstream news sources, which were previously found to be negatively related to belief in pandemic-related conspiracies, were associated with a net decrease in the strength of such beliefs over the study period. Additionally, we asked whether subsequent changes in pandemic conspiracy beliefs related to the use of media were also related to subsequent mask wearing and vaccination intentions. Methods: A survey that we conducted with a national US probability sample in March of 2020 and again in July with the same 840 respondents assessed belief in pandemic-related conspiracies, use of various types of media information sources, actions taken to prevent the spread of the disease and intentions to vaccinate, and various demographic characteristics. Change across the two waves was analyzed using path analytic techniques. Results: We found that conservative media use predicted an increase in conspiracy beliefs (?=.17, 99% CI .10-.25) and that reliance on mainstream print predicted a decrease in their belief (?=?.08, 99% CI ?.14 to ?.02). Although many social media platforms reported downgrading or removing false or misleading content, ongoing use of such platforms by respondents predicted growth in conspiracy beliefs as well (?=.072, 99% CI .018-.123). Importantly, conspiracy belief changes related to media use between the two waves of the study were associated with the uptake of mask wearing and changes in vaccination intentions in July. Unlike other media, use of mainstream broadcast television predicted greater mask wearing (?=.17, 99% CI .09-.26) and vaccination intention (?=.08, 95% CI .02-.14), independent of conspiracy beliefs. Conclusions: The findings point to the need for greater efforts on the part of commentators, reporters, and guests on conservative media to report verifiable information about the pandemic. The results also suggest that social media platforms need to be more aggressive in downgrading, blocking, and counteracting claims about COVID-19 vaccines, claims about mask wearing, and conspiracy beliefs that have been judged problematic by public health authorities. UR - https://www.jmir.org/2021/4/e25215 UR - http://dx.doi.org/10.2196/25215 UR - http://www.ncbi.nlm.nih.gov/pubmed/33857008 ID - info:doi/10.2196/25215 ER - TY - JOUR AU - Ovalle, Anaelia AU - Goldstein, Orpaz AU - Kachuee, Mohammad AU - Wu, C. Elizabeth S. AU - Hong, Chenglin AU - Holloway, W. Ian AU - Sarrafzadeh, Majid PY - 2021/4/26 TI - Leveraging Social Media Activity and Machine Learning for HIV and Substance Abuse Risk Assessment: Development and Validation Study JO - J Med Internet Res SP - e22042 VL - 23 IS - 4 KW - online social networks KW - machine learning KW - behavioral intervention KW - data mining KW - msm KW - public health N2 - Background: Social media networks provide an abundance of diverse information that can be leveraged for data-driven applications across various social and physical sciences. One opportunity to utilize such data exists in the public health domain, where data collection is often constrained by organizational funding and limited user adoption. Furthermore, the efficacy of health interventions is often based on self-reported data, which are not always reliable. Health-promotion strategies for communities facing multiple vulnerabilities, such as men who have sex with men, can benefit from an automated system that not only determines health behavior risk but also suggests appropriate intervention targets. Objective: This study aims to determine the value of leveraging social media messages to identify health risk behavior for men who have sex with men. Methods: The Gay Social Networking Analysis Program was created as a preliminary framework for intelligent web-based health-promotion intervention. The program consisted of a data collection system that automatically gathered social media data, health questionnaires, and clinical results for sexually transmitted diseases and drug tests across 51 participants over 3 months. Machine learning techniques were utilized to assess the relationship between social media messages and participants' offline sexual health and substance use biological outcomes. The F1 score, a weighted average of precision and recall, was used to evaluate each algorithm. Natural language processing techniques were employed to create health behavior risk scores from participant messages. Results: Offline HIV, amphetamine, and methamphetamine use were correctly identified using only social media data, with machine learning models obtaining F1 scores of 82.6%, 85.9%, and 85.3%, respectively. Additionally, constructed risk scores were found to be reasonably comparable to risk scores adapted from the Center for Disease Control. Conclusions: To our knowledge, our study is the first empirical evaluation of a social media?based public health intervention framework for men who have sex with men. We found that social media data were correlated with offline sexual health and substance use, verified through biological testing. The proof of concept and initial results validate that public health interventions can indeed use social media?based systems to successfully determine offline health risk behaviors. The findings demonstrate the promise of deploying a social media?based just-in-time adaptive intervention to target substance use and HIV risk behavior. UR - https://www.jmir.org/2021/4/e22042 UR - http://dx.doi.org/10.2196/22042 UR - http://www.ncbi.nlm.nih.gov/pubmed/33900200 ID - info:doi/10.2196/22042 ER - TY - JOUR AU - Tang, Lu AU - Liu, Wenlin AU - Thomas, Benjamin AU - Tran, Nga Hong Thoai AU - Zou, Wenxue AU - Zhang, Xueying AU - Zhi, Degui PY - 2021/4/26 TI - Texas Public Agencies? Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach JO - JMIR Public Health Surveill SP - e26720 VL - 7 IS - 4 KW - COVID-19 KW - public health agencies KW - natural language processing KW - Twitter KW - health belief model KW - public engagement KW - social media KW - belief KW - public health KW - engagement KW - communication KW - strategy KW - content analysis KW - dissemination N2 - Background: The ongoing COVID-19 pandemic is characterized by different morbidity and mortality rates across different states, cities, rural areas, and diverse neighborhoods. The absence of a national strategy for battling the pandemic also leaves state and local governments responsible for creating their own response strategies and policies. Objective: This study examines the content of COVID-19?related tweets posted by public health agencies in Texas and how content characteristics can predict the level of public engagement. Methods: All COVID-19?related tweets (N=7269) posted by Texas public agencies during the first 6 months of 2020 were classified in terms of each tweet?s functions (whether the tweet provides information, promotes action, or builds community), the preventative measures mentioned, and the health beliefs discussed, by using natural language processing. Hierarchical linear regressions were conducted to explore how tweet content predicted public engagement. Results: The information function was the most prominent function, followed by the action or community functions. Beliefs regarding susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets that served the information or action functions were more likely to be retweeted, while tweets that served the action and community functions were more likely to be liked. Tweets that provided susceptibility information resulted in the most public engagement in terms of the number of retweets and likes. Conclusions: Public health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve their strategies for designing social media messages about the benefits of disease prevention behaviors and audiences? self-efficacy. UR - https://publichealth.jmir.org/2021/4/e26720 UR - http://dx.doi.org/10.2196/26720 UR - http://www.ncbi.nlm.nih.gov/pubmed/33847587 ID - info:doi/10.2196/26720 ER - TY - JOUR AU - Szilagyi, Istvan-Szilard AU - Ullrich, Torsten AU - Lang-Illievich, Kordula AU - Klivinyi, Christoph AU - Schittek, Alexander Gregor AU - Simonis, Holger AU - Bornemann-Cimenti, Helmar PY - 2021/4/22 TI - Google Trends for Pain Search Terms in the World?s Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study JO - J Med Internet Res SP - e27214 VL - 23 IS - 4 KW - COVID-19 KW - data mining KW - Google Trends KW - incidence KW - internet KW - interest KW - pain KW - research KW - trend N2 - Background: Web-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade. Objective: This study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020. Methods: Search terms from the following regions were used for the analysis: India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. All statistical calculations were performed through exploratory data analysis and nonparametric Mann?Whitney U tests. Results: Although the overall search activity for pain-related terms increased, apart from pain entities such as headache, chest pain, and sore throat, we observed discordant search activity. Among the most populous regions, pain-related search parameters for shoulder, abdominal, and chest pain, headache, and toothache differed significantly before and after the first officially confirmed COVID-19 cases (for all, P<.001). In addition, we observed a heterogenous, marked increase or reduction in pain-related search parameters among the most populated regions. Conclusions: As internet searches are a surrogate for public interest, we assume that our data are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups. UR - https://www.jmir.org/2021/4/e27214 UR - http://dx.doi.org/10.2196/27214 UR - http://www.ncbi.nlm.nih.gov/pubmed/33844638 ID - info:doi/10.2196/27214 ER - TY - JOUR AU - Koyama, Sachiko AU - Ueha, Rumi AU - Kondo, Kenji PY - 2021/4/22 TI - Loss of Smell and Taste in Patients With Suspected COVID-19: Analyses of Patients? Reports on Social Media JO - J Med Internet Res SP - e26459 VL - 23 IS - 4 KW - COVID-19 KW - anosmia KW - ageusia KW - free reports on social media KW - symptomatic KW - asymptomatic KW - recovery of senses KW - symptom KW - social media KW - smell KW - taste KW - senses KW - patient-reported KW - benefit KW - limit KW - diagnosis N2 - Background: The year 2020 was the year of the global COVID-19 pandemic. The severity of the situation has become so substantial that many or even most of the patients with mild to moderate symptoms had to self-isolate without specific medical treatments or even without being tested for COVID-19. Many patients joined internet membership groups to exchange information and support each other. Objective: Our goal is to determine the benefits and limits of using social media to understand the symptoms of patients with suspected COVID-19 with mild to moderate symptoms and, in particular, their symptoms of anosmia (loss of the sense of smell) and ageusia (loss of the sense of taste). The voluntary reports on an internet website of a membership group will be the platform of the analyses. Methods: Posts and comments of members of an internet group known as COVID-19 Smell and Taste Loss, founded on March 24, 2020, to support patients with suspected COVID-19 were collected and analyzed daily. Demographic data were collected using the software mechanism called Group Insights on the membership group website. Results: Membership groups on social media have become rare sources of support for patients with suspected COVID-19 with mild to moderate symptoms. These groups provided mental support to their members and became resources for information on COVID-19 tests and medicines or supplements. However, the membership was voluntary, and often the members leave without notification. It is hard to be precise from the free voluntary reports. The number of women in the group (6995/9227, 75.38% as of October 12, 2020) was about three times more than men (2272/9227, 24.62% as of October 12, 2020), and the peak age of members was between 20-40 years in both men and women. Patients who were asymptomatic other than the senses comprised 14.93% (53/355) of the total patients. Recovery of the senses was higher in the patients who were asymptomatic besides having anosmia and ageusia. Most (112/123, 91.06%) patients experienced other symptoms first and then lost their senses, on average, 4.2 days later. Patients without other symptoms tended to recover earlier (P=.02). Patients with anosmia and ageusia occasionally reported distorted smell and taste (parosmia and dysgeusia) as well as experiencing or perceiving the smell and taste without the sources of the smell or taste (phantosmia and phantogeusia). Conclusions: Our analysis of the social media database of suspected COVID-19 patients? voices demonstrated that, although accurate diagnosis of patients is not always obtained with social media?based analyses, it may be a useful tool to collect a large amount of data on symptoms and the clinical course of worldwide rapidly growing infectious diseases. UR - https://www.jmir.org/2021/4/e26459 UR - http://dx.doi.org/10.2196/26459 UR - http://www.ncbi.nlm.nih.gov/pubmed/33788699 ID - info:doi/10.2196/26459 ER - TY - JOUR AU - Furstrand, Dorthe AU - Pihl, Andreas AU - Orbe, Bayram Elif AU - Kingod, Natasja AU - Sřndergaard, Jens PY - 2021/4/20 TI - ?Ask a Doctor About Coronavirus?: How Physicians on Social Media Can Provide Valid Health Information During a Pandemic JO - J Med Internet Res SP - e24586 VL - 23 IS - 4 KW - COVID-19 KW - coronavirus KW - digital health literacy KW - eHealth literacy KW - Facebook KW - framework KW - health information KW - health literacy KW - health promotion KW - infodemic KW - infodemiology KW - mental health KW - misinformation KW - pandemic KW - patient-physician relationship KW - public health KW - social media KW - trust KW - web-based community UR - https://www.jmir.org/2021/4/e24586 UR - http://dx.doi.org/10.2196/24586 UR - http://www.ncbi.nlm.nih.gov/pubmed/33835935 ID - info:doi/10.2196/24586 ER - TY - JOUR AU - Gerts, Dax AU - Shelley, D. Courtney AU - Parikh, Nidhi AU - Pitts, Travis AU - Watson Ross, Chrysm AU - Fairchild, Geoffrey AU - Vaquera Chavez, Yadria Nidia AU - Daughton, R. Ashlynn PY - 2021/4/14 TI - ?Thought I?d Share First? and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study JO - JMIR Public Health Surveill SP - e26527 VL - 7 IS - 4 KW - COVID-19 KW - coronavirus KW - social media KW - misinformation KW - health communication KW - Twitter KW - infodemic KW - infodemiology KW - conspiracy theories KW - vaccine hesitancy KW - 5G KW - unsupervised learning KW - random forest KW - active learning KW - supervised learning KW - machine learning KW - conspiracy KW - communication KW - vaccine KW - public health N2 - Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated. UR - https://publichealth.jmir.org/2021/4/e26527 UR - http://dx.doi.org/10.2196/26527 UR - http://www.ncbi.nlm.nih.gov/pubmed/33764882 ID - info:doi/10.2196/26527 ER - TY - JOUR AU - Griffith, Janessa AU - Marani, Husayn AU - Monkman, Helen PY - 2021/4/13 TI - COVID-19 Vaccine Hesitancy in Canada: Content Analysis of Tweets Using the Theoretical Domains Framework JO - J Med Internet Res SP - e26874 VL - 23 IS - 4 KW - vaccine hesitancy KW - vaccine KW - COVID-19 KW - immunization KW - Twitter KW - infodemiology KW - infoveillance KW - social media KW - behavioral science KW - behavior KW - Canada KW - content analysis KW - framework KW - hesitancy N2 - Background: With the approval of two COVID-19 vaccines in Canada, many people feel a sense of relief, as hope is on the horizon. However, only about 75% of people in Canada plan to receive one of the vaccines. Objective: The purpose of this study is to determine the reasons why people in Canada feel hesitant toward receiving a COVID-19 vaccine. Methods: We screened 3915 tweets from public Twitter profiles in Canada by using the search words ?vaccine? and ?COVID.? The tweets that met the inclusion criteria (ie, those about COVID-19 vaccine hesitancy) were coded via content analysis. Codes were then organized into themes and interpreted by using the Theoretical Domains Framework. Results: Overall, 605 tweets were identified as those about COVID-19 vaccine hesitancy. Vaccine hesitancy stemmed from the following themes: concerns over safety, suspicion about political or economic forces driving the COVID-19 pandemic or vaccine development, a lack of knowledge about the vaccine, antivaccine or confusing messages from authority figures, and a lack of legal liability from vaccine companies. This study also examined mistrust toward the medical industry not due to hesitancy, but due to the legacy of communities marginalized by health care institutions. These themes were categorized into the following five Theoretical Domains Framework constructs: knowledge, beliefs about consequences, environmental context and resources, social influence, and emotion. Conclusions: With the World Health Organization stating that one of the worst threats to global health is vaccine hesitancy, it is important to have a comprehensive understanding of the reasons behind this reluctance. By using a behavioral science framework, this study adds to the emerging knowledge about vaccine hesitancy in relation to COVID-19 vaccines by analyzing public discourse in tweets in real time. Health care leaders and clinicians may use this knowledge to develop public health interventions that are responsive to the concerns of people who are hesitant to receive vaccines. UR - https://www.jmir.org/2021/4/e26874 UR - http://dx.doi.org/10.2196/26874 UR - http://www.ncbi.nlm.nih.gov/pubmed/33769946 ID - info:doi/10.2196/26874 ER - TY - JOUR AU - Chrzanowski, J?drzej AU - So?ek, Julia AU - Fendler, Wojciech AU - Jemielniak, Dariusz PY - 2021/4/12 TI - Assessing Public Interest Based on Wikipedia?s Most Visited Medical Articles During the SARS-CoV-2 Outbreak: Search Trends Analysis JO - J Med Internet Res SP - e26331 VL - 23 IS - 4 KW - COVID-19 KW - pandemic KW - media KW - Wikipedia KW - internet KW - online health information KW - information seeking KW - interest KW - retrospective KW - surveillance KW - infodemiology KW - infoveillance N2 - Background: In the current era of widespread access to the internet, we can monitor public interest in a topic via information-targeted web browsing. We sought to provide direct proof of the global population?s altered use of Wikipedia medical knowledge resulting from the new COVID-19 pandemic and related global restrictions. Objective: We aimed to identify temporal search trends and quantify changes in access to Wikipedia Medicine Project articles that were related to the COVID-19 pandemic. Methods: We performed a retrospective analysis of medical articles across nine language versions of Wikipedia and country-specific statistics for registered COVID-19 deaths. The observed patterns were compared to a forecast model of Wikipedia use, which was trained on data from 2015 to 2019. The model comprehensively analyzed specific articles and similarities between access count data from before (ie, several years prior) and during the COVID-19 pandemic. Wikipedia articles that were linked to those directly associated with the pandemic were evaluated in terms of degrees of separation and analyzed to identify similarities in access counts. We assessed the correlation between article access counts and the number of diagnosed COVID-19 cases and deaths to identify factors that drove interest in these articles and shifts in public interest during the subsequent phases of the pandemic. Results: We observed a significant (P<.001) increase in the number of entries on Wikipedia medical articles during the pandemic period. The increased interest in COVID-19?related articles temporally correlated with the number of global COVID-19 deaths and consistently correlated with the number of region-specific COVID-19 deaths. Articles with low degrees of separation were significantly similar (P<.001) in terms of access patterns that were indicative of information-seeking patterns. Conclusions: The analysis of Wikipedia medical article popularity could be a viable method for epidemiologic surveillance, as it provides important information about the reasons behind public attention and factors that sustain public interest in the long term. Moreover, Wikipedia users can potentially be directed to credible and valuable information sources that are linked with the most prominent articles. UR - https://www.jmir.org/2021/4/e26331 UR - http://dx.doi.org/10.2196/26331 UR - http://www.ncbi.nlm.nih.gov/pubmed/33667176 ID - info:doi/10.2196/26331 ER - TY - JOUR AU - Heckman, Carolyn AU - Lin, Yong AU - Riley, Mary AU - Wang, Yaqun AU - Bhurosy, Trishnee AU - Mitarotondo, Anna AU - Xu, Baichen AU - Stapleton, Jerod PY - 2021/4/9 TI - Association Between State Indoor Tanning Legislation and Google Search Trends Data in the United States From 2006 to 2019: Time-Series Analysis JO - JMIR Dermatol SP - e26707 VL - 4 IS - 1 KW - adolescents KW - dermatology KW - Google Trends KW - indoor tanning KW - internet KW - policy KW - prevention KW - skin cancer KW - skin cancer prevention KW - tanning KW - trend KW - time series KW - web-based health information KW - young adult KW - youth N2 - Background: Exposure to ultraviolet radiation from the sun or indoor tanning is the cause of most skin cancers. Although indoor tanning has decreased in recent years, it remains most common among adolescents and young adults, whose skin is particularly vulnerable to long-term damage. US states have adopted several types of legislation to attempt to minimize indoor tanning among minors: a ban on indoor tanning among all minors, a partial minor ban by age (eg, <14 years), or the requirement of parental consent or accompaniment for tanning. Currently, only 6 US states have no indoor tanning legislation for minors. Objective: This study investigated whether internet searches (as an indicator of interest) related to indoor tanning varied across US states by the type of indoor tanning legislation, using data from Google Trends from 2006 to 2019. Methods: We conducted a time-series analysis of Google Trends data on indoor tanning from 2006 to 2019 by US state. Time-series linear regression models were generated to assess the Google Trends data over time by the type of indoor tanning legislation. Results: We found that indoor tanning search rates decreased significantly for all 50 states and the District of Columbia over time (P<.01). The searches peaked in 2012 when indoor tanning received marked attention (eg, indoor tanning was banned for all minors by the first state?California). The reduction in search rates was more marked for states with a complete ban among minors compared to those with less restrictive types of legislation. Conclusions: Our findings are consistent with those of other studies on the association between indoor tanning regulations and attitudinal and behavioral trends related to indoor tanning. The main limitation of the study is that raw search data were not available for more precise analysis. With changes in interest and norms, indoor tanning and skin cancer risk among young people may change. Future studies should continue to determine the impact of such public health policies in order to inform policy efforts and minimize risks to public health. UR - https://derma.jmir.org/2021/1/e26707 UR - http://dx.doi.org/10.2196/26707 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632845 ID - info:doi/10.2196/26707 ER - TY - JOUR AU - Platt, Moritz AU - Hasselgren, Anton AU - Román-Belmonte, Manuel Juan AU - Tuler de Oliveira, Marcela AU - De la Corte-Rodríguez, Hortensia AU - Delgado Olabarriaga, Sílvia AU - Rodríguez-Merchán, Carlos E. AU - Mackey, Ken Tim PY - 2021/4/6 TI - Test, Trace, and Put on the Blockchain?: A Viewpoint Evaluating the Use of Decentralized Systems for Algorithmic Contact Tracing to Combat a Global Pandemic JO - JMIR Public Health Surveill SP - e26460 VL - 7 IS - 4 KW - COVID-19 KW - public health KW - blockchain KW - distributed ledger technology KW - mobile apps KW - pandemic mitigation KW - contact tracing KW - epidemiological monitoring UR - https://publichealth.jmir.org/2021/4/e26460 UR - http://dx.doi.org/10.2196/26460 UR - http://www.ncbi.nlm.nih.gov/pubmed/33727212 ID - info:doi/10.2196/26460 ER - TY - JOUR AU - Asgari Mehrabadi, Milad AU - Dutt, Nikil AU - Rahmani, M. Amir PY - 2021/4/6 TI - The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis JO - JMIR Public Health Surveill SP - e22880 VL - 7 IS - 4 KW - bars KW - coronavirus KW - COVID-19 KW - deep learning KW - infodemiology KW - infoveillance KW - Google Trends KW - LSTM KW - machine learning KW - restaurants N2 - Background: The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. Objective: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. Methods: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends. Results: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test. Conclusions: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes. UR - https://publichealth.jmir.org/2021/4/e22880 UR - http://dx.doi.org/10.2196/22880 UR - http://www.ncbi.nlm.nih.gov/pubmed/33690143 ID - info:doi/10.2196/22880 ER - TY - JOUR AU - Oyebode, Oladapo AU - Ndulue, Chinenye AU - Adib, Ashfaq AU - Mulchandani, Dinesh AU - Suruliraj, Banuchitra AU - Orji, Anulika Fidelia AU - Chambers, T. Christine AU - Meier, Sandra AU - Orji, Rita PY - 2021/4/6 TI - Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach JO - JMIR Med Inform SP - e22734 VL - 9 IS - 4 KW - social media KW - COVID-19 KW - coronavirus KW - infodemiology KW - infoveillance KW - natural language processing KW - text mining KW - thematic analysis KW - interventions KW - health issues KW - psychosocial issues KW - social issues N2 - Background: The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. Objective: This paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data. Methods: We applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19?related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes. Results: A total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. Conclusions: We uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics. UR - https://medinform.jmir.org/2021/4/e22734 UR - http://dx.doi.org/10.2196/22734 UR - http://www.ncbi.nlm.nih.gov/pubmed/33684052 ID - info:doi/10.2196/22734 ER - TY - JOUR AU - Hussain, Amir AU - Tahir, Ahsen AU - Hussain, Zain AU - Sheikh, Zakariya AU - Gogate, Mandar AU - Dashtipour, Kia AU - Ali, Azhar AU - Sheikh, Aziz PY - 2021/4/5 TI - Artificial Intelligence?Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study JO - J Med Internet Res SP - e26627 VL - 23 IS - 4 KW - artificial intelligence KW - COVID-19 KW - deep learning KW - Facebook KW - health informatics KW - natural language processing KW - public health KW - sentiment analysis KW - social media KW - Twitter KW - infodemiology KW - vaccination N2 - Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence?based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning?based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence?enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake. UR - https://www.jmir.org/2021/4/e26627 UR - http://dx.doi.org/10.2196/26627 UR - http://www.ncbi.nlm.nih.gov/pubmed/33724919 ID - info:doi/10.2196/26627 ER - TY - JOUR AU - Zhou, Xinyu AU - Song, Yi AU - Jiang, Hao AU - Wang, Qian AU - Qu, Zhiqiang AU - Zhou, Xiaoyu AU - Jit, Mark AU - Hou, Zhiyuan AU - Lin, Leesa PY - 2021/4/5 TI - Comparison of Public Responses to Containment Measures During the Initial Outbreak and Resurgence of COVID-19 in China: Infodemiology Study JO - J Med Internet Res SP - e26518 VL - 23 IS - 4 KW - COVID-19 KW - engagement KW - latent Dirichlet allocation KW - public response KW - sentiment KW - social media KW - topic modeling N2 - Background: COVID-19 cases resurged worldwide in the second half of 2020. Not much is known about the changes in public responses to containment measures from the initial outbreak to resurgence. Monitoring public responses is crucial to inform policy measures to prepare for COVID-19 resurgence. Objective: This study aimed to assess and compare public responses to containment measures during the initial outbreak and resurgence of COVID-19 in China. Methods: We curated all COVID-19?related posts from Sina Weibo (China?s version of Twitter) during the initial outbreak and resurgence of COVID-19 in Beijing, China. With a Python script, we constructed subsets of Weibo posts focusing on 3 containment measures: lockdown, the test-trace-isolate strategy, and suspension of gatherings. The Baidu open-source sentiment analysis model and latent Dirichlet allocation topic modeling, a widely used machine learning algorithm, were used to assess public engagement, sentiments, and frequently discussed topics on each containment measure. Results: A total of 8,985,221 Weibo posts were curated. In China, the containment measures evolved from a complete lockdown for the general population during the initial outbreak to a more targeted response strategy for high-risk populations during COVID-19 resurgence. Between the initial outbreak and resurgence, the average daily proportion of Weibo posts with negative sentiments decreased from 57% to 47% for the lockdown, 56% to 51% for the test-trace-isolate strategy, and 55% to 48% for the suspension of gatherings. Among the top 3 frequently discussed topics on lockdown measures, discussions on containment measures accounted for approximately 32% in both periods, but those on the second-most frequently discussed topic shifted from the expression of negative emotions (11%) to its impacts on daily life or work (26%). The public expressed a high level of panic (21%) during the initial outbreak but almost no panic (1%) during resurgence. The more targeted test-trace-isolate measure received the most support (60%) among all 3 containment measures in the initial outbreak, and its support rate approached 90% during resurgence. Conclusions: Compared to the initial outbreak, the public expressed less engagement and less negative sentiments on containment measures and were more supportive toward containment measures during resurgence. Targeted test-trace-isolate strategies were more acceptable to the public. Our results indicate that when COVID-19 resurges, more targeted test-trace-isolate strategies for high-risk populations should be promoted to balance pandemic control and its impact on daily life and the economy. UR - https://www.jmir.org/2021/4/e26518 UR - http://dx.doi.org/10.2196/26518 UR - http://www.ncbi.nlm.nih.gov/pubmed/33750739 ID - info:doi/10.2196/26518 ER - TY - JOUR AU - Al-Ramahi, Mohammad AU - Elnoshokaty, Ahmed AU - El-Gayar, Omar AU - Nasralah, Tareq AU - Wahbeh, Abdullah PY - 2021/4/5 TI - Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data JO - JMIR Public Health Surveill SP - e26780 VL - 7 IS - 4 KW - pandemic KW - coronavirus KW - masks KW - social medial, opinion analysis KW - COVID-19 N2 - Background: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. Objective: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. Methods: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. Results: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. Conclusions: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics. UR - https://publichealth.jmir.org/2021/4/e26780 UR - http://dx.doi.org/10.2196/26780 UR - http://www.ncbi.nlm.nih.gov/pubmed/33720841 ID - info:doi/10.2196/26780 ER - TY - JOUR AU - Schück, Stéphane AU - Foulquié, Pierre AU - Mebarki, Adel AU - Faviez, Carole AU - Khadhar, Mickaďl AU - Texier, Nathalie AU - Katsahian, Sandrine AU - Burgun, Anita AU - Chen, Xiaoyi PY - 2021/4/5 TI - Concerns Discussed on Chinese and French Social Media During the COVID-19 Lockdown: Comparative Infodemiology Study Based on Topic Modeling JO - JMIR Form Res SP - e23593 VL - 5 IS - 4 KW - comparative analysis KW - content analysis KW - topic model KW - social media KW - COVID-19 KW - lockdown KW - China KW - France KW - impact KW - population N2 - Background: During the COVID-19 pandemic, numerous countries, including China and France, have implemented lockdown measures that have been effective in controlling the epidemic. However, little is known about the impact of these measures on the population as expressed on social media from different cultural contexts. Objective: This study aims to assess and compare the evolution of the topics discussed on Chinese and French social media during the COVID-19 lockdown. Methods: We extracted posts containing COVID-19?related or lockdown-related keywords in the most commonly used microblogging social media platforms (ie, Weibo in China and Twitter in France) from 1 week before lockdown to the lifting of the lockdown. A topic model was applied independently for three periods (prelockdown, early lockdown, and mid to late lockdown) to assess the evolution of the topics discussed on Chinese and French social media. Results: A total of 6395; 23,422; and 141,643 Chinese Weibo messages, and 34,327; 119,919; and 282,965 French tweets were extracted in the prelockdown, early lockdown, and mid to late lockdown periods, respectively, in China and France. Four categories of topics were discussed in a continuously evolving way in all three periods: epidemic news and everyday life, scientific information, public measures, and solidarity and encouragement. The most represented category over all periods in both countries was epidemic news and everyday life. Scientific information was far more discussed on Weibo than in French tweets. Misinformation circulated through social media in both countries; however, it was more concerned with the virus and epidemic in China, whereas it was more concerned with the lockdown measures in France. Regarding public measures, more criticisms were identified in French tweets than on Weibo. Advantages and data privacy concerns regarding tracing apps were also addressed in French tweets. All these differences were explained by the different uses of social media, the different timelines of the epidemic, and the different cultural contexts in these two countries. Conclusions: This study is the first to compare the social media content in eastern and western countries during the unprecedented COVID-19 lockdown. Using general COVID-19?related social media data, our results describe common and different public reactions, behaviors, and concerns in China and France, even covering the topics identified in prior studies focusing on specific interests. We believe our study can help characterize country-specific public needs and appropriately address them during an outbreak. UR - https://formative.jmir.org/2021/4/e23593 UR - http://dx.doi.org/10.2196/23593 UR - http://www.ncbi.nlm.nih.gov/pubmed/33750736 ID - info:doi/10.2196/23593 ER - TY - JOUR AU - Wang, Dandan AU - Qian, Yuxing PY - 2021/3/25 TI - Echo Chamber Effect in Rumor Rebuttal Discussions About COVID-19 in China: Social Media Content and Network Analysis Study JO - J Med Internet Res SP - e27009 VL - 23 IS - 3 KW - rumor rebuttal KW - infodemiology KW - infodemic KW - infoveillance KW - echo chamber effect KW - attitude KW - COVID-19 KW - Weibo N2 - Background: The dissemination of rumor rebuttal content on social media is vital for rumor control and disease containment during public health crises. Previous research on the effectiveness of rumor rebuttal, to a certain extent, ignored or simplified the structure of dissemination networks and users? cognition as well as decision-making and interaction behaviors. Objective: This study aimed to roughly evaluate the effectiveness of rumor rebuttal; dig deeply into the attitude-based echo chamber effect on users? responses to rumor rebuttal under multiple topics on Weibo, a Chinese social media platform, in the early stage of the COVID-19 epidemic; and evaluate the echo chamber?s impact on the information characteristics of user interaction content. Methods: We used Sina Weibo?s application programming interface to crawl rumor rebuttal content related to COVID-19 from 10 AM on January 23, 2020, to midnight on April 8, 2020. Using content analysis, sentiment analysis, social network analysis, and statistical analysis, we first analyzed whether and to what extent there was an echo chamber effect on the shaping of individuals? attitudes when retweeting or commenting on others? tweets. Then, we tested the heterogeneity of attitude distribution within communities and the homophily of interactions between communities. Based on the results at user and community levels, we made comprehensive judgments. Finally, we examined users? interaction content from three dimensions?sentiment expression, information seeking and sharing, and civility?to test the impact of the echo chamber effect. Results: Our results indicated that the retweeting mechanism played an essential role in promoting polarization, and the commenting mechanism played a role in consensus building. Our results showed that there might not be a significant echo chamber effect on community interactions and verified that, compared to like-minded interactions, cross-cutting interactions contained significantly more negative sentiment, information seeking and sharing, and incivility. We found that online users? information-seeking behavior was accompanied by incivility, and information-sharing behavior was accompanied by more negative sentiment, which was often accompanied by incivility. Conclusions: Our findings revealed the existence and degree of an echo chamber effect from multiple dimensions, such as topic, interaction mechanism, and interaction level, and its impact on interaction content. Based on these findings, we provide several suggestions for preventing or alleviating group polarization to achieve better rumor rebuttal. UR - https://www.jmir.org/2021/3/e27009 UR - http://dx.doi.org/10.2196/27009 UR - http://www.ncbi.nlm.nih.gov/pubmed/33690145 ID - info:doi/10.2196/27009 ER - TY - JOUR AU - Saha, Koustuv AU - Torous, John AU - Kiciman, Emre AU - De Choudhury, Munmun PY - 2021/3/19 TI - Understanding Side Effects of Antidepressants: Large-scale Longitudinal Study on Social Media Data JO - JMIR Ment Health SP - e26589 VL - 8 IS - 3 KW - antidepressants KW - symptoms KW - side effects KW - digital pharmacovigilance KW - social media KW - mental health KW - linguistic markers KW - digital health N2 - Background: Antidepressants are known to show heterogeneous effects across individuals and conditions, posing challenges to understanding their efficacy in mental health treatment. Social media platforms enable individuals to share their day-to-day concerns with others and thereby can function as unobtrusive, large-scale, and naturalistic data sources to study the longitudinal behavior of individuals taking antidepressants. Objective: We aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media. Methods: On a large-scale Twitter data set of individuals who self-reported using antidepressants, a quasi-experimental study using unsupervised language analysis was conducted to extract keywords that distinguish individuals who improved and who did not improve following the use of antidepressants. The net data set consists of over 8 million Twitter posts made by over 300,000 users in a 4-year period between January 1, 2014, and February 15, 2018. Results: Five major side effects of antidepressants were studied: sleep, weight, eating, pain, and sexual issues. Social media language revealed keywords related to these side effects. In particular, antidepressants were found to show a spectrum of effects from decrease to increase in each of these side effects. Conclusions: This work enhances the understanding of the side effects of antidepressants by identifying distinct linguistic markers in the longitudinal social media data of individuals showing the most and least improvement following the self-reported intake of antidepressants. One implication of this work concerns the potential of social media data as an effective means to support digital pharmacovigilance and digital therapeutics. These results can inform clinicians in tailoring their discussion and assessment of side effects and inform patients about what to potentially expect and what may or may not be within the realm of normal aftereffects of antidepressants. UR - https://mental.jmir.org/2021/3/e26589 UR - http://dx.doi.org/10.2196/26589 UR - http://www.ncbi.nlm.nih.gov/pubmed/33739296 ID - info:doi/10.2196/26589 ER - TY - JOUR AU - Yu, Shaobin AU - Eisenman, David AU - Han, Ziqiang PY - 2021/3/18 TI - Temporal Dynamics of Public Emotions During the COVID-19 Pandemic at the Epicenter of the Outbreak: Sentiment Analysis of Weibo Posts From Wuhan JO - J Med Internet Res SP - e27078 VL - 23 IS - 3 KW - public health emergencies KW - emotion KW - infodemiology KW - temporal dynamics KW - sentiment analysis KW - COVID-19 N2 - Background: The ongoing COVID-19 pandemic has led to an increase in anxiety, depression, posttraumatic stress disorder, and psychological stress experienced by the general public in various degrees worldwide. However, effective, tailored mental health services and interventions cannot be achieved until we understand the patterns of mental health issues emerging after a public health crisis, especially in the context of the rapid transmission of COVID-19. Understanding the public's emotions and needs and their distribution attributes are therefore critical for creating appropriate public policies and eventually responding to the health crisis effectively, efficiently, and equitably. Objective: This study aims to detect the temporal patterns in emotional fluctuation, significant events during the COVID-19 pandemic that affected emotional changes and variations, and hourly variations of emotions within a single day by analyzing data from the Chinese social media platform Weibo. Methods: Based on a longitudinal dataset of 816,556 posts published by 27,912 Weibo users in Wuhan, China, from December 31, 2019, to April 31, 2020, we processed general sentiment inclination rating and the type of sentiments of Weibo posts by using pandas and SnowNLP Python libraries. We also grouped the publication times into 5 time groups to measure changes in netizens? sentiments during different periods in a single day. Results: Overall, negative emotions such as surprise, fear, and anger were the most salient emotions detected on Weibo. These emotions were triggered by certain milestone events such as the confirmation of human-to-human transmission of COVID-19. Emotions varied within a day. Although all emotions were more prevalent in the afternoon and night, fear and anger were more dominant in the morning and afternoon, whereas depression was more salient during the night. Conclusions: Various milestone events during the COVID-19 pandemic were the primary events that ignited netizens? emotions. In addition, Weibo users? emotions varied within a day. Our findings provide insights into providing better-tailored mental health services and interventions. UR - https://www.jmir.org/2021/3/e27078 UR - http://dx.doi.org/10.2196/27078 UR - http://www.ncbi.nlm.nih.gov/pubmed/33661755 ID - info:doi/10.2196/27078 ER - TY - JOUR AU - Shao, Ruosi AU - Shi, Zhen AU - Zhang, Di PY - 2021/3/16 TI - Social Media and Emotional Burnout Regulation During the COVID-19 Pandemic: Multilevel Approach JO - J Med Internet Res SP - e27015 VL - 23 IS - 3 KW - COVID-19 KW - pandemic KW - emotion regulation KW - emotional exhaustion KW - multilevel approach KW - well-being KW - emotion KW - mental health KW - social media KW - perspective KW - strategy KW - effective KW - modeling KW - buffer N2 - Background: In February 2020, the Chinese government imposed a complete lockdown of Wuhan and other cities in Hubei Province to contain a spike of COVID-19 cases. Although such measures are effective in preventing the spread of the virus, medical professionals strongly voiced a caveat concerning the pandemic emotional burnout at the individual level. Although the lockdown limited individuals? interpersonal communication with people in their social networks, it is common that individuals turn to social media to seek and share health information, exchange social support, and express pandemic-generated feelings. Objective: Based on a holistic and multilevel perspective, this study examines how pandemic-related emotional exhaustion enacts intrapersonal, interpersonal, and hyperpersonal emotional regulation strategies, and then evaluates the effectiveness of these strategies, with a particular interest in understanding the role of hyperpersonal-level regulation or social media?based regulation. Methods: Using an online panel, this study sampled 538 Chinese internet users from Hubei Province, the epicenter of the COVID-19 outbreak in China. Survey data collection lasted for 12 days from February 7-18, 2020, two weeks after Hubei Province was placed under quarantine. The sample had an average age of 35 (SD 10.65, range 18-78) years, and a majority were married (n=369, 68.6%). Results: Using structural equation modeling, this study found that intrapersonal-level (B=0.22; ?=.24; P<.001) and interpersonal-level (B=0.35; ?=.49; P<.001) emotional regulation strategies were positively associated with individuals? outcome reappraisal. In contrast with intrapersonal and interpersonal regulations, hyperpersonal (social media?based) regulation strategies, such as disclosing and retweeting negative emotions, were negatively related to the outcome reappraisal (B=?1.00; ?=?.80; P<.001). Conclusions: Consistent with previous literature, intrapersonal-level regulation (eg, cognitive reappraisal, mindfulness, and self-kindness) and interpersonal-level supportive interaction may generate a buffering effect on emotional exhaustion and promote individuals? reappraisal toward the stressful situation. However, hyperpersonal-level regulation may exacerbate the experienced negative emotions and impede reappraisal of the pandemic situation. It is speculated that retweeting content that contains pandemic-related stress and anxiety may cause a digital emotion contagion. Individuals who share other people?s negative emotional expressions on social media are likely to be affected by the negative affect contagion. More importantly, the possible benefits of intrapersonal and interpersonal emotion regulations may be counteracted by social media or hyperpersonal regulation. This suggests the necessity to conduct social media?based health communication interventions to mitigate the social media?wide negative affect contagion if lockdown policies related to highly infectious diseases are initiated. UR - https://www.jmir.org/2021/3/e27015 UR - http://dx.doi.org/10.2196/27015 UR - http://www.ncbi.nlm.nih.gov/pubmed/33661753 ID - info:doi/10.2196/27015 ER - TY - JOUR AU - Liu, Wenhui AU - Wei, Zhiru AU - Cheng, Xu AU - Pang, Ran AU - Zhang, Han AU - Li, Guangshuai PY - 2021/3/16 TI - Public Interest in Cosmetic Surgical and Minimally Invasive Plastic Procedures During the COVID-19 Pandemic: Infodemiology Study of Twitter Data JO - J Med Internet Res SP - e23970 VL - 23 IS - 3 KW - COVID-19 KW - Twitter KW - Google Trends KW - plastic procedure KW - trend KW - survey KW - surgery KW - social media N2 - Background: The unprecedented COVID-19 pandemic has brought drastic changes to the field of plastic surgery. It is critical for stakeholders in this field to identify the changes in public interest in plastic procedures to be adequately prepared to meet the challenges of the pandemic. Objective: The aim of this study is to examine tweets related to the public interest in plastic procedures during the COVID-19 pandemic and to help stakeholders in the field of plastic surgery adjust their practices and sustain their operations during the current difficult situation of the pandemic. Methods: Using a web crawler, 73,963 publicly accessible tweets about the most common cosmetic surgical and minimally invasive plastic procedures were collected. The tweets were grouped into three phases, and the tweeting frequencies and Google Trends indices were examined. Tweeting frequency, sentiment, and word frequency analyses were performed with Python modules. Results: Tweeting frequency increased by 24.0% in phase 2 and decreased by 9.1% in phase 3. Tweets about breast augmentation, liposuction, and abdominoplasty (?tummy tuck?) procedures consecutively increased over the three phases of the pandemic. Interest in Botox and chemical peel procedures revived first when the lockdown was lifted. The COVID-19 pandemic was associated with a negative impact on public sentiment about plastic procedures. The word frequency pattern significantly changed after phase 1 and then remained relatively stable. Conclusions: According to Twitter data, the public maintained their interest in plastic procedures during the COVID-19 pandemic. Stakeholders should consider refocusing on breast augmentation, liposuction, and abdominoplasty procedures during the current phase of the pandemic. In the case of a second wave of COVID-19, stakeholders should prepare for a temporary surge of Botox and chemical peel procedures. UR - https://www.jmir.org/2021/3/e23970 UR - http://dx.doi.org/10.2196/23970 UR - http://www.ncbi.nlm.nih.gov/pubmed/33608248 ID - info:doi/10.2196/23970 ER - TY - JOUR AU - Park, Sungkyu AU - Han, Sungwon AU - Kim, Jeongwook AU - Molaie, Majid Mir AU - Vu, Dieu Hoang AU - Singh, Karandeep AU - Han, Jiyoung AU - Lee, Wonjae AU - Cha, Meeyoung PY - 2021/3/16 TI - COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication JO - J Med Internet Res SP - e23272 VL - 23 IS - 3 KW - COVID-19 KW - coronavirus KW - infodemic KW - infodemiology KW - infoveillance KW - Twitter KW - topic phase detection KW - topic modeling KW - latent Dirichlet allocation KW - risk communication N2 - Background: COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. Objective: This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. Methods: We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time?topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. Results: This research found that each government?s official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. Conclusions: This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic. UR - https://www.jmir.org/2021/3/e23272 UR - http://dx.doi.org/10.2196/23272 UR - http://www.ncbi.nlm.nih.gov/pubmed/33684054 ID - info:doi/10.2196/23272 ER - TY - JOUR AU - Gbashi, Sefater AU - Adebo, Ayodeji Oluwafemi AU - Doorsamy, Wesley AU - Njobeh, Berka Patrick PY - 2021/3/16 TI - Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study JO - JMIR Med Inform SP - e22916 VL - 9 IS - 3 KW - COVID-19 KW - coronavirus KW - vaccine KW - infodemiology KW - infoveillance KW - infodemic KW - sentiment analysis KW - natural language processing KW - media KW - computation KW - linguistic KW - model KW - communication N2 - Background: The global onset of COVID-19 has resulted in substantial public health and socioeconomic impacts. An immediate medical breakthrough is needed. However, parallel to the emergence of the COVID-19 pandemic is the proliferation of information regarding the pandemic, which, if uncontrolled, cannot only mislead the public but also hinder the concerted efforts of relevant stakeholders in mitigating the effect of this pandemic. It is known that media communications can affect public perception and attitude toward medical treatment, vaccination, or subject matter, particularly when the population has limited knowledge on the subject. Objective: This study attempts to systematically scrutinize media communications (Google News headlines or snippets and Twitter posts) to understand the prevailing sentiments regarding COVID-19 vaccines in Africa. Methods: A total of 637 Twitter posts and 569 Google News headlines or descriptions, retrieved between February 2 and May 5, 2020, were analyzed using three standard computational linguistics models (ie, TextBlob, Valence Aware Dictionary and Sentiment Reasoner, and Word2Vec combined with a bidirectional long short-term memory neural network). Results: Our findings revealed that, contrary to general perceptions, Google News headlines or snippets and Twitter posts within the stated period were generally passive or positive toward COVID-19 vaccines in Africa. It was possible to understand these patterns in light of increasingly sustained efforts by various media and health actors in ensuring the availability of factual information about the pandemic. Conclusions: This type of analysis could contribute to understanding predominant polarities and associated potential attitudinal inclinations. Such knowledge could be critical in informing relevant public health and media engagement policies. UR - https://medinform.jmir.org/2021/3/e22916 UR - http://dx.doi.org/10.2196/22916 UR - http://www.ncbi.nlm.nih.gov/pubmed/33667172 ID - info:doi/10.2196/22916 ER - TY - JOUR AU - Chew, Robert AU - Kery, Caroline AU - Baum, Laura AU - Bukowski, Thomas AU - Kim, Annice AU - Navarro, Mario PY - 2021/3/16 TI - Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation JO - JMIR Public Health Surveill SP - e25807 VL - 7 IS - 3 KW - Reddit KW - social media KW - age KW - machine learning KW - classification N2 - Background: Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users? demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. Objective: We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. Methods: This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users? age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. Results: The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. Conclusions: We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users? posting behavior, linguistic patterns, and account features that distinguish adolescents from adults. UR - https://publichealth.jmir.org/2021/3/e25807 UR - http://dx.doi.org/10.2196/25807 UR - http://www.ncbi.nlm.nih.gov/pubmed/33724195 ID - info:doi/10.2196/25807 ER - TY - JOUR AU - Gesser-Edelsburg, Anat PY - 2021/3/15 TI - Using Narrative Evidence to Convey Health Information on Social Media: The Case of COVID-19 JO - J Med Internet Res SP - e24948 VL - 23 IS - 3 KW - health and risk communication KW - social media KW - narrative evidence KW - crisis KW - pandemic KW - misinformation KW - infodemic KW - infodemiology KW - COVID-19 KW - policy KW - segmentation KW - barrier reduction KW - role models KW - empathy and support KW - strengthening self/community-efficacy KW - coping tools KW - preventing stigmatization KW - at-risk populations KW - communicating uncertainty KW - positive deviance KW - tailor messaging KW - targeted behavioral change UR - https://www.jmir.org/2021/3/e24948 UR - http://dx.doi.org/10.2196/24948 UR - http://www.ncbi.nlm.nih.gov/pubmed/33674257 ID - info:doi/10.2196/24948 ER - TY - JOUR AU - Martino, Florentine AU - Brooks, Ruby AU - Browne, Jennifer AU - Carah, Nicholas AU - Zorbas, Christina AU - Corben, Kirstan AU - Saleeba, Emma AU - Martin, Jane AU - Peeters, Anna AU - Backholer, Kathryn PY - 2021/3/12 TI - The Nature and Extent of Online Marketing by Big Food and Big Alcohol During the COVID-19 Pandemic in Australia: Content Analysis Study JO - JMIR Public Health Surveill SP - e25202 VL - 7 IS - 3 KW - alcohol KW - food and beverage KW - COVID-19 KW - marketing KW - social media N2 - Background: Emerging evidence demonstrates that obesity is associated with a higher risk of COVID-19 morbidity and mortality. Excessive alcohol consumption and ?comfort eating? as coping mechanisms during times of high stress have been shown to further exacerbate mental and physical ill-health. Global examples suggest that unhealthy food and alcohol brands and companies are using the COVID-19 pandemic to further market their products. However, there has been no systematic, in-depth analysis of how ?Big Food? and ?Big Alcohol? are capitalizing on the COVID-19 pandemic to market their products and brands. Objective: We aimed to quantify the extent and nature of online marketing by alcohol and unhealthy food and beverage companies during the COVID-19 pandemic in Australia. Methods: We conducted a content analysis of all COVID-19-related social media posts made by leading alcohol and unhealthy food and beverage brands (n=42) and their parent companies (n=12) over a 4-month period (February to May 2020) during the COVID-19 pandemic in Australia. Results: Nearly 80% of included brands and all parent companies posted content related to COVID-19 during the 4-month period. Quick service restaurants (QSRs), food and alcohol delivery companies, alcohol brands, and bottle shops were the most active in posting COVID-19-related content. The most common themes for COVID-19-related marketing were isolation activities and community support. Promotion of hygiene and home delivery was also common, particularly for QSRs and alcohol and food delivery companies. Parent companies were more likely to post about corporate social responsibility (CSR) initiatives, such as donations of money and products, and to offer health advice. Conclusions: This is the first study to show that Big Food and Big Alcohol are incessantly marketing their products and brands on social media platforms using themes related to COVID-19, such as isolation activities and community support. Parent companies are frequently posting about CSR initiatives, such as donations of money and products, thereby creating a fertile environment to loosen current regulation or resist further industry regulation. ?COVID-washing? by large alcohol brands, food and beverage brands, and their parent companies is both common and concerning. The need for comprehensive regulations to restrict unhealthy food and alcohol marketing, as recommended by the World Health Organization, is particularly acute in the COVID-19 context and is urgently required to ?build back better? in a post-COVID-19 world. UR - https://publichealth.jmir.org/2021/3/e25202 UR - http://dx.doi.org/10.2196/25202 UR - http://www.ncbi.nlm.nih.gov/pubmed/33709935 ID - info:doi/10.2196/25202 ER - TY - JOUR AU - Slavik, E. Catherine AU - Buttle, Charlotte AU - Sturrock, L. Shelby AU - Darlington, Connor J. AU - Yiannakoulias, Niko PY - 2021/3/11 TI - Examining Tweet Content and Engagement of Canadian Public Health Agencies and Decision Makers During COVID-19: Mixed Methods Analysis JO - J Med Internet Res SP - e24883 VL - 23 IS - 3 KW - COVID-19 KW - coronavirus KW - pandemic KW - public health KW - Twitter KW - social media KW - engagement KW - risk communication KW - infodemiology KW - content analysis N2 - Background: Effective communication during a health crisis can ease public concerns and promote the adoption of important risk-mitigating behaviors. Public health agencies and leaders have served as the primary communicators of information related to COVID-19, and a key part of their public outreach has taken place on social media platforms. Objective: This study examined the content and engagement of COVID-19 tweets authored by Canadian public health agencies and decision makers. We propose ways for public health accounts to adjust their tweeting practices during public health crises to improve risk communication and maximize engagement. Methods: We retrieved data from tweets by Canadian public health agencies and decision makers from January 1, 2020, to June 30, 2020. The Twitter accounts were categorized as belonging to either a public health agency, regional or local health department, provincial health authority, medical health officer, or minister of health. We analyzed trends in COVID-19 tweet engagement and conducted a content analysis on a stratified random sample of 485 tweets to examine the message functions and risk communication strategies used by each account type. Results: We analyzed 32,737 tweets authored by 118 Canadian public health Twitter accounts, of which 6982 tweets were related to COVID-19. Medical health officers authored the largest percentage of COVID-19?related tweets (n=1337, 35%) relative to their total number of tweets and averaged the highest number of retweets per COVID-19 tweet (112 retweets per tweet). Public health agencies had the highest frequency of daily tweets about COVID-19 throughout the study period. Compared to tweets containing media and user mentions, hashtags and URLs were used in tweets more frequently by all account types, appearing in 69% (n=4798 tweets) and 68% (n=4781 tweets) of COVID-19?related tweets, respectively. Tweets containing hashtags also received the highest average retweets (47 retweets per tweet). Our content analysis revealed that of the three tweet message functions analyzed (information, action, community), tweets providing information were the most commonly used across most account types, constituting 39% (n=181) of all tweets; however, tweets promoting actions from users received higher than average retweets (55 retweets per tweet). When examining tweets that received one or more retweet (n=359), the difference between mean retweets across the message functions was statistically significant (P<.001). The risk communication strategies that we examined were not widely used by any account type, appearing in only 262 out of 485 tweets. However, when these strategies were used, these tweets received more retweets compared to tweets that did not use any risk communication strategies (P<.001) (61 retweets versus 13 retweets on average). Conclusions: Public health agencies and decision makers should examine what messaging best meets the needs of their Twitter audiences to maximize sharing of their communications. Public health accounts that do not currently employ risk communication strategies in their tweets may be missing an important opportunity to engage with users about the mitigation of health risks related to COVID-19. UR - https://www.jmir.org/2021/3/e24883 UR - http://dx.doi.org/10.2196/24883 UR - http://www.ncbi.nlm.nih.gov/pubmed/33651705 ID - info:doi/10.2196/24883 ER - TY - JOUR AU - Han, Yangyang AU - Jiang, Binshan AU - Guo, Rui PY - 2021/3/11 TI - Factors Affecting Public Adoption of COVID-19 Prevention and Treatment Information During an Infodemic: Cross-sectional Survey Study JO - J Med Internet Res SP - e23097 VL - 23 IS - 3 KW - information adoption KW - infodemic KW - China KW - health information KW - infodemiology KW - COVID-19 KW - public health N2 - Background: With the spread of COVID-19, an infodemic is also emerging. In public health emergencies, the use of information to enable disease prevention and treatment is incredibly important. Although both the information adoption model (IAM) and health belief model (HBM) have their own merits, they only focus on information or public influence factors, respectively, to explain the public?s intention to adopt online prevention and treatment information. Objective: The aim of this study was to fill this gap by using a combination of the IAM and the HBM as the framework for exploring the influencing factors and paths in public health events that affect the public?s adoption of online health information and health behaviors, focusing on both objective and subjective factors. Methods: We carried out an online survey to collect responses from participants in China (N=501). Structural equation modeling was used to evaluate items, and confirmatory factor analysis was used to calculate construct reliability and validity. The goodness of fit of the model and mediation effects were analyzed. Results: The overall fitness indices for the model developed in this study indicated an acceptable fit. Adoption intention was predicted by information characteristics (?=.266, P<.001) and perceived usefulness (?=.565, P<.001), which jointly explained nearly 67% of the adoption intention variance. Information characteristics (?=.244, P<.001), perceived drawbacks (?=?.097, P=.002), perceived benefits (?=.512, P<.001), and self-efficacy (?=.141, P<.001) jointly determined perceived usefulness and explained about 81% of the variance of perceived usefulness. However, social influence did not have a statistically significant impact on perceived usefulness, and self-efficacy did not significantly influence adoption intention directly. Conclusions: By integrating IAM and HBM, this study provided the insight and understanding that perceived usefulness and adoption intention of online health information could be influenced by information characteristics, people?s perceptions of information drawbacks and benefits, and self-efficacy. Moreover, people also exhibited proactive behavior rather than reactive behavior to adopt information. Thus, we should consider these factors when helping the informed public obtain useful information via two approaches: one is to improve the quality of government-based and other official information, and the other is to improve the public?s capacity to obtain information, in order to promote truth and fight rumors. This will, in turn, contribute to saving lives as the pandemic continues to unfold and run its course. UR - https://www.jmir.org/2021/3/e23097 UR - http://dx.doi.org/10.2196/23097 UR - http://www.ncbi.nlm.nih.gov/pubmed/33600348 ID - info:doi/10.2196/23097 ER - TY - JOUR AU - Ahn, Euijoon AU - Liu, Na AU - Parekh, Tej AU - Patel, Ronak AU - Baldacchino, Tanya AU - Mullavey, Tracy AU - Robinson, Amanda AU - Kim, Jinman PY - 2021/3/9 TI - A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study JO - JMIR Public Health Surveill SP - e14837 VL - 7 IS - 3 KW - public health KW - infectious disease reporting KW - mobile app KW - disease notification KW - mobile phone N2 - Background: Outbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mobile health technology provides new capabilities that can help better capture, monitor, and manage infectious diseases, including the ability to quickly identify potential outbreaks. Objective: This study aims to develop a new infectious disease surveillance (IDS) system comprising a mobile app for accurate data capturing and dashboard for better health care planning and decision making. Methods: We developed the IDS system using a 2-pronged approach: a literature review on available and similar disease surveillance systems to understand the fundamental requirements and face-to-face interviews to collect specific user requirements from the local public health unit team at the Nepean Hospital, Nepean Blue Mountains Local Health District, New South Wales, Australia. Results: We identified 3 fundamental requirements when designing an electronic IDS system, which are the ability to capture and report outbreak data accurately, completely, and in a timely fashion. We then developed our IDS system based on the workflow, scope, and specific requirements of the public health unit team. We also produced detailed design and requirement guidelines. In our system, the outbreak data are captured and sent from anywhere using a mobile device or a desktop PC (web interface). The data are processed using a client-server architecture and, therefore, can be analyzed in real time. Our dashboard is designed to provide a daily, weekly, monthly, and historical summary of outbreak information, which can be potentially used to develop a future intervention plan. Specific information about certain outbreaks can also be visualized interactively to understand the unique characteristics of emerging infectious diseases. Conclusions: We demonstrated the design and development of our IDS system. We suggest that the use of a mobile app and dashboard will simplify the overall data collection, reporting, and analysis processes, thereby improving the public health responses and providing accurate registration of outbreak information. Accurate data reporting and collection are a major step forward in creating a better intervention plan for future outbreaks of infectious diseases. UR - https://publichealth.jmir.org/2021/3/e14837 UR - http://dx.doi.org/10.2196/14837 UR - http://www.ncbi.nlm.nih.gov/pubmed/33687334 ID - info:doi/10.2196/14837 ER - TY - JOUR AU - Zhang, Chunyan AU - Xu, Songhua AU - Li, Zongfang AU - Hu, Shunxu PY - 2021/3/5 TI - Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study JO - J Med Internet Res SP - e26482 VL - 23 IS - 3 KW - COVID-19 KW - Twitter mining KW - infodemiology KW - infoveillance KW - pandemic KW - concerns KW - sentiments KW - population groups KW - disparities N2 - Background: Since the beginning of the COVID-19 pandemic in late 2019, its far-reaching impacts have been witnessed globally across all aspects of human life, such as health, economy, politics, and education. Such widely penetrating impacts cast significant and profound burdens on all population groups, incurring varied concerns and sentiments among them. Objective: This study aims to identify the concerns, sentiments, and disparities of various population groups during the COVID-19 pandemic through a cross-sectional study conducted via large-scale Twitter data mining infoveillance. Methods: This study consisted of three steps: first, tweets posted during the pandemic were collected and preprocessed on a large scale; second, the key population attributes, concerns, sentiments, and emotions were extracted via a collection of natural language processing procedures; third, multiple analyses were conducted to reveal concerns, sentiments, and disparities among population groups during the pandemic. Overall, this study implemented a quick, effective, and economical approach for analyzing population-level disparities during a public health event. The source code developed in this study was released for free public use at GitHub. Results: A total of 1,015,655 original English tweets posted from August 7 to 12, 2020, were acquired and analyzed to obtain the following results. Organizations were significantly more concerned about COVID-19 (odds ratio [OR] 3.48, 95% CI 3.39-3.58) and expressed more fear and depression emotions than individuals. Females were less concerned about COVID-19 (OR 0.73, 95% CI 0.71-0.75) and expressed less fear and depression emotions than males. Among all age groups (ie, ?18, 19-29, 30-39, and ?40 years of age), the attention ORs of COVID-19 fear and depression increased significantly with age. It is worth noting that not all females paid less attention to COVID-19 than males. In the age group of 40 years or older, females were more concerned than males, especially regarding the economic and education topics. In addition, males 40 years or older and 18 years or younger were the least positive. Lastly, in all sentiment analyses, the sentiment polarities regarding political topics were always the lowest among the five topics of concern across all population groups. Conclusions: Through large-scale Twitter data mining, this study revealed that meaningful differences regarding concerns and sentiments about COVID-19-related topics existed among population groups during the study period. Therefore, specialized and varied attention and support are needed for different population groups. In addition, the efficient analysis method implemented by our publicly released code can be utilized to dynamically track the evolution of each population group during the pandemic or any other major event for better informed public health research and interventions. UR - https://www.jmir.org/2021/3/e26482 UR - http://dx.doi.org/10.2196/26482 UR - http://www.ncbi.nlm.nih.gov/pubmed/33617460 ID - info:doi/10.2196/26482 ER - TY - JOUR AU - Kammrath Betancor, Paola AU - Tizek, Linda AU - Zink, Alexander AU - Reinhard, Thomas AU - Böhringer, Daniel PY - 2021/3/3 TI - Estimating the Incidence of Conjunctivitis by Comparing the Frequency of Google Search Terms With Clinical Data: Retrospective Study JO - JMIR Public Health Surveill SP - e22645 VL - 7 IS - 3 KW - epidemic keratoconjunctivitis KW - big data KW - Google search KW - Freiburg clinical data N2 - Background: Infectious conjunctivitis is contagious and may lead to an outbreak. Prevention systems can help to avoid an outbreak. Objective: We aimed to evaluate if Google search data on conjunctivitis and associated terms can be used to estimate the incidence and if the data can provide an estimation for outbreaks. Methods: We obtained Google search data over 4 years for the German term for conjunctivitis (?Bindehautentzündung?) and 714 associated terms in 12 selected German cities and Germany as a whole using the Google AdWords Keyword Planner. The search volume from Freiburg was correlated with clinical data from the Freiburg emergency practice (Eye Center University of Freiburg). Results: The search volume for the German term for conjunctivitis in Germany as a whole and in the 12 German cities showed a highly uniform seasonal pattern. Cross-correlation between the temporal search frequencies in Germany as a whole and the 12 selected cities was high without any lag. Cross-correlation of the search volume in Freiburg with the frequency of conjunctivitis (International Statistical Classification of Diseases and Related Health Problems [ICD] code group ?H10.-?) from the centralized ophthalmologic emergency practice in Freiburg revealed a considerable temporal association, with the emergency practice lagging behind the frequency. Additionally, Pearson correlation between the count of patients per month and the count of searches per month in Freiburg was statistically significant (P=.04). Conclusions: We observed a close correlation between the Google search volume for the signs and symptoms of conjunctivitis and the frequency of patients with a congruent diagnosis in the Freiburg region. Regional deviations from the nationwide average search volume may therefore indicate a regional outbreak of infectious conjunctivitis. UR - https://publichealth.jmir.org/2021/3/e22645 UR - http://dx.doi.org/10.2196/22645 UR - http://www.ncbi.nlm.nih.gov/pubmed/33656450 ID - info:doi/10.2196/22645 ER - TY - JOUR AU - Herbuela, Marquez Von Ralph Dane AU - Karita, Tomonori AU - Carvajal, Marzo Thaddeus AU - Ho, Tsai Howell AU - Lorena, Olea John Michael AU - Regalado, Arce Rachele AU - Sobrepeńa, Dirilo Girly AU - Watanabe, Kozo PY - 2021/3/1 TI - Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study JO - JMIR Public Health Surveill SP - e19034 VL - 7 IS - 3 KW - dengue fever KW - mHealth KW - public health surveillance KW - health communication KW - behavior modification KW - dengue outbreak N2 - Background: While early detection and effective control of epidemics depend on appropriate surveillance methods, the Philippines bases its dengue fever surveillance system on a passive surveillance method (notifications from barangay/village health centers, municipal or city health offices, hospitals, and clinics). There is no available mHealth (mobile health) app for dengue fever that includes all the appropriate surveillance methods in early detection of disease outbreaks in the country. Objective: This study aimed to evaluate the usability of the Mozzify app in terms of objective quality (engagement, functionality, aesthetics, information) and app subjective and app-specific qualities and compare total app mean score ratings by sociodemographic profile and self and family dengue fever history to see what factors are associated with high app mean score rating among school-based young adult samples and health care professionals. Individual interviews and focus group discussions were also conducted among participants to develop themes from their comments and suggestions to help structure further improvement and future development of the app. Methods: User experience sessions were conducted among participants, and the Mobile Application Rating Scale (MARS) professional and user versions (uMARS) were administered followed by individual interviews and focus group discussions. Descriptive statistical analysis of the MARS and uMARS score ratings was performed. The total app mean score ratings by sociodemographic and dengue fever history using nonparametric mean difference analyses were also conducted. Thematic synthesis was used to develop themes from the comments and suggestions raised in individual interviews and focus group discussions. Results: Mozzify obtained an overall >4 (out of 5) mean score ratings in the MARS and uMARS app objective quality (4.45), subjective (4.17), and specific (4.55) scales among 948 participants (79 health care professionals and 869 school-based samples). Mean difference analyses revealed that total app mean score ratings were not significantly different across ages and gender among health care professionals and across age, income categories, and self and family dengue fever history but not gender (P<.001) among the school-based samples. Thematic syntheses revealed 7 major themes: multilanguage options and including other diseases; Android version availability; improvements on the app?s content, design, and engagement; inclusion of users from low-income and rural areas; Wi-Fi connection and app size concerns; data credibility and issues regarding user security and privacy. Conclusions: With its acceptable performance as perceived by health care professionals and school-based young adults, Mozzify has the potential to be used as a strategic health intervention system for early detection of disease outbreaks in the Philippines. It can be used by health care professionals of any age and gender and by school-based samples of any age, socioeconomic status, and dengue fever history. The study also highlights the feasibility of school-based young adults to use health-related apps for disease prevention. UR - https://publichealth.jmir.org/2021/3/e19034 UR - http://dx.doi.org/10.2196/19034 UR - http://www.ncbi.nlm.nih.gov/pubmed/33646128 ID - info:doi/10.2196/19034 ER - TY - JOUR AU - Huynh Dagher, Solene AU - Lamé, Guillaume AU - Hubiche, Thomas AU - Ezzedine, Khaled AU - Duong, Anh Tu PY - 2021/2/25 TI - The Influence of Media Coverage and Governmental Policies on Google Queries Related to COVID-19 Cutaneous Symptoms: Infodemiology Study JO - JMIR Public Health Surveill SP - e25651 VL - 7 IS - 2 KW - chilblains KW - COVID-19 KW - dermatology KW - Google Trends KW - infodemiology KW - lesion KW - media KW - media coverage KW - online health information KW - skin lesions KW - trend N2 - Background: During COVID-19, studies have reported the appearance of internet searches for disease symptoms before their validation by the World Health Organization. This suggested that monitoring of these searches with tools including Google Trends may help monitor the pandemic itself. In Europe and North America, dermatologists reported an unexpected outbreak of cutaneous acral lesions (eg, chilblain-like lesions) in April 2020. However, external factors such as public communications may also hinder the use of Google Trends as an infodemiology tool. Objective: The study aimed to assess the impact of media announcements and lockdown enforcement on internet searches related to cutaneous acral lesions during the COVID-19 outbreak in 2020. Methods: Two searches on Google Trends, including daily relative search volumes for (1) ?toe? or ?chilblains? and (2) ?coronavirus,? were performed from January 1 to May 16, 2020, with the United States, the United Kingdom, France, Italy, Spain, and Germany as the countries of choice. The ratio of interest over time in ?chilblains? and ?coronavirus? was plotted. To assess the impact of lockdown enforcement and media coverage on these internet searches, we performed an interrupted time-series analysis for each country. Results: The ratio of interest over time in ?chilblains? to ?coronavirus? showed a constant upward trend. In France, Italy, and the United Kingdom, lockdown enforcement was associated with a significant slope change for ?chilblain? searches with a variation coefficient of 1.06 (SE 0.42) (P=0.01), 1.04 (SE 0.28) (P<.01), and 1.21 (SE 0.44) (P=0.01), respectively. After media announcements, these ratios significantly increased in France, Spain, Italy, and the United States with variation coefficients of 18.95 (SE 5.77) (P=.001), 31.31 (SE 6.31) (P<.001), 14.57 (SE 6.33) (P=.02), and 11.24 (SE 4.93) (P=.02), respectively, followed by a significant downward trend in France (?1.82 [SE 0.45]), Spain (?1.10 [SE 0.38]), and Italy (?0.93 [SE 0.33]) (P<.001, P=0.004, and P<.001, respectively). The adjusted R2 values were 0.311, 0.351, 0.325, and 0.305 for France, Spain, Italy, and the United States, respectively, suggesting an average correlation between time and the search volume; however, this correlation was weak for Germany and the United Kingdom. Conclusions: To date, the association between chilblain-like lesions and COVID-19 remains controversial; however, our results indicate that Google queries of ?chilblain? were highly influenced by media coverage and government policies, indicating that caution should be exercised when using Google Trends as a monitoring tool for emerging diseases. UR - https://publichealth.jmir.org/2021/2/e25651 UR - http://dx.doi.org/10.2196/25651 UR - http://www.ncbi.nlm.nih.gov/pubmed/33513563 ID - info:doi/10.2196/25651 ER - TY - JOUR AU - Cha, Meeyoung AU - Cha, Chiyoung AU - Singh, Karandeep AU - Lima, Gabriel AU - Ahn, Yong-Yeol AU - Kulshrestha, Juhi AU - Varol, Onur PY - 2021/2/13 TI - Prevalence of Misinformation and Factchecks on the COVID-19 Pandemic in 35 Countries: Observational Infodemiology Study JO - JMIR Hum Factors SP - e23279 VL - 8 IS - 1 KW - COVID-19 KW - coronavirus KW - infodemic KW - infodemiology KW - misinformation KW - vulnerability KW - LMIC countries N2 - Background: The COVID-19 pandemic has been accompanied by an infodemic, in which a plethora of false information has been rapidly disseminated online, leading to serious harm worldwide. Objective: This study aims to analyze the prevalence of common misinformation related to the COVID-19 pandemic. Methods: We conducted an online survey via social media platforms and a survey company to determine whether respondents have been exposed to a broad set of false claims and fact-checked information on the disease. Results: We obtained more than 41,000 responses from 1257 participants in 85 countries, but for our analysis, we only included responses from 35 countries that had at least 15 respondents. We identified a strong negative correlation between a country?s Gross Domestic Product per-capita and the prevalence of misinformation, with poorer countries having a higher prevalence of misinformation (Spearman ?=?0.72; P<.001). We also found that fact checks spread to a lesser degree than their respective false claims, following a sublinear trend (?=.64). Conclusions: Our results imply that the potential harm of misinformation could be more substantial for low-income countries than high-income countries. Countries with poor infrastructures might have to combat not only the spreading pandemic but also the COVID-19 infodemic, which can derail efforts in saving lives. UR - https://humanfactors.jmir.org/2021/1/e23279 UR - http://dx.doi.org/10.2196/23279 UR - http://www.ncbi.nlm.nih.gov/pubmed/33395395 ID - info:doi/10.2196/23279 ER - TY - JOUR AU - Zheng, Chengda AU - Xue, Jia AU - Sun, Yumin AU - Zhu, Tingshao PY - 2021/2/23 TI - Public Opinions and Concerns Regarding the Canadian Prime Minister?s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques JO - J Med Internet Res SP - e23957 VL - 23 IS - 2 KW - Canada KW - PM Trudeau KW - YouTube KW - machine learning KW - big data KW - infodemiology KW - infodemic KW - public concerns KW - communication KW - concern KW - social media KW - video N2 - Background: During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government?s responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. Objective: The aim of this study was to examine comments on Canadian Prime Minister Trudeau?s COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. Methods: We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau?s COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. Results: We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau?s policies, essential work and frontline workers, individuals? financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China?s relationship, vaccines, and reopening. Conclusions: This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau?s daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies. UR - https://www.jmir.org/2021/2/e23957 UR - http://dx.doi.org/10.2196/23957 UR - http://www.ncbi.nlm.nih.gov/pubmed/33544690 ID - info:doi/10.2196/23957 ER - TY - JOUR AU - Wang, Hanyin AU - Li, Yikuan AU - Hutch, Meghan AU - Naidech, Andrew AU - Luo, Yuan PY - 2021/2/22 TI - Using Tweets to Understand How COVID-19?Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study JO - J Med Internet Res SP - e26302 VL - 23 IS - 2 KW - COVID-19 KW - social media KW - health belief KW - Twitter KW - infodemic KW - infodemiology KW - machine learning KW - natural language processing N2 - Background: The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. Objective: This study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media. Methods: We used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures. Results: The machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R0 of 7.62 for trends in the number of Twitter users posting health belief?related content over the study period. The fluctuations in the number of health belief?related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians? speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (P=.78 and P=.92 for perceived benefits and perceived barriers, respectively). Conclusions: As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is ?unhealthy? that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians? speeches, might not be endorsed by substantial evidence and could sometimes be misleading. UR - https://www.jmir.org/2021/2/e26302 UR - http://dx.doi.org/10.2196/26302 UR - http://www.ncbi.nlm.nih.gov/pubmed/33529155 ID - info:doi/10.2196/26302 ER - TY - JOUR AU - Wong, Zheng Mark Yu AU - Gunasekeran, Visva Dinesh AU - Nusinovici, Simon AU - Sabanayagam, Charumathi AU - Yeo, Keong Khung AU - Cheng, Ching-Yu AU - Tham, Yih-Chung PY - 2021/2/19 TI - Telehealth Demand Trends During the COVID-19 Pandemic in the Top 50 Most Affected Countries: Infodemiological Evaluation JO - JMIR Public Health Surveill SP - e24445 VL - 7 IS - 2 KW - COVID-19 KW - infodemiology KW - telehealth KW - telemedicine KW - internet N2 - Background: The COVID-19 pandemic has led to urgent calls for the adoption of telehealth solutions. However, public interest and demand for telehealth during the pandemic remain unknown. Objective: We used an infodemiological approach to estimate the worldwide demand for telehealth services during COVID-19, focusing on the 50 most affected countries and comparing the demand for such services with the level of information and communications technology (ICT) infrastructure available. Methods: We used Google Trends, the Baidu Index (China), and Yandex Keyword Statistics (Russia) to extract data on worldwide and individual countries? telehealth-related internet searches from January 1 to July 7, 2020, presented as relative search volumes (RSV; range 0-100). Daily COVID-19 cases and deaths were retrieved from the World Health Organization. Individual countries? ICT infrastructure profiles were retrieved from the World Economic Forum Report. Results: Across the 50 countries, the mean RSV was 18.5 (SD 23.2), and the mean ICT index was 62.1 (SD 15.0). An overall spike in worldwide telehealth-related RSVs was observed from March 11, 2020 (RSV peaked to 76.0), which then tailed off in June-July 2020 (mean RSV for the period was 25.8), but remained higher than pre-March RSVs (mean 7.29). By country, 42 (84%) manifested increased RSVs over the evaluation period, with the highest observed in Canada (RSV=100) and the United States (RSV=96). When evaluating associations between RSV and the ICT index, both the United States and Canada demonstrated high RSVs and ICT scores (?70.3). In contrast, European countries had relatively lower RSVs (range 3.4-19.5) despite high ICT index scores (mean 70.3). Several Latin American (Brazil, Chile, Colombia) and South Asian (India, Bangladesh, Pakistan) countries demonstrated relatively higher RSVs (range 13.8-73.3) but low ICT index scores (mean 44.6), indicating that the telehealth demand outstrips the current ICT infrastructure. Conclusions: There is generally increased interest and demand for telehealth services across the 50 countries most affected by COVID-19, highlighting the need to scale up telehealth capabilities, during and beyond the pandemic. UR - http://publichealth.jmir.org/2021/2/e24445/ UR - http://dx.doi.org/10.2196/24445 UR - http://www.ncbi.nlm.nih.gov/pubmed/33605883 ID - info:doi/10.2196/24445 ER - TY - JOUR AU - Andy, U. Anietie AU - Guntuku, C. Sharath AU - Adusumalli, Srinath AU - Asch, A. David AU - Groeneveld, W. Peter AU - Ungar, H. Lyle AU - Merchant, M. Raina PY - 2021/2/19 TI - Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models JO - JMIR Cardio SP - e24473 VL - 5 IS - 1 KW - ASCVD KW - machine learning KW - natural language processing KW - atherosclerotic KW - cardiovascular disease KW - social media language KW - social media N2 - Background: Current atherosclerotic cardiovascular disease (ASCVD) predictive models have limitations; thus, efforts are underway to improve the discriminatory power of ASCVD models. Objective: We sought to evaluate the discriminatory power of social media posts to predict the 10-year risk for ASCVD as compared to that of pooled cohort risk equations (PCEs). Methods: We consented patients receiving care in an urban academic emergency department to share access to their Facebook posts and electronic medical records (EMRs). We retrieved Facebook status updates up to 5 years prior to study enrollment for all consenting patients. We identified patients (N=181) without a prior history of coronary heart disease, an ASCVD score in their EMR, and more than 200 words in their Facebook posts. Using Facebook posts from these patients, we applied a machine-learning model to predict 10-year ASCVD risk scores. Using a machine-learning model and a psycholinguistic dictionary, Linguistic Inquiry and Word Count, we evaluated if language from posts alone could predict differences in risk scores and the association of certain words with risk categories, respectively. Results: The machine-learning model predicted the 10-year ASCVD risk scores for the categories <5%, 5%-7.4%, 7.5%-9.9%, and ?10% with area under the curve (AUC) values of 0.78, 0.57, 0.72, and 0.61, respectively. The machine-learning model distinguished between low risk (<10%) and high risk (>10%) with an AUC of 0.69. Additionally, the machine-learning model predicted the ASCVD risk score with Pearson r=0.26. Using Linguistic Inquiry and Word Count, patients with higher ASCVD scores were more likely to use words associated with sadness (r=0.32). Conclusions: Language used on social media can provide insights about an individual?s ASCVD risk and inform approaches to risk modification. UR - http://cardio.jmir.org/2021/1/e24473/ UR - http://dx.doi.org/10.2196/24473 UR - http://www.ncbi.nlm.nih.gov/pubmed/33605888 ID - info:doi/10.2196/24473 ER - TY - JOUR AU - Reuter, Katja AU - Lee, Delphine PY - 2021/2/18 TI - Perspectives Toward Seeking Treatment Among Patients With Psoriasis: Protocol for a Twitter Content Analysis JO - JMIR Res Protoc SP - e13731 VL - 10 IS - 2 KW - infodemiology KW - infoveillance KW - internet KW - surveillance KW - patient opinion KW - psoriasis, treatment KW - Twitter KW - social media KW - social network N2 - Background: Psoriasis is an autoimmune disease estimated to affect more than 6 million adults in the United States. It poses a significant public health problem and contributes to rising health care costs, affecting people?s quality of life and ability to work. Previous research showed that nontreatment and undertreatment of patients with psoriasis remain a significant problem. Perspectives of patients toward seeking psoriasis treatment are understudied. Social media offers a new data source of user-generated content. Researchers suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues. Objective: The objective of this study is to conduct a content analysis of Twitter posts (in English) published by users in the United States between February 1, 2016, and October 31, 2018, to examine perspectives that potentially influence the treatment decision among patients with psoriasis. Methods: User-generated Twitter posts that include keywords related to psoriasis will be analyzed using text classifiers to identify themes related to the research questions. We will use Symplur Signals, a health care social media analytics platform, to access the Twitter data. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among people with psoriasis. Results: This study is supported by the National Center for Advancing Translational Science through a Clinical and Translational Science Award award. Study approval was obtained from the institutional review board at the University of Southern California. Data extraction and cleaning are complete. For the time period from February 1, 2016, to October 31, 2018, we obtained 95,040 Twitter posts containing terms related to ?psoriasis? from users in the United States published in English. After removing duplicates, retweets, and non-English tweets, we found that 75.51% (52,301/69,264) of the psoriasis-related posts were sent by commercial or bot-like accounts, while 16,963 posts were noncommercial and will be included in the analysis to assess the patient perspective. Analysis was completed in Summer 2020. Conclusions: This protocol paper provides a detailed description of a social media research project including the process of data extraction, cleaning, and analysis. It is our goal to contribute to the development of more transparent social media research efforts. Our findings will shed light on whether Twitter provides a promising data source for garnering patient perspective data about psoriasis treatment decisions. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of psoriasis and treatment options among patients and implementing related health interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/13731 UR - http://www.researchprotocols.org/2021/2/e13731/ UR - http://dx.doi.org/10.2196/13731 UR - http://www.ncbi.nlm.nih.gov/pubmed/33599620 ID - info:doi/10.2196/13731 ER - TY - JOUR AU - Li, Zhengyi AU - Du, Xiangyu AU - Liao, Xiaojing AU - Jiang, Xiaoqian AU - Champagne-Langabeer, Tiffany PY - 2021/2/17 TI - Demystifying the Dark Web Opioid Trade: Content Analysis on Anonymous Market Listings and Forum Posts JO - J Med Internet Res SP - e24486 VL - 23 IS - 2 KW - opioids KW - black market KW - anonymous markets and forums KW - opioid supply chain KW - text mining KW - machine learning KW - opioid crisis KW - opioid epidemic KW - drug abuse N2 - Background: Opioid use disorder presents a public health issue afflicting millions across the globe. There is a pressing need to understand the opioid supply chain to gain new insights into the mitigation of opioid use and effectively combat the opioid crisis. The role of anonymous online marketplaces and forums that resemble eBay or Amazon, where anyone can post, browse, and purchase opioid commodities, has become increasingly important in opioid trading. Therefore, a greater understanding of anonymous markets and forums may enable public health officials and other stakeholders to comprehend the scope of the crisis. However, to the best of our knowledge, no large-scale study, which may cross multiple anonymous marketplaces and is cross-sectional, has been conducted to profile the opioid supply chain and unveil characteristics of opioid suppliers, commodities, and transactions. Objective: We aimed to profile the opioid supply chain in anonymous markets and forums via a large-scale, longitudinal measurement study on anonymous market listings and posts. Toward this, we propose a series of techniques to collect data; identify opioid jargon terms used in the anonymous marketplaces and forums; and profile the opioid commodities, suppliers, and transactions. Methods: We first conducted a whole-site crawl of anonymous online marketplaces and forums to solicit data. We then developed a suite of opioid domain?specific text mining techniques (eg, opioid jargon detection and opioid trading information retrieval) to recognize information relevant to opioid trading activities (eg, commodities, price, shipping information, and suppliers). Subsequently, we conducted a comprehensive, large-scale, longitudinal study to demystify opioid trading activities in anonymous markets and forums. Results: A total of 248,359 listings from 10 anonymous online marketplaces and 1,138,961 traces (ie, threads of posts) from 6 underground forums were collected. Among them, we identified 28,106 opioid product listings and 13,508 opioid-related promotional and review forum traces from 5147 unique opioid suppliers? IDs and 2778 unique opioid buyers? IDs. Our study characterized opioid suppliers (eg, activeness and cross-market activities), commodities (eg, popular items and their evolution), and transactions (eg, origins and shipping destination) in anonymous marketplaces and forums, which enabled a greater understanding of the underground trading activities involved in international opioid supply and demand. Conclusions: The results provide insight into opioid trading in the anonymous markets and forums and may prove an effective mitigation data point for illuminating the opioid supply chain. UR - http://www.jmir.org/2021/2/e24486/ UR - http://dx.doi.org/10.2196/24486 UR - http://www.ncbi.nlm.nih.gov/pubmed/33595442 ID - info:doi/10.2196/24486 ER - TY - JOUR AU - Yang, Qian AU - Tai-Seale, Ming AU - Liu, Stephanie AU - Shen, Yi AU - Zhang, Xiaobin AU - Xiao, Xiaohua AU - Zhang, Kejun PY - 2021/2/16 TI - Measuring Public Reaction to Violence Against Doctors in China: Interrupted Time Series Analysis of Media Reports JO - J Med Internet Res SP - e19651 VL - 23 IS - 2 KW - violence against doctors KW - government intervention KW - public opinion KW - patient?physician relationship N2 - Background: Violence against doctors in China is a serious problem that has attracted attention from both domestic and international media. Objective: This study investigates readers? responses to media reports on violence against doctors to identify attitudes toward perpetrators and physicians and examine if such trends are influenced by national policies. Methods: We searched 17 Chinese violence against doctors reports in international media sources from 2011 to 2020. We then tracked back the original reports and web crawled the 19,220 comments in China. To ascertain the possible turning point of public opinion, we searched violence against doctors?related policies from Tsinghua University ipolicy database from 2011 to 2020, and found 19 policies enacted by the Chinese central government aimed at alleviating the intense patient?physician relationship. We then conducted a series of interrupted time series analyses to examine the influence of these policies on public sentiment toward violence against doctors over time. Results: The interrupted time series analysis (ITSA) showed that the change in public sentiment toward violence against doctors reports was temporally associated with government interventions. The declarations of 10 of the public policies were followed by increases in the proportion of online public opinion in support of doctors (average slope changes of 0.010, P<.05). A decline in the proportion of online public opinion that blamed doctors (average level change of ?0.784, P<.05) followed the declaration of 3 policies. Conclusions: The government?s administrative interventions effectively shaped public opinion but only temporarily. Continued public policy interventions are needed to sustain the reduction of hostility toward medical doctors. UR - http://www.jmir.org/2021/2/e19651/ UR - http://dx.doi.org/10.2196/19651 UR - http://www.ncbi.nlm.nih.gov/pubmed/33591282 ID - info:doi/10.2196/19651 ER - TY - JOUR AU - Hassan, Lamiece AU - Nenadic, Goran AU - Tully, Patricia Mary PY - 2021/2/16 TI - A Social Media Campaign (#datasaveslives) to Promote the Benefits of Using Health Data for Research Purposes: Mixed Methods Analysis JO - J Med Internet Res SP - e16348 VL - 23 IS - 2 KW - social media KW - public engagement KW - social network analysis KW - medical research N2 - Background: Social media provides the potential to engage a wide audience about scientific research, including the public. However, little empirical research exists to guide health scientists regarding what works and how to optimize impact. We examined the social media campaign #datasaveslives established in 2014 to highlight positive examples of the use and reuse of health data in research. Objective: This study aims to examine how the #datasaveslives hashtag was used on social media, how often, and by whom; thus, we aim to provide insights into the impact of a major social media campaign in the UK health informatics research community and further afield. Methods: We analyzed all publicly available posts (tweets) that included the hashtag #datasaveslives (N=13,895) on the microblogging platform Twitter between September 1, 2016, and August 31, 2017. Using a combination of qualitative and quantitative analyses, we determined the frequency and purpose of tweets. Social network analysis was used to analyze and visualize tweet sharing (retweet) networks among hashtag users. Results: Overall, we found 4175 original posts and 9720 retweets featuring #datasaveslives by 3649 unique Twitter users. In total, 66.01% (2756/4175) of the original posts were retweeted at least once. Higher frequencies of tweets were observed during the weeks of prominent policy publications, popular conferences, and public engagement events. Cluster analysis based on retweet relationships revealed an interconnected series of groups of #datasaveslives users in academia, health services and policy, and charities and patient networks. Thematic analysis of tweets showed that #datasaveslives was used for a broader range of purposes than indexing information, including event reporting, encouraging participation and action, and showing personal support for data sharing. Conclusions: This study shows that a hashtag-based social media campaign was effective in encouraging a wide audience of stakeholders to disseminate positive examples of health research. Furthermore, the findings suggest that the campaign supported community building and bridging practices within and between the interdisciplinary sectors related to the field of health data science and encouraged individuals to demonstrate personal support for sharing health data. UR - http://www.jmir.org/2021/2/e16348/ UR - http://dx.doi.org/10.2196/16348 UR - http://www.ncbi.nlm.nih.gov/pubmed/33591280 ID - info:doi/10.2196/16348 ER - TY - JOUR AU - Basch, H. Corey AU - Fera, Joseph AU - Pierce, Isabela AU - Basch, E. Charles PY - 2021/2/12 TI - Promoting Mask Use on TikTok: Descriptive, Cross-sectional Study JO - JMIR Public Health Surveill SP - e26392 VL - 7 IS - 2 KW - TikTok KW - COVID-19 KW - social media KW - infodemiology KW - infoveillance KW - mask use KW - prevention KW - promotion KW - communication KW - public health KW - cross-sectional KW - content analysis KW - transmission N2 - Background: Over the past decade, there has been an increasing secular trend in the number of studies on social media and health. Objective: The purpose of this cross-sectional study was to examine the content and characteristics of TikTok videos that are related to an important aspect of community mitigation?the use of masks as a method for interrupting the transmission of SARS-CoV-2. Methods: In total, 100 trending videos with the hashtag #WearAMask (ie, a campaign on TikTok), along with 32 videos that were posted by the World Health Organization (WHO) and involved masks in any way (ie, all related WHO videos at the time of this study), were included in our sample. We collected the metadata of each post, and created content categories based on fact sheets that were provided by the WHO and the US Centers for Disease Control and Prevention. We used these fact sheets to code the characteristics of mask use. Results: Videos that were posted on TikTok and had the hashtag #WearAMask garnered almost 500 million views, and videos that were posted by the WHO garnered almost 57 million views. Although the ratio of the number of trending #WearAMask videos to the number of WHO videos was around 3:1, the #WearAMask videos received almost 10 times as many cumulative views as the WHO videos. In total, 68% (68/100) of the trending #WearAMask videos involved humor and garnered over 355 million cumulative views. However, only 9% (3/32) of the WHO videos involved humor. Furthermore, 27% (27/100) of the trending #WearAMask videos involved dance and garnered over 130 million cumulative views, whereas none of the WHO videos involved dance. Conclusions: This study is one of the first to describe how TikTok is being used to mitigate the community spread of COVID-19 by promoting mask use. Due to the platform?s incredible reach, TikTok has great potential in conveying important public health messages to various segments of the population. UR - http://publichealth.jmir.org/2021/2/e26392/ UR - http://dx.doi.org/10.2196/26392 UR - http://www.ncbi.nlm.nih.gov/pubmed/33523823 ID - info:doi/10.2196/26392 ER - TY - JOUR AU - Yin, Fulian AU - Shao, Xueying AU - Ji, Meiqi AU - Wu, Jianhong PY - 2021/2/12 TI - Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study JO - J Med Internet Res SP - e25734 VL - 23 IS - 2 KW - COVID-19 KW - delay transmission KW - dynamic model KW - Sina Microblog KW - social media KW - communication KW - online health information KW - health information KW - public health KW - opinion KW - strategy KW - model KW - information transmission KW - delay KW - infodemiology KW - infoveillance N2 - Background: In a fast-evolving public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially on a social media platform. The interval between subsequent posting times may have a different impact on the transmission and cross-propagation of the old and new information that results in a different peak value and a final size of forwarding users of the new information, depending on the content correlation and whether the new information is posted during the outbreak or quasi?steady-state phase of the old information. Objective: This study aims to help in designing effective communication strategies to ensure information is delivered to the maximal number of users. Methods: We developed and analyzed two classes of susceptible-forwarding-immune information propagation models with delay in transmission to describe the cross-propagation process of relevant information. A total of 28,661 retweets of typical information were posted frequently by each opinion leader related to COVID-19 with high influence (data acquisition up to February 19, 2020). The information was processed into discrete points with a frequency of 10 minutes, and the real data were fitted by the model numerical simulation. Furthermore, the influence of parameters on information dissemination and the design of a publishing strategy were analyzed. Results: The current epidemic outbreak situation, epidemic prevention, and other related authoritative information cannot be timely and effectively browsed by the public. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterized our models using real data from Sina Microblog and used the parameterized models to define and evaluate mutual attractiveness indexes, and we used these indexes and parameter sensitivity analyses to inform optimal strategies for new information to be effectively propagated in the microblog. The results of the parameter analysis showed that using different attractiveness indexes as the key parameters can control the information transmission with different release intervals, so it is considered as a key link in the design of an information communication strategy. At the same time, the dynamic process of information was analyzed through index evaluation. Conclusions: Our model can carry out an accurate numerical simulation of information at different release intervals and achieve a dynamic evaluation of information transmission by constructing an indicator system so as to provide theoretical support and strategic suggestions for government decision making. This study optimizes information posting strategies to maximize communication efforts for delivering key public health messages to the public for better outcomes of public health emergency management. UR - http://www.jmir.org/2021/2/e25734/ UR - http://dx.doi.org/10.2196/25734 UR - http://www.ncbi.nlm.nih.gov/pubmed/33529153 ID - info:doi/10.2196/25734 ER - TY - JOUR AU - Wong, Chun Frankie Ho AU - Liu, Tianyin AU - Leung, Yi Dara Kiu AU - Zhang, Y. Anna AU - Au, Hong Walker Siu AU - Kwok, Wai Wai AU - Shum, Y. Angie K. AU - Wong, Yan Gloria Hoi AU - Lum, Yat-Sang Terry PY - 2021/2/11 TI - Consuming Information Related to COVID-19 on Social Media Among Older Adults and Its Association With Anxiety, Social Trust in Information, and COVID-Safe Behaviors: Cross-sectional Telephone Survey JO - J Med Internet Res SP - e26570 VL - 23 IS - 2 KW - COVID-19 KW - anxiety KW - social media KW - infodemic KW - Hong Kong N2 - Background: COVID-19-related information on social media is overabundant and sometimes questionable, resulting in an ?infodemic? during the pandemic. While previous studies suggest social media usage increases the risk of developing anxiety symptoms, how induced anxiety affects attitudes and behaviors is less discussed, let alone during a global pandemic. Little is known about the relationship between older adults using social media during a pandemic and their anxiety, their attitudes toward social trust in information, and behaviors to avoid contracting COVID-19. Objective: The goal of this study was to investigate the associations between using social media for COVID-19-related information and anxiety symptoms as well as the mediation effect of anxiety symptoms on social trust in information and COVID-safe behaviors among older adults. Methods: A cross-sectional telephone survey was conducted in Hong Kong between May and August 2020. A rapid warm-call protocol was developed to train social workers and volunteers from participant nongovernmental organizations to conduct the telephone surveys. Questions related to COVID-safe behaviors, social trust in information, social media use, anxiety and depressive symptoms, and sociodemographic information were asked. The number of confirmed COVID-19 cases at the community level was used to account for the risk of contracting COVID-19. Ordinary least squares regressions examined the associations between social media use and anxiety symptoms, and how they were associated with social trust in information and COVID-safe behaviors. Structural equation modeling further mapped out these relationships to identify the mediation effects of anxiety symptoms. Results: This study collected information regarding 3421 adults aged 60 years and older. Use of social media for COVID-19-related information was associated with more anxiety symptoms and lower social trust in information but had no significant relationship with COVID-safe behaviors. Anxiety symptoms predicted lower social trust in information and higher COVID-safe behaviors. Lower social trust in information was predicted by using social media for COVID-19 information, mediated by anxiety symptoms, while no mediation effect was found for COVID-safe behaviors. Conclusions: Older adults who rely on social media for COVID-19-related information exhibited more anxiety symptoms, while showing mixed effects on attitudes and behaviors. Social trust in information may be challenged by unverified and contradictory information online. The negligible impact on COVID-safe behaviors suggested that social media may have caused more confusion than consolidating a consistent effort against the pandemic. Media literacy education is recommended to promote critical evaluation of COVID-19-related information and responsible sharing among older adults. UR - http://www.jmir.org/2021/2/e26570/ UR - http://dx.doi.org/10.2196/26570 UR - http://www.ncbi.nlm.nih.gov/pubmed/33523825 ID - info:doi/10.2196/26570 ER - TY - JOUR AU - Payton, Ashley AU - Woo, P. Benjamin K. PY - 2021/2/11 TI - Instagram Content Addressing Pruritic Urticarial Papules and Plaques of Pregnancy: Observational Study JO - JMIR Dermatol SP - e26200 VL - 4 IS - 1 KW - pruritic urticarial papules and plaques of pregnancy KW - dermatology KW - rash KW - pregnancy KW - obstetrics KW - dermatosis KW - Instagram KW - social media KW - patient education N2 - Background: Pruritic urticarial papules and plaques of pregnancy (PUPPP) is the most commonly diagnosed pregnancy-specific dermatosis. It presents with intense pruritus and can be difficult to manage, which encourages mothers to look to social media for camaraderie and advice. Objective: This study aimed to characterize the sources and thematic content of Instagram posts in order to define influential groups of users. Our goal was to determine the status of online discourse surrounding PUPPP and elucidate any potential space for health care provider intervention via creation of Instagram accounts dedicated to information dissemination for patient populations. Methods: Three hashtag categories were selected (#PUPPP, #PUPPPs, and #PUPPPrash), and the top public posts from each were analyzed and organized by source and by thematic content. The numbers of likes and comments were also recorded. Results: Among the top 150 posts in each hashtag category, only 428 posts in total were eligible for this analysis. Majority (316/428, 73.8%) of posts were created by mothers who experienced PUPPP. These posts were testimonial accounts in nature. A small fraction of posts (14/428, 3.3%) were generated by physician accounts. Posts from blogs with extensive followings garnered the most attention in the form of likes and comments. Conclusions: Mothers experiencing PUPPP comprised the majority of accounts posting under the hashtags selected. The most common themes included pictures of the rash and personal testimonies. Posts under blog posts received the most likes and comments on average. There is space for physician and health care specialists to improve their social media presence when it comes to discourse surrounding PUPPP. Patients are seeking out communities on social media, like Instagram, in order to have questions answered and obtain advice on management. Accounts with large followings tend to have more likes and more comments, which encourages information dissemination and awareness. Thus, we suggest that physicians create content and potentially partner with blog-type accounts to improve outreach. UR - http://derma.jmir.org/2021/1/e26200/ UR - http://dx.doi.org/10.2196/26200 UR - http://www.ncbi.nlm.nih.gov/pubmed/37632847 ID - info:doi/10.2196/26200 ER - TY - JOUR AU - Jang, Hyeju AU - Rempel, Emily AU - Roth, David AU - Carenini, Giuseppe AU - Janjua, Zafar Naveed PY - 2021/2/10 TI - Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis JO - J Med Internet Res SP - e25431 VL - 23 IS - 2 KW - COVID-19 KW - Twitter KW - topic modeling KW - aspect-based sentiment analysis KW - racism KW - anti-Asians KW - Canada KW - North America KW - sentiment analysis KW - social media KW - discourse KW - reaction KW - public health N2 - Background: Social media is a rich source where we can learn about people?s reactions to social issues. As COVID-19 has impacted people?s lives, it is essential to capture how people react to public health interventions and understand their concerns. Objective: We aim to investigate people?s reactions and concerns about COVID-19 in North America, especially in Canada. Methods: We analyzed COVID-19?related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people?s sentiment about COVID-19?related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. Results: Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, ?vaccines,? ?economy,? and ?masks?) and 60 opinion terms such as ?infectious? (negative) and ?professional? (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. Conclusions: Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19?related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions. UR - http://www.jmir.org/2021/2/e25431/ UR - http://dx.doi.org/10.2196/25431 UR - http://www.ncbi.nlm.nih.gov/pubmed/33497352 ID - info:doi/10.2196/25431 ER - TY - JOUR AU - de Melo, Tiago AU - Figueiredo, S. Carlos M. PY - 2021/2/10 TI - Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach JO - JMIR Public Health Surveill SP - e24585 VL - 7 IS - 2 KW - COVID-19 KW - Twitter KW - infodemiology KW - news KW - sentiment analysis KW - social media KW - Brazil KW - monitoring KW - topic modeling KW - entity recognition KW - text analysis N2 - Background: The COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet. Objective: The purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil. Methods: This work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts. Results: In this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic. Conclusions: This study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities. UR - http://publichealth.jmir.org/2021/2/e24585/ UR - http://dx.doi.org/10.2196/24585 UR - http://www.ncbi.nlm.nih.gov/pubmed/33480853 ID - info:doi/10.2196/24585 ER - TY - JOUR AU - Kim, Stephanie AU - Mourali, Alia AU - Allem, Jon-Patrick AU - Unger, B. Jennifer AU - Boley Cruz, Tess AU - Smiley, L. Sabrina PY - 2021/2/9 TI - Instagram Posts Related to Backwoods Cigarillo Blunts: Content Analysis JO - JMIR Public Health Surveill SP - e22946 VL - 7 IS - 2 KW - Instagram KW - blunts KW - Backwoods cigarillos KW - smoking N2 - Background: Instagram, one of the most popular social media platforms among youth, offers a unique opportunity to examine blunts?partially or fully hollowed-out large cigars, little cigars, and cigarillos that are filled with marijuana. Cigarillo brands like Backwoods (Imperial Tobacco Group Brands LLC) have product features that facilitate blunt making, including a variety of brand-specific flavors that enhance the smoking experience (eg, honey, dark stout). Backwoods has an active online presence with a user-friendly website. Objective: This study examined the extent to which Backwoods cigarillo?related posts on Instagram showed blunt making. Instagram offers a unique opportunity to examine blunt making as Instagram accounts will contain images reflective of behavior occurring without the prime of a researcher. Methods: Data consisted of publicly available Instagram posts with the hashtag #backwoods collected from August 30 to September 12, 2018. Inclusion criteria for this study included an Instagram post with the hashtag ?#backwoods?. Rules were established to content analyze posts. Categories included Type of post (ie, photo, video, or both); Blunt-related hashtags (ie, the corresponding post caption contained one or more hashtags like #blunts, #cannabis, and #weed that were identified in previous social media research); Rolling blunts (ie, the post contained an image of one or more individuals rolling a Backwoods cigarillo visibly containing marijuana); and Smoking blunts (ie, the post contained an image of one or more individuals blowing smoke or holding a lit blunt). We coded images for Product flavor reference, where a code of 1 showed a Backwoods cigarillo pack with a brand-specific flavor (eg, honey, dark stout, Russian crčme) visible in the blunt-related image, and a code of 0 indicated that it was not visible anywhere in the image. Results: Among all posts (N=1206), 871 (72.2%) were coded as Blunt-related hashtags. A total of 125 (10.4%) images were coded as Smoking blunts, and 25 (2.1%) were coded as Rolling blunts (ie, Backwoods cigarillo explicitly used to roll blunts). Among blunt images, 434 of 836 (51.9%) were coded as Product flavor (ie, a Backwoods pack with a brand-specific flavor was visible). Conclusions: Most Backwoods cigarillo?related Instagram images were blunt-related, and these blunt-related images showed Backwoods packages indicating flavor preference. Continued monitoring and surveillance of blunt-related posts on Instagram is needed to inform policies and interventions that reduce the risk that youth may experiment with blunts. Specific policies could include restrictions on product features (eg, flavors, perforated lines, attractive resealable foil pouches, sale as singles) that facilitate blunt making. UR - http://publichealth.jmir.org/2021/2/e22946/ UR - http://dx.doi.org/10.2196/22946 UR - http://www.ncbi.nlm.nih.gov/pubmed/33560242 ID - info:doi/10.2196/22946 ER - TY - JOUR AU - Karafillakis, Emilie AU - Martin, Sam AU - Simas, Clarissa AU - Olsson, Kate AU - Takacs, Judit AU - Dada, Sara AU - Larson, Jane Heidi PY - 2021/2/8 TI - Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review JO - JMIR Public Health Surveill SP - e17149 VL - 7 IS - 2 KW - vaccination KW - antivaccination movement KW - vaccination refusal KW - social media KW - internet KW - research design KW - review KW - media monitoring KW - social listening KW - infodemiology KW - infoveillance N2 - Background: Social media has changed the communication landscape, exposing individuals to an ever-growing amount of information while also allowing them to create and share content. Although vaccine skepticism is not new, social media has amplified public concerns and facilitated their spread globally. Multiple studies have been conducted to monitor vaccination discussions on social media. However, there is currently insufficient evidence on the best methods to perform social media monitoring. Objective: The aim of this study was to identify the methods most commonly used for monitoring vaccination-related topics on different social media platforms, along with their effectiveness and limitations. Methods: A systematic scoping review was conducted by applying a comprehensive search strategy to multiple databases in December 2018. The articles? titles, abstracts, and full texts were screened by two reviewers using inclusion and exclusion criteria. After data extraction, a descriptive analysis was performed to summarize the methods used to monitor and analyze social media, including data extraction tools; ethical considerations; search strategies; periods monitored; geolocalization of content; and sentiments, content, and reach analyses. Results: This review identified 86 articles on social media monitoring of vaccination, most of which were published after 2015. Although 35 out of the 86 studies used manual browser search tools to collect data from social media, this was time-consuming and only allowed for the analysis of small samples compared to social media application program interfaces or automated monitoring tools. Although simple search strategies were considered less precise, only 10 out of the 86 studies used comprehensive lists of keywords (eg, with hashtags or words related to specific events or concerns). Partly due to privacy settings, geolocalization of data was extremely difficult to obtain, limiting the possibility of performing country-specific analyses. Finally, 20 out of the 86 studies performed trend or content analyses, whereas most of the studies (70%, 60/86) analyzed sentiments toward vaccination. Automated sentiment analyses, performed using leverage, supervised machine learning, or automated software, were fast and provided strong and accurate results. Most studies focused on negative (n=33) and positive (n=31) sentiments toward vaccination, and may have failed to capture the nuances and complexity of emotions around vaccination. Finally, 49 out of the 86 studies determined the reach of social media posts by looking at numbers of followers and engagement (eg, retweets, shares, likes). Conclusions: Social media monitoring still constitutes a new means to research and understand public sentiments around vaccination. A wide range of methods are currently used by researchers. Future research should focus on evaluating these methods to offer more evidence and support the development of social media monitoring as a valuable research design. UR - http://publichealth.jmir.org/2021/2/e17149/ UR - http://dx.doi.org/10.2196/17149 UR - http://www.ncbi.nlm.nih.gov/pubmed/33555267 ID - info:doi/10.2196/17149 ER - TY - JOUR AU - Zhang, Shuai AU - Pian, Wenjing AU - Ma, Feicheng AU - Ni, Zhenni AU - Liu, Yunmei PY - 2021/2/5 TI - Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study JO - JMIR Public Health Surveill SP - e26090 VL - 7 IS - 2 KW - COVID-19 KW - infodemic KW - infodemiology KW - epidemic KW - misinformation KW - spread characteristics KW - social media KW - China KW - exploratory KW - dissemination N2 - Background: The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people?s health and governance systems. Objective: This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic. Methods: We collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time. Results: The daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: ?conspiracy theories? (648/2745, 23.61%), ?government response? (544/2745, 19.82%), ?prevention action? (411/2745, 14.97%), ?new cases? (365/2745, 13.30%), ?transmission routes? (244/2745, 8.89%), ?origin and nomenclature? (228/2745, 8.30%), ?vaccines and medicines? (154/2745, 5.61%), and ?symptoms and detection? (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations. Conclusions: Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic. UR - http://publichealth.jmir.org/2021/2/e26090/ UR - http://dx.doi.org/10.2196/26090 UR - http://www.ncbi.nlm.nih.gov/pubmed/33460391 ID - info:doi/10.2196/26090 ER - TY - JOUR AU - Bacsu, Juanita-Dawne AU - O'Connell, E. Megan AU - Cammer, Allison AU - Azizi, Mahsa AU - Grewal, Karl AU - Poole, Lisa AU - Green, Shoshana AU - Sivananthan, Saskia AU - Spiteri, J. Raymond PY - 2021/2/3 TI - Using Twitter to Understand the COVID-19 Experiences of People With Dementia: Infodemiology Study JO - J Med Internet Res SP - e26254 VL - 23 IS - 2 KW - Twitter KW - social media KW - dementia KW - COVID-19 KW - health policy KW - experience KW - support KW - disorder KW - theme KW - collaborate KW - quality of life N2 - Background: The COVID-19 pandemic is affecting people with dementia in numerous ways. Nevertheless, there is a paucity of research on the COVID-19 impact on people with dementia and their care partners. Objective: Using Twitter, the purpose of this study is to understand the experiences of COVID-19 for people with dementia and their care partners. Methods: We collected tweets on COVID-19 and dementia using the GetOldTweets application in Python from February 15 to September 7, 2020. Thematic analysis was used to analyze the tweets. Results: From the 5063 tweets analyzed with line-by-line coding, we identified 4 main themes including (1) separation and loss; (2) COVID-19 confusion, despair, and abandonment; (3) stress and exhaustion exacerbation; and (4) unpaid sacrifices by formal care providers. Conclusions: There is an imminent need for governments to rethink using a one-size-fits-all response to COVID-19 policy and use a collaborative approach to support people with dementia. Collaboration and more evidence-informed research are essential to reducing COVID-19 mortality and improving the quality of life for people with dementia and their care partners. UR - https://www.jmir.org/2021/2/e26254 UR - http://dx.doi.org/10.2196/26254 UR - http://www.ncbi.nlm.nih.gov/pubmed/33468449 ID - info:doi/10.2196/26254 ER - TY - JOUR AU - Geronikolou, Styliani AU - Chrousos, George PY - 2021/2/3 TI - COVID-19?Induced Fear in Infoveillance Studies: Pilot Meta-analysis Study of Preliminary Results JO - JMIR Form Res SP - e21156 VL - 5 IS - 2 KW - COVID-19 KW - social media KW - misinformation KW - infodemics KW - infodemiology KW - infoveillance KW - fear KW - meta-analysis N2 - Background: The World Health Organization named the phenomenon of misinformation spread through social media as an ?infodemic? and recognized the need to curb it. Misinformation infodemics undermine not only population safety but also compliance to the suggestions and prophylactic measures recommended during pandemics. Objective: The aim of this pilot study is to review the impact of social media on general population fear in ?infoveillance? studies during the COVID-19 pandemic. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol was followed, and 6 out of 20 studies were retrieved, meta-analyzed, and had their findings presented in the form of a forest plot. Results: The summary random and significant event rate was 0.298 (95% CI 0.213-0.400), suggesting that social media?circulated misinformation related to COVID-19 triggered public fear and other psychological manifestations. These findings merit special attention by public health authorities. Conclusions: Infodemiology and infoveillance are valid tools in the hands of epidemiologists to help prevent dissemination of false information, which has potentially damaging effects. UR - https://formative.jmir.org/2021/2/e21156 UR - http://dx.doi.org/10.2196/21156 UR - http://www.ncbi.nlm.nih.gov/pubmed/33400681 ID - info:doi/10.2196/21156 ER - TY - JOUR AU - Bapaye, Amol Jay AU - Bapaye, Amol Harsh PY - 2021/1/30 TI - Demographic Factors Influencing the Impact of Coronavirus-Related Misinformation on WhatsApp: Cross-sectional Questionnaire Study JO - JMIR Public Health Surveill SP - e19858 VL - 7 IS - 1 KW - coronavirus KW - COVID-19 KW - SARS?CoV?2 KW - WhatsApp KW - social media KW - misinformation KW - infodemiology KW - infodemic KW - pandemic KW - medical informatics N2 - Background: The risks of misinformation on social networking sites is a global issue, especially in light of the COVID-19 infodemic. WhatsApp is being used as an important source of COVID-19?related information during the current pandemic. Unlike Facebook and Twitter, limited studies have investigated the role of WhatsApp as a source of communication, information, or misinformation during crisis situations. Objective: Our study aimed to evaluate the vulnerability of demographic cohorts in a developing country toward COVID-19?related misinformation shared via WhatsApp. We also aimed to identify characteristics of WhatsApp messages associated with increased credibility of misinformation. Methods: We conducted a web-based questionnaire survey and designed a scoring system based on theories supported by the existing literature. Vulnerability (K) was measured as a ratio of the respondent?s score to the maximum score. Respondents were stratified according to age and occupation, and Kmean was calculated and compared among each subgroup using single-factor analysis of variance and Hochberg GT2 tests. The questionnaire evaluated the respondents? opinion of the veracity of coronavirus-related WhatsApp messages. The responses to the false-proven messages were compared using z test between the 2 groups: coronavirus-related WhatsApp messages with an attached link and/or source and those without. Results: We analyzed 1137 responses from WhatsApp users in India. Users aged over 65 years had the highest vulnerability (Kmean=0.38, 95% CI 0.341-0.419) to misinformation. Respondents in the age group 19-25 years had significantly lower vulnerability (Kmean=0.31, 95% CI 0.301-0.319) than those aged over 25 years (P<.05). The vulnerability of users employed in elementary occupations was the highest (Kmean=0.38, 95% CI 0.356-0.404), and it was significantly higher than that of professionals and students (P<.05). Interestingly, the vulnerability of healthcare workers was not significantly different from that of other occupation groups (P>.05). We found that false CRWMs with an attached link and/or source were marked true 6 times more often than false CRWMs without an attached link or source (P<.001). Conclusions: Our study demonstrates that in a developing country, WhatsApp users aged over 65 years and those involved in elementary occupations were found to be the most vulnerable to false information disseminated via WhatsApp. Health care workers, who are otherwise considered as experts with regard to this global health care crisis, also shared this vulnerability to misinformation with other occupation groups. Our findings also indicated that the presence of an attached link and/or source falsely validating an incorrect message adds significant false credibility, making it appear true. These results indicate an emergent need to address and rectify the current usage patterns of WhatsApp users. This study also provides metrics that can be used by health care organizations and government authorities of developing countries to formulate guidelines to contain the spread of WhatsApp-related misinformation. UR - http://publichealth.jmir.org/2021/1/e19858/ UR - http://dx.doi.org/10.2196/19858 UR - http://www.ncbi.nlm.nih.gov/pubmed/33444152 ID - info:doi/10.2196/19858 ER - TY - JOUR AU - Nutley, K. Sara AU - Falise, M. Alyssa AU - Henderson, Rebecca AU - Apostolou, Vasiliki AU - Mathews, A. Carol AU - Striley, W. Catherine PY - 2021/1/27 TI - Impact of the COVID-19 Pandemic on Disordered Eating Behavior: Qualitative Analysis of Social Media Posts JO - JMIR Ment Health SP - e26011 VL - 8 IS - 1 KW - eating disorders KW - anorexia nervosa KW - binge eating disorder KW - COVID-19 KW - coronavirus KW - Reddit KW - social media KW - disorder KW - eating KW - qualitative KW - experience KW - mental health KW - theme N2 - Background: A growing body of evidence is suggesting a significant association between the COVID-19 pandemic and population-level mental health. Study findings suggest that individuals with a lifetime history of disordered eating behavior may be negatively affected by COVID-19?related anxiety, and prevention measures may disrupt daily functioning and limit access to treatment. However, data describing the influence of the COVID-19 pandemic on disordered eating behaviors are limited, and most findings focus on individuals in treatment settings. Objective: The aim of this study is to characterize the experiences of Reddit users worldwide who post in eating disorder (ED)?related discussion forums describing the influence of the COVID-19 pandemic on their overall mental health and disordered eating behavior. Methods: Data were collected from popular subreddits acknowledging EDs as their primary discussion topic. Unique discussion posts dated from January 1 to May 31, 2020 that referenced the COVID-19 pandemic were extracted and evaluated using inductive, thematic data analysis. Results: Six primary themes were identified: change in ED symptoms, change in exercise routine, impact of quarantine on daily life, emotional well-being, help-seeking behavior, and associated risks and health outcomes. The majority of users reported that the COVID-19 pandemic and associated public health prevention measures negatively impacted their psychiatric health and contributed to increased disordered eating behaviors. Feelings of isolation, frustration, and anxiety were common. Many individuals used Reddit forums to share personal experiences, seek advice, and offer shared accountability. Conclusions: Reddit discussion forums have provided a therapeutic community for individuals to share experiences and provide support for peers with ED during a period of increased psychiatric distress. Future research is needed to assess the impact of the COVID-19 pandemic on disordered eating behavior and to evaluate the role of social media discussion forums in mental health treatment, especially during periods of limited treatment access. UR - http://mental.jmir.org/2021/1/e26011/ UR - http://dx.doi.org/10.2196/26011 UR - http://www.ncbi.nlm.nih.gov/pubmed/33465035 ID - info:doi/10.2196/26011 ER - TY - JOUR AU - Shimkhada, Riti AU - Attai, Deanna AU - Scheitler, AJ AU - Babey, Susan AU - Glenn, Beth AU - Ponce, Ninez PY - 2021/1/15 TI - Using a Twitter Chat to Rapidly Identify Barriers and Policy Solutions for Metastatic Breast Cancer Care: Qualitative Study JO - JMIR Public Health Surveill SP - e23178 VL - 7 IS - 1 KW - metastatic breast cancer KW - Twitter KW - infodemiology KW - infoveillance KW - health care barriers KW - health care policy KW - social media KW - policy KW - breast cancer N2 - Background: Real-time, rapid assessment of barriers to care experienced by patients can be used to inform relevant health care legislation. In recent years, online communities have become a source of support for patients as well as a vehicle for discussion and collaboration among patients, clinicians, advocates, and researchers. The Breast Cancer Social Media (#BCSM) community has hosted weekly Twitter chats since 2011. Topics vary each week, and chats draw a diverse group of participants. Partnering with the #BCSM community, we used Twitter to gather data on barriers to care for patients with metastatic breast cancer and potential policy solutions. Metastatic breast cancer survival rates are low and in large part conditioned by time-sensitive access to care factors that might be improved through policy changes. Objective: This study was part of an assessment of the barriers to care for metastatic breast cancer with the goal of offering policy solutions for the legislative session in California. Methods: We provided 5 questions for a chat specific to metastatic breast cancer care barriers and potential policy solutions. These were discussed during the course of a #BCSM chat on November 18, 2019. We used Symplur (Symplur LLC) analytics to generate a transcript of tweets and a profile of participants. Responses to the questions are presented in this paper. Results: There were 288 tweets from 42 users, generating 2.1 million impressions during the 1-hour chat. Participants included 23 patient advocates (most of whom were patients themselves), 7 doctors, 6 researchers or academics, 3 health care providers (2 nurses, 1 clinical psychologist), and 2 advocacy organizations. Participants noted communication gaps between patient and provider especially as related to the need for individualized medication dosing to minimize side effects and maximize quality of life. Timeliness of insurance company response, for example, to authorize treatments, was also a concern. Chat participants noted that palliative care is not well integrated into metastatic breast cancer care and that insurance company denials of coverage for these services were common. Regarding financial challenges, chat participants mentioned unexpected copays, changes in insurance drug formularies that made it difficult to anticipate drug costs, and limits on the number of physical therapy visits covered by insurance. Last, on the topic of disability benefits, participants expressed frustration about how to access disability benefits. When prompted for input regarding what health system and policy changes are necessary, participants suggested a number of ideas, including expanding the availability of nurse navigation for metastatic breast cancer, developing and offering a guide for the range of treatment and support resources patients with metastatic breast cancer, and improving access to clinical trials. Conclusions: Rapid assessments drawing from online community insights may be a critical source of data that can be used to ensure more responsive policy action to improve patient care. UR - http://publichealth.jmir.org/2021/1/e23178/ UR - http://dx.doi.org/10.2196/23178 UR - http://www.ncbi.nlm.nih.gov/pubmed/33315017 ID - info:doi/10.2196/23178 ER - TY - JOUR AU - Golder, Su AU - Bach, Millie AU - O'Connor, Karen AU - Gross, Robert AU - Hennessy, Sean AU - Gonzalez Hernandez, Graciela PY - 2021/1/26 TI - Public Perspectives on Anti-Diabetic Drugs: Exploratory Analysis of Twitter Posts JO - JMIR Diabetes SP - e24681 VL - 6 IS - 1 KW - diabetes KW - insulin KW - Twitter KW - social media KW - infodemiology KW - infoveillance KW - social listening KW - cost KW - rationing N2 - Background: Diabetes mellitus is a major global public health issue where self-management is critical to reducing disease burden. Social media has been a powerful tool to understand public perceptions. Public perception of the drugs used for the treatment of diabetes may be useful for orienting interventions to increase adherence. Objective: The aim of this study was to explore the public perceptions of anti-diabetic drugs through the analysis of health-related tweets mentioning such medications. Methods: This study uses an infoveillance social listening approach to monitor public discourse using Twitter data. We coded 4000 tweets from January 1, 2019 to October 1, 2019 containing key terms related to anti-diabetic drugs by using qualitative content analysis. Tweets were coded for whether they were truly about an anti-diabetic drug and whether they were health-related. Health-related tweets were further coded based on who was tweeting, which anti-diabetic drug was being tweeted about, and the content discussed in the tweet. The main outcome of the analysis was the themes identified by analyzing the content of health-related tweets on anti-diabetic drugs. Results: We identified 1664 health-related tweets on 33 anti-diabetic drugs. A quarter (415/1664) of the tweets were confirmed to have been from people with diabetes, 17.9% (298/1664) from people posting about someone else, and 2.7% (45/1664) from health care professionals. However, the role of the tweeter was unidentifiable in two-thirds of the tweets. We identified 13 themes, with the health consequences of the cost of anti-diabetic drugs being the most extensively discussed, followed by the efficacy and availability. We also identified issues that patients may conceal from health care professionals, such as purchasing medications from unofficial sources. Conclusions: This study uses an infoveillance approach using Twitter data to explore public perceptions related to anti-diabetic drugs. This analysis gives an insight into the real-life issues that an individual faces when taking anti-diabetic drugs, and such findings may be incorporated into health policies to improve compliance and efficacy. This study suggests that there is a fear of not having access to anti-diabetic drugs due to cost or physical availability and highlights the impact of the sacrifices made to access anti-diabetic drugs. Along with screening for diabetes-related health issues, health care professionals should also ask their patients about any non?health-related concerns regarding their anti-diabetic drugs. The positive tweets about dietary changes indicate that people with type 2 diabetes may be more open to self-management than what the health care professionals believe. UR - http://diabetes.jmir.org/2021/1/e24681/ UR - http://dx.doi.org/10.2196/24681 UR - http://www.ncbi.nlm.nih.gov/pubmed/33496671 ID - info:doi/10.2196/24681 ER - TY - JOUR AU - Erinoso, Olufemi AU - Wright, Ololade Kikelomo AU - Anya, Samuel AU - Kuyinu, Yetunde AU - Abdur-Razzaq, Hussein AU - Adewuya, Abiodun PY - 2021/1/25 TI - Predictors of COVID-19 Information Sources and Their Perceived Accuracy in Nigeria: Online Cross-sectional Study JO - JMIR Public Health Surveill SP - e22273 VL - 7 IS - 1 KW - COVID-19 KW - communication KW - health information KW - public health KW - infodemiology KW - infodemic KW - accuracy KW - cross-sectional KW - risk KW - information source KW - predictor KW - Nigeria N2 - Background: Effective communication is critical for mitigating the public health risks associated with the COVID-19 pandemic. Objective: This study assesses the source(s) of COVID-19 information among people in Nigeria, as well as the predictors and the perceived accuracy of information from these sources. Methods: We conducted an online survey of consenting adults residing in Nigeria between April and May 2020 during the lockdown and first wave of COVID-19. The major sources of information about COVID-19 were distilled from 7 potential sources (family and friends, places of worship, health care providers, internet, workplace, traditional media, and public posters/banners). An open-ended question was asked to explore how respondents determined accuracy of information. Statistical analysis was conducted using STATA 15.0 software (StataCorp Texas) with significance placed at P<.05. Approval to conduct this study was obtained from the Lagos State University Teaching Hospital Health Research Ethics Committee. Results: A total of 719 respondents completed the survey. Most respondents (n=642, 89.3%) obtained COVID-19?related information from the internet. The majority (n=617, 85.8%) considered their source(s) of information to be accurate, and 32.6% (n=234) depended on only 1 out of the 7 potential sources of COVID-19 information. Respondents earning a monthly income between NGN 70,000-120,000 had lower odds of obtaining COVID-19 information from the internet compared to respondents earning less than NGN 20,000 (odds ratio [OR] 0.49, 95% CI 0.24-0.98). In addition, a significant proportion of respondents sought accurate information from recognized health organizations, such as the Nigeria Centre for Disease Control and the World Health Organization. Conclusions: The internet was the most common source of COVID-19 information, and the population sampled had a relatively high level of perceived accuracy for the COVID-19 information received. Effective communication requires dissemination of information via credible communication channels, as identified from this study. This can be potentially beneficial for risk communication to control the pandemic. UR - http://publichealth.jmir.org/2021/1/e22273/ UR - http://dx.doi.org/10.2196/22273 UR - http://www.ncbi.nlm.nih.gov/pubmed/33428580 ID - info:doi/10.2196/22273 ER - TY - JOUR AU - Klein, Z. Ari AU - Magge, Arjun AU - O'Connor, Karen AU - Flores Amaro, Ivan Jesus AU - Weissenbacher, Davy AU - Gonzalez Hernandez, Graciela PY - 2021/1/22 TI - Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set JO - J Med Internet Res SP - e25314 VL - 23 IS - 1 KW - natural language processing KW - social media KW - data mining KW - COVID-19 KW - coronavirus KW - pandemics KW - epidemiology KW - infodemiology N2 - Background: In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. Objective: The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. Methods: Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out ?reported speech? (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. Results: Interannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen ?). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state?level geolocations. Conclusions: We have made the 13,714 tweets identified in this study, along with each tweet?s time stamp and US state?level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19. UR - http://www.jmir.org/2021/1/e25314/ UR - http://dx.doi.org/10.2196/25314 UR - http://www.ncbi.nlm.nih.gov/pubmed/33449904 ID - info:doi/10.2196/25314 ER - TY - JOUR AU - Suarez-Lledo, Victor AU - Alvarez-Galvez, Javier PY - 2021/1/20 TI - Prevalence of Health Misinformation on Social Media: Systematic Review JO - J Med Internet Res SP - e17187 VL - 23 IS - 1 KW - social media KW - health misinformation KW - infodemiology KW - infodemics KW - social networks KW - poor quality information KW - social contagion N2 - Background: Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated. Objective: This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern. Methods: We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms. Results: A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32%), drugs or smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%). Studies were mainly based on the following five methodological approaches: social network analysis (28%), evaluating content (26%), evaluating quality (24%), content/text analysis (16%), and sentiment analysis (6%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87% in some studies. Health misinformation about vaccines was also very common (43%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro?eating disorder arguments were moderate in comparison to the aforementioned topics (36%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30%). Conclusions: The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans. UR - http://www.jmir.org/2021/1/e17187/ UR - http://dx.doi.org/10.2196/17187 UR - http://www.ncbi.nlm.nih.gov/pubmed/33470931 ID - info:doi/10.2196/17187 ER - TY - JOUR AU - Itamura, Kyohei AU - Wu, Arthur AU - Illing, Elisa AU - Ting, Jonathan AU - Higgins, Thomas PY - 2021/1/14 TI - YouTube Videos Demonstrating the Nasopharyngeal Swab Technique for SARS-CoV-2 Specimen Collection: Content Analysis JO - JMIR Public Health Surveill SP - e24220 VL - 7 IS - 1 KW - COVID-19 KW - coronavirus KW - SARS-coV-2 KW - nasopharyngeal swab KW - viral testing KW - PCR KW - YouTube KW - infodemiology KW - digital epidemiology KW - testing KW - diagnostic KW - content analysis KW - video KW - error N2 - Background: Real-time polymerase chain reaction using nasopharyngeal swabs is currently the most widely used diagnostic test for SARS-CoV-2 detection. However, false negatives and the sensitivity of this mode of testing have posed challenges in the accurate estimation of the prevalence of SARS-CoV-2 infection rates. Objective: The purpose of this study was to evaluate whether technical and, therefore, correctable errors were being made with regard to nasopharyngeal swab procedures. Methods: We searched a web-based video database (YouTube) for videos demonstrating SARS-CoV-2 nasopharyngeal swab tests, posted from January 1 to May 15, 2020. Videos were rated by 3 blinded rhinologists for accuracy of swab angle and depth. The overall score for swab angle and swab depth for each nasopharyngeal swab demonstration video was determined based on the majority score with agreement between at least 2 of the 3 reviewers. We then comparatively evaluated video data collected from YouTube videos demonstrating the correct nasopharyngeal swab technique with data from videos demonstrating an incorrect nasopharyngeal swab technique. Multiple linear regression analysis with statistical significance set at P=.05 was performed to determine video data variables associated with the correct nasopharyngeal swab technique. Results: In all, 126 videos met the study inclusion and exclusion criteria. Of these, 52.3% (66/126) of all videos demonstrated the correct swab angle, and 46% (58/126) of the videos demonstrated an appropriate swab depth. Moreover, 45.2% (57/126) of the videos demonstrated both correct nasopharyngeal swab angle and appropriate depth, whereas 46.8% (59/126) of the videos demonstrated both incorrect nasopharyngeal swab angle and inappropriate depth. Videos with correct nasopharyngeal swab technique were associated with the swab operators identifying themselves as a medical professional or as an Ear, Nose, Throat?related medical professional. We also found an association between correct nasopharyngeal swab techniques and recency of video publication date (relative to May 15, 2020). Conclusions: Our findings show that over half of the videos documenting the nasopharyngeal swab test showed an incorrect technique, which could elevate false-negative test rates. Therefore, greater attention needs to be provided toward educating frontline health care workers who routinely perform nasopharyngeal swab procedures. UR - http://publichealth.jmir.org/2021/1/e24220/ UR - http://dx.doi.org/10.2196/24220 UR - http://www.ncbi.nlm.nih.gov/pubmed/33406478 ID - info:doi/10.2196/24220 ER - TY - JOUR AU - van Stekelenburg, Aart AU - Schaap, Gabi AU - Veling, Harm AU - Buijzen, Moniek PY - 2021/1/12 TI - Investigating and Improving the Accuracy of US Citizens? Beliefs About the COVID-19 Pandemic: Longitudinal Survey Study JO - J Med Internet Res SP - e24069 VL - 23 IS - 1 KW - infodemic KW - infodemiology KW - misinformation KW - COVID-19 pandemic KW - belief accuracy KW - boosting KW - trust in scientists KW - political orientation KW - media use N2 - Background: The COVID-19 infodemic, a surge of information and misinformation, has sparked worry about the public?s perception of the coronavirus pandemic. Excessive information and misinformation can lead to belief in false information as well as reduce the accurate interpretation of true information. Such incorrect beliefs about the COVID-19 pandemic might lead to behavior that puts people at risk of both contracting and spreading the virus. Objective: The objective of this study was two-fold. First, we attempted to gain insight into public beliefs about the novel coronavirus and COVID-19 in one of the worst hit countries: the United States. Second, we aimed to test whether a short intervention could improve people?s belief accuracy by empowering them to consider scientific consensus when evaluating claims related to the pandemic. Methods: We conducted a 4-week longitudinal study among US citizens, starting on April 27, 2020, just after daily COVID-19 deaths in the United States had peaked. Each week, we measured participants? belief accuracy related to the coronavirus and COVID-19 by asking them to indicate to what extent they believed a number of true and false statements (split 50/50). Furthermore, each new survey wave included both the original statements and four new statements: two false and two true statements. Half of the participants were exposed to an intervention aimed at increasing belief accuracy. The intervention consisted of a short infographic that set out three steps to verify information by searching for and verifying a scientific consensus. Results: A total of 1202 US citizens, balanced regarding age, gender, and ethnicity to approximate the US general public, completed the baseline (T0) wave survey. Retention rate for the follow-up waves? first follow-up wave (T1), second follow-up wave (T2), and final wave (T3)?was high (?85%). Mean scores of belief accuracy were high for all waves, with scores reflecting low belief in false statements and high belief in true statements; the belief accuracy scale ranged from ?1, indicating completely inaccurate beliefs, to 1, indicating completely accurate beliefs (T0 mean 0.75, T1 mean 0.78, T2 mean 0.77, and T3 mean 0.75). Accurate beliefs were correlated with self-reported behavior aimed at preventing the coronavirus from spreading (eg, social distancing) (r at all waves was between 0.26 and 0.29 and all P values were less than .001) and were associated with trust in scientists (ie, higher trust was associated with more accurate beliefs), political orientation (ie, liberal, Democratic participants held more accurate beliefs than conservative, Republican participants), and the primary news source (ie, participants reporting CNN or Fox News as the main news source held less accurate beliefs than others). The intervention did not significantly improve belief accuracy. Conclusions: The supposed infodemic was not reflected in US citizens? beliefs about the COVID-19 pandemic. Most people were quite able to figure out the facts in these relatively early days of the crisis, calling into question the prevalence of misinformation and the public?s susceptibility to misinformation. UR - http://www.jmir.org/2021/1/e24069/ UR - http://dx.doi.org/10.2196/24069 UR - http://www.ncbi.nlm.nih.gov/pubmed/33351776 ID - info:doi/10.2196/24069 ER - TY - JOUR AU - Maytin, Lauren AU - Maytin, Jason AU - Agarwal, Priya AU - Krenitsky, Anna AU - Krenitsky, JoAnn AU - Epstein, S. Robert PY - 2021/1/8 TI - Attitudes and Perceptions Toward COVID-19 Digital Surveillance: Survey of Young Adults in the United States JO - JMIR Form Res SP - e23000 VL - 5 IS - 1 KW - attitude KW - perception KW - young adult KW - COVID-19 KW - digital surveillance KW - population health technologies KW - surveillance KW - population KW - survey KW - adolescent N2 - Background: COVID-19 is an international health crisis of particular concern in the United States, which saw surges of infections with the lifting of lockdowns and relaxed social distancing. Young adults have proven to be a critical factor for COVID-19 transmission and are an important target of the efforts to contain the pandemic. Scalable digital public health technologies could be deployed to reduce COVID-19 transmission, but their use depends on the willingness of young adults to participate in surveillance. Objective: The aim of this study is to determine the attitudes of young adults regarding COVID-19 digital surveillance, including which aspects they would accept and which they would not, as well as to determine factors that may be associated with their willingness to participate in digital surveillance. Methods: We conducted an anonymous online survey of young adults aged 18-24 years throughout the United States in June 2020. The questionnaire contained predominantly closed-ended response options with one open-ended question. Descriptive statistics were applied to the data. Results: Of 513 young adult respondents, 383 (74.7%) agreed that COVID-19 represents a public health crisis. However, only 231 (45.1%) agreed to actively share their COVID-19 status or symptoms for monitoring and only 171 (33.4%) reported a willingness to allow access to their cell phone for passive location tracking or contact tracing. Conclusions: Despite largely agreeing that COVID-19 represents a serious public health risk, the majority of young adults sampled were reluctant to participate in digital monitoring to manage the pandemic. This was true for both commonly used methods of public health surveillance (such as contact tracing) and novel methods designed to facilitate a return to normal (such as frequent symptom checking through digital apps). This is a potential obstacle to ongoing containment measures (many of which rely on widespread surveillance) and may reflect a need for greater education on the benefits of public health digital surveillance for young adults. UR - http://formative.jmir.org/2021/1/e23000/ UR - http://dx.doi.org/10.2196/23000 UR - http://www.ncbi.nlm.nih.gov/pubmed/33347420 ID - info:doi/10.2196/23000 ER - TY - JOUR AU - Pickles, Kristen AU - Cvejic, Erin AU - Nickel, Brooke AU - Copp, Tessa AU - Bonner, Carissa AU - Leask, Julie AU - Ayre, Julie AU - Batcup, Carys AU - Cornell, Samuel AU - Dakin, Thomas AU - Dodd, H. Rachael AU - Isautier, J. Jennifer M. AU - McCaffery, J. Kirsten PY - 2021/1/7 TI - COVID-19 Misinformation Trends in Australia: Prospective Longitudinal National Survey JO - J Med Internet Res SP - e23805 VL - 23 IS - 1 KW - COVID-19 KW - coronavirus KW - misinformation KW - infodemic KW - myths KW - conspiracy KW - digital health KW - literacy KW - social media KW - trust N2 - Background: Misinformation about COVID-19 is common and has been spreading rapidly across the globe through social media platforms and other information systems. Understanding what the public knows about COVID-19 and identifying beliefs based on misinformation can help shape effective public health communications to ensure efforts to reduce viral transmission are not undermined. Objective: This study aimed to investigate the prevalence and factors associated with COVID-19 misinformation in Australia and their changes over time. Methods: This prospective, longitudinal national survey was completed by adults (18 years and above) across April (n=4362), May (n=1882), and June (n=1369) 2020. Results: Stronger agreement with misinformation was associated with younger age, male gender, lower education level, and language other than English spoken at home (P<.01 for all). After controlling for these variables, misinformation beliefs were significantly associated (P<.001) with lower levels of digital health literacy, perceived threat of COVID-19, confidence in government, and trust in scientific institutions. Analyses of specific government-identified misinformation revealed 3 clusters: prevention (associated with male gender and younger age), causation (associated with lower education level and greater social disadvantage), and cure (associated with younger age). Lower institutional trust and greater rejection of official government accounts were associated with stronger agreement with COVID-19 misinformation. Conclusions: The findings of this study highlight important gaps in communication effectiveness, which must be addressed to ensure effective COVID-19 prevention. UR - https://www.jmir.org/2021/1/e23805 UR - http://dx.doi.org/10.2196/23805 UR - http://www.ncbi.nlm.nih.gov/pubmed/33302250 ID - info:doi/10.2196/23805 ER - TY - JOUR AU - Ojo, Ayotomiwa AU - Guntuku, Chandra Sharath AU - Zheng, Margaret AU - Beidas, S. Rinad AU - Ranney, L. Megan PY - 2021/1/6 TI - How Health Care Workers Wield Influence Through Twitter Hashtags: Retrospective Cross-sectional Study of the Gun Violence and COVID-19 Public Health Crises JO - JMIR Public Health Surveill SP - e24562 VL - 7 IS - 1 KW - COVID-19 KW - firearm injury KW - social media KW - online advocacy KW - Twitter KW - infodemiology KW - infoveillance KW - tweet KW - campaign KW - health care worker KW - influence KW - public health KW - crisis KW - policy N2 - Background: Twitter has emerged as a novel way for physicians to share ideas and advocate for policy change. #ThisIsOurLane (firearm injury) and #GetUsPPE (COVID-19) are examples of nationwide health care?led Twitter campaigns that went viral. Health care?initiated Twitter hashtags regarding major public health topics have gained national attention, but their content has not been systematically examined. Objective: We hypothesized that Twitter discourse on two epidemics (firearm injury and COVID-19) would differ between tweets with health care?initiated hashtags (#ThisIsOurLane and #GetUsPPE) versus those with non?health care?initiated hashtags (#GunViolence and #COVID19). Methods: Using natural language processing, we compared content, affect, and authorship of a random 1% of tweets using #ThisIsOurLane (Nov 2018-Oct 2019) and #GetUsPPE (March-May 2020), compared to #GunViolence and #COVID19 tweets, respectively. We extracted the relative frequency of single words and phrases and created two sets of features: (1) an open-vocabulary feature set to create 50 data-driven?determined word clusters to evaluate the content of tweets; and (2) a closed-vocabulary feature for psycholinguistic categorization among case and comparator tweets. In accordance with conventional linguistic analysis, we used a P<.001, after adjusting for multiple comparisons using the Bonferroni correction, to identify potentially meaningful correlations between language features and outcomes. Results: In total, 67% (n=4828) of?#ThisIsOurLane tweets and 36.6% (n=7907) of #GetUsPPE tweets were authored by health care professionals, compared to 16% (n=1152) of #GunViolence and 9.8% (n=2117) of #COVID19 tweets. Tweets using #ThisIsOurLane and #GetUsPPE were more likely to contain health care?specific language; more language denoting positive emotions, affiliation, and group identity; and more action-oriented content compared to tweets with #GunViolence or #COVID19, respectively. Conclusions: Tweets with health care?led hashtags expressed more positivity and more action-oriented language than the comparison hashtags. As social media is increasingly used for news discourse, public education, and grassroots organizing, the public health community can take advantage of social media?s broad reach to amplify truthful, actionable messages around public health issues. UR - https://publichealth.jmir.org/2021/1/e24562 UR - http://dx.doi.org/10.2196/24562 UR - http://www.ncbi.nlm.nih.gov/pubmed/33315578 ID - info:doi/10.2196/24562 ER - TY - JOUR AU - Tang, Lu AU - Fujimoto, Kayo AU - Amith, (Tuan) Muhammad AU - Cunningham, Rachel AU - Costantini, A. Rebecca AU - York, Felicia AU - Xiong, Grace AU - Boom, A. Julie AU - Tao, Cui PY - 2021/1/5 TI - ?Down the Rabbit Hole? of Vaccine Misinformation on YouTube: Network Exposure Study JO - J Med Internet Res SP - e23262 VL - 23 IS - 1 KW - vaccine KW - misinformation KW - infodemiology KW - infodemic KW - YouTube KW - network analysis N2 - Background: Social media platforms such as YouTube are hotbeds for the spread of misinformation about vaccines. Objective: The aim of this study was to explore how individuals are exposed to antivaccine misinformation on YouTube based on whether they start their viewing from a keyword-based search or from antivaccine seed videos. Methods: Four networks of videos based on YouTube recommendations were collected in November 2019. Two search networks were created from provaccine and antivaccine keywords to resemble goal-oriented browsing. Two seed networks were constructed from conspiracy and antivaccine expert seed videos to resemble direct navigation. Video contents and network structures were analyzed using the network exposure model. Results: Viewers are more likely to encounter antivaccine videos through direct navigation starting from an antivaccine video than through goal-oriented browsing. In the two seed networks, provaccine videos, antivaccine videos, and videos containing health misinformation were all found to be more likely to lead to more antivaccine videos. Conclusions: YouTube has boosted the search rankings of provaccine videos to combat the influence of antivaccine information. However, when viewers are directed to antivaccine videos on YouTube from another site, the recommendation algorithm is still likely to expose them to additional antivaccine information. UR - https://www.jmir.org/2021/1/e23262 UR - http://dx.doi.org/10.2196/23262 UR - http://www.ncbi.nlm.nih.gov/pubmed/33399543 ID - info:doi/10.2196/23262 ER - TY - JOUR AU - Burnett, Dayle AU - Eapen, Valsamma AU - Lin, Ping-I PY - 2020/12/30 TI - Time Trends of the Public?s Attention Toward Suicide During the COVID-19 Pandemic: Retrospective, Longitudinal Time-Series Study JO - JMIR Public Health Surveill SP - e24694 VL - 6 IS - 4 KW - COVID-19 KW - suicide KW - infodemiology KW - infoveillance KW - Google Trends KW - time trend KW - school closure KW - attention KW - mental health KW - crisis KW - time series N2 - Background: The COVID-19 pandemic has overwhelmed health care systems around the world. Emerging evidence has suggested that substantially few patients seek help for suicidality at clinical settings during the COVID-19 pandemic, which has elicited concerns of an imminent mental health crisis as the course of the pandemic continues to unfold. Clarifying the relationship between the public?s attention to knowledge about suicide and the public?s attention to knowledge about the COVID-19 pandemic may provide insight into developing prevention strategies for a putative surge of suicide in relation to the impact of the COVID-19 pandemic. Objective: The goal of this retrospective, longitudinal time-series study is to understand the relationship between temporal trends of interest for the search term ?suicide? and those of COVID-19?related terms, such as ?social distancing,? ?school closure,? and ?lockdown.? Methods: We used the Google Trends platform to collect data on daily interest levels for search terms related to suicide, several other mental health-related issues, and COVID-19 over the period between February 14, 2020 and May 13, 2020. A correlational analysis was performed to determine the association between the search term ??suicide?? and COVID-19?related search terms in 16 countries. The Mann-Kendall test was used to examine significant differences between interest levels for the search term ?suicide? before and after school closure. Results: We found that interest levels for the search term ?suicide? statistically significantly inversely correlated with interest levels for the search terms ?COVID-19? or ?coronavirus? in nearly all countries between February 14, 2020 and May 13, 2020. Additionally, search interest for the term ??suicide?? significantly and negatively correlated with that of many COVID-19?related search terms, and search interest varied between countries. The Mann-Kendall test was used to examine significant differences between search interest levels for the term ?suicide? before and after school closure. The Netherlands (P=.19), New Zealand (P=.003), the United Kingdom (P=.006), and the United States (P=.049) showed significant negative trends in interest levels for suicide in the 2-week period preceding school closures. In contrast, interest levels for suicide had a significant positive trend in Canada (P<.001) and the United States (P=.002) after school closures. Conclusions: The public?s attention to suicide might inversely correlate with the public?s attention to COVID-19?related issues. Additionally, several anticontagion policies, such as school closure, might have led to a turning point for mental health crises, because the attention to suicidality increased after restrictions were implemented. Our results suggest that an increased risk of suicidal ideation may ensue due to the ongoing anticontagion policies. Timely intervention strategies for suicides should therefore be an integral part of efforts to flatten the epidemic curve. UR - http://publichealth.jmir.org/2020/4/e24694/ UR - http://dx.doi.org/10.2196/24694 UR - http://www.ncbi.nlm.nih.gov/pubmed/33326407 ID - info:doi/10.2196/24694 ER - TY - JOUR AU - Leis, Angela AU - Ronzano, Francesco AU - Mayer, Angel Miguel AU - Furlong, I. Laura AU - Sanz, Ferran PY - 2020/12/18 TI - Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study JO - J Med Internet Res SP - e20920 VL - 22 IS - 12 KW - depression KW - antidepressant drugs KW - serotonin uptake inhibitors KW - mental health KW - social media KW - infodemiology KW - data mining N2 - Background: Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed drugs for the treatment of depressive disorders. Some people share information about their experiences with antidepressants on social media platforms such as Twitter. Analysis of the messages posted by Twitter users under SSRI treatment can yield useful information on how these antidepressants affect users? behavior. Objective: This study aims to compare the behavioral and linguistic characteristics of the tweets posted while users were likely to be under SSRI treatment, in comparison to the tweets posted by the same users when they were less likely to be taking this medication. Methods: In the first step, the timelines of Twitter users mentioning SSRI antidepressants in their tweets were selected using a list of 128 generic and brand names of SSRIs. In the second step, two datasets of tweets were created, the in-treatment dataset (made up of the tweets posted throughout the 30 days after mentioning an SSRI) and the unknown-treatment dataset (made up of tweets posted more than 90 days before or more than 90 days after any tweet mentioning an SSRI). For each user, the changes in behavioral and linguistic features between the tweets classified in these two datasets were analyzed. 186 users and their timelines with 668,842 tweets were finally included in the study. Results: The number of tweets generated per day by the users when they were in treatment was higher than it was when they were in the unknown-treatment period (P=.001). When the users were in treatment, the mean percentage of tweets posted during the daytime (from 8 AM to midnight) increased in comparison to the unknown-treatment period (P=.002). The number of characters and words per tweet was higher when the users were in treatment (P=.03 and P=.02, respectively). Regarding linguistic features, the percentage of pronouns that were first-person singular was higher when users were in treatment (P=.008). Conclusions: Behavioral and linguistic changes have been detected when users with depression are taking antidepressant medication. These features can provide interesting insights for monitoring the evolution of this disease, as well as offering additional information related to treatment adherence. This information may be especially useful in patients who are receiving long-term treatments such as people suffering from depression. UR - http://www.jmir.org/2020/12/e20920/ UR - http://dx.doi.org/10.2196/20920 UR - http://www.ncbi.nlm.nih.gov/pubmed/33337338 ID - info:doi/10.2196/20920 ER - TY - JOUR AU - Li, Zhenlong AU - Li, Xiaoming AU - Porter, Dwayne AU - Zhang, Jiajia AU - Jiang, Yuqin AU - Olatosi, Bankole AU - Weissman, Sharon PY - 2020/12/18 TI - Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics JO - JMIR Res Protoc SP - e24432 VL - 9 IS - 12 KW - big data KW - human movement KW - spatial computing KW - COVID-19 KW - artificial intelligence N2 - Background: Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective: Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods: We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results: This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions: Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID): DERR1-10.2196/24432 UR - http://www.researchprotocols.org/2020/12/e24432/ UR - http://dx.doi.org/10.2196/24432 UR - http://www.ncbi.nlm.nih.gov/pubmed/33301418 ID - info:doi/10.2196/24432 ER - TY - JOUR AU - Nsoesie, Okanyene Elaine AU - Cesare, Nina AU - Müller, Martin AU - Ozonoff, Al PY - 2020/12/15 TI - COVID-19 Misinformation Spread in Eight Countries: Exponential Growth Modeling Study JO - J Med Internet Res SP - e24425 VL - 22 IS - 12 KW - misinformation KW - internet KW - COVID-19 KW - social media KW - rumors N2 - Background: The epidemic of misinformation about COVID-19 transmission, prevention, and treatment has been going on since the start of the pandemic. However, data on the exposure and impact of misinformation is not readily available. Objective: We aim to characterize and compare the start, peak, and doubling time of COVID-19 misinformation topics across 8 countries using an exponential growth model usually employed to study infectious disease epidemics. Methods: COVID-19 misinformation topics were selected from the World Health Organization Mythbusters website. Data representing exposure was obtained from the Google Trends application programming interface for 8 English-speaking countries. Exponential growth models were used in modeling trends for each country. Results: Searches for ?coronavirus AND 5G? started at different times but peaked in the same week for 6 countries. Searches for 5G also had the shortest doubling time across all misinformation topics, with the shortest time in Nigeria and South Africa (approximately 4-5 days). Searches for ?coronavirus AND ginger? started at the same time (the week of January 19, 2020) for several countries, but peaks were incongruent, and searches did not always grow exponentially after the initial week. Searches for ?coronavirus AND sun? had different start times across countries but peaked at the same time for multiple countries. Conclusions: Patterns in the start, peak, and doubling time for ?coronavirus AND 5G? were different from the other misinformation topics and were mostly consistent across countries assessed, which might be attributable to a lack of public understanding of 5G technology. Understanding the spread of misinformation, similarities and differences across different contexts can help in the development of appropriate interventions for limiting its impact similar to how we address infectious disease epidemics. Furthermore, the rapid proliferation of misinformation that discourages adherence to public health interventions could be predictive of future increases in disease cases. UR - http://www.jmir.org/2020/12/e24425/ UR - http://dx.doi.org/10.2196/24425 UR - http://www.ncbi.nlm.nih.gov/pubmed/33264102 ID - info:doi/10.2196/24425 ER - TY - JOUR AU - Valdez, Danny AU - ten Thij, Marijn AU - Bathina, Krishna AU - Rutter, A. Lauren AU - Bollen, Johan PY - 2020/12/14 TI - Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data JO - J Med Internet Res SP - e21418 VL - 22 IS - 12 KW - social media KW - analytics KW - infodemiology KW - infoveillance KW - COVID-19 KW - United States KW - mental health KW - informatics KW - sentiment analysis KW - Twitter N2 - Background: The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world?s mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population?s mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being. Objective: This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic? Methods: We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. Results: LDA topics generated in the early months of the data set corresponded to major COVID-19?specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March. Conclusions: Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts. UR - http://www.jmir.org/2020/12/e21418/ UR - http://dx.doi.org/10.2196/21418 UR - http://www.ncbi.nlm.nih.gov/pubmed/33284783 ID - info:doi/10.2196/21418 ER - TY - JOUR AU - Alshalan, Raghad AU - Al-Khalifa, Hend AU - Alsaeed, Duaa AU - Al-Baity, Heyam AU - Alshalan, Shahad PY - 2020/12/8 TI - Detection of Hate Speech in COVID-19?Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach JO - J Med Internet Res SP - e22609 VL - 22 IS - 12 KW - COVID-19 KW - coronavirus KW - Twitter KW - hate speech KW - social network analysis KW - social media KW - public health KW - pandemic KW - deep learning KW - non-negative matrix factorization KW - NMF KW - convolutional neural network KW - CNN N2 - Background: The massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech detection problem as a supervised text classification task using classical machine learning methods or, more recently, deep learning methods. However, work investigating this problem in Arabic cyberspace is still limited compared to the published work on English text. Objective: This study aims to identify hate speech related to the COVID-19 pandemic posted by Twitter users in the Arab region and to discover the main issues discussed in tweets containing hate speech. Methods: We used the ArCOV-19 dataset, an ongoing collection of Arabic tweets related to COVID-19, starting from January 27, 2020. Tweets were analyzed for hate speech using a pretrained convolutional neural network (CNN) model; each tweet was given a score between 0 and 1, with 1 being the most hateful text. We also used nonnegative matrix factorization to discover the main issues and topics discussed in hate tweets. Results: The analysis of hate speech in Twitter data in the Arab region identified that the number of non?hate tweets greatly exceeded the number of hate tweets, where the percentage of hate tweets among COVID-19 related tweets was 3.2% (11,743/547,554). The analysis also revealed that the majority of hate tweets (8385/11,743, 71.4%) contained a low level of hate based on the score provided by the CNN. This study identified Saudi Arabia as the Arab country from which the most COVID-19 hate tweets originated during the pandemic. Furthermore, we showed that the largest number of hate tweets appeared during the time period of March 1-30, 2020, representing 51.9% of all hate tweets (6095/11,743). Contrary to what was anticipated, in the Arab region, it was found that the spread of COVID-19?related hate speech on Twitter was weakly related with the dissemination of the pandemic based on the Pearson correlation coefficient (r=0.1982, P=.50). The study also identified the commonly discussed topics in hate tweets during the pandemic. Analysis of the 7 extracted topics showed that 6 of the 7 identified topics were related to hate speech against China and Iran. Arab users also discussed topics related to political conflicts in the Arab region during the COVID-19 pandemic. Conclusions: The COVID-19 pandemic poses serious public health challenges to nations worldwide. During the COVID-19 pandemic, frequent use of social media can contribute to the spread of hate speech. Hate speech on the web can have a negative impact on society, and hate speech may have a direct correlation with real hate crimes, which increases the threat associated with being targeted by hate speech and abusive language. This study is the first to analyze hate speech in the context of Arabic COVID-19?related tweets in the Arab region. UR - http://www.jmir.org/2020/12/e22609/ UR - http://dx.doi.org/10.2196/22609 UR - http://www.ncbi.nlm.nih.gov/pubmed/33207310 ID - info:doi/10.2196/22609 ER - TY - JOUR AU - Xu, Qing AU - Shen, Ziyi AU - Shah, Neal AU - Cuomo, Raphael AU - Cai, Mingxiang AU - Brown, Matthew AU - Li, Jiawei AU - Mackey, Tim PY - 2020/12/7 TI - Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis JO - JMIR Public Health Surveill SP - e24125 VL - 6 IS - 4 KW - COVID-19 KW - infodemiology KW - infoveillance KW - infodemic KW - Weibo KW - social media KW - content analysis KW - China KW - data mining KW - knowledge KW - attitude KW - behavior N2 - Background: The COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people?s knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences. Objective: This study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese. Methods: We used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19?related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures. Results: We identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak?s seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior. Conclusions: Between the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond. UR - http://publichealth.jmir.org/2020/4/e24125/ UR - http://dx.doi.org/10.2196/24125 UR - http://www.ncbi.nlm.nih.gov/pubmed/33175693 ID - info:doi/10.2196/24125 ER - TY - JOUR AU - Agley, Jon AU - Xiao, Yunyu AU - Thompson, E. Esi AU - Golzarri-Arroyo, Lilian PY - 2020/12/7 TI - COVID-19 Misinformation Prophylaxis: Protocol for a Randomized Trial of a Brief Informational Intervention JO - JMIR Res Protoc SP - e24383 VL - 9 IS - 12 KW - COVID-19 KW - misinformation KW - infodemic KW - infodemiology KW - trust KW - trust in science KW - protocol KW - intervention KW - health information KW - prevention KW - behavior N2 - Background: As the COVID-19 pandemic continues to affect life in the United States, the important role of nonpharmaceutical preventive behaviors (such as wearing a face mask) in reducing the risk of infection has become clear. During the pandemic, researchers have observed the rapid proliferation of misinformed or inconsistent narratives about COVID-19. There is growing evidence that such misinformed narratives are associated with various forms of undesirable behavior (eg, burning down cell towers). Furthermore, individuals? adherence to recommended COVID-19 preventive guidelines has been inconsistent, and such mandates have engendered opposition and controversy. Recent research suggests the possibility that trust in science and scientists may be an important thread to weave throughout these seemingly disparate components of the modern public health landscape. Thus, this paper describes the protocol for a randomized trial of a brief, digital intervention designed to increase trust in science. Objective: The objective of this study is to examine whether exposure to a curated infographic can increase trust in science, reduce the believability of misinformed narratives, and increase the likelihood to engage in preventive behaviors. Methods: This is a randomized, placebo-controlled, superiority trial comprising 2 parallel groups. A sample of 1000 adults aged ?18 years who are representative of the population of the United States by gender, race and ethnicity, and age will be randomly assigned (via a 1:1 allocation) to an intervention or a placebo-control arm. The intervention will be a digital infographic with content based on principles of trust in science, developed by a health communications expert. The intervention will then be both pretested and pilot-tested to determine its viability. Study outcomes will include trust in science, a COVID-19 narrative belief latent profile membership, and the likelihood to engage in preventive behaviors, which will be controlled by 8 theoretically selected covariates. Results: This study was funded in August 2020, approved by the Indiana University Institutional Review Board on September 15, 2020, and prospectively registered with ClinicalTrials.gov. Conclusions: COVID-19 misinformation prophylaxis is crucial. This proposed experiment investigates the impact of a brief yet actionable intervention that can be easily disseminated to increase individuals? trust in science, with the intention of affecting misinformation believability and, consequently, preventive behavioral intentions. Trial Registration: ClinicalTrials.gov NCT04557241; https://clinicaltrials.gov/ct2/show/NCT04557241 International Registered Report Identifier (IRRID): PRR1-10.2196/24383 UR - http://www.researchprotocols.org/2020/12/e24383/ UR - http://dx.doi.org/10.2196/24383 UR - http://www.ncbi.nlm.nih.gov/pubmed/33175694 ID - info:doi/10.2196/24383 ER - TY - JOUR AU - Majmundar, Anuja AU - Le, NamQuyen AU - Moran, Bridgid Meghan AU - Unger, B. Jennifer AU - Reuter, Katja PY - 2020/12/7 TI - Public Response to a Social Media Tobacco Prevention Campaign: Content Analysis JO - JMIR Public Health Surveill SP - e20649 VL - 6 IS - 4 KW - social media KW - health campaign KW - tobacco KW - online KW - health communication KW - internet KW - Twitter KW - Facebook KW - Instagram N2 - Background: Prior research suggests that social media?based public health campaigns are often targeted by countercampaigns. Objective: Using reactance theory as the theoretical framework, this research characterizes the nature of public response to tobacco prevention messages disseminated via a social media?based campaign. We also examine whether agreement with the prevention messages is associated with comment tone and nature of the contribution to the overall discussion. Methods: User comments to tobacco prevention messages, posted between April 19, 2017 and July 12, 2017, were extracted from Twitter, Facebook, and Instagram. Two coders categorized comments in terms of tone, agreement with message, nature of contribution, mentions of government agency and regulation, promotional or spam comments, and format of comment. Chi-square analyses tested associations between agreement with the message and tone of the public response and the nature of contributions to the discussions. Results: Of the 1242 comments received (Twitter: n=1004; Facebook: n=176; Instagram: n=62), many comments used a negative tone (42.75%) and disagreed with the health messages (39.77%), while the majority made healthy contributions to the discussions (84.38%). Only 0.56% of messages mentioned government agencies, and only 0.48% of the comments were antiregulation. Comments employing a positive tone (84.13%) or making healthy contributions (69.11%) were more likely to agree with the campaign messages (P=0.01). Comments employing a negative tone (71.25%) or making toxic contributions (36.26%) generally disagreed with the messages (P=0.01). Conclusions: The majority of user comments in response to a tobacco prevention campaign made healthy contributions. Our findings encourage the use of social media to promote dialogue about controversial health topics such as smoking. However, toxicity was characteristic of comments that disagreed with the health messages. Managing negative and toxic comments on social media is a crucial issue for social media?based tobacco prevention campaigns to consider. UR - http://publichealth.jmir.org/2020/4/e20649/ UR - http://dx.doi.org/10.2196/20649 UR - http://www.ncbi.nlm.nih.gov/pubmed/33284120 ID - info:doi/10.2196/20649 ER - TY - JOUR AU - Gallagher, John AU - Lawrence, Y. Heidi PY - 2020/12/4 TI - Rhetorical Appeals and Tactics in New York Times Comments About Vaccines: Qualitative Analysis JO - J Med Internet Res SP - e19504 VL - 22 IS - 12 KW - vaccination KW - qualitative research KW - quantitative research KW - rhetoric KW - online comments KW - anti-vaccination KW - pro-vaccination N2 - Background: Improving persuasion in response to vaccine skepticism is a long-standing problem. Elective nonvaccination emerging from skepticism about vaccine safety and efficacy jeopardizes herd immunity, exposing those who are most vulnerable to the risk of serious diseases. Objective: This article analyzes vaccine sentiments in the New York Times as a way of improving understanding of why existing persuasive approaches may be ineffective and offers insight into how existing methods might be improved. We categorize pro-vaccine and anti-vaccine arguments, offering an in-depth analysis of pro-vaccine appeals and tactics in particular to enhance current understanding of arguments that support vaccines. Methods: Qualitative thematic analyses were used to analyze themes in rhetorical appeals across 808 vaccine-specific comments. Pro-vaccine and anti-vaccine comments were categorized to provide a broad analysis of the overall context of vaccine comments across viewpoints, with in-depth rhetorical analysis of pro-vaccine comments to address current gaps in understanding of pro-vaccine arguments in particular. Results: Appeals across 808 anti-vaccine and pro-vaccine comments were similar, though these appeals diverged in tactics and conclusions. Anti-vaccine arguments were more heterogeneous, deploying a wide range of arguments against vaccines. Additional analysis of pro-vaccine comments reveals that these comments use rhetorical strategies that could be counterproductive to producing persuasion. Pro-vaccine comments more frequently used tactics such as ad hominem arguments levied at those who refuse vaccines or used appeals to science to correct beliefs in vaccine skepticism, both of which can be ineffective when attempting to persuade a skeptical audience. Conclusions: Further study of pro-vaccine argumentation appeals and tactics could illuminate how persuasiveness could be improved in online forums. UR - http://www.jmir.org/2020/12/e19504/ UR - http://dx.doi.org/10.2196/19504 UR - http://www.ncbi.nlm.nih.gov/pubmed/33275110 ID - info:doi/10.2196/19504 ER - TY - JOUR AU - Berkovic, Danielle AU - Ackerman, N. Ilana AU - Briggs, M. Andrew AU - Ayton, Darshini PY - 2020/12/3 TI - Tweets by People With Arthritis During the COVID-19 Pandemic: Content and Sentiment Analysis JO - J Med Internet Res SP - e24550 VL - 22 IS - 12 KW - COVID-19 KW - SARS-CoV-2 KW - novel coronavirus KW - social media KW - Twitter KW - content analysis KW - sentiment analysis KW - microblogging KW - arthritis N2 - Background: Emerging evidence suggests that people with arthritis are reporting increased physical pain and psychological distress during the COVID-19 pandemic. At the same time, Twitter?s daily usage has surged by 23% throughout the pandemic period, presenting a unique opportunity to assess the content and sentiment of tweets. Individuals with arthritis use Twitter to communicate with peers, and to receive up-to-date information from health professionals and services about novel therapies and management techniques. Objective: The aim of this research was to identify proxy topics of importance for individuals with arthritis during the COVID-19 pandemic, and to explore the emotional context of tweets by people with arthritis during the early phase of the pandemic. Methods: From March 20 to April 20, 2020, publicly available tweets posted in English and with hashtag combinations related to arthritis and COVID-19 were extracted retrospectively from Twitter. Content analysis was used to identify common themes within tweets, and sentiment analysis was used to examine positive and negative emotions in themes to understand the COVID-19 experiences of people with arthritis. Results: In total, 149 tweets were analyzed. The majority of tweeters were female and were from the United States. Tweeters reported a range of arthritis conditions, including rheumatoid arthritis, systemic lupus erythematosus, and psoriatic arthritis. Seven themes were identified: health care experiences, personal stories, links to relevant blogs, discussion of arthritis-related symptoms, advice sharing, messages of positivity, and stay-at-home messaging. Sentiment analysis demonstrated marked anxiety around medication shortages, increased physical symptom burden, and strong desire for trustworthy information and emotional connection. Conclusions: Tweets by people with arthritis highlight the multitude of concurrent concerns during the COVID-19 pandemic. Understanding these concerns, which include heightened physical and psychological symptoms in the context of treatment misinformation, may assist clinicians to provide person-centered care during this time of great health uncertainty. UR - https://www.jmir.org/2020/12/e24550 UR - http://dx.doi.org/10.2196/24550 UR - http://www.ncbi.nlm.nih.gov/pubmed/33170802 ID - info:doi/10.2196/24550 ER - TY - JOUR AU - Massey, M. Philip AU - Kearney, D. Matthew AU - Hauer, K. Michael AU - Selvan, Preethi AU - Koku, Emmanuel AU - Leader, E. Amy PY - 2020/12/3 TI - Dimensions of Misinformation About the HPV Vaccine on Instagram: Content and Network Analysis of Social Media Characteristics JO - J Med Internet Res SP - e21451 VL - 22 IS - 12 KW - social media KW - cancer KW - vaccination KW - health communication KW - public health KW - HPV, human papillomavirus N2 - Background: The human papillomavirus (HPV) vaccine is a major advancement in cancer prevention and this primary prevention tool has the potential to reduce and eliminate HPV-associated cancers; however, the safety and efficacy of vaccines in general and the HPV vaccine specifically have come under attack, particularly through the spread of misinformation on social media. The popular social media platform Instagram represents a significant source of exposure to health (mis)information; 1 in 3 US adults use Instagram. Objective: The objective of this analysis was to characterize pro- and anti-HPV vaccine networks on Instagram, and to describe misinformation within the anti-HPV vaccine network. Methods: From April 2018 to December 2018, we collected publicly available English-language Instagram posts containing hashtags #HPV, #HPVVaccine, or #Gardasil using Netlytic software (n=16,607). We randomly selected 10% of the sample and content analyzed relevant posts (n=580) for text, image, and social media features as well as holistic attributes (eg, sentiments, personal stories). Among antivaccine posts, we organized elements of misinformation within four broad dimensions: 1) misinformation theoretical domains, 2) vaccine debate topics, 3) evidence base, and 4) health beliefs. We conducted univariate, bivariate, and network analyses on the subsample of posts to quantify the role and position of individual posts in the network. Results: Compared to provaccine posts (324/580, 55.9%), antivaccine posts (256/580, 44.1%) were more likely to originate from individuals (64.1% antivaccine vs 25.0% provaccine; P<.001) and include personal narratives (37.1% vs 25.6%; P=.003). In the antivaccine network, core misinformation characteristics included mentioning #Gardasil, purporting to reveal a lie (ie, concealment), conspiracy theories, unsubstantiated claims, and risk of vaccine injury. Information/resource posts clustered around misinformation domains including falsification, nanopublications, and vaccine-preventable disease, whereas personal narrative posts clustered around different domains of misinformation, including concealment, injury, and conspiracy theories. The most liked post (6634 likes) in our full subsample was a positive personal narrative post, created by a non-health individual; the most liked post (5604 likes) in our antivaccine subsample was an informational post created by a health individual. Conclusions: Identifying characteristics of misinformation related to HPV vaccine on social media will inform targeted interventions (eg, network opinion leaders) and help sow corrective information and stories tailored to different falsehoods. UR - https://www.jmir.org/2020/12/e21451 UR - http://dx.doi.org/10.2196/21451 UR - http://www.ncbi.nlm.nih.gov/pubmed/33270038 ID - info:doi/10.2196/21451 ER - TY - JOUR AU - Weiger, Caitlin AU - Smith, C. Katherine AU - Cohen, E. Joanna AU - Dredze, Mark AU - Moran, Bridgid Meghan PY - 2020/12/2 TI - How Internet Contracts Impact Research: Content Analysis of Terms of Service on Consumer Product Websites JO - JMIR Public Health Surveill SP - e23579 VL - 6 IS - 4 KW - marketing KW - contracts KW - internet KW - jurisprudence KW - ethics N2 - Background: Companies use brand websites as a promotional tool to engage consumers on the web, which can increase product use. Given that some products are harmful to the health of consumers, it is important for marketing associated with these products to be subject to public health surveillance. However, terms of service (TOS) governing the use of brand website content may impede such important research. Objective: The aim of this study is to explore the TOS for brand websites with public health significance to assess possible legal and ethical challenges for conducting research on consumer product websites. Methods: Using Statista, we purposefully constructed a sample of 15 leading American tobacco, alcohol, psychiatric pharmaceutical, fast-food, and gun brands that have associated websites. We developed and implemented a structured coding system for the TOS on these websites and coded for the presence versus absence of different types of restriction that might impact the ability to conduct research. Results: All TOS stated that by accessing the website, users agreed to abide by the TOS (15/15, 100%). A total of 11 out of 15 (73%) websites had age restrictions in their TOS. All alcohol brand websites (5/15, 33%) required users to enter their age or date of birth before viewing website content. Both websites for tobacco brands (2/15, 13%) further required that users register and verify their age and identity to access any website content and agree that they use tobacco products. Only one website (1/15, 7%) allowed users to display, download, copy, distribute, and translate the website content as long as it was for personal and not commercial use. A total of 33% (5/15) of TOS unconditionally prohibited or put substantial restrictions on all of these activities and/or failed to specify if they were allowed or prohibited. Moreover, 87% (13/15) of TOS indicated that website access could be restricted at any time. A total of 73% (11/15) of websites specified that violating TOS could result in deleting user content from the website, revoking access by having the user?s Internet Protocol address blocked, terminating log-in credentials, or enforcing legal action resulting in civil or criminal penalties. Conclusions: TOS create complications for public health surveillance related to e-marketing on brand websites. Recent court opinions have reduced the risk of federal criminal charges for violating TOS on public websites, but this risk remains unclear for private websites. The public health community needs to establish standards to guide and protect researchers from the possibility of legal repercussions related to such efforts. UR - http://publichealth.jmir.org/2020/4/e23579/ UR - http://dx.doi.org/10.2196/23579 UR - http://www.ncbi.nlm.nih.gov/pubmed/33263555 ID - info:doi/10.2196/23579 ER - TY - JOUR AU - Singh, Tavleen AU - Roberts, Kirk AU - Cohen, Trevor AU - Cobb, Nathan AU - Wang, Jing AU - Fujimoto, Kayo AU - Myneni, Sahiti PY - 2020/11/30 TI - Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review JO - JMIR Public Health Surveill SP - e21660 VL - 6 IS - 4 KW - social media KW - infodemiology KW - infoveillance KW - online health communities KW - risky health behaviors KW - data mining KW - machine learning KW - natural language processing KW - text mining N2 - Background: Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change. Objective: The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms. Methods: We performed a systematic review of the literature in September 2020 by searching three databases?PubMed, Web of Science, and Scopus?using relevant keywords, such as ?social media,? ?online health communities,? ?machine learning,? ?data mining,? etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. Results: The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies. Conclusions: Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels. UR - http://publichealth.jmir.org/2020/4/e21660/ UR - http://dx.doi.org/10.2196/21660 UR - http://www.ncbi.nlm.nih.gov/pubmed/33252345 ID - info:doi/10.2196/21660 ER - TY - JOUR AU - Dong, Wei AU - Tao, Jinhu AU - Xia, Xiaolin AU - Ye, Lin AU - Xu, Hanli AU - Jiang, Peiye AU - Liu, Yangyang PY - 2020/11/25 TI - Public Emotions and Rumors Spread During the COVID-19 Epidemic in China: Web-Based Correlation Study JO - J Med Internet Res SP - e21933 VL - 22 IS - 11 KW - public emotions KW - rumor KW - infodemic KW - infodemiology KW - infoveillance KW - China KW - COVID-19 N2 - Background: Various online rumors have led to inappropriate behaviors among the public in response to the COVID-19 epidemic in China. These rumors adversely affect people?s physical and mental health. Therefore, a better understanding of the relationship between public emotions and rumors during the epidemic may help generate useful strategies for guiding public emotions and dispelling rumors. Objective: This study aimed to explore whether public emotions are related to the dissemination of online rumors in the context of COVID-19. Methods: We used the web-crawling tool Scrapy to gather data published by People?s Daily on Sina Weibo, a popular social media platform in China, after January 8, 2020. Netizens? comments under each Weibo post were collected. Nearly 1 million comments thus collected were divided into 5 categories: happiness, sadness, anger, fear, and neutral, based on the underlying emotional information identified and extracted from the comments by using a manual identification process. Data on rumors spread online were collected through Tencent?s Jiaozhen platform. Time-lagged cross-correlation analyses were performed to examine the relationship between public emotions and rumors. Results: Our results indicated that the angrier the public felt, the more rumors there would likely be (r=0.48, P<.001). Similar results were observed for the relationship between fear and rumors (r=0.51, P<.001) and between sadness and rumors (r=0.47, P<.001). Furthermore, we found a positive correlation between happiness and rumors, with happiness lagging the emergence of rumors by 1 day (r=0.56, P<.001). In addition, our data showed a significant positive correlation between fear and fearful rumors (r=0.34, P=.02). Conclusions: Our findings confirm that public emotions are related to the rumors spread online in the context of COVID-19 in China. Moreover, these findings provide several suggestions, such as the use of web-based monitoring methods, for relevant authorities and policy makers to guide public emotions and behavior during this public health emergency. UR - http://www.jmir.org/2020/11/e21933/ UR - http://dx.doi.org/10.2196/21933 UR - http://www.ncbi.nlm.nih.gov/pubmed/33112757 ID - info:doi/10.2196/21933 ER - TY - JOUR AU - Chang, Angela AU - Schulz, Johannes Peter AU - Tu, ShengTsung AU - Liu, Tingchi Matthew PY - 2020/11/25 TI - Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques JO - J Med Internet Res SP - e21504 VL - 22 IS - 11 KW - placing blame KW - culprits KW - sentiment analysis KW - infodemic analysis KW - political grievances KW - COVID-19 KW - communication KW - pandemic KW - social media KW - negativity KW - infodemic KW - infodemiology KW - infoveillance KW - blame KW - stigma N2 - Background: Information about a new coronavirus emerged in 2019 and rapidly spread around the world, gaining significant public attention and attracting negative bias. The use of stigmatizing language for the purpose of blaming sparked a debate. Objective: This study aims to identify social stigma and negative sentiment toward the blameworthy agents in social communities. Methods: We enabled a tailored text-mining platform to identify data in their natural settings by retrieving and filtering online sources, and constructed vocabularies and learning word representations from natural language processing for deductive analysis along with the research theme. The data sources comprised of ten news websites, eleven discussion forums, one social network, and two principal media sharing networks in Taiwan. A synthesis of news and social networking analytics was present from December 30, 2019, to March 31, 2020. Results: We collated over 1.07 million Chinese texts. Almost two-thirds of the texts on COVID-19 came from news services (n=683,887, 63.68%), followed by Facebook (n=297,823, 27.73%), discussion forums (n=62,119, 5.78%), and Instagram and YouTube (n=30,154, 2.81%). Our data showed that online news served as a hotbed for negativity and for driving emotional social posts. Online information regarding COVID-19 associated it with China?and a specific city within China through references to the ?Wuhan pneumonia??potentially encouraging xenophobia. The adoption of this problematic moniker had a high frequency, despite the World Health Organization guideline to avoid biased perceptions and ethnic discrimination. Social stigma is disclosed through negatively valenced responses, which are associated with the most blamed targets. Conclusions: Our sample is sufficiently representative of a community because it contains a broad range of mainstream online media. Stigmatizing language linked to the COVID-19 pandemic shows a lack of civic responsibility that encourages bias, hostility, and discrimination. Frequently used stigmatizing terms were deemed offensive, and they might have contributed to recent backlashes against China by directing blame and encouraging xenophobia. The implications ranging from health risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak are emphasized. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount. We propose solidarity with communication professionals in combating the COVID-19 outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative. UR - http://www.jmir.org/2020/11/e21504/ UR - http://dx.doi.org/10.2196/21504 UR - http://www.ncbi.nlm.nih.gov/pubmed/33108306 ID - info:doi/10.2196/21504 ER - TY - JOUR AU - Xue, Jia AU - Chen, Junxiang AU - Hu, Ran AU - Chen, Chen AU - Zheng, Chengda AU - Su, Yue AU - Zhu, Tingshao PY - 2020/11/25 TI - Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach JO - J Med Internet Res SP - e20550 VL - 22 IS - 11 KW - machine learning KW - Twitter data KW - COVID-19 KW - infodemic KW - infodemiology KW - infoveillance KW - public discussion KW - public sentiment KW - Twitter KW - social media KW - virus N2 - Background: It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring. Objective: The objective of this study is to examine COVID-19?related discussions, concerns, and sentiments using tweets posted by Twitter users. Methods: We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, ?coronavirus,? ?COVID-19,? ?quarantine?) from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. Results: Popular unigrams included ?virus,? ?lockdown,? and ?quarantine.? Popular bigrams included ?COVID-19,? ?stay home,? ?corona virus,? ?social distancing,? and ?new cases.? We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. Conclusions: This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic. UR - http://www.jmir.org/2020/11/e20550/ UR - http://dx.doi.org/10.2196/20550 UR - http://www.ncbi.nlm.nih.gov/pubmed/33119535 ID - info:doi/10.2196/20550 ER - TY - JOUR AU - Saha, Koustuv AU - Torous, John AU - Caine, D. Eric AU - De Choudhury, Munmun PY - 2020/11/24 TI - Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media JO - J Med Internet Res SP - e22600 VL - 22 IS - 11 KW - social media KW - Twitter KW - language KW - psychosocial effects KW - mental health KW - transfer learning KW - depression KW - anxiety KW - stress KW - social support KW - emotions KW - COVID-19 KW - coronavirus KW - crisis N2 - Background: The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a ?mental health tsunami?, the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances. Objective: Our study aims to provide insights regarding people?s psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context. Methods: We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people?s social media self-disclosure. Using these data sets, we studied people?s self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets. Results: We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis?mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis. Conclusions: We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people?s mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their ?new normal.? Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis. UR - http://www.jmir.org/2020/11/e22600/ UR - http://dx.doi.org/10.2196/22600 UR - http://www.ncbi.nlm.nih.gov/pubmed/33156805 ID - info:doi/10.2196/22600 ER - TY - JOUR AU - Niburski, Kacper AU - Niburski, Oskar PY - 2020/11/20 TI - Impact of Trump's Promotion of Unproven COVID-19 Treatments and Subsequent Internet Trends: Observational Study JO - J Med Internet Res SP - e20044 VL - 22 IS - 11 KW - COVID-19 KW - behavioral economics KW - public health KW - behavior KW - economics KW - media KW - influence KW - infodemic KW - infodemiology KW - infoveillance KW - Twitter KW - analysis KW - trend N2 - Background: Individuals with large followings can influence public opinions and behaviors, especially during a pandemic. In the early days of the pandemic, US president Donald J Trump has endorsed the use of unproven therapies. Subsequently, a death attributed to the wrongful ingestion of a chloroquine-containing compound occurred. Objective: We investigated Donald J Trump?s speeches and Twitter posts, as well as Google searches and Amazon purchases, and television airtime for mentions of hydroxychloroquine, chloroquine, azithromycin, and remdesivir. Methods: Twitter sourcing was catalogued with Factba.se, and analytics data, both past and present, were analyzed with Tweet Binder to assess average analytics data on key metrics. Donald J Trump?s time spent discussing unverified treatments on the United States? 5 largest TV stations was catalogued with the Global Database of Events, Language, and Tone, and his speech transcripts were obtained from White House briefings. Google searches and shopping trends were analyzed with Google Trends. Amazon purchases were assessed using Helium 10 software. Results: From March 1 to April 30, 2020, Donald J Trump made 11 tweets about unproven therapies and mentioned these therapies 65 times in White House briefings, especially touting hydroxychloroquine and chloroquine. These tweets had an impression reach of 300% above Donald J Trump?s average. Following these tweets, at least 2% of airtime on conservative networks for treatment modalities like azithromycin and continuous mentions of such treatments were observed on stations like Fox News. Google searches and purchases increased following his first press conference on March 19, 2020, and increased again following his tweets on March 21, 2020. The same is true for medications on Amazon, with purchases for medicine substitutes, such as hydroxychloroquine, increasing by 200%. Conclusions: Individuals in positions of power can sway public purchasing, resulting in undesired effects when the individuals? claims are unverified. Public health officials must work to dissuade the use of unproven treatments for COVID-19. UR - http://www.jmir.org/2020/11/e20044/ UR - http://dx.doi.org/10.2196/20044 UR - http://www.ncbi.nlm.nih.gov/pubmed/33151895 ID - info:doi/10.2196/20044 ER - TY - JOUR AU - Alanazi, Eisa AU - Alashaikh, Abdulaziz AU - Alqurashi, Sarah AU - Alanazi, Aued PY - 2020/11/18 TI - Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis JO - J Med Internet Res SP - e21329 VL - 22 IS - 11 KW - health KW - informatics KW - social networks KW - Twitter KW - anosmia KW - Arabic KW - COVID-19 KW - symptom N2 - Background: A substantial amount of COVID-19?related data is generated by Twitter users every day. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more about common symptoms and whether their order of appearance differs among different groups in the community. These data may be used to develop a COVID-19 risk assessment system that is tailored toward a specific group of people. Objective: The aim of this study was to identify the most common symptoms reported by patients with COVID-19, as well as the order of symptom appearance, by examining tweets in Arabic. Methods: We searched Twitter posts in Arabic for personal reports of COVID-19 symptoms from March 1 to May 27, 2020. We identified 463 Arabic users who had tweeted about testing positive for COVID-19 and extracted the symptoms they associated with the disease. Furthermore, we asked them directly via personal messaging to rank the appearance of the first 3 symptoms they had experienced immediately before (or after) their COVID-19 diagnosis. Finally, we tracked their Twitter timeline to identify additional symptoms that were mentioned within ±5 days from the day of the first tweet on their COVID-19 diagnosis. In total, 270 COVID-19 self-reports were collected, and symptoms were (at least partially) ranked. Results: The collected self-reports contained 893 symptoms from 201 (74%) male and 69 (26%) female Twitter users. The majority (n=270, 82%) of the tracked users were living in Saudi Arabia (n=125, 46%) and Kuwait (n=98, 36%). Furthermore, 13% (n=36) of the collected reports were from asymptomatic individuals. Of the 234 users with symptoms, 66% (n=180) provided a chronological order of appearance for at least 3 symptoms. Fever (n=139, 59%), headache (n=101, 43%), and anosmia (n=91, 39%) were the top 3 symptoms mentioned in the self-reports. Additionally, 28% (n=65) reported that their COVID-19 experience started with a fever, 15% (n=34) with a headache, and 12% (n=28) with anosmia. Of the 110 symptomatic cases from Saudi Arabia, the most common 3 symptoms were fever (n=65, 59%), anosmia (n=46, 42%), and headache (n=42, 38%). Conclusions: This study identified the most common symptoms of COVID-19 from tweets in Arabic. These symptoms can be further analyzed in clinical settings and may be incorporated into a real-time COVID-19 risk estimator. UR - http://www.jmir.org/2020/11/e21329/ UR - http://dx.doi.org/10.2196/21329 UR - http://www.ncbi.nlm.nih.gov/pubmed/33119539 ID - info:doi/10.2196/21329 ER - TY - JOUR AU - Lee, Jae Jung AU - Kang, Kyung-Ah AU - Wang, Ping Man AU - Zhao, Zhi Sheng AU - Wong, Ha Janet Yuen AU - O'Connor, Siobhan AU - Yang, Ching Sook AU - Shin, Sunhwa PY - 2020/11/13 TI - Associations Between COVID-19 Misinformation Exposure and Belief With COVID-19 Knowledge and Preventive Behaviors: Cross-Sectional Online Study JO - J Med Internet Res SP - e22205 VL - 22 IS - 11 KW - COVID-19 KW - misinformation KW - infodemic KW - infodemiology KW - anxiety KW - depression KW - PTSD KW - knowledge KW - preventive behaviors KW - prevention KW - behavior N2 - Background: Online misinformation proliferation during the COVID-19 pandemic has become a major public health concern. Objective: We aimed to assess the prevalence of COVID-19 misinformation exposure and beliefs, associated factors including psychological distress with misinformation exposure, and the associations between COVID-19 knowledge and number of preventive behaviors. Methods: A cross-sectional online survey was conducted with 1049 South Korean adults in April 2020. Respondents were asked about receiving COVID-19 misinformation using 12 items identified by the World Health Organization. Logistic regression was used to compute adjusted odds ratios (aORs) for the association of receiving misinformation with sociodemographic characteristics, source of information, COVID-19 misinformation belief, and psychological distress, as well as the associations of COVID-19 misinformation belief with COVID-19 knowledge and the number of COVID-19 preventive behaviors among those who received the misinformation. All data were weighted according to the Korea census data in 2018. Results: Overall, 67.78% (n=711) of respondents reported exposure to at least one COVID-19 misinformation item. Misinformation exposure was associated with younger age, higher education levels, and lower income. Sources of information associated with misinformation exposure were social networking services (aOR 1.67, 95% CI 1.20-2.32) and instant messaging (aOR 1.79, 1.27-2.51). Misinformation exposure was also associated with psychological distress including anxiety (aOR 1.80, 1.24-2.61), depressive (aOR 1.47, 1.09-2.00), and posttraumatic stress disorder symptoms (aOR 1.97, 1.42-2.73), as well as misinformation belief (aOR 7.33, 5.17-10.38). Misinformation belief was associated with poorer COVID-19 knowledge (high: aOR 0.62, 0.45-0.84) and fewer preventive behaviors (?7 behaviors: aOR 0.54, 0.39-0.74). Conclusions: COVID-19 misinformation exposure was associated with misinformation belief, while misinformation belief was associated with fewer preventive behaviors. Given the potential of misinformation to undermine global efforts in COVID-19 disease control, up-to-date public health strategies are required to counter the proliferation of misinformation. UR - http://www.jmir.org/2020/11/e22205/ UR - http://dx.doi.org/10.2196/22205 UR - http://www.ncbi.nlm.nih.gov/pubmed/33048825 ID - info:doi/10.2196/22205 ER - TY - JOUR AU - Xu, Chenjie AU - Cao, Zhi AU - Yang, Hongxi AU - Gao, Ying AU - Sun, Li AU - Hou, Yabing AU - Cao, Xinxi AU - Jia, Peng AU - Wang, Yaogang PY - 2020/11/12 TI - Leveraging Internet Search Data to Improve the Prediction and Prevention of Noncommunicable Diseases: Retrospective Observational Study JO - J Med Internet Res SP - e18998 VL - 22 IS - 11 KW - noncommunicable diseases KW - internet searches KW - Google Trends KW - infodemiology KW - infoveillance KW - early warning model KW - United States N2 - Background: As human society enters an era of vast and easily accessible social media, a growing number of people are exploiting the internet to search and exchange medical information. Because internet search data could reflect population interest in particular health topics, they provide a new way of understanding health concerns regarding noncommunicable diseases (NCDs) and the role they play in their prevention. Objective: We aimed to explore the association of internet search data for NCDs with published disease incidence and mortality rates in the United States and to grasp the health concerns toward NCDs. Methods: We tracked NCDs by examining the correlations among the incidence rates, mortality rates, and internet searches in the United States from 2004 to 2017, and we established forecast models based on the relationship between the disease rates and internet searches. Results: Incidence and mortality rates of 29 diseases in the United States were statistically significantly correlated with the relative search volumes (RSVs) of their search terms (P<.05). From the perspective of the goodness of fit of the multiple regression prediction models, the results were closest to 1 for diabetes mellitus, stroke, atrial fibrillation and flutter, Hodgkin lymphoma, and testicular cancer; the coefficients of determination of their linear regression models for predicting incidence were 80%, 88%, 96%, 80%, and 78%, respectively. Meanwhile, the coefficient of determination of their linear regression models for predicting mortality was 82%, 62%, 94%, 78%, and 62%, respectively. Conclusions: An advanced understanding of search behaviors could augment traditional epidemiologic surveillance and could be used as a reference to aid in disease prediction and prevention. UR - http://www.jmir.org/2020/11/e18998/ UR - http://dx.doi.org/10.2196/18998 UR - http://www.ncbi.nlm.nih.gov/pubmed/33180022 ID - info:doi/10.2196/18998 ER - TY - JOUR AU - Boon-Itt, Sakun AU - Skunkan, Yukolpat PY - 2020/11/11 TI - Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study JO - JMIR Public Health Surveill SP - e21978 VL - 6 IS - 4 KW - COVID-19 KW - Twitter KW - social media KW - infoveillance KW - infodemiology KW - infodemic KW - data KW - health informatics KW - mining KW - perception KW - topic modeling N2 - Background: COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. Objective: The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. Methods: Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. Results: The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. Conclusions: Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease. UR - http://publichealth.jmir.org/2020/4/e21978/ UR - http://dx.doi.org/10.2196/21978 UR - http://www.ncbi.nlm.nih.gov/pubmed/33108310 ID - info:doi/10.2196/21978 ER - TY - JOUR AU - Xue, Jia AU - Chen, Junxiang AU - Chen, Chen AU - Hu, Ran AU - Zhu, Tingshao PY - 2020/11/6 TI - The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets JO - J Med Internet Res SP - e24361 VL - 22 IS - 11 KW - Twitter KW - family violence KW - COVID-19 KW - machine learning KW - big data KW - infodemiology KW - infoveillance N2 - Background: Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. Objective: This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter. Methods: We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets. Results: We extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence?related news (eg, Tara Reade, Melissa DeRosa). Conclusions: This study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks. UR - http://www.jmir.org/2020/11/e24361/ UR - http://dx.doi.org/10.2196/24361 UR - http://www.ncbi.nlm.nih.gov/pubmed/33108315 ID - info:doi/10.2196/24361 ER - TY - JOUR AU - Johnson, Kristen Amy AU - Bhaumik, Runa AU - Tabidze, Irina AU - Mehta, D. Supriya PY - 2020/11/5 TI - Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends JO - JMIR Public Health Surveill SP - e20588 VL - 6 IS - 4 KW - health information technology KW - sexually transmitted infections KW - surveillance KW - infoveillance KW - infodemiology KW - Google Trends N2 - Background: Sexually transmitted infections (STIs) pose a significant public health challenge in the United States. Traditional surveillance systems are adversely affected by data quality issues, underreporting of cases, and reporting delays, resulting in missed prevention opportunities to respond to trends in disease prevalence. Search engine data can potentially facilitate an efficient and economical enhancement to surveillance reporting systems established for STIs. Objective: We aimed to develop and train a predictive model using reported STI case data from Chicago, Illinois, and to investigate the model?s predictive capacity, timeliness, and ability to target interventions to subpopulations using Google Trends data. Methods: Deidentified STI case data for chlamydia, gonorrhea, and primary and secondary syphilis from 2011-2017 were obtained from the Chicago Department of Public Health. The data set included race/ethnicity, age, and birth sex. Google Correlate was used to identify the top 100 correlated search terms with ?STD symptoms,? and an autocrawler was established using Google Health Application Programming Interface to collect the search volume for each term. Elastic net regression was used to evaluate prediction accuracy, and cross-correlation analysis was used to identify timeliness of prediction. Subgroup elastic net regression analysis was performed for race, sex, and age. Results: For gonorrhea and chlamydia, actual and predicted STI values correlated moderately in 2011 (chlamydia: r=0.65; gonorrhea: r=0.72) but correlated highly (chlamydia: r=0.90; gonorrhea: r=0.94) from 2012 to 2017. However, for primary and secondary syphilis, the high correlation was observed only for 2012 (r=0.79), 2013 (r=0.77), 2016 (0.80), and 2017 (r=0.84), with 2011, 2014, and 2015 showing moderate correlations (r=0.55-0.70). Model performance was the most accurate (highest correlation and lowest mean absolute error) for gonorrhea. Subgroup analyses improved model fit across disease and year. Regression models using search terms selected from the cross-correlation analysis improved the prediction accuracy and timeliness across diseases and years. Conclusions: Integrating nowcasting with Google Trends in surveillance activities can potentially enhance the prediction and timeliness of outbreak detection and response as well as target interventions to subpopulations. Future studies should prospectively examine the utility of Google Trends applied to STI surveillance and response. UR - http://publichealth.jmir.org/2020/4/e20588/ UR - http://dx.doi.org/10.2196/20588 UR - http://www.ncbi.nlm.nih.gov/pubmed/33151162 ID - info:doi/10.2196/20588 ER - TY - JOUR AU - McCausland, Kahlia AU - Maycock, Bruce AU - Leaver, Tama AU - Wolf, Katharina AU - Freeman, Becky AU - Thomson, Katie AU - Jancey, Jonine PY - 2020/11/5 TI - E-Cigarette Promotion on Twitter in Australia: Content Analysis of Tweets JO - JMIR Public Health Surveill SP - e15577 VL - 6 IS - 4 KW - electronic cigarette KW - e-cigarette KW - electronic nicotine delivery systems KW - vaping KW - vape KW - social media KW - twitter KW - content analysis KW - public health KW - public policy N2 - Background: The sale of electronic cigarettes (e-cigarettes) containing nicotine is prohibited in all Australian states and territories; yet, the growing availability and convenience of the internet enable the promotion and exposure of e-cigarettes across countries. Social media?s increasing pervasiveness has provided a powerful avenue to market products and influence social norms and risk behaviors. At present, there is no evidence of how e-cigarettes and vaping are promoted on social media in Australia. Objective: This study aimed to investigate how e-cigarettes are portrayed and promoted on Twitter through a content analysis of vaping-related tweets containing an image posted and retweeted by Australian users and how the portrayal and promotion have emerged and trended over time. Methods: In total, we analyzed 1303 tweets and accompanying images from 2012, 2014, 2016, and 2018 collected through the Tracking Infrastructure for Social Media Analysis (TrISMA), a contemporary technical and organizational infrastructure for the tracking of public communication by Australian users of social media, via a list of 15 popular e-cigarette?related terms. Results: Despite Australia?s cautious approach toward e-cigarettes and the limited evidence supporting them as an efficacious smoking cessation aid, it is evident that there is a concerted effort by some Twitter users to promote these devices as a health-conducive (91/129, 70.5%), smoking cessation product (266/1303, 20.41%). Further, Twitter is being used in an attempt to circumvent Australian regulation and advocate for a more liberal approach to personal vaporizers (90/1303, 6.90%). A sizeable proportion of posts was dedicated to selling or promoting vape products (347/1303, 26.63%), and 19.95% (260/1303) were found to be business listings. These posts used methods to try and expand their clientele further than immediate followers by touting competitions and giveaways, with those wanting to enter having to perform a sequence of steps such as liking, tagging, and reposting, ultimately exposing the post among the user?s network and to others not necessarily interested in vaping. Conclusions: The borderless nature of social media presents a clear challenge for enforcing Article 13 of the World Health Organization Framework Convention on Tobacco Control, which requires all ratifying nations to implement a ban on tobacco advertising, promotion, and sponsorship. Countering the advertising and promotion of these products is a public health challenge that will require cross-border cooperation with other World Health Organization Framework Convention on Tobacco Control parties. Further research aimed at developing strategies to counter the advertising and promotion of e-cigarettes is therefore needed. UR - http://publichealth.jmir.org/2020/4/e15577/ UR - http://dx.doi.org/10.2196/15577 UR - http://www.ncbi.nlm.nih.gov/pubmed/33151159 ID - info:doi/10.2196/15577 ER - TY - JOUR AU - Schäfer, Florent AU - Faviez, Carole AU - Voillot, Paméla AU - Foulquié, Pierre AU - Najm, Matthieu AU - Jeanne, Jean-François AU - Fagherazzi, Guy AU - Schück, Stéphane AU - Le Nevé, Boris PY - 2020/11/3 TI - Mapping and Modeling of Discussions Related to Gastrointestinal Discomfort in French-Speaking Online Forums: Results of a 15-Year Retrospective Infodemiology Study JO - J Med Internet Res SP - e17247 VL - 22 IS - 11 KW - gastrointestinal discomfort KW - disorders of gut-brain interactions KW - social media KW - infodemiology KW - topic modeling N2 - Background: Gastrointestinal (GI) discomfort is prevalent and known to be associated with impaired quality of life. Real-world information on factors of GI discomfort and solutions used by people is, however, limited. Social media, including online forums, have been considered a new source of information to examine the health of populations in real-life settings. Objective: The aims of this retrospective infodemiology study are to identify discussion topics, characterize users, and identify perceived determinants of GI discomfort in web-based messages posted by users of French social media. Methods: Messages related to GI discomfort posted between January 2003 and August 2018 were extracted from 14 French-speaking general and specialized publicly available online forums. Extracted messages were cleaned and deidentified. Relevant medical concepts were determined on the basis of the Medical Dictionary for Regulatory Activities and vernacular terms. The identification of discussion topics was carried out by using a correlated topic model on the basis of the latent Dirichlet allocation. A nonsupervised clustering algorithm was applied to cluster forum users according to the reported symptoms of GI discomfort, discussion topics, and activity on online forums. Users? age and gender were determined by linear regression and application of a support vector machine, respectively, to characterize the identified clusters according to demographic parameters. Perceived factors of GI discomfort were classified by a combined method on the basis of syntactic analysis to identify messages with causality terms and a second topic modeling in a relevant segment of phrases. Results: A total of 198,866 messages associated with GI discomfort were included in the analysis corpus after extraction and cleaning. These messages were posted by 36,989 separate web users, most of them being women younger than 40 years. Everyday life, diet, digestion, abdominal pain, impact on the quality of life, and tips to manage stress were among the most discussed topics. Segmentation of users identified 5 clusters corresponding to chronic and acute GI concerns. Diet topic was associated with each cluster, and stress was strongly associated with abdominal pain. Psychological factors, food, and allergens were perceived as the main causes of GI discomfort by web users. Conclusions: GI discomfort is actively discussed by web users. This study reveals a complex relationship between food, stress, and GI discomfort. Our approach has shown that identifying web-based discussion topics associated with GI discomfort and its perceived factors is feasible and can serve as a complementary source of real-world evidence for caregivers. UR - https://www.jmir.org/2020/11/e17247 UR - http://dx.doi.org/10.2196/17247 UR - http://www.ncbi.nlm.nih.gov/pubmed/33141087 ID - info:doi/10.2196/17247 ER - TY - JOUR AU - Park, Heum Tae AU - Kim, Il Woo AU - Park, Suyeon AU - Ahn, Jaeouk AU - Cho, Kyun Moon AU - Kim, Sooyoung PY - 2020/10/26 TI - Public Interest in Acne on the Internet: Comparison of Search Information From Google Trends and Naver JO - J Med Internet Res SP - e19427 VL - 22 IS - 10 KW - acne vulgaris KW - internet KW - infodemiology KW - infoveillance KW - cosmetics KW - diet KW - dermatology KW - Google N2 - Background: Acne vulgaris is a common skin disease primarily affecting young adults. Given that the internet has become a major source of health information, especially among the young, the internet is a powerful tool of communication and has a significant influence on patients. Objective: This study aimed to clarify the features of patients? interest in and evaluate the quality of information about acne vulgaris on the internet. Methods: We compared the search volumes on acne vulgaris with those of other dermatological diseases using Google Trends from January 2004 to August 2019. We also determined the search volumes for relevant keywords of acne vulgaris on Google and Naver and evaluated the quality of answers to the queries in KnowledgeiN. Results: The regression analysis of Google Trends data demonstrated that the patients? interest in acne vulgaris was higher than that for other dermatological diseases, such as atopic dermatitis (?=?20.33, 95% CI ?22.27 to ?18.39, P<.001) and urticaria (?=?27.09, 95% CI ?29.03 to ?25.15, P<.001) and has increased yearly (?=2.38, 95% CI 2.05 to 2.71, P<.001). The search volume for acne vulgaris was significantly higher in the summer than in the spring (?=?5.04, 95% CI ?9.21 to ?0.88, P=.018) and on weekends than on weekdays (?=?6.68, 95% CI ?13.18 to ?0.18, P=.044). The most frequently searched relevant keywords with ?acne vulgaris? and ?cause? were ?stress,? ?food,? and ?cosmetics.? Among food, the 2 highest acne vulgaris?related keywords were milk and wheat in Naver and coffee and ramen in Google. The queries in Naver KnowledgeiN were mostly answered by a Korean traditional medicine doctor (53.4%) or the public (33.6%), but only 12.0% by dermatologists. Conclusions: Physicians should be aware of patients? interest in and beliefs about acne vulgaris to provide the best patient education and care, both online and in the clinic. UR - http://www.jmir.org/2020/10/e19427/ UR - http://dx.doi.org/10.2196/19427 UR - http://www.ncbi.nlm.nih.gov/pubmed/33104003 ID - info:doi/10.2196/19427 ER - TY - JOUR AU - Chandrasekaran, Ranganathan AU - Mehta, Vikalp AU - Valkunde, Tejali AU - Moustakas, Evangelos PY - 2020/10/23 TI - Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study JO - J Med Internet Res SP - e22624 VL - 22 IS - 10 KW - coronavirus KW - infodemiology KW - infoveillance KW - infodemic KW - twitter KW - COVID-19 KW - social media KW - sentiment analysis KW - trends KW - topic modeling KW - disease surveillance N2 - Background: With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. Objective: The aims of this study were to examine key themes and topics of English-language COVID-19?related tweets posted by individuals and to explore the trends and variations in how the COVID-19?related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. Methods: Building on the emergent stream of studies examining COVID-19?related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19?related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. Results: Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19?related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. Conclusions: Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic. UR - http://www.jmir.org/2020/10/e22624/ UR - http://dx.doi.org/10.2196/22624 UR - http://www.ncbi.nlm.nih.gov/pubmed/33006937 ID - info:doi/10.2196/22624 ER - TY - JOUR AU - Gong, Xue AU - Han, Yangyang AU - Hou, Mengchi AU - Guo, Rui PY - 2020/10/22 TI - Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index JO - JMIR Public Health Surveill SP - e23098 VL - 6 IS - 4 KW - Baidu Index KW - public attention KW - time lag cross-correlation analysis KW - COVID-19 N2 - Background: The COVID-19 pandemic has become a global public health event, attracting worldwide attention. As a tool to monitor public awareness, internet search engines have been widely used in public health emergencies. Objective: This study aims to use online search data (Baidu Index) to monitor the public?s attention and verify internet search engines? function in public attention monitoring of public health emergencies. Methods: We collected the Baidu Index and the case monitoring data from January 20, 2020, to April 20, 2020. We combined the Baidu Index of keywords related to COVID-19 to describe the public attention?s temporal trend and spatial distribution, and conducted the time lag cross-correlation analysis. Results: The Baidu Index temporal trend indicated that the changes of the Baidu Index had a clear correspondence with the development time node of the pandemic. The Baidu Index spatial distribution showed that in the regions of central and eastern China, with denser populations, larger internet user bases, and higher economic development levels, the public was more concerned about COVID-19. In addition, the Baidu Index was significantly correlated with six case indicators of new confirmed cases, new death cases, new cured discharge cases, cumulative confirmed cases, cumulative death cases, and cumulative cured discharge cases. Moreover, the Baidu Index was 0-4 days earlier than new confirmed and new death cases, and about 20 days earlier than new cured and discharged cases while 3-5 days later than the change of cumulative cases. Conclusions: The national public?s demand for epidemic information is urgent regardless of whether it is located in the hardest hit area. The public was more sensitive to the daily new case data that represents the progress of the epidemic, but the public?s attention to the epidemic situation in other areas may lag behind. We could set the Baidu Index as the sentinel and the database in the online infoveillance system for infectious disease and public health emergencies. According to the monitoring data, the government needs to prevent and control the possible outbreak in advance and communicate the risks to the public so as to ensure the physical and psychological health of the public in the epidemic. UR - http://publichealth.jmir.org/2020/4/e23098/ UR - http://dx.doi.org/10.2196/23098 UR - http://www.ncbi.nlm.nih.gov/pubmed/32960177 ID - info:doi/10.2196/23098 ER - TY - JOUR AU - Younis, Joseph AU - Freitag, Harvy AU - Ruthberg, S. Jeremy AU - Romanes, P. Jonathan AU - Nielsen, Craig AU - Mehta, Neil PY - 2020/10/20 TI - Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study JO - JMIR Public Health Surveill SP - e21340 VL - 6 IS - 4 KW - COVID-19 KW - social media KW - Google Trends KW - Twitter KW - Instagram KW - reproduction number KW - estimated reproduction number KW - social distancing KW - public health surveillance KW - social media surveillance KW - Google Maps KW - Apple Maps KW - pandemic KW - epidemic N2 - Background:  The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible. Objective: We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (Rt) as compared to social mobility estimates reported from Google and Apple Maps. Methods:  In this observational study, the estimated Rt was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of ?social distancing? or ?#socialdistancing? on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between Rt and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (?) with significance set to P<.05. Results: Negative correlations were found between Google search interest for ?social distancing? and Rt in the United States (P<.001), and between search interest and state-specific Rt for 9 states with the highest COVID-19 cases (P<.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag ?#socialdistancing? and at 6 days for Twitter (P<.001). Significant correlations between Rt and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at ?6 and ?4 days. Meanwhile, changes in social mobility correlated best with Rt at ?2 days and +1 day for workplace and grocery/pharmacy, respectively. Conclusions: Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with Rt when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior. UR - http://publichealth.jmir.org/2020/4/e21340/ UR - http://dx.doi.org/10.2196/21340 UR - http://www.ncbi.nlm.nih.gov/pubmed/33001831 ID - info:doi/10.2196/21340 ER - TY - JOUR AU - Dhaliwal, Dhamanpreet AU - Mannion, Cynthia PY - 2020/10/20 TI - Antivaccine Messages on Facebook: Preliminary Audit JO - JMIR Public Health Surveill SP - e18878 VL - 6 IS - 4 KW - antivaccine KW - vaccines KW - vaccination KW - immunization KW - communicable disease N2 - Background: The World Health Organization lists vaccine hesitancy as one of 10 threats to global health. The antivaccine movement uses Facebook to promote messages on the alleged dangers and consequences of vaccinating, leading to a reluctance to immunize against preventable communicable diseases. Objective: We would like to know more about the messages these websites are sharing via social media that can influence readers and consumers. What messages is the public receiving on Facebook about immunization? What content (news articles, testimonials, videos, scientific studies) is being promoted? Methods: We proposed using a social media audit tool and 3 categorical lists to capture information on websites and posts, respectively. The keywords ?vaccine,? ?vaccine truth,? and ?anti-vax? were entered in the Facebook search bar. A Facebook page was examined if it had between 2500 and 150,000 likes. Data about beliefs, calls to action, and testimonials were recorded from posts and listed under the categories Myths, Truths, and Consequences. Website data were entered in a social media audit template. Results: Users? posts reflected fear and vaccine hesitancy resulting from the alleged dangers of immunization featured on the website links. Vaccines were blamed for afflictions such as autism, cancer, and infertility. Mothers shared testimonies on alleged consequences their children suffered due to immunization, which have influenced other parents to not vaccinate their children. Users denied the current measles outbreaks in the United States to be true, retaliating against the government in protests for fabricating news. Conclusions: Some Facebook messages encourage prevailing myths about the safety and consequences of vaccines and likely contribute to parents? vaccine hesitancy. Deeply concerning is the mistrust social media has the potential to cast upon the relationship between health care providers and the public. A grasp of common misconceptions can help support health care provider practice. UR - http://publichealth.jmir.org/2020/4/e18878/ UR - http://dx.doi.org/10.2196/18878 UR - http://www.ncbi.nlm.nih.gov/pubmed/33079072 ID - info:doi/10.2196/18878 ER - TY - JOUR AU - Scheerer, Cora AU - Rüth, Melvin AU - Tizek, Linda AU - Köberle, Martin AU - Biedermann, Tilo AU - Zink, Alexander PY - 2020/10/16 TI - Googling for Ticks and Borreliosis in Germany: Nationwide Google Search Analysis From 2015 to 2018 JO - J Med Internet Res SP - e18581 VL - 22 IS - 10 KW - Google KW - infodemiology KW - infoveillance KW - public health KW - seasonal health trend KW - medical internet research KW - tick-borne disease KW - tick bites, borreliosis KW - Lyme disease N2 - Background: Borreliosis is the most frequently transmitted tick-borne disease in Europe. It is difficult to estimate the incidence of tick bites and associated diseases in the German population due to the lack of an obligation to register across all 16 federal states of Germany. Objective: The aim of this study is to show that Google data can be used to generate general trends of infectious diseases on the basis of borreliosis and tick bites. In addition, the possibility of using Google AdWord data to estimate incidences of infectious diseases, where there is inconsistency in the obligation to notify authorities, is investigated with the perspective to facilitate public health studies. Methods: Google AdWords Keyword Planner was used to identify search terms related to ticks and borreliosis in Germany from January 2015 to December 2018. The search volume data from the identified search terms was assessed using Excel version 15.23. In addition, SPSS version 24.0 was used to calculate the correlation between search volumes, registered cases, and temperature. Results: A total of 1999 tick-related and 542 borreliosis-related search terms were identified, with a total of 209,679,640 Google searches in all 16 German federal states in the period under review. The analysis showed a high correlation between temperature and borreliosis (r=0.88), and temperature and tick bite (r=0.83), and a very high correlation between borreliosis and tick bite (r=0.94). Furthermore, a high to very high correlation between Google searches and registered cases in each federal state was observed (Brandenburg r=0.80, Mecklenburg-West Pomerania r= 0.77, Saxony r= 0.74, and Saxony-Anhalt r=0.90; all P<.001). Conclusions: Our study provides insight into annual trends concerning interest in ticks and borreliosis that are relevant to the German population exemplary in the data of a large internet search engine. Public health studies collecting incidence data may benefit from the results indicating a significant correlation between internet search data and incidences of infectious diseases. UR - http://www.jmir.org/2020/10/e18581/ UR - http://dx.doi.org/10.2196/18581 UR - http://www.ncbi.nlm.nih.gov/pubmed/33064086 ID - info:doi/10.2196/18581 ER - TY - JOUR AU - Dubey, Dutt Akash PY - 2020/10/15 TI - The Resurgence of Cyber Racism During the COVID-19 Pandemic and its Aftereffects: Analysis of Sentiments and Emotions in Tweets JO - JMIR Public Health Surveill SP - e19833 VL - 6 IS - 4 KW - COVID-19 KW - pandemic KW - China KW - racism KW - WHO KW - Twitter KW - infodemiology KW - infodemic N2 - Background: With increasing numbers of patients with COVID-19 globally, China and the World Health Organization have been blamed by some for the spread of this disease. Consequently, instances of racism and hateful acts have been reported around the world. When US President Donald Trump used the term ?Chinese Virus,? this issue gained momentum, and ethnic Asians are now being targeted. The online situation looks similar, with increases in hateful comments and posts. Objective: The aim of this paper is to analyze the increasing instances of cyber racism during the COVID-19 pandemic, by assessing emotions and sentiments associated with tweets on Twitter. Methods: In total, 16,000 tweets from April 11-16, 2020, were analyzed to determine their associated sentiments and emotions. Statistical analysis was carried out using R. Twitter API and the sentimentr package were used to collect tweets and then evaluate their sentiments, respectively. This research analyzed the emotions and sentiments associated with terms like ?Chinese Virus,? ?Wuhan Virus,? and ?Chinese Corona Virus.? Results: The results suggest that the majority of the analyzed tweets were of negative sentiment and carried emotions of fear, sadness, anger, and disgust. There was a high usage of slurs and profane words. In addition, terms like ?China Lied People Died,? ?Wuhan Health Organization,? ?Kung Flu,? ?China Must Pay,? and ?CCP is Terrorist? were frequently used in these tweets. Conclusions: This study provides insight into the rise in cyber racism seen on Twitter. Based on the findings, it can be concluded that a substantial number of users are tweeting with mostly negative sentiments toward ethnic Asians, China, and the World Health Organization. UR - http://publichealth.jmir.org/2020/4/e19833/ UR - http://dx.doi.org/10.2196/19833 UR - http://www.ncbi.nlm.nih.gov/pubmed/32936772 ID - info:doi/10.2196/19833 ER - TY - JOUR AU - Wang, Wenjun AU - Wang, Yikai AU - Zhang, Xin AU - Jia, Xiaoli AU - Li, Yaping AU - Dang, Shuangsuo PY - 2020/10/5 TI - Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study JO - JMIR Mhealth Uhealth SP - e19589 VL - 8 IS - 10 KW - novel coronavirus KW - SARS KW - SARS-CoV-2 KW - COVID-19 KW - social media KW - WeChat KW - early detection KW - surveillance KW - infodemiology KW - infoveillance N2 - Background: A novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future. Objective: This study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media. Methods: WeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020. Results: WeChat Index hits for ?Feidian? (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority?s outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords ?SARS,? ?coronavirus,? ?novel coronavirus,? ?shortness of breath,? ?dyspnea,? and ?diarrhea,? but these terms were not as meaningful for the early detection of the outbreak as the term ?Feidian?. Conclusions: By using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks. UR - https://mhealth.jmir.org/2020/10/e19589 UR - http://dx.doi.org/10.2196/19589 UR - http://www.ncbi.nlm.nih.gov/pubmed/32931439 ID - info:doi/10.2196/19589 ER - TY - JOUR AU - Wang, Di AU - Lyu, Chen Joanne AU - Zhao, Xiaoyu PY - 2020/10/14 TI - Public Opinion About E-Cigarettes on Chinese Social Media: A Combined Study of Text Mining Analysis and Correspondence Analysis JO - J Med Internet Res SP - e19804 VL - 22 IS - 10 KW - e-cigarettes KW - public opinion KW - social media KW - infodemiology KW - infoveillance KW - regulation KW - China N2 - Background: Electronic cigarettes (e-cigarettes) have become increasingly popular. China has accelerated its legislation on e-cigarettes in recent years by issuing two policies to regulate their use: the first on August 26, 2018, and the second on November 1, 2019. Social media provide an efficient platform to access information on the public opinion of e-cigarettes. Objective: To gain insight into how policies have influenced the reaction of the Chinese public to e-cigarettes, this study aims to understand what the Chinese public say about e-cigarettes and how the focus of discussion might have changed in the context of policy implementation. Methods: This study uses a combination of text mining and correspondence analysis to content analyze 1160 e-cigarette?related questions and their corresponding answers from Zhihu, China?s largest question-and-answer platform and one of the country?s most trustworthy social media sources. From January 1, 2017, to December 31, 2019, Python was used to text mine the most frequently used words and phrases in public e-cigarette discussions on Zhihu. The correspondence analysis was used to examine the similarities and differences between high-frequency words and phrases across 3 periods (ie, January 1, 2017, to August 27, 2018; August 28, 2018, to October 31, 2019; and November 1, 2019, to January 1, 2020). Results: The results of the study showed that the consistent themes across time were comparisons with traditional cigarettes, health concerns, and how to choose e-cigarette products. The issuance of government policies on e-cigarettes led to a change in the focus of public discussion. The discussion of e-cigarettes in period 1 mainly focused on the use and experience of e-cigarettes. In period 2, the public?s attention was not only on the substances related to e-cigarettes but also on the smoking cessation functions of e-cigarettes. In period 3, the public shifted their attention to the e-cigarette industry and government policy on the banning of e-cigarette sales to minors. Conclusions: Social media are an informative source, which can help policy makers and public health professionals understand the public?s concerns over and understanding of e-cigarettes. When there was little regulation, public discussion was greatly influenced by industry claims about e-cigarettes; however, once e-cigarette policies were issued, these policies, to a large extent, set the agenda for public discussion. In addition, media reporting of these policies might have greatly influenced the way e-cigarette policies were discussed. Therefore, monitoring e-cigarette discussions on social media and responding to them in a timely manner will both help improve the public?s e-cigarette literacy and facilitate the implementation of e-cigarette?related policies. UR - http://www.jmir.org/2020/10/e19804/ UR - http://dx.doi.org/10.2196/19804 UR - http://www.ncbi.nlm.nih.gov/pubmed/33052127 ID - info:doi/10.2196/19804 ER - TY - JOUR AU - McCausland, Kahlia AU - Maycock, Bruce AU - Leaver, Tama AU - Wolf, Katharina AU - Freeman, Becky AU - Jancey, Jonine PY - 2020/10/14 TI - E-Cigarette Advocates on Twitter: Content Analysis of Vaping-Related Tweets JO - JMIR Public Health Surveill SP - e17543 VL - 6 IS - 4 KW - electronic nicotine delivery systems KW - electronic cigarettes KW - e-cigarette KW - infodemiology KW - infoveillance KW - vaping KW - Twitter KW - social media KW - public health KW - content analysis N2 - Background: As the majority of Twitter content is publicly available, the platform has become a rich data source for public health surveillance, providing insights into emergent phenomena, such as vaping. Although there is a growing body of literature that has examined the content of vaping-related tweets, less is known about the people who generate and disseminate these messages and the role of e-cigarette advocates in the promotion of these devices. Objective: This study aimed to identify key conversation trends and patterns over time, and discern the core voices, message frames, and sentiment surrounding e-cigarette discussions on Twitter. Methods: A random sample of data were collected from Australian Twitter users who referenced at least one of 15 identified e-cigarette related keywords during 2012, 2014, 2016, or 2018. Data collection was facilitated by TrISMA (Tracking Infrastructure for Social Media Analysis) and analyzed by content analysis. Results: A sample of 4432 vaping-related tweets posted and retweeted by Australian users was analyzed. Positive sentiment (3754/4432, 84.70%) dominated the discourse surrounding e-cigarettes, and vape retailers and manufacturers (1161/4432, 26.20%), the general public (1079/4432, 24.35%), and e-cigarette advocates (1038/4432, 23.42%) were the most prominent posters. Several tactics were used by e-cigarette advocates to communicate their beliefs, including attempts to frame e-cigarettes as safer than traditional cigarettes, imply that federal government agencies lack sufficient competence or evidence for the policies they endorse about vaping, and denounce as propaganda ?gateway? claims of youth progressing from e-cigarettes to combustible tobacco. Some of the most common themes presented in tweets were advertising or promoting e-cigarette products (2040/4432, 46.03%), promoting e-cigarette use or intent to use (970/4432, 21.89%), and discussing the potential of e-cigarettes to be used as a smoking cessation aid or tobacco alternative (716/4432, 16.16%), as well as the perceived health and safety benefits and consequences of e-cigarette use (681/4432, 15.37%). Conclusions: Australian Twitter content does not reflect the country?s current regulatory approach to e-cigarettes. Rather, the conversation on Twitter generally encourages e-cigarette use, promotes vaping as a socially acceptable practice, discredits scientific evidence of health risks, and rallies around the idea that e-cigarettes should largely be outside the bounds of health policy. The one-sided nature of the discussion is concerning, as is the lack of disclosure and transparency, especially among vaping enthusiasts who dominate the majority of e-cigarette discussions on Twitter, where it is unclear if comments are endorsed, sanctioned, or even supported by the industry. UR - http://publichealth.jmir.org/2020/4/e17543/ UR - http://dx.doi.org/10.2196/17543 UR - http://www.ncbi.nlm.nih.gov/pubmed/33052130 ID - info:doi/10.2196/17543 ER - TY - JOUR AU - Low, M. Daniel AU - Rumker, Laurie AU - Talkar, Tanya AU - Torous, John AU - Cecchi, Guillermo AU - Ghosh, S. Satrajit PY - 2020/10/12 TI - Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study JO - J Med Internet Res SP - e22635 VL - 22 IS - 10 KW - COVID-19 KW - mental health KW - psychiatry KW - infodemiology KW - infoveillance KW - infodemic KW - social media KW - Reddit KW - natural language processing KW - ADHD KW - eating disorders KW - anxiety KW - suicidality N2 - Background: The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. Objective: The aim of this study is to leverage natural language processing (NLP) with the goal of characterizing changes in 15 of the world?s largest mental health support groups (eg, r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with 11 non?mental health groups (eg, r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. Methods: We created and released the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyzed trends from 90 text-derived features such as sentiment analysis, personal pronouns, and semantic categories. Using supervised machine learning, we classified posts into their respective support groups and interpreted important features to understand how different problems manifest in language. We applied unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic. Results: We found that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately 2 months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories ?economic stress,? ?isolation,? and ?home,? while others such as ?motion? significantly decreased. We found that support groups related to attention-deficit/hyperactivity disorder, eating disorders, and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discovered that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (?=?0.96, P<.001). Using unsupervised clustering, we found the suicidality and loneliness clusters more than doubled in the number of posts during the pandemic. Specifically, the support groups for borderline personality disorder and posttraumatic stress disorder became significantly associated with the suicidality cluster. Furthermore, clusters surrounding self-harm and entertainment emerged. Conclusions: By using a broad set of NLP techniques and analyzing a baseline of prepandemic posts, we uncovered patterns of how specific mental health problems manifest in language, identified at-risk users, and revealed the distribution of concerns across Reddit, which could help provide better resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests. UR - http://www.jmir.org/2020/10/e22635/ UR - http://dx.doi.org/10.2196/22635 UR - http://www.ncbi.nlm.nih.gov/pubmed/32936777 ID - info:doi/10.2196/22635 ER - TY - JOUR AU - Gozzi, Nicolň AU - Tizzani, Michele AU - Starnini, Michele AU - Ciulla, Fabio AU - Paolotti, Daniela AU - Panisson, André AU - Perra, Nicola PY - 2020/10/12 TI - Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis JO - J Med Internet Res SP - e21597 VL - 22 IS - 10 KW - social media KW - news coverage KW - digital epidemiology KW - infodemiology KW - infoveillance KW - infodemic KW - data science KW - topic modeling KW - pandemic KW - COVID-19 KW - Reddit KW - Wikipedia KW - information KW - response KW - risk perception KW - behavior N2 - Background: The exposure and consumption of information during epidemic outbreaks may alter people?s risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. Objective: The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. Methods: We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19?related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users? collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. Results: Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions: Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people?s collective awareness and risk perception and thus on their tendencies toward behavioral change. UR - http://www.jmir.org/2020/10/e21597/ UR - http://dx.doi.org/10.2196/21597 UR - http://www.ncbi.nlm.nih.gov/pubmed/32960775 ID - info:doi/10.2196/21597 ER - TY - JOUR AU - Ahmed, Wasim AU - López Seguí, Francesc AU - Vidal-Alaball, Josep AU - Katz, S. Matthew PY - 2020/10/5 TI - COVID-19 and the ?Film Your Hospital? Conspiracy Theory: Social Network Analysis of Twitter Data JO - J Med Internet Res SP - e22374 VL - 22 IS - 10 KW - COVID-19 KW - coronavirus KW - Twitter KW - misinformation KW - fake news KW - social network analysis KW - public health KW - social media N2 - Background: During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are ?empty.? Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. Objective: This study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. Methods: Twitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. Results: The most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. Conclusions: Hashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content. UR - http://www.jmir.org/2020/10/e22374/ UR - http://dx.doi.org/10.2196/22374 UR - http://www.ncbi.nlm.nih.gov/pubmed/32936771 ID - info:doi/10.2196/22374 ER - TY - JOUR AU - van Draanen, Jenna AU - Tao, HaoDong AU - Gupta, Saksham AU - Liu, Sam PY - 2020/10/5 TI - Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study JO - JMIR Public Health Surveill SP - e18540 VL - 6 IS - 4 KW - marijuana KW - social media KW - sentiment KW - cannabis KW - Twitter N2 - Background: Infodemiology is an emerging field of research that utilizes user-generated health-related content, such as that found in social media, to help improve public health. Twitter has become an important venue for studying emerging patterns in health issues such as substance use because it can reflect trends in real-time and display messages generated directly by users, giving a uniquely personal voice to analyses. Over the past year, several states in the United States have passed legislation to legalize adult recreational use of cannabis and the federal government in Canada has done the same. There are few studies that examine the sentiment and content of tweets about cannabis since the recent legislative changes regarding cannabis have occurred in North America. Objective: To examine differences in the sentiment and content of cannabis-related tweets by state cannabis laws, and to examine differences in sentiment between the United States and Canada between 2017 and 2019. Methods: In total, 1,200,127 cannabis-related tweets were collected from January 1, 2017, to June 17, 2019, using the Twitter application programming interface. Tweets then were grouped geographically based on cannabis legal status (legal for adult recreational use, legal for medical use, and no legal use) in the locations from which the tweets came. Sentiment scoring for the tweets was done with VADER (Valence Aware Dictionary and sEntiment Reasoner), and differences in sentiment for states with different cannabis laws were tested using Tukey adjusted two-sided pairwise comparisons. Topic analysis to determine the content of tweets was done using latent Dirichlet allocation in Python, using a Java implementation, LdaMallet, with Gensim wrapper. Results: Significant differences were seen in tweet sentiment between US states with different cannabis laws (P=.001 for negative sentiment tweets in fully illegal compared to legal for adult recreational use states), as well as between the United States and Canada (P=.003 for positive sentiment and P=.001 for negative sentiment). In both cases, restrictive state policy environments (eg, those where cannabis use is fully illegal, or legal for medical use only) were associated with more negative tweet sentiment than less restrictive policy environments (eg, where cannabis is legal for adult recreational use). Six key topics were found in recent US tweet contents: fun and recreation (keywords, eg, love, life, high); daily life (today, start, live); transactions (buy, sell, money); places of use (room, car, house); medical use and cannabis industry (business, industry, company); and legalization (legalize, police, tax). The keywords representing content of tweets also differed between the United States and Canada. Conclusions: Knowledge about how cannabis is being discussed online, and geographic differences that exist in these conversations may help to inform public health planning and prevention efforts. Public health education about how to use cannabis in ways that promote safety and minimize harms may be especially important in places where cannabis is legal for adult recreational and medical use. UR - https://publichealth.jmir.org/2020/4/e18540 UR - http://dx.doi.org/10.2196/18540 UR - http://www.ncbi.nlm.nih.gov/pubmed/33016888 ID - info:doi/10.2196/18540 ER - TY - JOUR AU - Zhou, Zeyun AU - Hultgren, Emerson Kyle PY - 2020/9/30 TI - Complementing the US Food and Drug Administration Adverse Event Reporting System With Adverse Drug Reaction Reporting From Social Media: Comparative Analysis JO - JMIR Public Health Surveill SP - e19266 VL - 6 IS - 3 KW - adverse drug reactions KW - FAERS KW - social media reporting KW - pharmacovigilance N2 - Background: Adverse drug reactions (ADRs) can occur any time someone uses a medication. ADRs are systematically tracked and cataloged, with varying degrees of success, in order to better understand their etiology and develop methods of prevention. The US Food and Drug Administration (FDA) has developed the FDA Adverse Event Reporting System (FAERS) for this purpose. FAERS collects information from myriad sources, but the primary reporters have traditionally been medical professionals and pharmacovigilance data from manufacturers. Recent studies suggest that information shared publicly on social media platforms related to medication use could be of benefit in complementing FAERS data in order to have a richer picture of how medications are actually being used and the experiences people are having across large populations. Objective: The aim of this study is to validate the accuracy and precision of social media methodology and conduct evaluations of Twitter ADR reporting for commonly used pharmaceutical agents. Methods: ADR data from the 10 most prescribed medications according to pharmacy claims data were collected from both FAERS and Twitter. In order to obtain data from FAERS, the SafeRx database, a curated collection of FAERS data, was used to collect data from March 1, 2016, to March 31, 2017. Twitter data were manually scraped during the same time period to extract similar data using an algorithm designed to minimize noise and false signals in social media data. Results: A total of 40,539 FAERS ADR reports were obtained via SafeRx and more than 40,000 tweets containing the drug names were obtained from Twitter?s Advanced Search engine. While the FAERS data were specific to ADRs, the Twitter data were more limited. Only hydrocodone/acetaminophen, prednisone, amoxicillin, gabapentin, and metformin had a sufficient volume of ADR content for review and comparison. For metformin, diarrhea was the side effect that resulted in no difference between the two platforms (P=.30). For hydrocodone/acetaminophen, ineffectiveness as an ADR that resulted in no difference (P=.60). For gabapentin, there were no differences in terms of the ADRs ineffectiveness and fatigue (P=.15 and P=.67, respectively). For amoxicillin, hypersensitivity, nausea, and rash shared similar profiles between platforms (P=.35, P=.05, and P=.31, respectively). Conclusions: FAERS and Twitter shared similarities in types of data reported and a few unique items to each data set as well. The use of Twitter as an ADR pharmacovigilance platform should continue to be studied as a unique and complementary source of information rather than a validation tool of existing ADR databases. UR - https://publichealth.jmir.org/2020/3/e19266 UR - http://dx.doi.org/10.2196/19266 UR - http://www.ncbi.nlm.nih.gov/pubmed/32996889 ID - info:doi/10.2196/19266 ER - TY - JOUR AU - Husnayain, Atina AU - Shim, Eunha AU - Fuad, Anis AU - Su, Chia-Yu Emily PY - 2020/9/29 TI - Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study JO - J Med Internet Res SP - e19788 VL - 22 IS - 9 KW - Google Trends KW - risk KW - perception KW - communication KW - COVID-19 KW - South Korea KW - outbreak KW - infodemiology N2 - Background: South Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis. Objective: We attempt to explore patterns of community health risk perceptions of COVID-19 in South Korea using internet search data. Methods: Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19?related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access data set for the time period of December 5, 2019, to May 31, 2020. Time-lag correlations calculated by Spearman rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, and GT and NAVER RSVs in lag periods (of 1-3 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor of <5. Results: The numbers of COVID-19?related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a face mask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763-0.823; P<.001) and age groups ?29 years (r=0.726-0.821; P<.001), 30-44 years (r=0.701-0.826; P<.001), and ?50 years (r=0.706-0.725; P<.001). In terms of spatial distribution, internet search data were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704-0.804; P<.001) compared to those of desktop searches (r=0.705-0.717; P<.001), indicating changing behaviors in searching for online health information during the outbreak. These varied internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test?related information as being more important than disease-related knowledge. Meanwhile, younger, and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19?related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case?based model and potentially be used to predict epidemic curves. Conclusions: The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location. UR - http://www.jmir.org/2020/9/e19788/ UR - http://dx.doi.org/10.2196/19788 UR - http://www.ncbi.nlm.nih.gov/pubmed/32931446 ID - info:doi/10.2196/19788 ER - TY - JOUR AU - Pan, Peng AU - Yu, Changhua AU - Li, Tao AU - Zhou, Xilei AU - Dai, Tingting AU - Tian, Hanhan AU - Xiong, Yaozu PY - 2020/9/29 TI - Xigua Video as a Source of Information on Breast Cancer: Content Analysis JO - J Med Internet Res SP - e19668 VL - 22 IS - 9 KW - breast cancer KW - internet KW - Xigua Video KW - content analysis N2 - Background: Seeking health information on the internet is a popular trend. Xigua Video, a short video platform in China, ranks among the most accessed websites in the country and hosts an increasing number of videos with medical information. However, the nature of these videos is frequently unscientific, misleading, or even harmful. Objective: Little is known about Xigua Video as a source of information on breast cancer. Thus, the study aimed to investigate the contents, quality, and reliability of breast cancer?related content on Xigua Video. Methods: On February 4, 2020, a Xigua Video search was performed using the keyword ?breast cancer.? Videos were categorized by 2 doctors based on whether the video content provided useful or misleading information. Furthermore, the reliability and quality of the videos were assessed using the 5-point DISCERN tool and 5-point global quality score criteria. Results: Out of the 170 videos selected for the study, 64 (37.6%) were classified as useful, whereas 106 (62.4%) provided misleading information. A total of 41.8% videos (71/170) were generated by individuals compared to 19.4% videos (33/170) contributed by health care professionals. The topics mainly covered etiology, anatomy, symptoms, preventions, treatments, and prognosis. The top topic was ?treatments? (119/170, 70%). The reliability scores and global quality scores of the videos in the useful information group were high (P<.001). No differences were observed between the 2 groups in terms of video length, duration in months, and comments. The number of total views was higher for the misleading information group (819,478.5 vs 647,940) but did not reach a level of statistical significance (P=.112). The uploading sources of the videos were mainly health care professionals, health information websites, medical advertisements, and individuals. Statistical differences were found between the uploading source groups in terms of reliability scores and global quality scores (P<.001). In terms of total views, video length, duration, and comments, no statistical differences were indicated among the said groups. However, a statistical difference was noted between the useful and misleading information video groups with respect to the uploading sources (P<.001). Conclusions: A large number of Xigua videos pertaining to breast cancer contain misleading information. There is a need for accurate health information to be provided on Xigua Video and other social media; health care professionals should address this challenge. UR - http://www.jmir.org/2020/9/e19668/ UR - http://dx.doi.org/10.2196/19668 UR - http://www.ncbi.nlm.nih.gov/pubmed/32883651 ID - info:doi/10.2196/19668 ER - TY - JOUR AU - Tsai, Jiun-Yi AU - Phua, Joe AU - Pan, Shuya AU - Yang, Chia-chen PY - 2020/9/25 TI - Intergroup Contact, COVID-19 News Consumption, and the Moderating Role of Digital Media Trust on Prejudice Toward Asians in the United States: Cross-Sectional Study JO - J Med Internet Res SP - e22767 VL - 22 IS - 9 KW - COVID-19 KW - prejudice KW - news exposure KW - news trust KW - infodemic KW - media bias KW - racism KW - social media use KW - intergroup contact KW - regression KW - moderation analysis KW - cross-sectional survey N2 - Background: The perceived threat of a contagious virus may lead people to be distrustful of immigrants and out-groups. Since the COVID-19 outbreak, the salient politicized discourses of blaming Chinese people for spreading the virus have fueled over 2000 reports of anti-Asian racial incidents and hate crimes in the United States. Objective: The study aims to investigate the relationships between news consumption, trust, intergroup contact, and prejudicial attitudes toward Asians and Asian Americans residing in the United States during the COVID-19 pandemic. We compare how traditional news, social media use, and biased news exposure cultivate racial attitudes, and the moderating role of media use and trust on prejudice against Asians is examined. Methods: A cross-sectional study was completed in May 2020. A total of 430 US adults (mean age 36.75, SD 11.49 years; n=258, 60% male) participated in an online survey through Amazon?s Mechanical Turk platform. Respondents answered questions related to traditional news exposure, social media use, perceived trust, and their top three news channels for staying informed about the novel coronavirus. In addition, intergroup contact and racial attitudes toward Asians were assessed. We performed hierarchical regression analyses to test the associations. Moderation effects were estimated using simple slopes testing with a 95% bootstrap confidence interval approach. Results: Participants who identified as conservatives (?=.08, P=.02), had a personal infection history (?=.10, P=.004), and interacted with Asian people frequently in their daily lives (?=.46, P<.001) reported more negative attitudes toward Asians after controlling for sociodemographic variables. Relying more on traditional news media (?=.08, P=.04) and higher levels of trust in social media (?=.13, P=.007) were positively associated with prejudice against Asians. In contrast, consuming news from left-leaning outlets (?=?.15, P=.001) and neutral outlets (?=?.13, P=.003) was linked to less prejudicial attitudes toward Asians. Among those who had high trust in social media, exposure had a negative relationship with prejudice. At high levels of trust in digital websites and apps, frequent use was related to less unfavorable attitudes toward Asians. Conclusions: Experiencing racial prejudice among the Asian population during a challenging pandemic can cause poor psychological outcomes and exacerbate health disparities. The results suggest that conservative ideology, personal infection history, frequency of intergroup contact, traditional news exposure, and trust in social media emerge as positive predictors of prejudice against Asians and Asian Americans, whereas people who get COVID-19 news from left-leaning and balanced outlets show less prejudice. For those who have more trust in social media and digital news, frequent use of these two sources is associated with lower levels of prejudice. Our findings highlight the need to reshape traditional news discourses and use social media and mobile news apps to develop credible messages for combating racial prejudice against Asians. UR - http://www.jmir.org/2020/9/e22767/ UR - http://dx.doi.org/10.2196/22767 UR - http://www.ncbi.nlm.nih.gov/pubmed/32924948 ID - info:doi/10.2196/22767 ER - TY - JOUR AU - Liu, J. Jean C. AU - Tong, W. Eddie M. PY - 2020/9/25 TI - The Relation Between Official WhatsApp-Distributed COVID-19 News Exposure and Psychological Symptoms: Cross-Sectional Survey Study JO - J Med Internet Res SP - e22142 VL - 22 IS - 9 KW - mental health KW - social media KW - pandemic KW - depression KW - anxiety KW - stress KW - COVID-19 KW - app KW - risk factor KW - psychology N2 - Background: In a global pandemic, digital technology offers innovative methods to disseminate public health messages. As an example, the messenger app WhatsApp was adopted by both the World Health Organization and government agencies to provide updates on the coronavirus disease (COVID-19). During a time when rumors and excessive news threaten psychological well-being, these services allow for rapid transmission of information and may boost resilience. Objective: In this study, we sought to accomplish the following: (1) assess well-being during the pandemic; (2) replicate prior findings linking exposure to COVID-19 news with psychological distress; and (3) examine whether subscription to an official WhatsApp channel can mitigate this risk. Methods: Across 8 weeks of the COVID-19 outbreak (March 7 to April 21, 2020), we conducted a survey of 1145 adults in Singapore. As the primary outcome measure, participants completed the Depression, Anxiety, and Stress Scale (DASS-21). As predictor variables, participants also answered questions pertaining to the following: (1) their exposure to COVID-19 news; (2) their use of the Singapore government?s WhatsApp channel; and (3) their demographics. Results: Within the sample, 7.9% of participants had severe or extremely severe symptoms on at least one DASS-21 subscale. Depression scores were associated with increased time spent receiving COVID-19 updates, whereas use of the official WhatsApp channel emerged as a protective factor (b=?0.07, t[863]=?2.04, P=.04). Similarly, increased anxiety scores were associated with increased exposure to both updates and rumors, but this risk was mitigated by trust in the government?s WhatsApp messages (b=?0.05, t[863]=?2.13, P=.03). Finally, although stress symptoms increased with the amount of time spent receiving updates, these symptoms were not significantly related to WhatsApp use. Conclusions: Our findings suggest that messenger apps may be an effective medium for disseminating pandemic-related information, allowing official agencies to reach a broad sector of the population rapidly. In turn, this use may promote public well-being amid an ?infodemic.? Trial Registration: ClinicalTrials.gov NCT04305574; https://clinicaltrials.gov/ct2/show/NCT04305574 UR - http://www.jmir.org/2020/9/e22142/ UR - http://dx.doi.org/10.2196/22142 UR - http://www.ncbi.nlm.nih.gov/pubmed/32877349 ID - info:doi/10.2196/22142 ER - TY - JOUR AU - Lin, Yu-Hsuan AU - Chiang, Ting-Wei AU - Lin, Yu-Lun PY - 2020/9/21 TI - Increased Internet Searches for Insomnia as an Indicator of Global Mental Health During the COVID-19 Pandemic: Multinational Longitudinal Study JO - J Med Internet Res SP - e22181 VL - 22 IS - 9 KW - internet search KW - Google Trends KW - infodemiology KW - infoveillance KW - COVID-19 KW - insomnia KW - mental health N2 - Background: Real-time global mental health surveillance is urgently needed for tracking the long-term impact of the COVID-19 pandemic. Objective: This study aimed to use Google Trends data to investigate the impact of the pandemic on global mental health by analyzing three keywords indicative of mental distress: ?insomnia,? ?depression,? and ?suicide.? Methods: We examined increases in search queries for 19 countries. Significant increases were defined as the actual daily search value (from March 20 to April 19, 2020) being higher than the 95% CIs of the forecast from the 3-month baseline via ARIMA (autoregressive integrated moving average) modeling. We examined the correlation between increases in COVID-19?related deaths and the number of days with significant increases in search volumes for insomnia, depression, and suicide across multiple nations. Results: The countries with the greatest increases in searches for insomnia were Iran, Spain, the United States, and Italy; these countries exhibited a significant increase in insomnia searches on more than 10 of the 31 days observed. The number of COVID-19?related deaths was positively correlated to the number of days with an increase in searches for insomnia in the 19 countries (?=0.64, P=.003). By contrast, there was no significant correlation between the number of deaths and increases in searches for depression (?=?0.12, P=.63) or suicide (?=?0.07, P=.79). Conclusions: Our analysis suggests that insomnia could be a part of routine mental health screening during the COVID-19 pandemic. UR - http://www.jmir.org/2020/9/e22181/ UR - http://dx.doi.org/10.2196/22181 UR - http://www.ncbi.nlm.nih.gov/pubmed/32924951 ID - info:doi/10.2196/22181 ER - TY - JOUR AU - Adnan, Mehnaz AU - Gao, Xiaoying AU - Bai, Xiaohan AU - Newbern, Elizabeth AU - Sherwood, Jill AU - Jones, Nicholas AU - Baker, Michael AU - Wood, Tim AU - Gao, Wei PY - 2020/9/17 TI - Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering JO - JMIR Public Health Surveill SP - e18281 VL - 6 IS - 3 KW - Campylobacter KW - disease outbreaks KW - forecasting KW - spatio-temporal analysis N2 - Background: Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective: The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods: We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran?s I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results: Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions: Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice. UR - http://publichealth.jmir.org/2020/3/e18281/ UR - http://dx.doi.org/10.2196/18281 UR - http://www.ncbi.nlm.nih.gov/pubmed/32940617 ID - info:doi/10.2196/18281 ER - TY - JOUR AU - Hochberg, Irit AU - Orshalimy, Sharon AU - Yom-Tov, Elad PY - 2020/9/15 TI - Real-World Evidence on the Effect of Missing an Oral Contraceptive Dose: Analysis of Internet Search Engine Queries JO - J Med Internet Res SP - e20632 VL - 22 IS - 9 KW - search engines KW - birth control KW - abortion KW - miscarriage N2 - Background: Oral contraceptives (OCs) are a unique chronic medication with which a memory slip may result in a threat that could change a person?s life course. Subjective concerns of missed OC doses among women have been addressed infrequently. Anonymized queries to internet search engines provide unique access to concerns and information gaps faced by a large number of internet users. Objective: We aimed to quantitate the frequency of queries by women seeking information in an internet search engine, after missing one or more doses of an OC; their further queries on emergency contraception, abortion, and miscarriage; and their rate of reporting a pregnancy timed to the cycle of missing an OC. Methods: We extracted all English-language queries submitted to Bing in the United States during 2018, which mentioned a missed OC and subsequent queries of the same users on miscarriage, abortion, emergency contraceptives, and week of pregnancy. Results: We identified 26,395 Bing users in the United States who queried about missing OC pills and the fraction that further queried about miscarriage, abortion, emergency contraceptive, and week of pregnancy. Users under the age of 30 years who asked about forgetting an OC dose were more likely to ask about abortion (1.5 times) and emergency contraception (1.7 times) (P<.001 for both), while users at ages of 30-34 years were more likely to query about pregnancy (2.1 times) and miscarriage (5.4 times) (P<.001 for both). Conclusions: Our data indicate that many women missing a dose of OC might not have received sufficient information from their health care providers or chose to obtain it online. Queries about abortion and miscarriage peaking in the subsequent days indicate a common worry of possible pregnancy. These results reinforce the importance of providing comprehensive written information on missed pills when prescribing an OC. UR - http://www.jmir.org/2020/9/e20632/ UR - http://dx.doi.org/10.2196/20632 UR - http://www.ncbi.nlm.nih.gov/pubmed/32930672 ID - info:doi/10.2196/20632 ER - TY - JOUR AU - Ali, F. Khawla AU - Whitebridge, Simon AU - Jamal, H. Mohammad AU - Alsafy, Mohammad AU - Atkin, L. Stephen PY - 2020/9/8 TI - Perceptions, Knowledge, and Behaviors Related to COVID-19 Among Social Media Users: Cross-Sectional Study JO - J Med Internet Res SP - e19913 VL - 22 IS - 9 KW - COVID-19 KW - social media KW - public health KW - perception KW - knowledge KW - health information KW - health education KW - virus N2 - Background: Social media is one of the most rapid and impactful ways of obtaining and delivering information in the modern era. Objective: The aim of this study was to rapidly obtain information on public perceptions, knowledge, and behaviors related to COVID-19 in order to identify deficiencies in key areas of public education. Methods: Using a cross-sectional study design, a survey web link was posted on the social media and messaging platforms Instagram, Twitter, and WhatsApp by the study investigators. Participants, aged ?18 years, filled out the survey on a voluntary basis. The main outcomes measured were knowledge of COVID-19 symptoms, protective measures against COVID-19, and source(s) of information about COVID-19. Subgroup analyses were conducted to determine the effects of age, gender, underlying illness, and working or studying in the health care industry on the perceived likelihood of acquiring COVID-19 and getting vaccinated. Results: A total of 5677 subjects completed the survey over the course of 1 week. ?Fever or chills? (n=4973, 87.6%) and ?shortness of breath? (n=4695, 82.7%) were identified as the main symptoms of COVID-19. Washing and sanitizing hands (n=4990, 87.9%) and avoiding public places and crowds (n=4865, 85.7%) were identified as the protective measures most frequently used against COVID-19. Social media was the most utilized source for information on the disease (n=4740, 83.5%), followed by the World Health Organization (n=2844, 50.1%). Subgroup analysis revealed that younger subjects (<35 years), males, and those working or studying in health care reported a higher perceived likelihood of acquiring COVID-19, whereas older subjects, females, and those working or studying in non?health care areas reported a lower perceived likelihood of acquiring COVID-19. Similar trends were observed for vaccination against COVID-19, with older subjects, females, and those working or studying in non?health care sectors reporting a lower likelihood of vaccinating against COVID-19. Conclusions: Our results are indicative of a relatively well-informed cohort implementing appropriate protective measures. However, key knowledge deficiencies exist with regards to vaccination against COVID-19, which future efforts should aim at correcting. UR - http://www.jmir.org/2020/9/e19913/ UR - http://dx.doi.org/10.2196/19913 UR - http://www.ncbi.nlm.nih.gov/pubmed/32841153 ID - info:doi/10.2196/19913 ER - TY - JOUR AU - Benson, Ryzen AU - Hu, Mengke AU - Chen, T. Annie AU - Nag, Subhadeep AU - Zhu, Shu-Hong AU - Conway, Mike PY - 2020/9/2 TI - Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data JO - JMIR Public Health Surveill SP - e19975 VL - 6 IS - 3 KW - JUUL KW - electronic cigarettes KW - smoking cessation KW - natural language processing KW - NLP KW - Twitter KW - underage tobacco use KW - tobacco KW - e-cig KW - ENDS KW - electronic nicotine delivery system KW - machine learning KW - infodemiology KW - infoveillance KW - social media KW - public health N2 - Background: Increases in electronic nicotine delivery system (ENDS) use among high school students from 2017 to 2019 appear to be associated with the increasing popularity of the ENDS device JUUL. Objective: We employed a content analysis approach in conjunction with natural language processing methods using Twitter data to understand salient themes regarding JUUL use on Twitter, sentiment towards JUUL, and underage JUUL use. Methods: Between July 2018 and August 2019, 11,556 unique tweets containing a JUUL-related keyword were collected. We manually annotated 4000 tweets for JUUL-related themes of use and sentiment. We used 3 machine learning algorithms to classify positive and negative JUUL sentiments as well as underage JUUL mentions. Results: Of the annotated tweets, 78.80% (3152/4000) contained a specific mention of JUUL. Only 1.43% (45/3152) of tweets mentioned using JUUL as a method of smoking cessation, and only 6.85% (216/3152) of tweets mentioned the potential health effects of JUUL use. Of the machine learning methods used, the random forest classifier was the best performing algorithm among all 3 classification tasks (ie, positive sentiment, negative sentiment, and underage JUUL mentions). Conclusions: Our findings suggest that a vast majority of Twitter users are not using JUUL to aid in smoking cessation nor do they mention the potential health benefits or detriments of JUUL use. Using machine learning algorithms to identify tweets containing underage JUUL mentions can support the timely surveillance of JUUL habits and opinions, further assisting youth-targeted public health intervention strategies. UR - https://publichealth.jmir.org/2020/3/e19975 UR - http://dx.doi.org/10.2196/19975 UR - http://www.ncbi.nlm.nih.gov/pubmed/32876579 ID - info:doi/10.2196/19975 ER - TY - JOUR AU - Hasegawa, Shin AU - Suzuki, Teppei AU - Yagahara, Ayako AU - Kanda, Reiko AU - Aono, Tatsuo AU - Yajima, Kazuaki AU - Ogasawara, Katsuhiko PY - 2020/9/2 TI - Changing Emotions About Fukushima Related to the Fukushima Nuclear Power Station Accident?How Rumors Determined People?s Attitudes: Social Media Sentiment Analysis JO - J Med Internet Res SP - e18662 VL - 22 IS - 9 KW - Fukushima nuclear accident KW - Twitter messaging KW - radiation KW - radioactivity KW - radioactive hazard release KW - information dissemination KW - belief in rumors KW - disaster medicine KW - infodemiology KW - infoveillance KW - infodemic N2 - Background: Public interest in radiation rose after the Tokyo Electric Power Company (TEPCO) Fukushima Daiichi Nuclear Power Station accident was caused by an earthquake off the Pacific coast of Tohoku on March 11, 2011. Various reports on the accident and radiation were spread by the mass media, and people displayed their emotional reactions, which were thought to be related to information about the Fukushima accident, on Twitter, Facebook, and other social networking sites. Fears about radiation were spread as well, leading to harmful rumors about Fukushima and the refusal to test children for radiation. It is believed that identifying the process by which people emotionally responded to this information, and hence became gripped by an increased aversion to Fukushima, might be useful in risk communication when similar disasters and accidents occur in the future. There are few studies surveying how people feel about radiation in Fukushima and other regions in an unbiased form. Objective: The purpose of this study is to identify how the feelings of local residents toward radiation changed according to Twitter. Methods: We used approximately 19 million tweets in Japanese containing the words ?radiation? (???), ?radioactivity? (???), and ?radioactive substances? (?????) that were posted to Twitter over a 1-year period following the Fukushima nuclear accident. We used regional identifiers contained in tweets (ie, nouns, proper nouns, place names, postal codes, and telephone numbers) to categorize them according to their prefecture, and then analyzed the feelings toward those prefectures from the semantic orientation of the words contained in individual tweets (ie, positive impressions or negative impressions). Results: Tweets about radiation increased soon after the earthquake and then decreased, and feelings about radiation trended positively. We determined that, on average, tweets associating Fukushima Prefecture with radiation show more positive feelings than those about other prefectures, but have trended negatively over time. We also found that as other tweets have trended positively, only bots and retweets about Fukushima Prefecture have trended negatively. Conclusions: The number of tweets about radiation has decreased overall, and feelings about radiation have trended positively. However, the fact that tweets about Fukushima Prefecture trended negatively, despite decreasing in percentage, suggests that negative feelings toward Fukushima Prefecture have become more extreme. We found that while the bots and retweets that were not about Fukushima Prefecture gradually trended toward positive feelings, the bots and retweets about Fukushima Prefecture trended toward negative feelings. UR - https://www.jmir.org/2020/9/e18662 UR - http://dx.doi.org/10.2196/18662 UR - http://www.ncbi.nlm.nih.gov/pubmed/32876574 ID - info:doi/10.2196/18662 ER - TY - JOUR AU - Müller, Martin AU - Schneider, Manuel AU - Salathé, Marcel AU - Vayena, Effy PY - 2020/8/31 TI - Assessing Public Opinion on CRISPR-Cas9: Combining Crowdsourcing and Deep Learning JO - J Med Internet Res SP - e17830 VL - 22 IS - 8 KW - CRISPR KW - natural language processing KW - sentiment analysis KW - digital methods KW - infodemiology KW - infoveillace KW - empirical bioethics KW - social media N2 - Background: The discovery of the CRISPR-Cas9?based gene editing method has opened unprecedented new potential for biological and medical engineering, sparking a growing public debate on both the potential and dangers of CRISPR applications. Given the speed of technology development and the almost instantaneous global spread of news, it is important to follow evolving debates without much delay and in sufficient detail, as certain events may have a major long-term impact on public opinion and later influence policy decisions. Objective: Social media networks such as Twitter have shown to be major drivers of news dissemination and public discourse. They provide a vast amount of semistructured data in almost real-time and give direct access to the content of the conversations. We can now mine and analyze such data quickly because of recent developments in machine learning and natural language processing. Methods: Here, we used Bidirectional Encoder Representations from Transformers (BERT), an attention-based transformer model, in combination with statistical methods to analyze the entirety of all tweets ever published on CRISPR since the publication of the first gene editing application in 2013. Results: We show that the mean sentiment of tweets was initially very positive, but began to decrease over time, and that this decline was driven by rare peaks of strong negative sentiments. Due to the high temporal resolution of the data, we were able to associate these peaks with specific events and to observe how trending topics changed over time. Conclusions: Overall, this type of analysis can provide valuable and complementary insights into ongoing public debates, extending the traditional empirical bioethics toolset. UR - http://www.jmir.org/2020/8/e17830/ UR - http://dx.doi.org/10.2196/17830 UR - http://www.ncbi.nlm.nih.gov/pubmed/32865499 ID - info:doi/10.2196/17830 ER - TY - JOUR AU - Mackey, Ken Tim AU - Li, Jiawei AU - Purushothaman, Vidya AU - Nali, Matthew AU - Shah, Neal AU - Bardier, Cortni AU - Cai, Mingxiang AU - Liang, Bryan PY - 2020/8/25 TI - Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram JO - JMIR Public Health Surveill SP - e20794 VL - 6 IS - 3 KW - COVID-19 KW - coronavirus KW - infectious disease KW - social media KW - surveillance KW - infoveillance KW - infodemiology KW - infodemic KW - fraud KW - cybercrime N2 - Background: The coronavirus disease (COVID-19) pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel ?infodemic,? including the online marketing and sale of unapproved, illegal, and counterfeit COVID-19 health products including testing kits, treatments, and other questionable ?cures.? Enabling the proliferation of this content is the growing ubiquity of internet-based technologies, including popular social media platforms that now have billions of global users. Objective: This study aims to collect, analyze, identify, and enable reporting of suspected fake, counterfeit, and unapproved COVID-19?related health care products from Twitter and Instagram. Methods: This study is conducted in two phases beginning with the collection of COVID-19?related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter application programming interface for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing (NLP) and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence. Results: We collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March to April for Twitter and February to May for Instagram. After applying our NLP and deep learning approaches, we identified 1271 tweets and 596 Instagram posts associated with questionable sales of COVID-19?related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods. Conclusions: Results from this study provide initial insight into one front of the ?infodemic? fight against COVID-19 by characterizing what types of health products, selling claims, and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public. UR - http://publichealth.jmir.org/2020/3/e20794/ UR - http://dx.doi.org/10.2196/20794 UR - http://www.ncbi.nlm.nih.gov/pubmed/32750006 ID - info:doi/10.2196/20794 ER - TY - JOUR AU - Rovetta, Alessandro AU - Bhagavathula, Srikanth Akshaya PY - 2020/8/25 TI - Global Infodemiology of COVID-19: Analysis of Google Web Searches and Instagram Hashtags JO - J Med Internet Res SP - e20673 VL - 22 IS - 8 KW - COVID-19 KW - coronavirus KW - Google KW - Instagram KW - infodemiology KW - infodemic KW - social media N2 - Background: Although ?infodemiological? methods have been used in research on coronavirus disease (COVID-19), an examination of the extent of infodemic moniker (misinformation) use on the internet remains limited. Objective: The aim of this paper is to investigate internet search behaviors related to COVID-19 and examine the circulation of infodemic monikers through two platforms?Google and Instagram?during the current global pandemic. Methods: We have defined infodemic moniker as a term, query, hashtag, or phrase that generates or feeds fake news, misinterpretations, or discriminatory phenomena. Using Google Trends and Instagram hashtags, we explored internet search activities and behaviors related to the COVID-19 pandemic from February 20, 2020, to May 6, 2020. We investigated the names used to identify the virus, health and risk perception, life during the lockdown, and information related to the adoption of COVID-19 infodemic monikers. We computed the average peak volume with a 95% CI for the monikers. Results: The top six COVID-19?related terms searched in Google were ?coronavirus,? ?corona,? ?COVID,? ?virus,? ?corona virus,? and ?COVID-19.? Countries with a higher number of COVID-19 cases had a higher number of COVID-19 queries on Google. The monikers ?coronavirus ozone,? ?coronavirus laboratory,? ?coronavirus 5G,? ?coronavirus conspiracy,? and ?coronavirus bill gates? were widely circulated on the internet. Searches on ?tips and cures? for COVID-19 spiked in relation to the US president speculating about a ?miracle cure? and suggesting an injection of disinfectant to treat the virus. Around two thirds (n=48,700,000, 66.1%) of Instagram users used the hashtags ?COVID-19? and ?coronavirus? to disperse virus-related information. Conclusions: Globally, there is a growing interest in COVID-19, and numerous infodemic monikers continue to circulate on the internet. Based on our findings, we hope to encourage mass media regulators and health organizers to be vigilant and diminish the use and circulation of these infodemic monikers to decrease the spread of misinformation. UR - http://www.jmir.org/2020/8/e20673/ UR - http://dx.doi.org/10.2196/20673 UR - http://www.ncbi.nlm.nih.gov/pubmed/32748790 ID - info:doi/10.2196/20673 ER - TY - JOUR AU - Ngai, Bik Cindy Sing AU - Singh, Gill Rita AU - Lu, Wenze AU - Koon, Chun Alex PY - 2020/8/24 TI - Grappling With the COVID-19 Health Crisis: Content Analysis of Communication Strategies and Their Effects on Public Engagement on Social Media JO - J Med Internet Res SP - e21360 VL - 22 IS - 8 KW - COVID-19 KW - communication KW - public engagement KW - social media KW - infodemiology KW - infodemic KW - message style KW - health content frames KW - interactive features KW - framework KW - content analysis N2 - Background: The coronavirus disease (COVID-19) has posed an unprecedented challenge to governments worldwide. Effective government communication of COVID-19 information with the public is of crucial importance. Objective: We investigate how the most-read state-owned newspaper in China, People?s Daily, used an online social networking site, Sina Weibo, to communicate about COVID-19 and whether this could engage the public. The objective of this study is to develop an integrated framework to examine the content, message style, and interactive features of COVID-19?related posts and determine their effects on public engagement in the largest social media network in China. Methods: Content analysis was employed to scrutinize 608 COVID-19 posts, and coding was performed on three main dimensions: content, message style, and interactive features. The content dimension was coded into six subdimensions: action, new evidence, reassurance, disease prevention, health care services, and uncertainty, and the style dimension was coded into the subdimensions of narrative and nonnarrative. As for interactive features, they were coded into links to external sources, use of hashtags, use of questions to solicit feedback, and use of multimedia. Public engagement was measured in the form of the number of shares, comments, and likes on the People?s Daily?s Sina Weibo account from January 20, 2020, to March 11, 2020, to reveal the association between different levels of public engagement and communication strategies. A one-way analysis of variance followed by a post-hoc Tukey test and negative binomial regression analysis were employed to generate the results. Results: We found that although the content frames of action, new evidence, and reassurance delivered in a nonnarrative style were predominant in COVID-19 communication by the government, posts related to new evidence and a nonnarrative style were strong negative predictors of the number of shares. In terms of generating a high number of shares, it was found that disease prevention posts delivered in a narrative style were able to achieve this purpose. Additionally, an interaction effect was found between content and style. The use of a narrative style in disease prevention posts had a significant positive effect on generating comments and likes by the Chinese public, while links to external sources fostered sharing. Conclusions: These results have implications for governments, health organizations, medical professionals, the media, and researchers on their epidemic communication to engage the public. Selecting suitable communication strategies may foster active liking and sharing of posts on social media, which in turn, might raise the public?s awareness of COVID-19 and motivate them to take preventive measures. The sharing of COVID-19 posts is particularly important because this action can reach out to a large audience, potentially helping to contain the spread of the virus. UR - http://www.jmir.org/2020/8/e21360/ UR - http://dx.doi.org/10.2196/21360 UR - http://www.ncbi.nlm.nih.gov/pubmed/32750013 ID - info:doi/10.2196/21360 ER - TY - JOUR AU - Hswen, Yulin AU - Hawkins, B. Jared AU - Sewalk, Kara AU - Tuli, Gaurav AU - Williams, R. David AU - Viswanath, K. AU - Subramanian, V. S. AU - Brownstein, S. John PY - 2020/8/21 TI - Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter JO - J Med Internet Res SP - e17048 VL - 22 IS - 8 KW - racial disparities KW - race KW - patient experience KW - policy KW - social media KW - digital epidemiology KW - social determinants of health KW - health disparities KW - health inequities N2 - Background: Racial and ethnic minority groups often face worse patient experiences compared with the general population, which is directly related to poorer health outcomes within these minority populations. Evaluation of patient experience among racial and ethnic minority groups has been difficult due to lack of representation in traditional health care surveys. Objective: This study aims to assess the feasibility of Twitter for identifying racial and ethnic disparities in patient experience across the United States from 2013 to 2016. Methods: In total, 851,973 patient experience tweets with geographic location information from the United States were collected from 2013 to 2016. Patient experience tweets included discussions related to care received in a hospital, urgent care, or any other health institution. Ordinary least squares multiple regression was used to model patient experience sentiment and racial and ethnic groups over the 2013 to 2016 period and in relation to the implementation of the Patient Protection and Affordable Care Act (ACA) in 2014. Results: Racial and ethnic distribution of users on Twitter was highly correlated with population estimates from the United States Census Bureau?s 5-year survey from 2016 (r2=0.99; P<.001). From 2013 to 2016, the average patient experience sentiment was highest for White patients, followed by Asian/Pacific Islander, Hispanic/Latino, and American Indian/Alaska Native patients. A reduction in negative patient experience sentiment on Twitter for all racial and ethnic groups was seen from 2013 to 2016. Twitter users who identified as Hispanic/Latino showed the greatest improvement in patient experience, with a 1.5 times greater increase (P<.001) than Twitter users who identified as White. Twitter users who identified as Black had the highest increase in patient experience postimplementation of the ACA (2014-2016) compared with preimplementation of the ACA (2013), and this change was 2.2 times (P<.001) greater than Twitter users who identified as White. Conclusions: The ACA mandated the implementation of the measurement of patient experience of care delivery. Considering that quality assessment of care is required, Twitter may offer the ability to monitor patient experiences across diverse racial and ethnic groups and inform the evaluation of health policies like the ACA. UR - http://www.jmir.org/2020/8/e17048/ UR - http://dx.doi.org/10.2196/17048 UR - http://www.ncbi.nlm.nih.gov/pubmed/32821062 ID - info:doi/10.2196/17048 ER - TY - JOUR AU - Hung, Man AU - Lauren, Evelyn AU - Hon, S. Eric AU - Birmingham, C. Wendy AU - Xu, Julie AU - Su, Sharon AU - Hon, D. Shirley AU - Park, Jungweon AU - Dang, Peter AU - Lipsky, S. Martin PY - 2020/8/18 TI - Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence JO - J Med Internet Res SP - e22590 VL - 22 IS - 8 KW - COVID-19 KW - coronavirus KW - sentiment KW - social network KW - Twitter KW - infodemiology KW - infodemic KW - pandemic KW - crisis KW - public health KW - business economy KW - artificial intelligence N2 - Background: The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. Objective: The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. Methods: This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19?related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. Results: There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19?related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. Conclusions: This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public?s response to COVID-19 and help officials navigate the pandemic. UR - http://www.jmir.org/2020/8/e22590/ UR - http://dx.doi.org/10.2196/22590 UR - http://www.ncbi.nlm.nih.gov/pubmed/32750001 ID - info:doi/10.2196/22590 ER - TY - JOUR AU - Liu, Dianbo AU - Clemente, Leonardo AU - Poirier, Canelle AU - Ding, Xiyu AU - Chinazzi, Matteo AU - Davis, Jessica AU - Vespignani, Alessandro AU - Santillana, Mauricio PY - 2020/8/17 TI - Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models JO - J Med Internet Res SP - e20285 VL - 22 IS - 8 KW - COVID-19 KW - coronavirus KW - digital epidemiology KW - modeling KW - modeling disease outbreaks KW - emerging outbreak KW - machine learning KW - precision public health KW - machine learning in public health KW - forecasting KW - digital data KW - mechanistic model KW - hybrid simulation KW - hybrid model KW - simulation N2 - Background: The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. Objective: We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. Methods: Our method uses the following as inputs: (a) official health reports, (b) COVID-19?related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. Results: Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. Conclusions: Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention. UR - http://www.jmir.org/2020/8/e20285/ UR - http://dx.doi.org/10.2196/20285 UR - http://www.ncbi.nlm.nih.gov/pubmed/32730217 ID - info:doi/10.2196/20285 ER - TY - JOUR AU - Yamaguchi, Yoichiro AU - Lee, Deokcheol AU - Nagai, Takuya AU - Funamoto, Taro AU - Tajima, Takuya AU - Chosa, Etsuo PY - 2020/8/14 TI - Googling Musculoskeletal-Related Pain and Ranking of Medical Associations? Patient Information Pages: Google Ads Keyword Planner Analysis JO - J Med Internet Res SP - e18684 VL - 22 IS - 8 KW - Google KW - ad words KW - infodemiology KW - musculoskeletal-related pain KW - patient education KW - medical information N2 - Background: Most people currently use the internet to obtain information about many subjects, including health information. Thus, medical associations need to provide accurate medical information websites. Although medical associations have their own patient education pages, it is not clear if these websites actually show up in search results. Objective: The aim of this study was to evaluate how well medical associations function as online information providers by searching for information about musculoskeletal-related pain online and determining the ranking of the websites of medical associations. Methods: We conducted a Google search for frequently searched keywords. Keywords were extracted using Google Ads Keyword Planner associated with ?pain? relevant to the musculoskeletal system from June 2016 to December 2019. The top 20 search queries were extracted and searched using the Google search engine in Japan and the United States. Results: The number of suggested queries for ?pain? provided by Google Ads Keyword Planner was 930 in the United States and 2400 in Japan. Among the top 20 musculoskeletal-related pain queries chosen, the probability that the medical associations? websites would appear in the top 10 results was 30% in the United States and 45% in Japan. In five queries each, the associations? websites did not appear among the top 100 results. No significant difference was found in the rank of the associations? website search results (P=.28). Conclusions: To provide accurate medical information to patients, it is essential to undertake effective measures for search engine optimization. For orthopedic associations, it is necessary that their websites should appear among the top search results. UR - https://www.jmir.org/2020/8/e18684 UR - http://dx.doi.org/10.2196/18684 UR - http://www.ncbi.nlm.nih.gov/pubmed/32795991 ID - info:doi/10.2196/18684 ER - TY - JOUR AU - Nasralah, Tareq AU - El-Gayar, Omar AU - Wang, Yong PY - 2020/8/13 TI - Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis JO - J Med Internet Res SP - e18350 VL - 22 IS - 8 KW - drug abuse KW - social media KW - infodemiology KW - infoveillance KW - text mining KW - opioid crisis N2 - Background: Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients? attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective: This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods: The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results: The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions: The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. UR - https://www.jmir.org/2020/8/e18350 UR - http://dx.doi.org/10.2196/18350 UR - http://www.ncbi.nlm.nih.gov/pubmed/32788147 ID - info:doi/10.2196/18350 ER - TY - JOUR AU - Zhang, Yan AU - Cao, Bolin AU - Wang, Yifan AU - Peng, Tai-Quan AU - Wang, Xiaohua PY - 2020/8/13 TI - When Public Health Research Meets Social Media: Knowledge Mapping From 2000 to 2018 JO - J Med Internet Res SP - e17582 VL - 22 IS - 8 KW - social media KW - public health KW - infodemiology KW - infoveillance KW - topic modeling KW - research theme KW - research method N2 - Background: Social media has substantially changed how people confront health issues. However, a comprehensive understanding of how social media has altered the foci and methods in public health research remains lacking. Objective: This study aims to examine research themes, the role of social media, and research methods in social media?based public health research published from 2000 to 2018. Methods: A dataset of 3419 valid studies was developed by searching a list of relevant keywords in the Web of Science and PubMed databases. In addition, this study employs an unsupervised text-mining technique and topic modeling to extract research themes of the published studies. Moreover, the role of social media and research methods adopted in those studies were analyzed. Results: This study identifies 25 research themes, covering different diseases, various population groups, physical and mental health, and other significant issues. Social media assumes two major roles in public health research: produce substantial research interest for public health research and furnish a research context for public health research. Social media provides substantial research interest for public health research when used for health intervention, human-computer interaction, as a platform of social influence, and for disease surveillance, risk assessment, or prevention. Social media acts as a research context for public health research when it is mere reference, used as a platform to recruit participants, and as a platform for data collection. While both qualitative and quantitative methods are frequently used in this emerging area, cutting edge computational methods play a marginal role. Conclusions: Social media enables scholars to study new phenomena and propose new research questions in public health research. Meanwhile, the methodological potential of social media in public health research needs to be further explored. UR - http://www.jmir.org/2020/8/e17582/ UR - http://dx.doi.org/10.2196/17582 UR - http://www.ncbi.nlm.nih.gov/pubmed/32788156 ID - info:doi/10.2196/17582 ER - TY - JOUR AU - Moon, Hana AU - Lee, Ho Geon PY - 2020/8/12 TI - Evaluation of Korean-Language COVID-19?Related Medical Information on YouTube: Cross-Sectional Infodemiology Study JO - J Med Internet Res SP - e20775 VL - 22 IS - 8 KW - COVID-19 KW - YouTube KW - social media KW - misinformation KW - public health surveillance KW - health communication KW - consumer health information KW - health education KW - infectious disease outbreaks KW - infodemiology KW - infoveillance KW - infodemic KW - internet KW - multimedia N2 - Background: In South Korea, the number of coronavirus disease (COVID-19) cases has declined rapidly and much sooner than in other countries. South Korea is one of the most digitalized countries in the world, and YouTube may have served as a rapid delivery mechanism for increasing public awareness of COVID-19. Thus, the platform may have helped the South Korean public fight the spread of the disease. Objective: The aim of this study is to compare the reliability, overall quality, title?content consistency, and content coverage of Korean-language YouTube videos on COVID-19, which have been uploaded by different sources. Methods: A total of 200 of the most viewed YouTube videos from January 1, 2020, to April 30, 2020, were screened, searching in Korean for the terms ?Coronavirus,? ?COVID,? ?Corona,? ?Wuhan virus,? and ?Wuhan pneumonia.? Non-Korean videos and videos that were duplicated, irrelevant, or livestreamed were excluded. Source and video metrics were collected. The videos were scored based on the following criteria: modified DISCERN index, Journal of the American Medical Association Score (JAMAS) benchmark criteria, global quality score (GQS), title?content consistency index (TCCI), and medical information and content index (MICI). Results: Of the 105 total videos, 37.14% (39/105) contained misleading information; independent user?generated videos showed the highest proportion of misleading information at 68.09% (32/47), while all of the government-generated videos were useful. Government agency?generated videos achieved the highest median score of DISCERN (5.0, IQR 5.0-5.0), JAMAS (4.0, IQR 4.0-4.0), GQS (4.0, IQR 3.0-4.5), and TCCI (5.0, IQR 5.0-5.0), while independent user?generated videos achieved the lowest median score of DISCERN (2.0, IQR 1.0-3.0), JAMAS (2.0, IQR 1.5-2.0), GQS (2.0, IQR 1.5-2.0), and TCCI (3.0, IQR 3.0-4.0). However, the total MICI was not significantly different among sources. ?Transmission and precautionary measures? were the most commonly covered content by government agencies, news agencies, and independent users. In contrast, the most mentioned content by news agencies was ?prevalence,? followed by ?transmission and precautionary measures.? Conclusions: Misleading videos had more likes, fewer comments, and longer running times than useful videos. Korean-language YouTube videos on COVID-19 uploaded by different sources varied significantly in terms of reliability, overall quality, and title?content consistency, but the content coverage was not significantly different. Government-generated videos had higher reliability, overall quality, and title?content consistency than independent user?generated videos. UR - http://www.jmir.org/2020/8/e20775/ UR - http://dx.doi.org/10.2196/20775 UR - http://www.ncbi.nlm.nih.gov/pubmed/32730221 ID - info:doi/10.2196/20775 ER - TY - JOUR AU - Visweswaran, Shyam AU - Colditz, B. Jason AU - O?Halloran, Patrick AU - Han, Na-Rae AU - Taneja, B. Sanya AU - Welling, Joel AU - Chu, Kar-Hai AU - Sidani, E. Jaime AU - Primack, A. Brian PY - 2020/8/12 TI - Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study JO - J Med Internet Res SP - e17478 VL - 22 IS - 8 KW - vaping KW - social media KW - infodemiology KW - infoveillance KW - machine learning KW - deep learning N2 - Background: Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. Objective: This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. Methods: We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. Results: LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. Conclusions: We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system. UR - https://www.jmir.org/2020/8/e17478 UR - http://dx.doi.org/10.2196/17478 UR - http://www.ncbi.nlm.nih.gov/pubmed/32784184 ID - info:doi/10.2196/17478 ER - TY - JOUR AU - Pawar, S. Aditya AU - Nagpal, Sajan AU - Pawar, Neha AU - Lerman, O. Lilach AU - Eirin, Alfonso PY - 2020/8/11 TI - General Public?s Information-Seeking Patterns of Topics Related to Obesity: Google Trends Analysis JO - JMIR Public Health Surveill SP - e20923 VL - 6 IS - 3 KW - obesity KW - normalization KW - public awareness KW - infodemiology KW - infoveillance N2 - Background: Obesity is a major public health challenge, and recent literature sheds light on the concept of ?normalization? of obesity. Objective: We aimed to study the worldwide pattern of web-based information seeking by public on obesity and on its related terms and topics using Google Trends. Methods: We compared the relative frequency of obesity-related search terms and topics between 2004 and 2019 on Google Trends. The mean relative interest scores for these terms over the 4-year quartiles were compared. Results: The mean relative interest score of the search term ?obesity? consistently decreased with time in all four quartiles (2004-2019), whereas the relative interest scores of the search topics ?weight loss? and ?abdominal obesity? increased. The topic ?weight loss? was popular during the month of January, and its median relative interest score for January was higher than that for other months for the entire study period (P<.001). The relative interest score for the search term ?obese? decreased over time, whereas those scores for the terms ?body positivity? and ?self-love? increased after 2013. Conclusions: Despite a worldwide increase in the prevalence of obesity, its popularity as an internet search term diminished over time. The reason for peaks in months should be explored and applied to the awareness campaigns for better effectiveness. These patterns suggest normalization of obesity in society and a rise of public curiosity about image-related obesity rather than its medical implications and harm. UR - http://publichealth.jmir.org/2020/3/e20923/ UR - http://dx.doi.org/10.2196/20923 UR - http://www.ncbi.nlm.nih.gov/pubmed/32633725 ID - info:doi/10.2196/20923 ER - TY - JOUR AU - Sousa-Pinto, Bernardo AU - Anto, Aram AU - Czarlewski, Wienia AU - Anto, M. Josep AU - Fonseca, Almeida Joăo AU - Bousquet, Jean PY - 2020/8/10 TI - Assessment of the Impact of Media Coverage on COVID-19?Related Google Trends Data: Infodemiology Study JO - J Med Internet Res SP - e19611 VL - 22 IS - 8 KW - COVID-19 KW - infodemiology KW - infodemic KW - Google Trends KW - media coverage KW - media KW - coronavirus KW - symptom KW - monitoring KW - trend KW - pandemic N2 - Background: The influence of media coverage on web-based searches may hinder the role of Google Trends (GT) in monitoring coronavirus disease (COVID-19). Objective: The aim of this study was to assess whether COVID-19?related GT data, particularly those related to ageusia and anosmia, were primarily related to media coverage or to epidemic trends. Methods: We retrieved GT query data for searches on coronavirus, cough, anosmia, and ageusia and plotted them over a period of 5 years. In addition, we analyzed the trends of those queries for 17 countries throughout the year 2020 with a particular focus on the rises and peaks of the searches. For anosmia and ageusia, we assessed whether the respective GT data correlated with COVID-19 cases and deaths both throughout 2020 and specifically before March 16, 2020 (ie, the date when the media started reporting that these symptoms can be associated with COVID-19). Results: Over the last five years, peaks for coronavirus searches in GT were only observed during the winter of 2020. Rises and peaks in coronavirus searches appeared at similar times in the 17 different assessed countries irrespective of their epidemic situations. In 15 of these countries, rises in anosmia and ageusia searches occurred in the same week or 1 week after they were identified in the media as symptoms of COVID-19. When data prior to March 16, 2020 were analyzed, anosmia and ageusia GT data were found to have variable correlations with COVID-19 cases and deaths in the different countries. Conclusions: Our results indicate that COVID-19?related GT data are more closely related to media coverage than to epidemic trends. UR - https://www.jmir.org/2020/8/e19611 UR - http://dx.doi.org/10.2196/19611 UR - http://www.ncbi.nlm.nih.gov/pubmed/32530816 ID - info:doi/10.2196/19611 ER - TY - JOUR AU - Hou, Zhiyuan AU - Du, Fanxing AU - Zhou, Xinyu AU - Jiang, Hao AU - Martin, Sam AU - Larson, Heidi AU - Lin, Leesa PY - 2020/8/3 TI - Cross-Country Comparison of Public Awareness, Rumors, and Behavioral Responses to the COVID-19 Epidemic: Infodemiology Study JO - J Med Internet Res SP - e21143 VL - 22 IS - 8 KW - COVID-19 KW - internet KW - surveillance KW - infodemic KW - infodemiology KW - infoveillance KW - Google Trends KW - public response KW - behavior KW - rumor KW - trend N2 - Background: Understanding public behavioral responses to the coronavirus disease (COVID-19) epidemic and the accompanying infodemic is crucial to controlling the epidemic. Objective: The aim of this study was to assess real-time public awareness and behavioral responses to the COVID-19 epidemic across 12 selected countries. Methods: Internet surveillance was used to collect real-time data from the general public to assess public awareness and rumors (China: Baidu; worldwide: Google Trends) and behavior responses (China: Ali Index; worldwide: Google Shopping). These indices measured the daily number of searches or purchases and were compared with the numbers of daily COVID-19 cases. The trend comparisons across selected countries were observed from December 1, 2019 (prepandemic baseline) to April 11, 2020 (at least one month after the governments of selected countries took actions for the pandemic). Results: We identified missed windows of opportunity for early epidemic control in 12 countries, when public awareness was very low despite the emerging epidemic. China's epidemic and the declaration of a public health emergency of international concern did not prompt a worldwide public reaction to adopt health-protective measures; instead, most countries and regions only responded to the epidemic after their own case counts increased. Rumors and misinformation led to a surge of sales in herbal remedies in China and antimalarial drugs worldwide, and timely clarification of rumors mitigated the rush to purchase unproven remedies. Conclusions: Our comparative study highlights the urgent need for international coordination to promote mutual learning about epidemic characteristics and effective control measures as well as to trigger early and timely responses in individual countries. Early release of official guidelines and timely clarification of rumors led by governments are necessary to guide the public to take rational action. UR - https://www.jmir.org/2020/8/e21143 UR - http://dx.doi.org/10.2196/21143 UR - http://www.ncbi.nlm.nih.gov/pubmed/32701460 ID - info:doi/10.2196/21143 ER - TY - JOUR AU - Morley, Jessica AU - Cowls, Josh AU - Taddeo, Mariarosaria AU - Floridi, Luciano PY - 2020/8/3 TI - Public Health in the Information Age: Recognizing the Infosphere as a Social Determinant of Health JO - J Med Internet Res SP - e19311 VL - 22 IS - 8 KW - COVID-19 KW - public health KW - misinformation KW - disinformation KW - infodemic KW - infodemiology KW - infosphere KW - social determinants of health KW - information ethics UR - https://www.jmir.org/2020/8/e19311 UR - http://dx.doi.org/10.2196/19311 UR - http://www.ncbi.nlm.nih.gov/pubmed/32648850 ID - info:doi/10.2196/19311 ER - TY - JOUR AU - Hswen, Yulin AU - Zhang, Amanda AU - Sewalk, C. Kara AU - Tuli, Gaurav AU - Brownstein, S. John AU - Hawkins, B. Jared PY - 2020/7/31 TI - Investigation of Geographic and Macrolevel Variations in LGBTQ Patient Experiences: Longitudinal Social Media Analysis JO - J Med Internet Res SP - e17087 VL - 22 IS - 7 KW - LGBTQ KW - sexual and gender minorities KW - health care quality KW - health care disparities KW - social media KW - digital health KW - sentiment analysis KW - infodemiology N2 - Background: Discrimination in the health care system contributes to worse health outcomes among lesbian, gay, bisexual, transgender, and queer (LGBTQ) patients. Objective: The aim of this study is to examine disparities in patient experience among LGBTQ persons using social media data. Methods: We collected patient experience data from Twitter from February 2013 to February 2017 in the United States. We compared the sentiment of patient experience tweets between Twitter users who self-identified as LGBTQ and non-LGBTQ. The effect of state-level partisan identity on patient experience sentiment and differences between LGBTQ users and non-LGBTQ users were analyzed. Results: We observed lower (more negative) patient experience sentiment among 13,689 LGBTQ users compared to 1,362,395 non-LGBTQ users. Increasing state-level liberal political identification was associated with higher patient experience sentiment among all users but had stronger effects for LGBTQ users. Conclusions: Our findings highlight that social media data can yield insights about patient experience for LGBTQ persons and suggest that a state-level sociopolitical environment influences patient experience for this group. Efforts are needed to reduce disparities in patient care for LGBTQ persons while taking into context the effect of the political climate on these inequities. UR - http://www.jmir.org/2020/7/e17087/ UR - http://dx.doi.org/10.2196/17087 UR - http://www.ncbi.nlm.nih.gov/pubmed/33137713 ID - info:doi/10.2196/17087 ER - TY - JOUR AU - Cousins, C. Henry AU - Cousins, C. Clara AU - Harris, Alon AU - Pasquale, R. Louis PY - 2020/7/30 TI - Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns JO - J Med Internet Res SP - e19483 VL - 22 IS - 7 KW - epidemiology KW - infoveillance KW - COVID-19 KW - internet activity KW - Google Trends KW - infectious disease KW - surveillance KW - public health N2 - Background: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. Objective: We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. Methods: We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. Results: Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. Conclusions: Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity. UR - http://www.jmir.org/2020/7/e19483/ UR - http://dx.doi.org/10.2196/19483 UR - http://www.ncbi.nlm.nih.gov/pubmed/32692691 ID - info:doi/10.2196/19483 ER - TY - JOUR AU - Safarnejad, Lida AU - Xu, Qian AU - Ge, Yaorong AU - Bagavathi, Arunkumar AU - Krishnan, Siddharth AU - Chen, Shi PY - 2020/7/28 TI - Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study JO - JMIR Public Health Surveill SP - e17175 VL - 6 IS - 3 KW - social media KW - infodemiology KW - infoveillance KW - infodemic KW - health emergency KW - tweeting dynamics KW - events detection KW - online influentials KW - Zika KW - public engagement N2 - Background: Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web. Objective: The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a time series of case counts; (2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization?s Public Health Emergency of International Concern (PHEIC) announcement, and (3) a few influential users? tweeting activities. Methods: All tweets and retweets (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques. Results: Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related tweeting activity were strongly associated with a few critical real-world events, both planned, such as the Rio Olympics, and unplanned, such as the PHEIC announcement. The Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users. Conclusions: Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter were decoupled from the actual disease epidemic in the United States but were closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises. UR - https://publichealth.jmir.org/2020/3/e17175 UR - http://dx.doi.org/10.2196/17175 UR - http://www.ncbi.nlm.nih.gov/pubmed/32348275 ID - info:doi/10.2196/17175 ER - TY - JOUR AU - Syamsuddin, Muhammad AU - Fakhruddin, Muhammad AU - Sahetapy-Engel, Marlen Jane Theresa AU - Soewono, Edy PY - 2020/7/24 TI - Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study JO - J Med Internet Res SP - e17633 VL - 22 IS - 7 KW - dengue KW - Google Trends KW - infodemiology KW - infoveillance KW - vector error correction model KW - Granger causality N2 - Background: The popularity of dengue can be inferred from Google Trends that summarizes Google searches of related topics. Both the disease and its Google Trends have a similar source of causation in the dengue virus, leading us to hypothesize that dengue incidence and Google Trends results have a long-run equilibrium. Objective: This research aimed to investigate the properties of this long-run equilibrium in the hope of using the information derived from Google Trends for the early detection of upcoming dengue outbreaks. Methods: This research used the cointegration method to assess a long-run equilibrium between dengue incidence and Google Trends results. The long-run equilibrium was characterized by their linear combination that generated a stationary process. The Dickey-Fuller test was adopted to check the stationarity of the processes. An error correction model (ECM) was then adopted to measure deviations from the long-run equilibrium to examine the short-term and long-term effects. The resulting models were used to determine the Granger causality between the two processes. Additional information about the two processes was obtained by examining the impulse response function and variance decomposition. Results: The Dickey-Fuller test supported an implicit null hypothesis that the dengue incidence and Google Trends results are nonstationary processes (P=.01). A further test showed that the processes were cointegrated (P=.01), indicating that their particular linear combination is a stationary process. These results permitted us to construct ECMs. The model showed the direction of causality of the two processes, indicating that Google Trends results will Granger-cause dengue incidence (not in the reverse order). Conclusions: Various hypothesis testing results in this research concluded that Google Trends results can be used as an initial indicator of upcoming dengue outbreaks. UR - http://www.jmir.org/2020/7/e17633/ UR - http://dx.doi.org/10.2196/17633 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706682 ID - info:doi/10.2196/17633 ER - TY - JOUR AU - Chen, Xi AU - Zhang, X. Stephen AU - Jahanshahi, Afshar Asghar AU - Alvarez-Risco, Aldo AU - Dai, Huiyang AU - Li, Jizhen AU - Ibarra, García Verónica PY - 2020/7/21 TI - Belief in a COVID-19 Conspiracy Theory as a Predictor of Mental Health and Well-Being of Health Care Workers in Ecuador: Cross-Sectional Survey Study JO - JMIR Public Health Surveill SP - e20737 VL - 6 IS - 3 KW - coronavirus KW - 2019-nCoV KW - mental health KW - psychiatric identification KW - Latin America KW - COVID-19 KW - conspiracy KW - well-being KW - health care worker KW - social media KW - prediction N2 - Background: During the coronavirus disease (COVID-19) pandemic, social media platforms have become active sites for the dissemination of conspiracy theories that provide alternative explanations of the cause of the pandemic, such as secret plots by powerful and malicious groups. However, the association of individuals? beliefs in conspiracy theories about COVID-19 with mental health and well-being issues has not been investigated. This association creates an assessable channel to identify and provide assistance to people with mental health and well-being issues during the pandemic. Objective: Our aim was to provide the first evidence that belief in conspiracy theories regarding the COVID-19 pandemic is a predictor of the mental health and well-being of health care workers. Methods: We conducted a survey of 252 health care workers in Ecuador from April 10 to May 2, 2020. We analyzed the data regarding distress and anxiety caseness with logistic regression and the data regarding life and job satisfaction with linear regression. Results: Among the 252 sampled health care workers in Ecuador, 61 (24.2%) believed that the virus was developed intentionally in a lab; 82 (32.5%) experienced psychological distress, and 71 (28.2%) had anxiety disorder. Compared to health care workers who were not sure where the virus originated, those who believed the virus was developed intentionally in a lab were more likely to report psychological distress and anxiety disorder and to have lower levels of job satisfaction and life satisfaction. Conclusions: This paper identifies belief in COVID-19 conspiracy theories as an important predictor of distress, anxiety, and job and life satisfaction among health care workers. This finding will enable mental health services to better target and provide help to mentally vulnerable health care workers during the ongoing COVID-19 pandemic. UR - http://publichealth.jmir.org/2020/3/e20737/ UR - http://dx.doi.org/10.2196/20737 UR - http://www.ncbi.nlm.nih.gov/pubmed/32658859 ID - info:doi/10.2196/20737 ER - TY - JOUR AU - Liu, Hejing AU - Li, Qiudan AU - Zhan, Yongcheng AU - Zhang, Zhu AU - Zeng, D. Daniel AU - Leischow, J. Scott PY - 2020/7/20 TI - Characterizing Social Media Messages Related to Underage JUUL E-Cigarette Buying and Selling: Cross-Sectional Analysis of Reddit Subreddits JO - J Med Internet Res SP - e16962 VL - 22 IS - 7 KW - JUUL KW - e-cigarette KW - Reddit KW - cross-sectional analysis KW - electronic nicotine delivery system KW - underage JUUL use N2 - Background: Stopping the epidemic of e-cigarette use among youth has become the common goal of both regulatory authorities and health departments. JUUL is currently the most popular e-cigarette brand on the market. Young people usually obtain and exchange information about JUUL with the help of social media platforms. Along with the rising prevalence of JUUL, posts about underage JUUL buying and selling have appeared on social media platforms such as Reddit, which sharply increase the risk of minors being exposed to JUUL. Objective: This study aims to analyze Reddit messages about JUUL buying and selling among the users of the UnderageJuul subreddit, and to further summarize the characteristics of those messages. The findings and insights can contribute to a better understanding of the patterns of underage JUUL use, and help public health officials provide timely education and guidance to minors who have intentions of accessing JUUL. Methods: We used a novel cross-subreddit method to analyze the Reddit messages on 2 subreddits. From July 9, 2017, to January 7, 2018, we collected data from the UnderageJuul subreddit, which was created for underage JUUL use discussion. The data set included 716 threads, 2935 comments, and 844 Reddit users (ie, Redditors). We collected our second data set, comprising 23,840 threads and 162,106 comments posted between July 9, 2017, and January 8, 2019, from the JUUL subreddit. We conducted analyses including the following: (1) annotation of users with buying/selling intention, (2) posting patterns discovery and topic comparison, and (3) posting activeness observation of discovered Redditors. Term frequency?inverse document frequency and regular expression-enhanced keyword search methods were applied during the content analysis to extract the posting patterns. The public posting records of the discovered users on the JUUL subreddit during the year after the UnderageJuul subreddit was shut down were analyzed to determine whether they were still active and interested in obtaining JUUL. Results: Our study revealed the following: (1) Among the 716 threads on the UnderageJuul subreddit, there were 214 threads related to JUUL sale and 168 threads related to JUUL purchase, which accounted for 53.5% (382/714) of threads. (2) Among the 844 Redditors of the UnderageJuul subreddit, 23.82% (201/844) of users were annotated with buying intention, and 21.10% (178/844) of users were annotated with selling intention. There were 34 users with buying/selling intention that self-reported as being <21 years old. (3) The most common key phrases used in selling threads were ?WTS,? ?want to sell,? ?for sale,? and ?selling? (154/214, 72.0%). The most common key phrases used in buying threads were ?look for/get JUUL/pods? (58/168, 34.5%) and ?WTB? (53/168, 31.5%). (4) The most important concern that UnderageJuul Redditors had in obtaining JUULs was the price (311/1306, 23.81%), followed by the delivery service (68/1306, 5.21%). (5) The most popular flavors among the users with buying/selling intention were mango, cucumber, and mint. The flavor preferences remained consistent on both subreddits. Adverse symptoms related to the mango flavor were reported by 3 users on the JUUL subreddit. (6) In total, 24.4% (49/201) of users wanted to buy JUULs and 46.6% (83/178) of users wanted to sell JUULs, including 11 self-reported underage users, who also participated in the discussions on the JUUL subreddit. (7) Within one year of the UnderageJuul subreddit shutting down, there were 40 users who continued to post 186 threads on the JUUL subreddit, including 10 threads indicating buying/selling willingness that were posted shortly after the UnderageJuul subreddit was closed. Conclusions: There were overlapping users active in the JUUL and UnderageJuul subreddits. The buying/selling-related content appeared in multiple venues with certain posting patterns from July 9, 2017, to January 7, 2018. Such content might lead to a high risk of health problems for minors, such as nicotine addiction. Based on these findings, this study provided some insights and suggestions that might contribute to the decision-making processes of regulators and public health officials. UR - http://www.jmir.org/2020/7/e16962/ UR - http://dx.doi.org/10.2196/16962 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706661 ID - info:doi/10.2196/16962 ER - TY - JOUR AU - Husain, Iltifat AU - Briggs, Blake AU - Lefebvre, Cedric AU - Cline, M. David AU - Stopyra, P. Jason AU - O'Brien, Claire Mary AU - Vaithi, Ramupriya AU - Gilmore, Scott AU - Countryman, Chase PY - 2020/7/17 TI - Fluctuation of Public Interest in COVID-19 in the United States: Retrospective Analysis of Google Trends Search Data JO - JMIR Public Health Surveill SP - e19969 VL - 6 IS - 3 KW - Infodemiology KW - COVID-19 KW - SARS-CoV-2 KW - digital health KW - Google Trends KW - trend KW - internet KW - public health N2 - Background: In the absence of vaccines and established treatments, nonpharmaceutical interventions (NPIs) are fundamental tools to control coronavirus disease (COVID-19) transmission. NPIs require public interest to be successful. In the United States, there is a lack of published research on the factors that influence public interest in COVID-19. Using Google Trends, we examined the US level of public interest in COVID-19 and how it correlated to testing and with other countries. Objective: The aim of this study was to determine how public interest in COVID-19 in the United States changed over time and the key factors that drove this change, such as testing. US public interest in COVID-19 was compared to that in countries that have been more successful in their containment and mitigation strategies. Methods: In this retrospective study, Google Trends was used to analyze the volume of internet searches within the United States relating to COVID-19, focusing on dates between December 31, 2019, and March 24, 2020. The volume of internet searches related to COVID-19 was compared to that in other countries. Results: Throughout January and February 2020, there was limited search interest in COVID-19 within the United States. Interest declined for the first 21 days of February. A similar decline was seen in geographical regions that were later found to be experiencing undetected community transmission in February. Between March 9 and March 12, 2020, there was a rapid rise in search interest. This rise in search interest was positively correlated with the rise of positive tests for SARS-CoV-2 (6.3, 95% CI ?2.9 to 9.7; P<.001). Within the United States, it took 52 days for search interest to rise substantially after the first positive case; in countries with more successful outbreak control, search interest rose in less than 15 days. Conclusions: Containment and mitigation strategies require public interest to be successful. The initial level of COVID-19 public interest in the United States was limited and even decreased during a time when containment and mitigation strategies were being established. A lack of public interest in COVID-19 existed in the United States when containment and mitigation policies were in place. Based on our analysis, it is clear that US policy makers need to develop novel methods of communicating COVID-19 public health initiatives. UR - https://publichealth.jmir.org/2020/3/e19969 UR - http://dx.doi.org/10.2196/19969 UR - http://www.ncbi.nlm.nih.gov/pubmed/32501806 ID - info:doi/10.2196/19969 ER - TY - JOUR AU - Rajan, Anjana AU - Sharaf, Ravi AU - Brown, S. Robert AU - Sharaiha, Z. Reem AU - Lebwohl, Benjamin AU - Mahadev, SriHari PY - 2020/7/17 TI - Association of Search Query Interest in Gastrointestinal Symptoms With COVID-19 Diagnosis in the United States: Infodemiology Study JO - JMIR Public Health Surveill SP - e19354 VL - 6 IS - 3 KW - COVID-19 KW - diarrhea KW - internet search queries KW - Google Trends KW - gastrointestinal KW - symptom KW - health information KW - pandemic KW - infectious disease KW - virus N2 - Background: Coronavirus disease (COVID-19) is a novel viral illness that has rapidly spread worldwide. While the disease primarily presents as a respiratory illness, gastrointestinal symptoms such as diarrhea have been reported in up to one-third of confirmed cases, and patients may have mild symptoms that do not prompt them to seek medical attention. Internet-based infodemiology offers an approach to studying symptoms at a population level, even in individuals who do not seek medical care. Objective: This study aimed to determine if a correlation exists between internet searches for gastrointestinal symptoms and the confirmed case count of COVID-19 in the United States. Methods: The search terms chosen for analysis in this study included common gastrointestinal symptoms such as diarrhea, nausea, vomiting, and abdominal pain. Furthermore, the search terms fever and cough were used as positive controls, and constipation was used as a negative control. Daily query shares for the selected symptoms were obtained from Google Trends between October 1, 2019 and June 15, 2020 for all US states. These shares were divided into two time periods: pre?COVID-19 (prior to March 1) and post?COVID-19 (March 1-June 15). Confirmed COVID-19 case numbers were obtained from the Johns Hopkins University Center for Systems Science and Engineering data repository. Moving averages of the daily query shares (normalized to baseline pre?COVID-19) were then analyzed against the confirmed disease case count and daily new cases to establish a temporal relationship. Results: The relative search query shares of many symptoms, including nausea, vomiting, abdominal pain, and constipation, remained near or below baseline throughout the time period studied; however, there were notable increases in searches for the positive control symptoms of fever and cough as well as for diarrhea. These increases in daily search queries for fever, cough, and diarrhea preceded the rapid rise in number of cases by approximately 10 to 14 days. The search volumes for these terms began declining after mid-March despite the continued rises in cumulative cases and daily new case counts. Conclusions: Google searches for symptoms may precede the actual rises in cases and hospitalizations during pandemics. During the current COVID-19 pandemic, this study demonstrates that internet search queries for fever, cough, and diarrhea increased prior to the increased confirmed case count by available testing during the early weeks of the pandemic in the United States. While the search volumes eventually decreased significantly as the number of cases continued to rise, internet query search data may still be a useful tool at a population level to identify areas of active disease transmission at the cusp of new outbreaks. UR - http://publichealth.jmir.org/2020/3/e19354/ UR - http://dx.doi.org/10.2196/19354 UR - http://www.ncbi.nlm.nih.gov/pubmed/32640418 ID - info:doi/10.2196/19354 ER - TY - JOUR AU - Cash, Scottye AU - Schwab-Reese, Marie Laura AU - Zipfel, Erin AU - Wilt, Megan AU - Moreno, Megan PY - 2020/7/17 TI - What College Students Post About Depression on Facebook and the Support They Perceive: Content Analysis JO - JMIR Form Res SP - e13650 VL - 4 IS - 7 KW - social media KW - depression KW - college students KW - qualitative N2 - Background: College students frequently use social media sites to connect with friends. Increasingly, research suggests college students and other young adults seek mental health-related support on social media, which may present a unique venue for intervention. Objective: The purpose of this study was to examine college students? perceptions about displaying feelings of depression on Facebook and, in turn, how their social media friends responded. Methods: A primarily quantitative online survey with open response questions was distributed to students at four US universities. Qualitative responses were analyzed using content analysis. Results: A total of 34 students provided qualitative responses for analysis, these students were 85.3% female, mean age 20.2 (SD=1.4) and 20.6% racial/ethnic minority. Students who reported posting about depression often expressed an emotion or feeling but did not use the word ?depression? in the post. Approximately 20% posted language about a bad day, and 15% posted a song or music video. Only one person reported posting a statement that directly asked for help. When friends responded to the posts, students generally perceived the responses as supportive or motivating gestures. Nearly 15% of friends contacted the individual outside of Facebook. One individual received a negative response and no responses suggested that the individual seek help. Conclusions: This study found that college students who post about depression often do so without directly referencing depression and that friends were generally supportive. However, no participants reported their social network suggested they seek help, which may suggest increasing mental health literacy, for both support seekers and responders, would be an opportunity to improve online mental health-related support. UR - https://formative.jmir.org/2020/7/e13650 UR - http://dx.doi.org/10.2196/13650 UR - http://www.ncbi.nlm.nih.gov/pubmed/32706687 ID - info:doi/10.2196/13650 ER - TY - JOUR AU - Viguria, Iranzu AU - Alvarez-Mon, Angel Miguel AU - Llavero-Valero, Maria AU - Asunsolo del Barco, Angel AU - Ortuńo, Felipe AU - Alvarez-Mon, Melchor PY - 2020/7/14 TI - Eating Disorder Awareness Campaigns: Thematic and Quantitative Analysis Using Twitter JO - J Med Internet Res SP - e17626 VL - 22 IS - 7 KW - awareness campaigns KW - eating disorders KW - Twitter KW - social media N2 - Background: Health awareness initiatives are frequent but their efficacy is a matter of controversy. We have investigated the effect of the Eating Disorder Awareness Week and Wake Up Weight Watchers campaigns on Twitter. Objective: We aimed to examine whether the Eating Disorder Awareness Week and Wake Up Weight Watchers initiatives increased the volume and dissemination of Twitter conversations related to eating disorders and investigate what content generates the most interest on Twitter. Methods: Over a period of 12 consecutive days in 2018, we collected tweets containing the hashtag #wakeupweightwatchers and hashtags related to Eating Disorder Awareness Week (#eatingdisorderawarenessweek, #eatingdisorderawareness, or #EDAW), with the hashtag #eatingdisorder as a control. The content of each tweet was rated as medical, testimony, help offer, awareness, pro-ana, or anti-ana. We analyzed the number of retweets and favorites generated, as well as the potential reach and impact of the hashtags and the characteristics of contributors. Results: The number of #wakeupweightwatchers tweets was higher than that of Eating Disorder Awareness Week and #eatingdisorder tweets (3900, 2056, and 1057, respectively). The content of tweets was significantly different between the hashtags analyzed (P<.001). Medical content was lower in the awareness campaigns. Awareness and help offer content were lower in #wakeupweightwatchers tweets. Retweet and favorite ratios were highest in #wakeupweightwatchers tweets. Eating Disorder Awareness Week achieved the highest impact, and very influential contributors participated. Conclusions: Both awareness campaigns effectively promoted tweeting about eating disorders. The majority of tweets did not promote any specific preventive or help-seeking behaviors. UR - http://www.jmir.org/2020/7/e17626/ UR - http://dx.doi.org/10.2196/17626 UR - http://www.ncbi.nlm.nih.gov/pubmed/32673225 ID - info:doi/10.2196/17626 ER - TY - JOUR AU - Gao, Shuqing AU - He, Lingnan AU - Chen, Yue AU - Li, Dan AU - Lai, Kaisheng PY - 2020/7/13 TI - Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media JO - J Med Internet Res SP - e16649 VL - 22 IS - 7 KW - artificial intelligence KW - public perception KW - social media KW - content analysis KW - medical care N2 - Background: High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public?s views on medical AI. Currently, the opinions of the general public on this matter remain unclear. Objective: The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. Methods: Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. Results: Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors. Conclusions: Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients? emotional needs instead of focusing merely on technical issues. UR - http://www.jmir.org/2020/7/e16649/ UR - http://dx.doi.org/10.2196/16649 UR - http://www.ncbi.nlm.nih.gov/pubmed/32673231 ID - info:doi/10.2196/16649 ER - TY - JOUR AU - Hswen, Yulin AU - Zhang, Amanda AU - Freifeld, Clark AU - Brownstein, S. John PY - 2020/7/10 TI - Evaluation of Volume of News Reporting and Opioid-Related Deaths in the United States: Comparative Analysis Study of Geographic and Socioeconomic Differences JO - J Med Internet Res SP - e17693 VL - 22 IS - 7 KW - opioid epidemic KW - news media KW - geographic KW - socioeconomic KW - addiction KW - overdose N2 - Background: News media coverage is a powerful influence on public attitude and government action. The digitization of news media covering the current opioid epidemic has changed the landscape of coverage and may have implications for how to effectively respond to the opioid crisis. Objective: This study aims to characterize the relationship between volume of online opioid news reporting and opioid-related deaths in the United States and how these measures differ across geographic and socioeconomic county-level factors. Methods: Online news reports from February 2018 to April 2019 on opioid-related events in the United States were extracted from Google News. News data were aggregated at the county level and compared against opioid-related death counts. Ordinary least squares regression was used to model opioid-related death rate and opioid news coverage with the inclusion of socioeconomic and geographic explanatory variables. Results: A total of 35,758 relevant news reports were collected representing 1789 counties. Regression analysis revealed that opioid-related death rate was positively associated with news reporting. However, opioid-related death rate and news reporting volume showed opposite correlations with educational attainment and rurality. When controlling for variation in death rate, counties in the Northeast were overrepresented by news coverage. Conclusions: Our results suggest that regional variation in the volume of opioid-related news reporting does not reflect regional variation in opioid-related death rate. Differences in the amount of media attention may influence perceptions of the severity of opioid epidemic. Future studies should investigate the influence of media reporting on public support and action on opioid issues. UR - http://www.jmir.org/2020/7/e17693/ UR - http://dx.doi.org/10.2196/17693 UR - http://www.ncbi.nlm.nih.gov/pubmed/32673248 ID - info:doi/10.2196/17693 ER - TY - JOUR AU - Buente, Wayne AU - Dalisay, Francis AU - Pokhrel, Pallav AU - Kramer, Kurihara Hanae AU - Pagano, Ian PY - 2020/7/9 TI - An Instagram-Based Study to Understand Betel Nut Use Culture in Micronesia: Exploratory Content Analysis JO - J Med Internet Res SP - e13954 VL - 22 IS - 7 KW - betel nut KW - areca catechu KW - areca KW - cancer KW - health KW - Guam KW - Micronesia KW - Instagram KW - mobile phone KW - culture N2 - Background: A 2012 World Health Organization report recognizes betel nut use as an urgent public health threat faced by the Western Pacific region. However, compared with other addictive substances, little is known about how betel nuts are depicted on social media platforms. In particular, image-based social media platforms can be powerful tools for health communication. Studying the content of substance use on visual social media may provide valuable insights into public health interventions. Objective: This study aimed to explore and document the ways that betel nut is portrayed on the photo-sharing site Instagram. The analysis focuses on the hashtag #pugua, which refers to the local term for betel nut in Guam and other parts of Micronesia. Methods: An exploratory content analysis of 242 Instagram posts tagged #pugua was conducted based on previous research on substance use and Instagram and betel nut practices in Micronesia. In addition, the study examined the social engagement of betel nut content on the image-based platform. Results: The study findings revealed content themes referencing the betel nut or betel nut tree, betel nut preparation practices, and the unique social and cultural context surrounding betel nut activity in Guam and Micronesia. In addition, certain practices and cultural themes encouraged social engagement on Instagram. Conclusions: The findings from this study emphasize the cultural relevance of betel nut use in Micronesia. These findings provide a basis for empirically testing hypotheses related to the etiological roles of cultural identity and pride in shaping betel nut use behavior among Micronesians, particularly youths and young adults. Such research is likely to inform the development of culturally relevant betel nut prevention and cessation programs. UR - https://www.jmir.org/2020/7/e13954 UR - http://dx.doi.org/10.2196/13954 UR - http://www.ncbi.nlm.nih.gov/pubmed/32673220 ID - info:doi/10.2196/13954 ER - TY - JOUR AU - Nguyen, T. Thu AU - Adams, Nikki AU - Huang, Dina AU - Glymour, Maria M. AU - Allen, M. Amani AU - Nguyen, C. Quynh PY - 2020/7/6 TI - The Association Between State-Level Racial Attitudes Assessed From Twitter Data and Adverse Birth Outcomes: Observational Study JO - JMIR Public Health Surveill SP - e17103 VL - 6 IS - 3 KW - social media KW - racial bias KW - birth outcomes KW - racial or ethnic minorities N2 - Background: In the United States, racial disparities in birth outcomes persist and have been widening. Interpersonal and structural racism are leading explanations for the continuing racial disparities in birth outcomes, but research to confirm the role of racism and evaluate trends in the impact of racism on health outcomes has been hampered by the challenge of measuring racism. Most research on discrimination relies on self-reported experiences of discrimination, and few studies have examined racial attitudes and bias at the US national level. Objective: This study aimed to investigate the associations between state-level Twitter-derived sentiments related to racial or ethnic minorities and birth outcomes. Methods: We utilized Twitter?s Streaming application programming interface to collect 26,027,740 tweets from June 2015 to December 2017, containing at least one race-related term. Sentiment analysis was performed using support vector machine, a supervised machine learning model. We constructed overall indicators of sentiment toward minorities and sentiment toward race-specific groups. For each year, state-level Twitter-derived sentiment data were merged with birth data for that year. The study participants were women who had singleton births with no congenital abnormalities from 2015 to 2017 and for whom data were available on gestational age (n=9,988,030) or birth weight (n=9,985,402). The main outcomes were low birth weight (birth weight ?2499 g) and preterm birth (gestational age <37 weeks). We estimated the incidence ratios controlling for individual-level maternal characteristics (sociodemographics, prenatal care, and health behaviors) and state-level demographics, using log binomial regression models. Results: The accuracy for identifying negative sentiments on comparing the machine learning model to manually labeled tweets was 91%. Mothers living in states in the highest tertile for negative sentiment tweets referencing racial or ethnic minorities had greater incidences of low birth weight (8% greater, 95% CI 4%-13%) and preterm birth (8% greater, 95% CI 0%-14%) compared with mothers living in states in the lowest tertile. More negative tweets referencing minorities were associated with adverse birth outcomes in the total population, including non-Hispanic white people and racial or ethnic minorities. In stratified subgroup analyses, more negative tweets referencing specific racial or ethnic minority groups (black people, Middle Eastern people, and Muslims) were associated with poor birth outcomes for black people and minorities. Conclusions: A negative social context related to race was associated with poor birth outcomes for racial or ethnic minorities, as well as non-Hispanic white people. UR - https://publichealth.jmir.org/2020/3/e17103 UR - http://dx.doi.org/10.2196/17103 UR - http://www.ncbi.nlm.nih.gov/pubmed/32298232 ID - info:doi/10.2196/17103 ER - TY - JOUR AU - Zepecki, Anne AU - Guendelman, Sylvia AU - DeNero, John AU - Prata, Ndola PY - 2020/7/6 TI - Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework JO - JMIR Res Protoc SP - e16543 VL - 9 IS - 7 KW - Google KW - search data KW - infodemiology KW - infoveillance KW - infodemic KW - reproductive health KW - abortion KW - birth control KW - Google Trends KW - APIs N2 - Background: Individuals are increasingly turning to search engines like Google to obtain health information and access resources. Analysis of Google search queries offers a novel approach, which is part of the methodological toolkit for infodemiology or infoveillance researchers, to understanding population health concerns and needs in real time or near-real time. While searches predominantly have been examined with the Google Trends website tool, newer application programming interfaces (APIs) are now available to academics to draw a richer landscape of searches. These APIs allow users to write code in languages like Python to retrieve sample data directly from Google servers. Objective: The purpose of this paper is to describe a novel protocol to determine the top queries, volume of queries, and the top sites reached by a population searching on the web for a specific health term. The protocol retrieves Google search data obtained from three Google APIs: Google Trends, Google Health Trends (also referred to as Flu Trends), and Google Custom Search. Methods: Our protocol consisted of four steps: (1) developing a master list of top search queries for an initial search term using Google Trends, (2) gathering information on relative search volume using Google Health Trends, (3) determining the most popular sites using Google Custom Search, and (4) calculating estimated total search volume. We tested the protocol following key procedures at each step and verified its usefulness by examining search traffic on birth control in 2017 in the United States. Two separate programmers working independently achieved similar results with insignificant variation due to sample variability. Results: We successfully tested the methodology on the initial search term birth control. We identified top search queries for birth control, of which birth control pill was the most popular and obtained the relative and estimated total search volume for the top queries: relative search volume was 0.54 for the pill, corresponding to an estimated 9.3-10.7 million searches. We used the estimates of the proportion of search activity for the top queries to arrive at a generated list of the most popular websites: for the pill, the Planned Parenthood website was the top site. Conclusions: The proposed methodological framework demonstrates how to retrieve Google query data from multiple Google APIs and provides thorough documentation required to systematically identify search queries and websites, as well as estimate relative and total search volume of queries in real time or near-real time in specific locations and time periods. Although the protocol needs further testing, it allows researchers to replicate the steps and shows promise in advancing our understanding of population-level health concerns. International Registered Report Identifier (IRRID): RR1-10.2196/16543 UR - https://www.researchprotocols.org/2020/7/e16543 UR - http://dx.doi.org/10.2196/16543 UR - http://www.ncbi.nlm.nih.gov/pubmed/32442159 ID - info:doi/10.2196/16543 ER - TY - JOUR AU - Campos-Castillo, Celeste AU - Laestadius, I. Linnea PY - 2020/7/3 TI - Racial and Ethnic Digital Divides in Posting COVID-19 Content on Social Media Among US Adults: Secondary Survey Analysis JO - J Med Internet Res SP - e20472 VL - 22 IS - 7 KW - COVID-19 KW - digital divides KW - user characteristics KW - race KW - ethnicity KW - algorithm bias KW - social media KW - bias KW - surveillance KW - public health N2 - Background: Public health surveillance experts are leveraging user-generated content on social media to track the spread and effects of COVID-19. However, racial and ethnic digital divides, which are disparities among people who have internet access and post on social media, can bias inferences. This bias is particularly problematic in the context of the COVID-19 pandemic because due to structural inequalities, members of racial and ethnic minority groups are disproportionately vulnerable to contracting the virus and to the deleterious economic and social effects from mitigation efforts. Further, important demographic intersections with race and ethnicity, such as gender and age, are rarely investigated in work characterizing social media users; however, they reflect additional axes of inequality shaping differential exposure to COVID-19 and its effects. Objective: The aim of this study was to characterize how the race and ethnicity of US adults are associated with their odds of posting COVID-19 content on social media and how gender and age modify these odds. Methods: We performed a secondary analysis of a survey conducted by the Pew Research Center from March 19 to 24, 2020, using a national probability sample (N=10,510). Respondents were recruited from an online panel, where panelists without an internet-enabled device were given one to keep at no cost. The binary dependent variable was responses to an item asking whether respondents ?used social media to share or post information about the coronavirus.? We used survey-weighted logistic regressions to estimate the odds of responding in the affirmative based on the race and ethnicity of respondents (white, black, Latino, other race/ethnicity), adjusted for covariates measuring sociodemographic background and COVID-19 experiences. We examined how gender (female, male) and age (18 to 30 years, 31 to 50 years, 51 to 64 years, and 65 years and older) intersected with race and ethnicity by estimating interactions. Results: Respondents who identified as black (odds ratio [OR] 1.29, 95% CI 1.02-1.64; P=.03), Latino (OR 1.66, 95% CI 1.36-2.04; P<.001), or other races/ethnicities (OR 1.33, 95% CI 1.02-1.72; P=.03) had higher odds than respondents who identified as white of reporting that they posted COVID-19 content on social media. Women had higher odds of posting than men regardless of race and ethnicity (OR 1.58, 95% CI 1.39-1.80; P<.001). Among men, respondents who identified as black, Latino, or members of other races/ethnicities were significantly more likely to post than respondents who identified as white. Older adults (65 years or older) had significantly lower odds (OR 0.73, 95% CI 0.57-0.94; P=.01) of posting compared to younger adults (18-29 years), particularly among those identifying as other races/ethnicities. Latino respondents were the most likely to report posting across all age groups. Conclusions: In the United States, members of racial and ethnic minority groups are most likely to contribute to COVID-19 content on social media, particularly among groups traditionally less likely to use social media (older adults and men). The next step is to ensure that data collection procedures capture this diversity by encompassing a breadth of search criteria and social media platforms. UR - https://www.jmir.org/2020/7/e20472 UR - http://dx.doi.org/10.2196/20472 UR - http://www.ncbi.nlm.nih.gov/pubmed/32568726 ID - info:doi/10.2196/20472 ER - TY - JOUR AU - Caldwell, K. Wendy AU - Fairchild, Geoffrey AU - Del Valle, Y. Sara PY - 2020/7/3 TI - Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset JO - J Med Internet Res SP - e14337 VL - 22 IS - 7 KW - influenza KW - surveillance KW - infoveillance KW - infodemiology KW - projections and predictions KW - internet KW - data sources N2 - Background: Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. Objective: This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. Methods: We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. We tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data. Results: Our results yielded an r2 value of 0.955 in the most successful case, promising results for some cases, and unsuccessful results for other cases. In the interest of scientific transparency to further the understanding of when internet data streams are an appropriate supplemental data source, we also included negative results (ie, unsuccessful models). Models that focused on a single influenza season were more successful than those that attempted to model multiple influenza seasons. Geographic resolution appeared to play a key role, with national and regional models being more successful, overall, than models at the state level. Conclusions: These results demonstrate that internet data may be able to complement traditional influenza surveillance in some cases but not in others. Specifically, our results show that the CDC website traffic may inform national- and division-level models but not models for each individual state. In addition, our results show better agreement when the data were broken up by seasons instead of aggregated over several years. We anticipate that this work will lead to more complex nowcasting and forecasting models using this data stream. UR - https://www.jmir.org/2020/7/e14337 UR - http://dx.doi.org/10.2196/14337 UR - http://www.ncbi.nlm.nih.gov/pubmed/32437327 ID - info:doi/10.2196/14337 ER - TY - JOUR AU - Xu, Chenjie AU - Zhang, Xinyu AU - Wang, Yaogang PY - 2020/7/2 TI - Mapping of Health Literacy and Social Panic Via Web Search Data During the COVID-19 Public Health Emergency: Infodemiological Study JO - J Med Internet Res SP - e18831 VL - 22 IS - 7 KW - COVID-19 KW - China KW - Baidu KW - infodemiology KW - web search KW - internet KW - public health KW - emergency KW - outbreak KW - infectious disease KW - pandemic KW - health literacy N2 - Background: Coronavirus disease (COVID-19) is a type of pneumonia caused by a novel coronavirus that was discovered in 2019. As of May 6, 2020, 84,407 cases and 4643 deaths have been confirmed in China. The Chinese population has expressed great concern since the COVID-19 outbreak. Meanwhile, an average of 1 billion people per day are using the Baidu search engine to find COVID-19?related health information. Objective: The aim of this paper is to analyze web search data volumes related to COVID-19 in China. Methods: We conducted an infodemiological study to analyze web search data volumes related to COVID-19. Using Baidu Index data, we assessed the search frequencies of specific search terms in Baidu to describe the impact of COVID-19 on public health, psychology, behaviors, lifestyles, and social policies (from February 11, 2020, to March 17, 2020). Results: The search frequency related to COVID-19 has increased significantly since February 11th. Our heat maps demonstrate that citizens in Wuhan, Hubei Province, express more concern about COVID-19 than citizens from other cities since the outbreak first occurred in Wuhan. Wuhan citizens frequently searched for content related to ?medical help,? ?protective materials,? and ?pandemic progress.? Web searches for ?return to work? and ?go back to school? have increased eight-fold compared to the previous month. Searches for content related to ?closed community and remote office? have continued to rise, and searches for ?remote office demand? have risen by 663% from the previous quarter. Employees who have returned to work have mainly engaged in the following web searches: ?return to work and prevention measures,? ?return to work guarantee policy,? and ?time to return to work.? Provinces with large, educated populations (eg, Henan, Hebei, and Shandong) have been focusing on ?online education? whereas medium-sized cities have been paying more attention to ?online medical care.? Conclusions: Our findings suggest that web search data may reflect changes in health literacy, social panic, and prevention and control policies in response to COVID-19. UR - https://www.jmir.org/2020/7/e18831 UR - http://dx.doi.org/10.2196/18831 UR - http://www.ncbi.nlm.nih.gov/pubmed/32540844 ID - info:doi/10.2196/18831 ER - TY - JOUR AU - Black, C. Joshua AU - Margolin, R. Zachary AU - Olson, A. Richard AU - Dart, C. Richard PY - 2020/6/29 TI - Online Conversation Monitoring to Understand the Opioid Epidemic: Epidemiological Surveillance Study JO - JMIR Public Health Surveill SP - e17073 VL - 6 IS - 2 KW - epidemiological surveillance KW - infoveillance KW - infodemiology KW - opioids KW - social media KW - misuse KW - abuse KW - addiction KW - overdose KW - death N2 - Background: Between 2016 and 2017, the national mortality rate involving opioids continued its escalation; opioid deaths rose from 42,249 to 47,600, bringing the public health crisis to a new height. Considering that 69% of adults in the United States use online social media sites, a resource that builds a more complete understanding of prescription drug misuse and abuse could supplement traditional surveillance instruments. The Food and Drug Administration has identified 5 key risks and consequences of opioid drugs?misuse, abuse, addiction, overdose, and death. Identifying posts that discuss these key risks could lead to novel information that is not typically captured by traditional surveillance systems. Objective: The goal of this study was to describe the trends of online posts (frequency over time) involving abuse, misuse, addiction, overdose, and death in the United States and to describe the types of websites that host these discussions. Internet posts that mentioned fentanyl, hydrocodone, oxycodone, or oxymorphone were examined. Methods: Posts that did not refer to personal experiences were removed, after which 3.1 million posts remained. A stratified sample of 61,000 was selected. Unstructured data were classified into 5 key risks by manually coding for key outcomes of misuse, abuse, addiction, overdose, and death. Sampling probabilities of the coded posts were used to estimate the total post volume for each key risk. Results: Addiction and misuse were the two most commonly discussed key risks for hydrocodone, oxycodone, and oxymorphone. For fentanyl, overdose and death were the most discussed key risks. Fentanyl had the highest estimated number of misuse-, overdose-, and death-related mentions (41,808, 42,659, and 94,169, respectively). Oxycodone had the highest estimated number of abuse- and addiction-related mentions (3548 and 12,679, respectively). The estimated volume of online posts for fentanyl increased by more than 10-fold in late 2017 and 2018. The odds of discussing fentanyl overdose (odds ratios [OR] 4.32, 95% CI 2.43-7.66) and death (OR 5.05, 95% CI 3.10-8.21) were higher for social media, while the odds of discussing fentanyl abuse (OR 0.10, 95% CI 0.04-0.22) and addiction (OR 0.24, 95% CI 0.15-0.38) were higher for blogs and forums. Conclusions: Of the 5 FDA-defined key risks, fentanyl overdose and death has dominated discussion in recent years, while discussion of oxycodone, hydrocodone, and oxymorphone has decreased. As drug-related deaths continue to increase, an understanding of the motivations, circumstances, and consequences of drug abuse would assist in developing policy responses. Furthermore, content was notably different based on media origin, and studies that exclusively use either social media sites (such as Twitter) or blogs and forums could miss important content. This study sets out sustainable, ongoing methodology for surveilling internet postings regarding these drugs. UR - http://publichealth.jmir.org/2020/2/e17073/ UR - http://dx.doi.org/10.2196/17073 UR - http://www.ncbi.nlm.nih.gov/pubmed/32597786 ID - info:doi/10.2196/17073 ER - TY - JOUR AU - Eysenbach, Gunther PY - 2020/6/29 TI - How to Fight an Infodemic: The Four Pillars of Infodemic Management JO - J Med Internet Res SP - e21820 VL - 22 IS - 6 KW - infodemiology KW - infodemic KW - COVID-19 KW - infoveillance KW - pandemic KW - epidemics KW - emergency management KW - public health UR - http://www.jmir.org/2020/6/e21820/ UR - http://dx.doi.org/10.2196/21820 UR - http://www.ncbi.nlm.nih.gov/pubmed/32589589 ID - info:doi/10.2196/21820 ER - TY - JOUR AU - Tangcharoensathien, Viroj AU - Calleja, Neville AU - Nguyen, Tim AU - Purnat, Tina AU - D?Agostino, Marcelo AU - Garcia-Saiso, Sebastian AU - Landry, Mark AU - Rashidian, Arash AU - Hamilton, Clayton AU - AbdAllah, Abdelhalim AU - Ghiga, Ioana AU - Hill, Alexandra AU - Hougendobler, Daniel AU - van Andel, Judith AU - Nunn, Mark AU - Brooks, Ian AU - Sacco, Luigi Pier AU - De Domenico, Manlio AU - Mai, Philip AU - Gruzd, Anatoliy AU - Alaphilippe, Alexandre AU - Briand, Sylvie PY - 2020/6/26 TI - Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation JO - J Med Internet Res SP - e19659 VL - 22 IS - 6 KW - COVID-19 KW - infodemic KW - knowledge translation KW - message amplification KW - misinformation KW - information-seeking behavior KW - access to information KW - information literacy KW - communications media KW - internet KW - risk communication KW - evidence synthesis N2 - Background: An infodemic is an overabundance of information?some accurate and some not?that occurs during an epidemic. In a similar manner to an epidemic, it spreads between humans via digital and physical information systems. It makes it hard for people to find trustworthy sources and reliable guidance when they need it. Objective: A World Health Organization (WHO) technical consultation on responding to the infodemic related to the coronavirus disease (COVID-19) pandemic was held, entirely online, to crowdsource suggested actions for a framework for infodemic management. Methods: A group of policy makers, public health professionals, researchers, students, and other concerned stakeholders was joined by representatives of the media, social media platforms, various private sector organizations, and civil society to suggest and discuss actions for all parts of society, and multiple related professional and scientific disciplines, methods, and technologies. A total of 594 ideas for actions were crowdsourced online during the discussions and consolidated into suggestions for an infodemic management framework. Results: The analysis team distilled the suggestions into a set of 50 proposed actions for a framework for managing infodemics in health emergencies. The consultation revealed six policy implications to consider. First, interventions and messages must be based on science and evidence, and must reach citizens and enable them to make informed decisions on how to protect themselves and their communities in a health emergency. Second, knowledge should be translated into actionable behavior-change messages, presented in ways that are understood by and accessible to all individuals in all parts of all societies. Third, governments should reach out to key communities to ensure their concerns and information needs are understood, tailoring advice and messages to address the audiences they represent. Fourth, to strengthen the analysis and amplification of information impact, strategic partnerships should be formed across all sectors, including but not limited to the social media and technology sectors, academia, and civil society. Fifth, health authorities should ensure that these actions are informed by reliable information that helps them understand the circulating narratives and changes in the flow of information, questions, and misinformation in communities. Sixth, following experiences to date in responding to the COVID-19 infodemic and the lessons from other disease outbreaks, infodemic management approaches should be further developed to support preparedness and response, and to inform risk mitigation, and be enhanced through data science and sociobehavioral and other research. Conclusions: The first version of this framework proposes five action areas in which WHO Member States and actors within society can apply, according to their mandate, an infodemic management approach adapted to national contexts and practices. Responses to the COVID-19 pandemic and the related infodemic require swift, regular, systematic, and coordinated action from multiple sectors of society and government. It remains crucial that we promote trusted information and fight misinformation, thereby helping save lives. UR - http://www.jmir.org/2020/6/e19659/ UR - http://dx.doi.org/10.2196/19659 UR - http://www.ncbi.nlm.nih.gov/pubmed/32558655 ID - info:doi/10.2196/19659 ER - TY - JOUR AU - Lin, Ro-Ting AU - Cheng, Yawen AU - Jiang, Yan-Cheng PY - 2020/6/26 TI - Exploring Public Awareness of Overwork Prevention With Big Data From Google Trends: Retrospective Analysis JO - J Med Internet Res SP - e18181 VL - 22 IS - 6 KW - overwork KW - working hours KW - policy KW - big data N2 - Background: To improve working conditions and prevent illness and deaths related to overwork, the Taiwanese government in 2015, 2016, and 2018 amended regulations regarding working time, overtime, shifts, and rest days. Such policy changes may lead to a rising public awareness of overwork-related issues, which may in turn reinforce policy development. Objective: This study aimed to investigate to what extent public awareness of overwork-related issues correlated with policy changes. Methods: Policies, laws, and regulations promulgated or amended in Taiwan between January 2004 and November 2019 were identified. We defined 3 working conditions (overwork, long working hours, and high job stress) related to overwork prevention, generated a keyword for each condition, and extracted the search volumes for each keyword on the Google search engine as proxy indicators of public awareness. We then calculated the monthly percentage change in the search volumes using the Joinpoint Regression Program. Results: Apparent peaks in search volumes were observed immediately after policy changes. Especially, policy changes in 2010 were followed by a remarkable peak in search volumes for both overwork and working hours, with the search volumes for overwork increased by 29% per month from June 2010 to March 2011. This increase was preceded by the implementation of new overwork recognition guidelines and media reports of several suspected overwork-related events. The search volumes for working hours also steadily increased, by 2% per month in September 2013 and afterward, reaching a peak in January 2017. The peak was likely due to the amendment to the Labor Standards Act, which called for ?1 fixed and 1 flexible day off per week,? in 2016. The search volumes for job stress significantly increased (P=.026) but only by 0.4% per month since March 2013. Conclusions: Over the past 15 years, Taiwanese authorities have revised and implemented several policies to prevent overwork-related health problems. Our study suggests a relationship between the implementation of policies that clearly defined the criteria for overwork and working hours and the rising public awareness of the importance of overwork prevention and shorter working hours. UR - https://www.jmir.org/2020/6/e18181 UR - http://dx.doi.org/10.2196/18181 UR - http://www.ncbi.nlm.nih.gov/pubmed/32589160 ID - info:doi/10.2196/18181 ER - TY - JOUR AU - Anwar, Mohd AU - Khoury, Dalia AU - Aldridge, P. Arnie AU - Parker, J. Stephanie AU - Conway, P. Kevin PY - 2020/6/24 TI - Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study JO - JMIR Public Health Surveill SP - e17574 VL - 6 IS - 2 KW - opioids KW - surveillance KW - social media N2 - Background: Over the last two decades, deaths associated with opioids have escalated in number and geographic spread, impacting more and more individuals, families, and communities. Reflecting on the shifting nature of the opioid overdose crisis, Dasgupta, Beletsky, and Ciccarone offer a triphasic framework to explain that opioid overdose deaths (OODs) shifted from prescription opioids for pain (beginning in 2000), to heroin (2010 to 2015), and then to synthetic opioids (beginning in 2013). Given the rapidly shifting nature of OODs, timelier surveillance data are critical to inform strategies that combat the opioid crisis. Using easily accessible and near real-time social media data to improve public health surveillance efforts related to the opioid crisis is a promising area of research. Objective: This study explored the potential of using Twitter data to monitor the opioid epidemic. Specifically, this study investigated the extent to which the content of opioid-related tweets corresponds with the triphasic nature of the opioid crisis and correlates with OODs in North Carolina between 2009 and 2017. Methods: Opioid-related Twitter posts were obtained using Crimson Hexagon, and were classified as relating to prescription opioids, heroin, and synthetic opioids using natural language processing. This process resulted in a corpus of 100,777 posts consisting of tweets, retweets, mentions, and replies. Using a random sample of 10,000 posts from the corpus, we identified opioid-related terms by analyzing word frequency for each year. OODs were obtained from the Multiple Cause of Death database from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Least squares regression and Granger tests compared patterns of opioid-related posts with OODs. Results: The pattern of tweets related to prescription opioids, heroin, and synthetic opioids resembled the triphasic nature of OODs. For prescription opioids, tweet counts and OODs were statistically unrelated. Tweets mentioning heroin and synthetic opioids were significantly associated with heroin OODs and synthetic OODs in the same year (P=.01 and P<.001, respectively), as well as in the following year (P=.03 and P=.01, respectively). Moreover, heroin tweets in a given year predicted heroin deaths better than lagged heroin OODs alone (P=.03). Conclusions: Findings support using Twitter data as a timely indicator of opioid overdose mortality, especially for heroin. UR - http://publichealth.jmir.org/2020/2/e17574/ UR - http://dx.doi.org/10.2196/17574 UR - http://www.ncbi.nlm.nih.gov/pubmed/32469322 ID - info:doi/10.2196/17574 ER - TY - JOUR AU - Stevens, Robin AU - Bonett, Stephen AU - Bannon, Jacqueline AU - Chittamuru, Deepti AU - Slaff, Barry AU - Browne, K. Safa AU - Huang, Sarah AU - Bauermeister, A. José PY - 2020/6/24 TI - Association Between HIV-Related Tweets and HIV Incidence in the United States: Infodemiology Study JO - J Med Internet Res SP - e17196 VL - 22 IS - 6 KW - HIV/AIDS KW - social media KW - youth KW - natural language processing KW - surveillance N2 - Background: Adolescents and young adults in the age range of 13-24 years are at the highest risk of developing HIV infections. As social media platforms are extremely popular among youths, researchers can utilize these platforms to curb the HIV epidemic by investigating the associations between the discourses on HIV infections and the epidemiological data of HIV infections. Objective: The goal of this study was to examine how Twitter activity among young men is related to the incidence of HIV infection in the population. Methods: We used integrated human-computer techniques to characterize the HIV-related tweets by male adolescents and young male adults (age range: 13-24 years). We identified tweets related to HIV risk and prevention by using natural language processing (NLP). Our NLP algorithm identified 89.1% (2243/2517) relevant tweets, which were manually coded by expert coders. We coded 1577 HIV-prevention tweets and 17.5% (940/5372) of general sex-related tweets (including emojis, gifs, and images), and we achieved reliability with intraclass correlation at 0.80 or higher on key constructs. Bivariate and multivariate analyses were performed to identify the spatial patterns in posting HIV-related tweets as well as the relationships between the tweets and local HIV infection rates. Results: We analyzed 2517 tweets that were identified as relevant to HIV risk and prevention tags; these tweets were geolocated in 109 counties throughout the United States. After adjusting for region, HIV prevalence, and social disadvantage index, our findings indicated that every 100-tweet increase in HIV-specific tweets per capita from noninstitutional accounts was associated with a multiplicative effect of 0.97 (95% CI [0.94-1.00]; P=.04) on the incidence of HIV infections in the following year in a given county. Conclusions: Twitter may serve as a proxy of public behavior related to HIV infections, and the association between the number of HIV-related tweets and HIV infection rates further supports the use of social media for HIV disease prevention. UR - https://www.jmir.org/2020/6/e17196 UR - http://dx.doi.org/10.2196/17196 UR - http://www.ncbi.nlm.nih.gov/pubmed/32579119 ID - info:doi/10.2196/17196 ER - TY - JOUR AU - Chen, Long AU - Lu, Xinyi AU - Yuan, Jianbo AU - Luo, Joyce AU - Luo, Jiebo AU - Xie, Zidian AU - Li, Dongmei PY - 2020/6/22 TI - A Social Media Study on the Associations of Flavored Electronic Cigarettes With Health Symptoms: Observational Study JO - J Med Internet Res SP - e17496 VL - 22 IS - 6 KW - e-cigarette KW - social media KW - eHealth N2 - Background: In recent years, flavored electronic cigarettes (e-cigarettes) have become popular among teenagers and young adults. Discussions about e-cigarettes and e-cigarette use (vaping) experiences are prevalent online, making social media an ideal resource for understanding the health risks associated with e-cigarette flavors from the users? perspective. Objective: This study aimed to investigate the potential associations between electronic cigarette liquid (e-liquid) flavors and the reporting of health symptoms using social media data. Methods: A dataset consisting of 2.8 million e-cigarette?related posts was collected using keyword filtering from Reddit, a social media platform, from January 2013 to April 2019. Temporal analysis for nine major health symptom categories was used to understand the trend of public concerns related to e-cigarettes. Sentiment analysis was conducted to obtain the proportions of positive and negative sentiment scores for all reported health symptom categories. Topic modeling was applied to reveal the topics related to e-cigarettes and health symptoms. Furthermore, generalized estimating equation (GEE) models were used to quantitatively measure potential associations between e-liquid flavors and the reporting of health symptoms. Results: Temporal analysis showed that the Respiratory category was consistently the most discussed health symptom category among all categories related to e-cigarettes on Reddit, followed by the Throat category. Sentiment analysis showed higher proportions of positive sentiment scores for all reported health symptom categories, except for the Cancer category. Topic modeling conducted on all health-related posts showed that 17 of the top 100 topics were flavor related. GEE models showed different associations between the reporting of health symptoms and e-liquid flavor categories, for example, lower association of the Beverage flavors with Respiratory compared with other flavors and higher association of the Fruit flavors with Cardiovascular than other flavors. Conclusions: This study identified different potential associations between e-liquid flavors and the reporting of health symptoms using social media data. The results of this study provide valuable information for further investigation of the health effects associated with different e-liquid flavors. UR - http://www.jmir.org/2020/6/e17496/ UR - http://dx.doi.org/10.2196/17496 UR - http://www.ncbi.nlm.nih.gov/pubmed/32568093 ID - info:doi/10.2196/17496 ER - TY - JOUR AU - Wahbeh, Abdullah AU - Nasralah, Tareq AU - Al-Ramahi, Mohammad AU - El-Gayar, Omar PY - 2020/6/18 TI - Mining Physicians? Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis JO - JMIR Public Health Surveill SP - e19276 VL - 6 IS - 2 KW - pandemic KW - coronavirus KW - COVID-19 KW - social media KW - infodemiology KW - infoveillance KW - medical professionals KW - opinion analysis N2 - Background: The coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control this pandemic. Objective: The objective of this paper was to identify topics, opinions, and recommendations about the COVID-19 pandemic discussed by medical professionals on the Twitter social medial platform. Methods: Using a mixed methods approach blending the capabilities of social media analytics and qualitative analysis, we analyzed COVID-19?related tweets posted by medical professionals and examined their content. We used qualitative analysis to explore the collected data to identify relevant tweets and uncover important concepts about the pandemic using qualitative coding. Unsupervised and supervised machine learning techniques and text analysis were used to identify topics and opinions. Results: Data were collected from 119 medical professionals on Twitter about the coronavirus pandemic. A total of 10,096 English tweets were collected from the identified medical professionals between December 1, 2019 and April 1, 2020. We identified eight topics, namely actions and recommendations, fighting misinformation, information and knowledge, the health care system, symptoms and illness, immunity, testing, and infection and transmission. The tweets mainly focused on needed actions and recommendations (2827/10,096, 28%) to control the pandemic. Many tweets warned about misleading information (2019/10,096, 20%) that could lead to infection of more people with the virus. Other tweets discussed general knowledge and information (911/10,096, 9%) about the virus as well as concerns about the health care systems and workers (909/10,096, 9%). The remaining tweets discussed information about symptoms associated with COVID-19 (810/10,096, 8%), immunity (707/10,096, 7%), testing (605/10,096, 6%), and virus infection and transmission (503/10,096, 5%). Conclusions: Our findings indicate that Twitter and social media platforms can help identify important and useful knowledge shared by medical professionals during a pandemic. UR - http://publichealth.jmir.org/2020/2/e19276/ UR - http://dx.doi.org/10.2196/19276 UR - http://www.ncbi.nlm.nih.gov/pubmed/32421686 ID - info:doi/10.2196/19276 ER - TY - JOUR AU - Tao, Zhuo-Ying AU - Chu, Guang AU - McGrath, Colman AU - Hua, Fang AU - Leung, Yan Yiu AU - Yang, Wei-Fa AU - Su, Yu-Xiong PY - 2020/6/15 TI - Nature and Diffusion of COVID-19?related Oral Health Information on Chinese Social Media: Analysis of Tweets on Weibo JO - J Med Internet Res SP - e19981 VL - 22 IS - 6 KW - COVID-19 KW - dentistry KW - oral health KW - online health KW - social media KW - tweet KW - Weibo KW - China KW - health information N2 - Background: Social media has become increasingly important as a source of information for the public and is widely used for health-related information. The outbreak of the coronavirus disease (COVID-19) has exerted a negative impact on dental practices. Objective: The aim of this study is to analyze the nature and diffusion of COVID-19?related oral health information on the Chinese social media site Weibo. Methods: A total of 15,900 tweets related to oral health and dentistry information from Weibo during the COVID-19 outbreak in China (December 31, 2019, to March 16, 2020) were included in our study. Two researchers coded 1000 of the total tweets in advance, and two main thematic categories with eight subtypes were refined. The included tweets were analyzed over time and geographic region, and coded into eight thematic categories. Additionally, the time distributions of tweets containing information about dental services, needs of dental treatment, and home oral care during the COVID-19 epidemic were further analyzed. Results: People reacted rapidly to the emerging severe acute respiratory syndrome coronavirus 2 threat to dental services, and a large amount of COVID-19?related oral health information was tweeted on Weibo. The time and geographic distribution of tweets shared similarities with epidemiological data of the COVID-19 outbreak in China. Tweets containing home oral care and dental services content were the most frequently exchanged information (n=4803/15,900, 30.20% and n=4478, 28.16%, respectively). Significant differences of public attention were found between various types of bloggers in dental services?related tweets (P<.001), and the tweets from the government and media engaged the most public attention. The distributions of tweets containing information about dental services, needs of dental treatment, and home oral care information dynamically changed with time. Conclusions: Our study overviewed and analyzed social media data on the dental services and oral health information during the COVID-19 epidemic, thus, providing insights for government organizations, media, and dental professionals to better facilitate oral health communication and efficiently shape public concern through social media when routine dental services are unavailable during an unprecedented event. The study of the nature and distribution of social media can serve as a useful adjunct tool to help make public health policies. UR - http://www.jmir.org/2020/6/e19981/ UR - http://dx.doi.org/10.2196/19981 UR - http://www.ncbi.nlm.nih.gov/pubmed/32501808 ID - info:doi/10.2196/19981 ER - TY - JOUR AU - Mackey, Tim AU - Purushothaman, Vidya AU - Li, Jiawei AU - Shah, Neal AU - Nali, Matthew AU - Bardier, Cortni AU - Liang, Bryan AU - Cai, Mingxiang AU - Cuomo, Raphael PY - 2020/6/8 TI - Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study JO - JMIR Public Health Surveill SP - e19509 VL - 6 IS - 2 KW - infoveillance KW - COVID-19 KW - Twitter KW - machine learning KW - surveillance N2 - Background: The coronavirus disease (COVID-19) pandemic is a global health emergency with over 6 million cases worldwide as of the beginning of June 2020. The pandemic is historic in scope and precedent given its emergence in an increasingly digital era. Importantly, there have been concerns about the accuracy of COVID-19 case counts due to issues such as lack of access to testing and difficulty in measuring recoveries. Objective: The aims of this study were to detect and characterize user-generated conversations that could be associated with COVID-19-related symptoms, experiences with access to testing, and mentions of disease recovery using an unsupervised machine learning approach. Methods: Tweets were collected from the Twitter public streaming application programming interface from March 3-20, 2020, filtered for general COVID-19-related keywords and then further filtered for terms that could be related to COVID-19 symptoms as self-reported by users. Tweets were analyzed using an unsupervised machine learning approach called the biterm topic model (BTM), where groups of tweets containing the same word-related themes were separated into topic clusters that included conversations about symptoms, testing, and recovery. Tweets in these clusters were then extracted and manually annotated for content analysis and assessed for their statistical and geographic characteristics. Results: A total of 4,492,954 tweets were collected that contained terms that could be related to COVID-19 symptoms. After using BTM to identify relevant topic clusters and removing duplicate tweets, we identified a total of 3465 (<1%) tweets that included user-generated conversations about experiences that users associated with possible COVID-19 symptoms and other disease experiences. These tweets were grouped into five main categories including first- and secondhand reports of symptoms, symptom reporting concurrent with lack of testing, discussion of recovery, confirmation of negative COVID-19 diagnosis after receiving testing, and users recalling symptoms and questioning whether they might have been previously infected with COVID-19. The co-occurrence of tweets for these themes was statistically significant for users reporting symptoms with a lack of testing and with a discussion of recovery. A total of 63% (n=1112) of the geotagged tweets were located in the United States. Conclusions: This study used unsupervised machine learning for the purposes of characterizing self-reporting of symptoms, experiences with testing, and mentions of recovery related to COVID-19. Many users reported symptoms they thought were related to COVID-19, but they were not able to get tested to confirm their concerns. In the absence of testing availability and confirmation, accurate case estimations for this period of the outbreak may never be known. Future studies should continue to explore the utility of infoveillance approaches to estimate COVID-19 disease severity. UR - http://publichealth.jmir.org/2020/2/e19509/ UR - http://dx.doi.org/10.2196/19509 UR - http://www.ncbi.nlm.nih.gov/pubmed/32490846 ID - info:doi/10.2196/19509 ER - TY - JOUR AU - Jo, Wonkwang AU - Lee, Jaeho AU - Park, Junli AU - Kim, Yeol PY - 2020/6/2 TI - Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis JO - J Med Internet Res SP - e19455 VL - 22 IS - 6 KW - coronavirus KW - anxiety KW - pandemic KW - online KW - health information exchange KW - topic modeling N2 - Background: In case of a population-wide infectious disease outbreak, such as the novel coronavirus disease (COVID-19), people?s online activities could significantly affect public concerns and health behaviors due to difficulty in accessing credible information from reliable sources, which in turn causes people to seek necessary information on the web. Therefore, measuring and analyzing online health communication and public sentiment is essential for establishing effective and efficient disease control policies, especially in the early stage of an outbreak. Objective: This study aimed to investigate the trends of online health communication, analyze the focus of people?s anxiety in the early stages of COVID-19, and evaluate the appropriateness of online information. Methods: We collected 13,148 questions and 29,040 answers related to COVID-19 from Naver, the most popular Korean web portal (January 20, 2020, to March 2, 2020). Three main methods were used in this study: (1) the structural topic model was used to examine the topics in the online questions; (2) word network analysis was conducted to analyze the focus of people?s anxiety and worry in the questions; and (3) two medical doctors assessed the appropriateness of the answers to the questions, which were primarily related to people?s anxiety. Results: A total of 50 topics and 6 cohesive topic communities were identified from the questions. Among them, topic community 4 (suspecting COVID-19 infection after developing a particular symptom) accounted for the largest portion of the questions. As the number of confirmed patients increased, the proportion of topics belonging to topic community 4 also increased. Additionally, the prolonged situation led to a slight increase in the proportion of topics related to job issues. People?s anxieties and worries were closely related with physical symptoms and self-protection methods. Although relatively appropriate to suspect physical symptoms, a high proportion of answers related to self-protection methods were assessed as misinformation or advertisements. Conclusions: Search activity for online information regarding the COVID-19 outbreak has been active. Many of the online questions were related to people?s anxieties and worries. A considerable portion of corresponding answers had false information or were advertisements. The study results could contribute reference information to various countries that need to monitor public anxiety and provide appropriate information in the early stage of an infectious disease outbreak, including COVID-19. Our research also contributes to developing methods for measuring public opinion and sentiment in an epidemic situation based on natural language data on the internet. UR - https://www.jmir.org/2020/6/e19455 UR - http://dx.doi.org/10.2196/19455 UR - http://www.ncbi.nlm.nih.gov/pubmed/32463367 ID - info:doi/10.2196/19455 ER - TY - JOUR AU - Jacobson, C. Nicholas AU - Lekkas, Damien AU - Price, George AU - Heinz, V. Michael AU - Song, Minkeun AU - O?Malley, James A. AU - Barr, J. Paul PY - 2020/6/1 TI - Flattening the Mental Health Curve: COVID-19 Stay-at-Home Orders Are Associated With Alterations in Mental Health Search Behavior in the United States JO - JMIR Ment Health SP - e19347 VL - 7 IS - 6 KW - COVID-19 KW - coronavirus KW - stay-at-home orders KW - mental health KW - suicide KW - anxiety KW - infodemiology KW - infoveillance KW - search trends KW - health information needs N2 - Background: The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people?s everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health. Objective: The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale. Methods: In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation. Results: Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety. Conclusions: These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders. UR - https://mental.jmir.org/2020/6/e19347 UR - http://dx.doi.org/10.2196/19347 UR - http://www.ncbi.nlm.nih.gov/pubmed/32459186 ID - info:doi/10.2196/19347 ER - TY - JOUR AU - Chen, Emily AU - Lerman, Kristina AU - Ferrara, Emilio PY - 2020/5/29 TI - Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set JO - JMIR Public Health Surveill SP - e19273 VL - 6 IS - 2 KW - COVID-19 KW - SARS-CoV-2 KW - social media KW - network analysis KW - computational social sciences N2 - Background: At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. Objective: In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. Methods: We started this ongoing data collection on January 28, 2020, leveraging Twitter?s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter?s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. Results: Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. Conclusions: It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic?and undoubtedly more. UR - http://publichealth.jmir.org/2020/2/e19273/ UR - http://dx.doi.org/10.2196/19273 UR - http://www.ncbi.nlm.nih.gov/pubmed/32427106 ID - info:doi/10.2196/19273 ER - TY - JOUR AU - Shen, Cuihua AU - Chen, Anfan AU - Luo, Chen AU - Zhang, Jingwen AU - Feng, Bo AU - Liao, Wang PY - 2020/5/28 TI - Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study JO - J Med Internet Res SP - e19421 VL - 22 IS - 5 KW - COVID-19 KW - SARS-CoV-2 KW - novel coronavirus KW - infectious disease KW - social media KW - Weibo KW - China KW - disease surveillance KW - surveillance KW - infoveillance KW - infodemiology N2 - Background: Coronavirus disease (COVID-19) has affected more than 200 countries and territories worldwide. This disease poses an extraordinary challenge for public health systems because screening and surveillance capacity is often severely limited, especially during the beginning of the outbreak; this can fuel the outbreak, as many patients can unknowingly infect other people. Objective: The aim of this study was to collect and analyze posts related to COVID-19 on Weibo, a popular Twitter-like social media site in China. To our knowledge, this infoveillance study employs the largest, most comprehensive, and most fine-grained social media data to date to predict COVID-19 case counts in mainland China. Methods: We built a Weibo user pool of 250 million people, approximately half the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19?related posts from our user pool from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify ?sick posts,? in which users report their own or other people?s symptoms and diagnoses related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China. Results: We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline. Conclusions: Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance. UR - http://www.jmir.org/2020/5/e19421/ UR - http://dx.doi.org/10.2196/19421 UR - http://www.ncbi.nlm.nih.gov/pubmed/32452804 ID - info:doi/10.2196/19421 ER - TY - JOUR AU - Liao, Qiuyan AU - Yuan, Jiehu AU - Dong, Meihong AU - Yang, Lin AU - Fielding, Richard AU - Lam, Tak Wendy Wing PY - 2020/5/26 TI - Public Engagement and Government Responsiveness in the Communications About COVID-19 During the Early Epidemic Stage in China: Infodemiology Study on Social Media Data JO - J Med Internet Res SP - e18796 VL - 22 IS - 5 KW - risk communication KW - social media KW - epidemic KW - COVID-19 KW - pandemic KW - outbreak KW - infectious disease KW - content analysis N2 - Background: Effective risk communication about the outbreak of a newly emerging infectious disease in the early stage is critical for managing public anxiety and promoting behavioral compliance. China has experienced the unprecedented epidemic of the coronavirus disease (COVID-19) in an era when social media has fundamentally transformed information production and consumption patterns. Objective: This study examined public engagement and government responsiveness in the communications about COVID-19 during the early epidemic stage based on an analysis of data from Sina Weibo, a major social media platform in China. Methods: Weibo data relevant to COVID-19 from December 1, 2019, to January 31, 2020, were retrieved. Engagement data (likes, comments, shares, and followers) of posts from government agency accounts were extracted to evaluate public engagement with government posts online. Content analyses were conducted for a random subset of 644 posts from personal accounts of individuals, and 273 posts from 10 relatively more active government agency accounts and the National Health Commission of China to identify major thematic contents in online discussions. Latent class analysis further explored main content patterns, and chi-square for trend examined how proportions of main content patterns changed by time within the study time frame. Results: The public response to COVID-19 seemed to follow the spread of the disease and government actions but was earlier for Weibo than the government. Online users generally had low engagement with posts relevant to COVID-19 from government agency accounts. The common content patterns identified in personal and government posts included sharing epidemic situations; general knowledge of the new disease; and policies, guidelines, and official actions. However, personal posts were more likely to show empathy to affected people (?21=13.3, P<.001), attribute blame to other individuals or government (?21=28.9, P<.001), and express worry about the epidemic (?21=32.1, P<.001), while government posts were more likely to share instrumental support (?21=32.5, P<.001) and praise people or organizations (?21=8.7, P=.003). As the epidemic evolved, sharing situation updates (for trend, ?21=19.7, P<.001) and policies, guidelines, and official actions (for trend, ?21=15.3, P<.001) became less frequent in personal posts but remained stable or increased significantly in government posts. Moreover, as the epidemic evolved, showing empathy and attributing blame (for trend, ?21=25.3, P<.001) became more frequent in personal posts, corresponding to a slight increase in sharing instrumental support, praising, and empathizing in government posts (for trend, ?21=9.0, P=.003). Conclusions: The government should closely monitor social media data to improve the timing of communications about an epidemic. As the epidemic evolves, merely sharing situation updates and policies may be insufficient to capture public interest in the messages. The government may adopt a more empathic communication style as more people are affected by the disease to address public concerns. UR - http://www.jmir.org/2020/5/e18796/ UR - http://dx.doi.org/10.2196/18796 UR - http://www.ncbi.nlm.nih.gov/pubmed/32412414 ID - info:doi/10.2196/18796 ER - TY - JOUR AU - Lwin, Oo May AU - Lu, Jiahui AU - Sheldenkar, Anita AU - Schulz, Johannes Peter AU - Shin, Wonsun AU - Gupta, Raj AU - Yang, Yinping PY - 2020/5/22 TI - Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends JO - JMIR Public Health Surveill SP - e19447 VL - 6 IS - 2 KW - COVID-19 KW - Twitter KW - pandemic KW - social sentiments KW - emotions KW - infodemic N2 - Background: With the World Health Organization?s pandemic declaration and government-initiated actions against coronavirus disease (COVID-19), sentiments surrounding COVID-19 have evolved rapidly. Objective: This study aimed to examine worldwide trends of four emotions?fear, anger, sadness, and joy?and the narratives underlying those emotions during the COVID-19 pandemic. Methods: Over 20 million social media twitter posts made during the early phases of the COVID-19 outbreak from January 28 to April 9, 2020, were collected using ?wuhan,? ?corona,? ?nCov,? and ?covid? as search keywords. Results: Public emotions shifted strongly from fear to anger over the course of the pandemic, while sadness and joy also surfaced. Findings from word clouds suggest that fears around shortages of COVID-19 tests and medical supplies became increasingly widespread discussion points. Anger shifted from xenophobia at the beginning of the pandemic to discourse around the stay-at-home notices. Sadness was highlighted by the topics of losing friends and family members, while topics related to joy included words of gratitude and good health. Conclusions: Overall, global COVID-19 sentiments have shown rapid evolutions within just the span of a few weeks. Findings suggest that emotion-driven collective issues around shared public distress experiences of the COVID-19 pandemic are developing and include large-scale social isolation and the loss of human lives. The steady rise of societal concerns indicated by negative emotions needs to be monitored and controlled by complementing regular crisis communication with strategic public health communication that aims to balance public psychological wellbeing. UR - http://publichealth.jmir.org/2020/2/e19447/ UR - http://dx.doi.org/10.2196/19447 UR - http://www.ncbi.nlm.nih.gov/pubmed/32412418 ID - info:doi/10.2196/19447 ER - TY - JOUR AU - Huang, Chunmei AU - Xu, Xinjie AU - Cai, Yuyang AU - Ge, Qinmin AU - Zeng, Guangwang AU - Li, Xiaopan AU - Zhang, Weide AU - Ji, Chen AU - Yang, Ling PY - 2020/5/17 TI - Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts JO - J Med Internet Res SP - e19087 VL - 22 IS - 5 KW - SARS-CoV-2 KW - COVID-19 KW - coronavirus disease KW - social media KW - Sina Weibo KW - help N2 - Background: In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help. Objective: This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo. Methods: We conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients? home and the nearest designated hospital was calculated using the geographic information system ArcGIS. Results: All patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12%) was the most common symptom. Ground-glass opacity (237/314, 75.48%) was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital. Conclusions: Our findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan?s lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help. UR - http://www.jmir.org/2020/5/e19087/ UR - http://dx.doi.org/10.2196/19087 UR - http://www.ncbi.nlm.nih.gov/pubmed/32401210 ID - info:doi/10.2196/19087 ER - TY - JOUR AU - King, Catherine AU - Judge, Ciaran AU - Byrne, Aideen AU - Conlon, Niall PY - 2020/5/13 TI - Googling Allergy in Ireland: Content Analysis JO - J Med Internet Res SP - e16763 VL - 22 IS - 5 KW - allergy KW - food allergy KW - food intolerance KW - technology KW - Ireland KW - immunology N2 - Background: Internet search engines are increasingly being utilized as the first port of call for medical information by the public. The prevalence of allergies in developed countries has risen steadily over time. There exists significant variability in the quality of health-related information available on the web. Inaccurately diagnosed and mismanaged allergic disease has major downstream effects on patients, general practitioners, and regional allergy services. Objective: This study aimed to verify whether Ireland has a relatively high rate of web-based allergy-related searches, to establish the proportion of medically accurate web pages encountered by the public, and to compare current search results localized to Dublin, Ireland with urban centers elsewhere. Methods: Google Trends was used to evaluate regional interest of allergy-related search terms over a 10-year period using terms ?allergy,? ?allergy test,? ?food allergy,? and ?food intolerance.? These terms were then inputted into Google search, localizing them to cities in Ireland, the United Kingdom, and the United States. Output for each search was reviewed by two independent clinicians and deemed rational or nonevidence based, as per current best practice guidelines. Searches localized to Dublin were initially completed in 2015 and repeated in 2019 to assess for changes in the quality of search results over time. Results: Ireland has a persistently high demand for web-based information relating to allergy and ranks first worldwide for ?allergy test,? second for ?food allergy? and ?food intolerance,? and seventh for ?allergy? over the specified 10-year timeframe. Results for each of the four subsearches in Dublin (2015) showed that over 60% of websites promoted nonevidence-based diagnostics. A marginal improvement in scientifically robust information was seen in 2019, but results for ?allergy test? and ?food intolerance? continued to promote alternative testing 57% (8/14) of the time. This strongly contrasted with results localized to Southampton and Rochester, where academic and hospital-affiliated web pages predominantly featured. Government-funded Department of Health websites did not feature in the top five results for Dublin searches ?allergy testing,? ?food allergy,? or ?food intolerance? in either 2015 or 2019. Conclusions: The Irish public demonstrates a keen interest in seeking allergy-related information on the web. The proportion of evidence-based websites encountered by the Irish public is considerably lower than that encountered by patients in other urban centers. Factors contributing to this are the lack of a specialist register for allergy in Ireland, inadequate funding for allergy centers currently in operation, and insufficient promotion by the health service of their web-based health database, which contains useful patient-oriented information on allergy. Increased funding of clinical allergology services will more meaningfully impact the health of patients if there is a parallel investment by the health service in information and communication technology consultancy to amplify their presence on the web. UR - https://www.jmir.org/2020/5/e16763 UR - http://dx.doi.org/10.2196/16763 UR - http://www.ncbi.nlm.nih.gov/pubmed/32401220 ID - info:doi/10.2196/16763 ER - TY - JOUR AU - Ahmed, Wasim AU - Vidal-Alaball, Josep AU - Downing, Joseph AU - López Seguí, Francesc PY - 2020/5/6 TI - COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data JO - J Med Internet Res SP - e19458 VL - 22 IS - 5 KW - COVID-19 KW - coronavirus KW - twitter KW - misinformation KW - fake news KW - 5G KW - social network analysis KW - social media KW - public health KW - pandemic N2 - Background: Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. Objective: The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. Methods: This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph?s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. Results: Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. Conclusions: The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future. UR - http://www.jmir.org/2020/5/e19458/ UR - http://dx.doi.org/10.2196/19458 UR - http://www.ncbi.nlm.nih.gov/pubmed/32352383 ID - info:doi/10.2196/19458 ER - TY - JOUR AU - Budhwani, Henna AU - Sun, Ruoyan PY - 2020/5/6 TI - Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the ?Chinese virus? on Twitter: Quantitative Analysis of Social Media Data JO - J Med Internet Res SP - e19301 VL - 22 IS - 5 KW - COVID-19 KW - coronavirus KW - Twitter KW - stigma KW - social media KW - public health N2 - Background: Stigma is the deleterious, structural force that devalues members of groups that hold undesirable characteristics. Since stigma is created and reinforced by society?through in-person and online social interactions?referencing the novel coronavirus as the ?Chinese virus? or ?China virus? has the potential to create and perpetuate stigma. Objective: The aim of this study was to assess if there was an increase in the prevalence and frequency of the phrases ?Chinese virus? and ?China virus? on Twitter after the March 16, 2020, US presidential reference of this term. Methods: Using the Sysomos software (Sysomos, Inc), we extracted tweets from the United States using a list of keywords that were derivatives of ?Chinese virus.? We compared tweets at the national and state levels posted between March 9 and March 15 (preperiod) with those posted between March 19 and March 25 (postperiod). We used Stata 16 (StataCorp) for quantitative analysis, and Python (Python Software Foundation) to plot a state-level heat map. Results: A total of 16,535 ?Chinese virus? or ?China virus? tweets were identified in the preperiod, and 177,327 tweets were identified in the postperiod, illustrating a nearly ten-fold increase at the national level. All 50 states witnessed an increase in the number of tweets exclusively mentioning ?Chinese virus? or ?China virus? instead of coronavirus disease (COVID-19) or coronavirus. On average, 0.38 tweets referencing ?Chinese virus? or ?China virus? were posted per 10,000 people at the state level in the preperiod, and 4.08 of these stigmatizing tweets were posted in the postperiod, also indicating a ten-fold increase. The 5 states with the highest number of postperiod ?Chinese virus? tweets were Pennsylvania (n=5249), New York (n=11,754), Florida (n=13,070), Texas (n=14,861), and California (n=19,442). Adjusting for population size, the 5 states with the highest prevalence of postperiod ?Chinese virus? tweets were Arizona (5.85), New York (6.04), Florida (6.09), Nevada (7.72), and Wyoming (8.76). The 5 states with the largest increase in pre- to postperiod ?Chinese virus? tweets were Kansas (n=697/58, 1202%), South Dakota (n=185/15, 1233%), Mississippi (n=749/54, 1387%), New Hampshire (n=582/41, 1420%), and Idaho (n=670/46, 1457%). Conclusions: The rise in tweets referencing ?Chinese virus? or ?China virus,? along with the content of these tweets, indicate that knowledge translation may be occurring online and COVID-19 stigma is likely being perpetuated on Twitter. UR - http://www.jmir.org/2020/5/e19301/ UR - http://dx.doi.org/10.2196/19301 UR - http://www.ncbi.nlm.nih.gov/pubmed/32343669 ID - info:doi/10.2196/19301 ER - TY - JOUR AU - Rovetta, Alessandro AU - Bhagavathula, Srikanth Akshaya PY - 2020/5/5 TI - COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study JO - JMIR Public Health Surveill SP - e19374 VL - 6 IS - 2 KW - novel coronavirus, COVID-19, Google search KW - Google Trends KW - infodemiology, infodemic monikers, Italy KW - behavior KW - public health KW - communication KW - digital health KW - online search N2 - Background: Since the beginning of the novel coronavirus disease (COVID-19) outbreak, fake news and misleading information have circulated worldwide, which can profoundly affect public health communication. Objective: We investigated online search behavior related to the COVID-19 outbreak and the attitudes of ?infodemic monikers? (ie, erroneous information that gives rise to interpretative mistakes, fake news, episodes of racism, etc) circulating in Italy. Methods: By using Google Trends to explore the internet search activity related to COVID-19 from January to March 2020, article titles from the most read newspapers and government websites were mined to investigate the attitudes of infodemic monikers circulating across various regions and cities in Italy. Search volume values and average peak comparison (APC) values were used to analyze the results. Results: Keywords such as ?novel coronavirus,? ?China coronavirus,? ?COVID-19,? ?2019-nCOV,? and ?SARS-COV-2? were the top infodemic and scientific COVID-19 terms trending in Italy. The top five searches related to health were ?face masks,? ?amuchina? (disinfectant), ?symptoms of the novel coronavirus,? ?health bulletin,? and ?vaccines for coronavirus.? The regions of Umbria and Basilicata recorded a high number of infodemic monikers (APC weighted total >140). Misinformation was widely circulated in the Campania region, and racism-related information was widespread in Umbria and Basilicata. These monikers were frequently searched (APC weighted total >100) in more than 10 major cities in Italy, including Rome. Conclusions: We identified a growing regional and population-level interest in COVID-19 in Italy. The majority of searches were related to amuchina, face masks, health bulletins, and COVID-19 symptoms. Since a large number of infodemic monikers were observed across Italy, we recommend that health agencies use Google Trends to predict human behavior as well as to manage misinformation circulation in Italy. UR - http://publichealth.jmir.org/2020/2/e19374/ UR - http://dx.doi.org/10.2196/19374 UR - http://www.ncbi.nlm.nih.gov/pubmed/32338613 ID - info:doi/10.2196/19374 ER - TY - JOUR AU - Park, Woo Han AU - Park, Sejung AU - Chong, Miyoung PY - 2020/5/5 TI - Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea JO - J Med Internet Res SP - e18897 VL - 22 IS - 5 KW - infodemiology KW - COVID-19 KW - SARS-CoV-2 KW - coronavirus KW - Twitter KW - South Korea KW - medical news KW - social media KW - pandemic KW - outbreak KW - infectious disease KW - public health N2 - Background: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations. Objective: Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak. Methods: Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources. Results: The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word ?Coronavirus? communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers? attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852). Conclusions: Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes. UR - http://www.jmir.org/2020/5/e18897/ UR - http://dx.doi.org/10.2196/18897 UR - http://www.ncbi.nlm.nih.gov/pubmed/32325426 ID - info:doi/10.2196/18897 ER - TY - JOUR AU - Liu, Qian AU - Zheng, Zequan AU - Zheng, Jiabin AU - Chen, Qiuyi AU - Liu, Guan AU - Chen, Sihan AU - Chu, Bojia AU - Zhu, Hongyu AU - Akinwunmi, Babatunde AU - Huang, Jian AU - Zhang, P. Casper J. AU - Ming, Wai-Kit PY - 2020/4/28 TI - Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach JO - J Med Internet Res SP - e19118 VL - 22 IS - 4 KW - coronavirus KW - COVID-19 KW - outbreak KW - health communication KW - mass media KW - public crisis KW - topic modeling N2 - Background: In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. Objective: The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. Methods: We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. Results: After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics? themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. Conclusions: Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media?s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data. UR - http://www.jmir.org/2020/4/e19118/ UR - http://dx.doi.org/10.2196/19118 UR - http://www.ncbi.nlm.nih.gov/pubmed/32302966 ID - info:doi/10.2196/19118 ER - TY - JOUR AU - Mavragani, Amaryllis PY - 2020/4/28 TI - Infodemiology and Infoveillance: Scoping Review JO - J Med Internet Res SP - e16206 VL - 22 IS - 4 KW - big data KW - infodemiology KW - infoveillance KW - internet KW - review KW - web-based data N2 - Background: Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade. Objective: The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment. Results: Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%). Conclusions: The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research. UR - http://www.jmir.org/2020/4/e16206/ UR - http://dx.doi.org/10.2196/16206 UR - http://www.ncbi.nlm.nih.gov/pubmed/32310818 ID - info:doi/10.2196/16206 ER - TY - JOUR AU - Wen, Wanting AU - Zhang, Zhu AU - Li, Ziqiang AU - Liang, Jiaqi AU - Zhan, Yongcheng AU - Zeng, D. Daniel AU - Leischow, J. Scott PY - 2020/4/27 TI - Public Reactions to the Cigarette Control Regulation on a Chinese Microblogging Platform: Empirical Analysis JO - J Med Internet Res SP - e14660 VL - 22 IS - 4 KW - cigarette smoking KW - regulations KW - social media KW - information networks N2 - Background: On January 1, 2019, a new regulation on the control of smoking in public places was officially implemented in Hangzhou, China. On the day of the implementation, a large number of Chinese media reported the contents of the regulation on the microblog platform Weibo, causing a strong response from and heated discussion among netizens. Objective: This study aimed to conduct a content and network analysis to examine topics and patterns in the social media response to the new regulation. Methods: We analyzed all microblogs on Weibo that mentioned and explained the regulation in the first 8 days following the implementation. We conducted a content analysis on these microblogs and used social network visualization and descriptive statistics to identify key users and key microblogs. Results: Of 7924 microblogs, 12.85% (1018/7924) were in support of the smoking control regulation, 84.12% (6666/7924) were neutral, and 1.31% (104/7924) were opposed to the smoking regulation control. For the negative posts, the public had doubts about the intentions of the policy, its implementation, and the regulations on electronic cigarettes. In addition, 1.72% (136/7924) were irrelevant to the smoking regulation control. Among the 1043 users who explicitly expressed their positive or negative attitude toward the policy, a large proportion of users showed supportive attitudes (956/1043, 91.66%). A total of 5 topics and 11 subtopics were identified. Conclusions: This study used a content and network analysis to examine topics and patterns in the social media response to the new smoking regulation. We found that the number of posts with a positive attitude toward the regulation was considerably higher than that of the posts with a negative attitude toward the regulation. Our findings may assist public health policy makers to better understand the policy?s intentions, scope, and potential effects on public interest and support evidence-based public health regulations in the future. UR - http://www.jmir.org/2020/4/e14660/ UR - http://dx.doi.org/10.2196/14660 UR - http://www.ncbi.nlm.nih.gov/pubmed/32338615 ID - info:doi/10.2196/14660 ER - TY - JOUR AU - Daughton, R. Ashlynn AU - Chunara, Rumi AU - Paul, J. Michael PY - 2020/4/24 TI - Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study JO - JMIR Public Health Surveill SP - e14986 VL - 6 IS - 2 KW - social media KW - infodemiology KW - influenza, human KW - selection bias KW - bias KW - logistic models N2 - Background: Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. Objective: This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. Methods: This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users? tweets. Results: Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants? tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P?.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P?.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). Conclusions: To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data. UR - http://publichealth.jmir.org/2020/2/e14986/ UR - http://dx.doi.org/10.2196/14986 UR - http://www.ncbi.nlm.nih.gov/pubmed/32329741 ID - info:doi/10.2196/14986 ER - TY - JOUR AU - Li, Jiawei AU - Xu, Qing AU - Cuomo, Raphael AU - Purushothaman, Vidya AU - Mackey, Tim PY - 2020/4/21 TI - Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study JO - JMIR Public Health Surveill SP - e18700 VL - 6 IS - 2 KW - COVID-19 KW - coronavirus KW - infectious disease KW - social media, surveillance KW - infoveillance KW - infodemiology N2 - Background: The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. Objective: The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. Methods: Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. Results: A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P<.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. Conclusions: The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication. UR - http://publichealth.jmir.org/2020/2/e18700/ UR - http://dx.doi.org/10.2196/18700 UR - http://www.ncbi.nlm.nih.gov/pubmed/32293582 ID - info:doi/10.2196/18700 ER - TY - JOUR AU - Abd-Alrazaq, Alaa AU - Alhuwail, Dari AU - Househ, Mowafa AU - Hamdi, Mounir AU - Shah, Zubair PY - 2020/4/21 TI - Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study JO - J Med Internet Res SP - e19016 VL - 22 IS - 4 KW - coronavirus, COVID-19 KW - SARS-CoV-2 KW - 2019-nCov KW - social media KW - public health KW - Twitter KW - infoveillance KW - infodemiology KW - health informatics KW - disease surveillance N2 - Background: The recent coronavirus disease (COVID-19) pandemic is taking a toll on the world?s health care infrastructure as well as the social, economic, and psychological well-being of humanity. Individuals, organizations, and governments are using social media to communicate with each other on a number of issues relating to the COVID-19 pandemic. Not much is known about the topics being shared on social media platforms relating to COVID-19. Analyzing such information can help policy makers and health care organizations assess the needs of their stakeholders and address them appropriately. Objective: This study aims to identify the main topics posted by Twitter users related to the COVID-19 pandemic. Methods: Leveraging a set of tools (Twitter?s search application programming interface (API), Tweepy Python library, and PostgreSQL database) and using a set of predefined search terms (?corona,? ?2019-nCov,? and ?COVID-19?), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) of public English language tweets from February 2, 2020, to March 15, 2020. We analyzed the collected tweets using word frequencies of single (unigrams) and double words (bigrams). We leveraged latent Dirichlet allocation for topic modeling to identify topics discussed in the tweets. We also performed sentiment analysis and extracted the mean number of retweets, likes, and followers for each topic and calculated the interaction rate per topic. Results: Out of approximately 2.8 million tweets included, 167,073 unique tweets from 160,829 unique users met the inclusion criteria. Our analysis identified 12 topics, which were grouped into four main themes: origin of the virus; its sources; its impact on people, countries, and the economy; and ways of mitigating the risk of infection. The mean sentiment was positive for 10 topics and negative for 2 topics (deaths caused by COVID-19 and increased racism). The mean for tweet topics of account followers ranged from 2722 (increased racism) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic loss), while the lowest was 3.94 (travel bans and warnings). Conclusions: Public health crisis response activities on the ground and online are becoming increasingly simultaneous and intertwined. Social media provides an opportunity to directly communicate health information to the public. Health systems should work on building national and international disease detection and surveillance systems through monitoring social media. There is also a need for a more proactive and agile public health presence on social media to combat the spread of fake news. UR - http://www.jmir.org/2020/4/e19016/ UR - http://dx.doi.org/10.2196/19016 UR - http://www.ncbi.nlm.nih.gov/pubmed/32287039 ID - info:doi/10.2196/19016 ER - TY - JOUR AU - Hoyos, R. Luis AU - Putra, Manesha AU - Armstrong, A. Abigail AU - Cheng, Y. Connie AU - Riestenberg, K. Carrie AU - Schooler, A. Tery AU - Dumesic, A. Daniel PY - 2020/4/21 TI - Measures of Patient Dissatisfaction With Health Care in Polycystic Ovary Syndrome: Retrospective Analysis JO - J Med Internet Res SP - e16541 VL - 22 IS - 4 KW - PCOS KW - fibroid KW - Google KW - healthcare quality KW - infoveillance KW - infodemiology KW - medical education KW - health care KW - internet KW - satisfaction N2 - Background: Polycystic ovary syndrome (PCOS) is a common reproductive and metabolic disorder in women; however, many clinicians may not be well versed in scientific advances that aid understanding of the associated reproductive, metabolic, and psychological abnormalities. Women with PCOS are dissatisfied with health care providers, the diagnostic process, and the initial treatment of PCOS and seek information through alternative sources. This has affected the patient-physician relationship by allowing medical information acquired through the internet, whether correct or not, to become accessible to patients and reshape their health care perspective. Patient dissatisfaction with health care providers regarding PCOS raises questions about the responsibilities of academic institutions to adequately train and maintain the competence of clinicians and government agencies to sufficiently support scientific investigation in this field. Objective: The primary aim was to examine internet searching behaviors of the public regarding PCOS vs another highly prevalent gynecologic disorder. The secondary aim was to explore satisfaction with health care among patients with PCOS and their internet use. The tertiary aim was to examine medical education in reproductive endocrinology and infertility (REI) during obstetrics and gynecology (Ob/Gyn) residency as a proxy for physician knowledge in this field. Methods: Google search trends and StoryBase quantified monthly Google absolute search volumes for search terms related to PCOS and fibroids (January 2004 to December 2017; United States). The reproductive disorder, fibroids, was selected as a comparison group because of its high prevalence among women. Between female groups, monthly absolute search volumes and their trends were compared. A Web-based questionnaire (June 2015 to March 2018) explored health care experiences and the internet use of women with PCOS. REI rotation information during Ob/Gyn residency in the United States was obtained from the Association of Professors of Gynecology and Obstetrics website. Results: For PCOS (R=0.89; P<.01), but not fibroids (R=0.09; P=.25), monthly absolute search volumes increased significantly. PCOS-related monthly absolute search volumes (mean 384,423 searches, SD 88,756) were significantly greater than fibroid-related monthly absolute search volumes (mean 348,502 searches, SD 37,317; P<.05). PCOS was diagnosed by an Ob/Gyn in 60.9% (462/759) of patients, and 57.3% (435/759) of patients were dissatisfied with overall care. Among patients with PCOS, 98.2% (716/729) searched for PCOS on the Web but only 18.8% (143/729) of patients joined an online PCOS support group or forum. On average, Ob/Gyn residencies dedicated only 4% (2/43) of total block time to REI, whereas 5.5% (11/200) of such residencies did not offer any REI rotations. Conclusions: Over time, PCOS has been increasingly searched on the Web compared with another highly prevalent gynecologic disorder. Patients with PCOS are dissatisfied with their health care providers, who would benefit from an improved understanding of PCOS during Ob/Gyn residency training. UR - http://www.jmir.org/2020/4/e16541/ UR - http://dx.doi.org/10.2196/16541 UR - http://www.ncbi.nlm.nih.gov/pubmed/32314967 ID - info:doi/10.2196/16541 ER - TY - JOUR AU - Mavragani, Amaryllis PY - 2020/4/20 TI - Tracking COVID-19 in Europe: Infodemiology Approach JO - JMIR Public Health Surveill SP - e18941 VL - 6 IS - 2 KW - big data KW - coronavirus KW - COVID-19 KW - infodemiology KW - infoveillance KW - Google Trends N2 - Background: Infodemiology (ie, information epidemiology) uses web-based data to inform public health and policy. Infodemiology metrics have been widely and successfully used to assess and forecast epidemics and outbreaks. Objective: In light of the recent coronavirus disease (COVID-19) pandemic that started in Wuhan, China in 2019, online search traffic data from Google are used to track the spread of the new coronavirus disease in Europe. Methods: Time series from Google Trends from January to March 2020 on the Topic (Virus) of ?Coronavirus? were retrieved and correlated with official data on COVID-19 cases and deaths worldwide and in the European countries that have been affected the most: Italy (at national and regional level), Spain, France, Germany, and the United Kingdom. Results: Statistically significant correlations are observed between online interest and COVID-19 cases and deaths. Furthermore, a critical point, after which the Pearson correlation coefficient starts declining (even if it is still statistically significant) was identified, indicating that this method is most efficient in regions or countries that have not yet peaked in COVID-19 cases. Conclusions: In the past, infodemiology metrics in general and data from Google Trends in particular have been shown to be useful in tracking and forecasting outbreaks, epidemics, and pandemics as, for example, in the cases of the Middle East respiratory syndrome, Ebola, measles, and Zika. With the COVID-19 pandemic still in the beginning stages, it is essential to explore and combine new methods of disease surveillance to assist with the preparedness of health care systems at the regional level. UR - http://publichealth.jmir.org/2020/2/e18941/ UR - http://dx.doi.org/10.2196/18941 UR - http://www.ncbi.nlm.nih.gov/pubmed/32250957 ID - info:doi/10.2196/18941 ER - TY - JOUR AU - Mazuz, Keren AU - Yom-Tov, Elad PY - 2020/4/20 TI - Analyzing Trends of Loneliness Through Large-Scale Analysis of Social Media Postings: Observational Study JO - JMIR Ment Health SP - e17188 VL - 7 IS - 4 KW - loneliness KW - text postings KW - behavior online KW - social media KW - computer-based analysis KW - online self-disclosure N2 - Background: Loneliness has become a public health problem described as an epidemic, and it has been argued that digital behavior such as social media posting affects loneliness. Objective: The aim of this study is to expand knowledge of the determinants of loneliness by investigating online postings in a social media forum devoted to loneliness. Specifically, this study aims to analyze the temporal trends in loneliness and their associations with topics of interest, especially with those related to mental health determinants. Methods: We collected a total of 19,668 postings from 11,054 users in the loneliness forum on Reddit. We asked seven crowdsourced workers to imagine themselves as writing 1 of 236 randomly chosen posts and to answer the short-form UCLA Loneliness Scale. After showing that these postings could provide an assessment of loneliness, we built a predictive model for loneliness scores based on the posts? text and applied it to all collected postings. We then analyzed trends in loneliness postings over time and their correlations with other topics of interest related to mental health determinants. Results: We found that crowdsourced workers can estimate loneliness (interclass correlation=0.19) and that predictive models are correlated with reported loneliness scores (Pearson r=0.38). Our results show that increases in loneliness are strongly associated with postings to a suicidality-related forum (hazard ratio 1.19) and to forums associated with other detrimental behaviors such as depression and illicit drug use. Clustering demonstrates that people who are lonely come from diverse demographics and from a variety of interests. Conclusions: The results demonstrate that it is possible for unrelated individuals to assess people?s social media postings for loneliness. Moreover, our findings show the multidimensional nature of online loneliness and its correlated behaviors. Our study shows the advantages of studying a hard-to-reach population through social media and suggests new directions for future studies. UR - http://mental.jmir.org/2020/4/e17188/ UR - http://dx.doi.org/10.2196/17188 UR - http://www.ncbi.nlm.nih.gov/pubmed/32310141 ID - info:doi/10.2196/17188 ER - TY - JOUR AU - Ayyoubzadeh, Mohammad Seyed AU - Ayyoubzadeh, Mehdi Seyed AU - Zahedi, Hoda AU - Ahmadi, Mahnaz AU - R Niakan Kalhori, Sharareh PY - 2020/4/14 TI - Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study JO - JMIR Public Health Surveill SP - e18828 VL - 6 IS - 2 KW - coronavirus KW - COVID-19 KW - prediction KW - incidence KW - Google Trends KW - linear regression KW - LSTM KW - pandemic KW - outbreak KW - public health N2 - Background: The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources? data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. Objective: This study aimed to predict the incidence of COVID-19 in Iran. Methods: Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. Results: The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). Conclusions: Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly. UR - http://publichealth.jmir.org/2020/2/e18828/ UR - http://dx.doi.org/10.2196/18828 UR - http://www.ncbi.nlm.nih.gov/pubmed/32234709 ID - info:doi/10.2196/18828 ER - TY - JOUR AU - Jimenez, Alberto AU - Santed-Germán, Miguel-Angel AU - Ramos, Victoria PY - 2020/4/13 TI - Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study JO - JMIR Public Health Surveill SP - e10919 VL - 6 IS - 2 KW - suicide KW - big data KW - infodemiology KW - infoveillance KW - incidence KW - help-seeking behaviors KW - searching behavior KW - early diagnosis N2 - Background: Different studies have suggested that web search data are useful in forecasting several phenomena from the field of economics to epidemiology or health issues. Objective: This study aimed to (1) evaluate the correlation between suicide rates released by the Spanish National Statistics Institute (INE) and internet search trends in Spain reported by Google Trends (GT) for 57 suicide-related terms representing major known risks of suicide and an analysis of these results using a linear regression model and (2) study the differential association between male and female suicide rates published by the INE and internet searches of these 57 terms. Methods: The study period was from 2004 to 2013. In this study, suicide data were collected from (1) Spain?s INE and (2) local internet search data from GT, both from January 2004 to December 2013. We investigated and validated 57 suicide-related terms already tested in scientific studies before 2015 that would be the best predictors of new suicide cases. We then evaluated the nowcasting effects of a GT search through a cross-correlation analysis and by linear regression of the suicide incidence data with the GT data. Results: Suicide rates in Spain in the study period were positively associated (r<-0.2) for the general population with the search volume for 7 terms and negatively for 1 from the 57 terms used in previous studies. Suicide rates for men were found to be significantly different than those of women. The search term, ?allergy,? demonstrated a lead effect for new suicide cases (r=0.513; P=.001). The next significant correlating terms for those 57 studied were ?antidepressant,? ?alcohol abstinence,? ?relationship breakup? (r=0.295, P=.001; r=0.295, P=.001; and r=0.268, P=.002, respectively). Significantly different results were obtained for men and women. Search terms that correlate with suicide rates of women are consistent with previous studies, showing that the incidence of depression is higher in women than in men, and showing different gender searching patterns. Conclusions: A better understanding of internet search behavior of both men and women in relation to suicide and related topics may help design effective suicide prevention programs based on information provided by search robots and other big data sources. UR - https://publichealth.jmir.org/2020/2/e10919 UR - http://dx.doi.org/10.2196/10919 UR - http://www.ncbi.nlm.nih.gov/pubmed/32281540 ID - info:doi/10.2196/10919 ER - TY - JOUR AU - Geldsetzer, Pascal PY - 2020/4/2 TI - Use of Rapid Online Surveys to Assess People's Perceptions During Infectious Disease Outbreaks: A Cross-sectional Survey on COVID-19 JO - J Med Internet Res SP - e18790 VL - 22 IS - 4 KW - rapid online surveys KW - perceptions KW - knowledge KW - coronavirus KW - SARS-CoV-2 KW - pandemic KW - infectious disease KW - outbreak KW - survey KW - COVID-19 KW - public health N2 - Background: Given the extensive time needed to conduct a nationally representative household survey and the commonly low response rate of phone surveys, rapid online surveys may be a promising method to assess and track knowledge and perceptions among the general public during fast-moving infectious disease outbreaks. Objective: This study aimed to apply rapid online surveying to determine knowledge and perceptions of coronavirus disease 2019 (COVID-19) among the general public in the United States and the United Kingdom. Methods: An online questionnaire was administered to 3000 adults residing in the United States and 3000 adults residing in the United Kingdom who had registered with Prolific Academic to participate in online research. Prolific Academic established strata by age (18-27, 28-37, 38-47, 48-57, or ?58 years), sex (male or female), and ethnicity (white, black or African American, Asian or Asian Indian, mixed, or ?other?), as well as all permutations of these strata. The number of participants who could enroll in each of these strata was calculated to reflect the distribution in the US and UK general population. Enrollment into the survey within each stratum was on a first-come, first-served basis. Participants completed the questionnaire between February 23 and March 2, 2020. Results: A total of 2986 and 2988 adults residing in the United States and the United Kingdom, respectively, completed the questionnaire. Of those, 64.4% (1924/2986) of US participants and 51.5% (1540/2988) of UK participants had a tertiary education degree, 67.5% (2015/2986) of US participants had a total household income between US $20,000 and US $99,999, and 74.4% (2223/2988) of UK participants had a total household income between Ł15,000 and Ł74,999. US and UK participants? median estimate for the probability of a fatal disease course among those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 5.0% (IQR 2.0%-15.0%) and 3.0% (IQR 2.0%-10.0%), respectively. Participants generally had good knowledge of the main mode of disease transmission and common symptoms of COVID-19. However, a substantial proportion of participants had misconceptions about how to prevent an infection and the recommended care-seeking behavior. For instance, 37.8% (95% CI 36.1%-39.6%) of US participants and 29.7% (95% CI 28.1%-31.4%) of UK participants thought that wearing a common surgical mask was ?highly effective? in protecting them from acquiring COVID-19, and 25.6% (95% CI 24.1%-27.2%) of US participants and 29.6% (95% CI 28.0%-31.3%) of UK participants thought it was prudent to refrain from eating at Chinese restaurants. Around half (53.8%, 95% CI 52.1%-55.6%) of US participants and 39.1% (95% CI 37.4%-40.9%) of UK participants thought that children were at an especially high risk of death when infected with SARS-CoV-2. Conclusions: The distribution of participants by total household income and education followed approximately that of the US and UK general population. The findings from this online survey could guide information campaigns by public health authorities, clinicians, and the media. More broadly, rapid online surveys could be an important tool in tracking the public?s knowledge and misperceptions during rapidly moving infectious disease outbreaks. UR - http://www.jmir.org/2020/4/e18790/ UR - http://dx.doi.org/10.2196/18790 UR - http://www.ncbi.nlm.nih.gov/pubmed/32240094 ID - info:doi/10.2196/18790 ER - TY - JOUR AU - Basch, H. Corey AU - Hillyer, C. Grace AU - Meleo-Erwin, C. Zoe AU - Jaime, Christie AU - Mohlman, Jan AU - Basch, E. Charles PY - 2020/4/2 TI - Preventive Behaviors Conveyed on YouTube to Mitigate Transmission of COVID-19: Cross-Sectional Study JO - JMIR Public Health Surveill SP - e18807 VL - 6 IS - 2 KW - YouTube KW - COVID-19 KW - social media KW - pandemic KW - outbreak KW - infectious disease KW - public health KW - prevention N2 - Background: Accurate information and guidance about personal behaviors that can reduce exposure to severe acute respiratory syndrome coronavirus 2 are among the most important elements in mitigating the spread of coronavirus disease 2019 (COVID-19). With over 2 billion users, YouTube is a media channel that millions turn to when seeking information. Objective: At the time of this study, there were no published studies investigating the content of YouTube videos related to COVID-19. This study aims to address this gap in the current knowledge. Methods: The 100 most widely viewed YouTube videos uploaded throughout the month of January 2020 were reviewed and the content covered was described. Collectively, these videos were viewed over 125 million times. Results: Fewer than one-third of the videos covered any of the seven key prevention behaviors listed on the US Centers for Disease Control and Prevention website. Conclusions: These results represent an important missed opportunity for disease prevention. UR - http://publichealth.jmir.org/2020/2/e18807/ UR - http://dx.doi.org/10.2196/18807 UR - http://www.ncbi.nlm.nih.gov/pubmed/32240096 ID - info:doi/10.2196/18807 ER - TY - JOUR AU - Rivas, Ryan AU - Sadah, A. Shouq AU - Guo, Yuhang AU - Hristidis, Vagelis PY - 2020/4/1 TI - Classification of Health-Related Social Media Posts: Evaluation of Post Content?Classifier Models and Analysis of User Demographics JO - JMIR Public Health Surveill SP - e14952 VL - 6 IS - 2 KW - social media KW - demographics KW - classification N2 - Background: The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. Objective: The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. Methods: We analyzed two different types of health-related social media: (1) health-related online forums?WebMD and DailyStrength?and (2) general online social networks?Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. Results: We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741/6769) of posts by male WebMD users asked for advice, and male users? WebMD posts were more likely to ask for medical advice than female users? posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were used to share experiences less frequently than posts on WebMD and DailyStrength. Conclusions: We studied and analyzed the content of health-related social media posts. Our results can guide health advocates and researchers to better target patient populations based on the application type. Given a research question or an outreach goal, our results can be used to choose the best online forums to answer the question or disseminate a message. UR - https://publichealth.jmir.org/2020/2/e14952 UR - http://dx.doi.org/10.2196/14952 UR - http://www.ncbi.nlm.nih.gov/pubmed/32234706 ID - info:doi/10.2196/14952 ER - TY - JOUR AU - Hernández-García, Ignacio AU - Giménez-Júlvez, Teresa PY - 2020/4/1 TI - Assessment of Health Information About COVID-19 Prevention on the Internet: Infodemiological Study JO - JMIR Public Health Surveill SP - e18717 VL - 6 IS - 2 KW - COVID-19 KW - coronavirus KW - prevention KW - internet KW - information KW - evaluation KW - authorship KW - World Health Organization KW - official public health organizations KW - digital media KW - infodemic KW - infodemiology N2 - Background: The internet is a large source of health information and has the capacity to influence its users. However, the information found on the internet often lacks scientific rigor, as anyone may upload content. This factor is a cause of great concern to scientific societies, governments, and users. Objective: The objective of our study was to investigate the information about the prevention of coronavirus disease 2019 (COVID-19) on the internet. Methods: On February 29, 2020, we performed a Google search with the terms ?Prevention coronavirus,? ?Prevention COVID-19,? ?Prevención coronavirus,? and ?Prevención COVID-19?. A univariate analysis was performed to study the association between the type of authorship, country of publication, and recommendations to avoid COVID-19 according to the World Health Organization (WHO). Results: In total, 80 weblinks were reviewed. Most of them were produced in the United States and Spain (n=58, 73%) by digital media sources and official public health organizations (n=60, 75%). The most mentioned WHO preventive measure was ?wash your hands frequently? (n=65, 81%). A less frequent recommendation was to ?stay home if you feel unwell? (n=26, 33%). The analysis by type of author (official public health organizations versus digital media) revealed significant differences regarding the recommendation to wear a mask when you are healthy only if caring for a person with suspected COVID-19 (odds ratio [OR] 4.39). According to the country of publication (Spain versus the United States), significant differences were detected regarding some recommendations such as ?wash your hands frequently? (OR 9.82), ?cover your mouth and nose with your bent elbow or tissue when you cough or sneeze? (OR 4.59), or ?stay home if you feel unwell? (OR 0.31). Conclusions: It is necessary to urge and promote the use of the websites of official public health organizations when seeking information on COVID-19 preventive measures on the internet. In this way, users will be able to obtain high-quality information more frequently, and such websites may improve their accessibility and positioning, given that search engines justify the positioning of links obtained in a search based on the frequency of access to them. UR - https://publichealth.jmir.org/2020/2/e18717 UR - http://dx.doi.org/10.2196/18717 UR - http://www.ncbi.nlm.nih.gov/pubmed/32217507 ID - info:doi/10.2196/18717 ER - TY - JOUR AU - Jordan, Lisa AU - Kalin, James AU - Dabrowski, Colleen PY - 2020/3/27 TI - Characteristics of Gun Advertisements on Social Media: Systematic Search and Content Analysis of Twitter and YouTube Posts JO - J Med Internet Res SP - e15736 VL - 22 IS - 3 KW - firearms KW - advertising KW - social media KW - internet KW - gender identity N2 - Background: Although gun violence has been identified as a major public health concern, the scope and significance of internet gun advertising is not known. Objective: This study aimed to quantify the characteristics of gun advertising on social media and to compare the reach of posts by manufacturers with those of influencers. Methods: Using a systematic search, we created a database of recent and popular Twitter and YouTube posts made public by major firearm manufacturers and influencers. From our sample of social media posts, we reviewed the content of the posts on the basis of 19 different characteristics, such as type of gun, presence of women, and military or police references. Our content analysis summarized statistical differences in the information conveyed in posts to compare advertising approaches across social media platforms. Results: Sample posts revealed that firearm manufacturers use social media to attract audiences to websites that sell firearms: 14.1% (131/928; ±2.9) of Twitter posts, 53.6% (228/425; ±6.2) of YouTube videos, and 89.5% (214/239; ±5.1) of YouTube influencer videos link to websites that facilitate sales. Advertisements included women in efforts to market handguns and pistols for the purpose of protection: videos with women included protection themes 2.5 times more often than videos without women. Top manufacturers of domestic firearms received 98 million channel views, compared with 6.1 billion channel views received by the top 12 YouTube influencers. Conclusions: Firearm companies use social media as an advertising platform to connect viewers to websites that sell guns. Gun manufacturers appropriate YouTube servers, video streaming services, and the work of YouTube influencers to reach large audiences to promote the widespread sale of consumer firearms. YouTube and Twitter subsidize gun advertising by offering server and streaming services at no cost to gun manufacturers, to the commercial benefit of Google and Twitter?s corporate ownership. UR - http://www.jmir.org/2020/3/e15736/ UR - http://dx.doi.org/10.2196/15736 UR - http://www.ncbi.nlm.nih.gov/pubmed/32217496 ID - info:doi/10.2196/15736 ER - TY - JOUR AU - Stevens, C. Robin AU - Brawner, M. Bridgette AU - Kranzler, Elissa AU - Giorgi, Salvatore AU - Lazarus, Elizabeth AU - Abera, Maramawit AU - Huang, Sarah AU - Ungar, Lyle PY - 2020/3/26 TI - Exploring Substance Use Tweets of Youth in the United States: Mixed Methods Study JO - JMIR Public Health Surveill SP - e16191 VL - 6 IS - 1 KW - social media KW - illicit drug KW - youth KW - adolescent N2 - Background: Substance use by youth remains a significant public health concern. Social media provides the opportunity to discuss and display substance use?related beliefs and behaviors, suggesting that the act of posting drug-related content, or viewing posted content, may influence substance use in youth. This aligns with empirically supported theories, which posit that behavior is influenced by perceptions of normative behavior. Nevertheless, few studies have explored the content of posts by youth related to substance use. Objective: This study aimed to identify the beliefs and behaviors of youth related to substance use by characterizing the content of youths? drug-related tweets. Using a sequential explanatory mixed methods approach, we sampled drug-relevant tweets and qualitatively examined their content. Methods: We used natural language processing to determine the frequency of drug-related words in public tweets (from 2011 to 2015) among youth Twitter users geolocated to Pennsylvania. We limited our sample by age (13-24 years), yielding approximately 23 million tweets from 20,112 users. We developed a list of drug-related keywords and phrases and selected a random sample of tweets with the most commonly used keywords to identify themes (n=249). Results: We identified two broad classes of emergent themes: functional themes and relational themes. Functional themes included posts that explicated a function of drugs in one?s life, with subthemes indicative of pride, longing, coping, and reminiscing as they relate to drug use and effects. Relational themes emphasized a relational nature of substance use, capturing substance use as a part of social relationships, with subthemes indicative of drug-related identity and companionship. We also identified topical areas in tweets related to drug use, including reference to polysubstance use, pop culture, and antidrug content. Across the tweets, the themes of pride (63/249, 25.3%) and longing (39/249, 15.7%) were the most popular. Most tweets that expressed pride (46/63, 73%) were explicitly related to marijuana. Nearly half of the tweets on coping (17/36, 47%) were related to prescription drugs. Very few of the tweets contained antidrug content (9/249, 3.6%). Conclusions: Data integration indicates that drugs are typically discussed in a positive manner, with content largely reflective of functional and relational patterns of use. The dissemination of this information, coupled with the relative absence of antidrug content, may influence youth such that they perceive drug use as normative and justified. Strategies to address the underlying causes of drug use (eg, coping with stressors) and engage antidrug messaging on social media may reduce normative perceptions and associated behaviors among youth. The findings of this study warrant research to further examine the effects of this content on beliefs and behaviors and to identify ways to leverage social media to decrease substance use in this population. UR - http://publichealth.jmir.org/2020/1/e16191/ UR - http://dx.doi.org/10.2196/16191 UR - http://www.ncbi.nlm.nih.gov/pubmed/32213472 ID - info:doi/10.2196/16191 ER - TY - JOUR AU - Butler, F. Stephen AU - Oyedele, K. Natasha AU - Dailey Govoni, Taryn AU - Green, L. Jody PY - 2020/3/25 TI - How Motivations for Using Buprenorphine Products Differ From Using Opioid Analgesics: Evidence from an Observational Study of Internet Discussions Among Recreational Users JO - JMIR Public Health Surveill SP - e16038 VL - 6 IS - 1 KW - buprenorphine-naloxone combination KW - buprenorphine KW - motivation KW - controlled substance diversion KW - addiction, opioid KW - opioid medication-assisted treatment N2 - Background: Opioid use disorder (OUD) poses medical and societal concerns. Although most individuals with OUD in the United States are not in drug abuse treatment, buprenorphine is considered a safe and effective OUD treatment, which reduces illicit opioid use, mortality, and other drug-related harms. However, as buprenorphine prescriptions increase, so does evidence of misused, abused, or diverted buprenorphine. Users? motivations for extratreatment use of buprenorphine (ie, misuse or abuse of one?s own prescription or use of diverted medication) may be different from the motivations involved in analgesic opioid products. Previous research is based on small sample sizes and use surveys, and none directly compare the motivations for using buprenorphine products (ie, tablet or film) with other opioid products having known abuse potential. Objective: The aim of the study was to describe and compare the motivation-to-use buprenorphine products, including buprenorphine/naloxone (BNX) sublingual film and oxycodone extended-release (ER), as discussed in online forums. Methods: Web-based posts from 2012 to 2016 were collected from online forums using the Web Informed Services internet monitoring archive. A random sample of posts was coded for motivation to use. These posts were coded into the following motivation categories: (1) use to avoid withdrawal, (2) pain relief, (3) tapering from other drugs, (4) opioid addiction treatment, (5) recreational use (ie, to get high), and (6) other use. Oxycodone ER, an opioid analgesic with known abuse potential, was selected as a comparator. Results: Among all posts, 0.81% (30,576/3,788,922) discussed motivation to use one of the target products. The examination of query-selected posts revealed significantly greater discussion of buprenorphine products than oxycodone ER (P<.001). The posts mentioning buprenorphine products were more likely than oxycodone ER to discuss treatment for OUD, tapering down use, and/or withdrawal management (P<.001). Buprenorphine-related posts discussed recreational use (375/1020, 36.76%), although much less often than in oxycodone ER posts (425/508, 83.7%). Despite some differences, the overall pattern of motivation to use was similar for BNX sublingual film and other buprenorphine products. Conclusions: An analysis of spontaneous, Web-based discussion among recreational substance users who post on online drug forums supports the contention that motivation-to-use patterns associated with buprenorphine products are different from those reported for oxycodone ER. Although the findings presented here are not expected to reflect the actual use of the target products, they may represent the interests and motivations of those posting on the online forums. Buprenorphine-related posts were more likely to discuss treatment for OUD, tapering, and withdrawal management than oxycodone ER. Although the findings are consistent with a purported link between the limited availability of medication-assisted therapies for substance use disorders and use of diverted buprenorphine products for self-treatment, recreational use was a motivation expressed in more than one-third of buprenorphine posts. UR - http://publichealth.jmir.org/2020/1/e16038/ UR - http://dx.doi.org/10.2196/16038 UR - http://www.ncbi.nlm.nih.gov/pubmed/32209533 ID - info:doi/10.2196/16038 ER - TY - JOUR AU - Barros, M. Joana AU - Duggan, Jim AU - Rebholz-Schuhmann, Dietrich PY - 2020/3/13 TI - The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review JO - J Med Internet Res SP - e13680 VL - 22 IS - 3 KW - medical informatics KW - public health informatics KW - public health KW - infectious diseases KW - chronic diseases KW - infodemiology KW - infoveillance N2 - Background: Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance. Objective: This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods. Methods: A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria. Results: Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias. Conclusions: IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population?s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health. UR - http://www.jmir.org/2020/3/e13680/ UR - http://dx.doi.org/10.2196/13680 UR - http://www.ncbi.nlm.nih.gov/pubmed/32167477 ID - info:doi/10.2196/13680 ER - TY - JOUR AU - Xu, Chenjie AU - Yang, Hongxi AU - Sun, Li AU - Cao, Xinxi AU - Hou, Yabing AU - Cai, Qiliang AU - Jia, Peng AU - Wang, Yaogang PY - 2020/3/12 TI - Detecting Lung Cancer Trends by Leveraging Real-World and Internet-Based Data: Infodemiology Study JO - J Med Internet Res SP - e16184 VL - 22 IS - 3 KW - lung cancer KW - incidence KW - mortality KW - internet searches KW - infodemiology N2 - Background: Internet search data on health-related terms can reflect people?s concerns about their health status in near real time, and hence serve as a supplementary metric of disease characteristics. However, studies using internet search data to monitor and predict chronic diseases at a geographically finer state-level scale are sparse. Objective: The aim of this study was to explore the associations of internet search volumes for lung cancer with published cancer incidence and mortality data in the United States. Methods: We used Google relative search volumes, which represent the search frequency of specific search terms in Google. We performed cross-sectional analyses of the original and disease metrics at both national and state levels. A smoothed time series of relative search volumes was created to eliminate the effects of irregular changes on the search frequencies and obtain the long-term trends of search volumes for lung cancer at both the national and state levels. We also performed analyses of decomposed Google relative search volume data and disease metrics at the national and state levels. Results: The monthly trends of lung cancer-related internet hits were consistent with the trends of reported lung cancer rates at the national level. Ohio had the highest frequency for lung cancer-related search terms. At the state level, the relative search volume was significantly correlated with lung cancer incidence rates in 42 states, with correlation coefficients ranging from 0.58 in Virginia to 0.94 in Oregon. Relative search volume was also significantly correlated with mortality in 47 states, with correlation coefficients ranging from 0.58 in Oklahoma to 0.94 in North Carolina. Both the incidence and mortality rates of lung cancer were correlated with decomposed relative search volumes in all states excluding Vermont. Conclusions: Internet search behaviors could reflect public awareness of lung cancer. Research on internet search behaviors could be a novel and timely approach to monitor and estimate the prevalence, incidence, and mortality rates of a broader range of cancers and even more health issues. UR - http://www.jmir.org/2020/3/e16184/ UR - http://dx.doi.org/10.2196/16184 UR - http://www.ncbi.nlm.nih.gov/pubmed/32163035 ID - info:doi/10.2196/16184 ER - TY - JOUR AU - Reukers, M. Daphne F. AU - Marbus, D. Sierk AU - Smit, Hella AU - Schneeberger, Peter AU - Donker, Gé AU - van der Hoek, Wim AU - van Gageldonk-Lafeber, B. Arianne PY - 2020/3/4 TI - Media Reports as a Source for Monitoring Impact of Influenza on Hospital Care: Qualitative Content Analysis JO - JMIR Public Health Surveill SP - e14627 VL - 6 IS - 1 KW - influenza KW - severe acute respiratory infections KW - SARI KW - surveillance KW - media reports KW - news articles KW - hospital care N2 - Background: The Netherlands, like most European countries, has a robust influenza surveillance system in primary care. However, there is a lack of real-time nationally representative data on hospital admissions for complications of influenza. Anecdotal information about hospital capacity problems during influenza epidemics can, therefore, not be substantiated. Objective: The aim of this study was to assess whether media reports could provide relevant information for estimating the impact of influenza on hospital capacity, in the absence of hospital surveillance data. Methods: Dutch news articles on influenza in hospitals during the influenza season (week 40 of 2017 until week 20 of 2018) were searched in a Web-based media monitoring program (Coosto). Trends in the number of weekly articles were compared with trends in 5 different influenza surveillance systems. A content analysis was performed on a selection of news articles, and information on the hospital, department, problem, and preventive or response measures was collected. Results: The trend in weekly news articles correlated significantly with the trends in all 5 surveillance systems, including severe acute respiratory infections (SARI) surveillance. However, the peak in all 5 surveillance systems preceded the peak in news articles. Content analysis showed hospitals (N=69) had major capacity problems (46/69, 67%), resulting in admission stops (9/46, 20%), postponement of nonurgent surgical procedures (29/46, 63%), or both (8/46, 17%). Only few hospitals reported the use of point-of-care testing (5/69, 7%) or a separate influenza ward (3/69, 4%) to accelerate clinical management, but most resorted to ad hoc crisis management (34/69, 49%). Conclusions: Media reports showed that the 2017/2018 influenza epidemic caused serious problems in hospitals throughout the country. However, because of the time lag in media reporting, it is not a suitable alternative for near real-time SARI surveillance. A robust SARI surveillance program is important to inform decision making. UR - http://publichealth.jmir.org/2020/1/e14627/ UR - http://dx.doi.org/10.2196/14627 UR - http://www.ncbi.nlm.nih.gov/pubmed/32130197 ID - info:doi/10.2196/14627 ER - TY - JOUR AU - O'Connor, Karen AU - Sarker, Abeed AU - Perrone, Jeanmarie AU - Gonzalez Hernandez, Graciela PY - 2020/2/26 TI - Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines JO - J Med Internet Res SP - e15861 VL - 22 IS - 2 KW - prescription drug misuse KW - social media KW - substance abuse detection KW - natural language processing KW - machine learning KW - infodemiology KW - infoveillance N2 - Background: Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. Objective: This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse?related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. Methods: We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes?abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. Results: Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271). Conclusions: Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks. UR - http://www.jmir.org/2020/2/e15861/ UR - http://dx.doi.org/10.2196/15861 UR - http://www.ncbi.nlm.nih.gov/pubmed/32130117 ID - info:doi/10.2196/15861 ER - TY - JOUR AU - Kim, Gyu Myeong AU - Kim, Jungu AU - Kim, Cheol Su AU - Jeong, Jaegwon PY - 2020/2/24 TI - Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study JO - J Med Internet Res SP - e16466 VL - 22 IS - 2 KW - methylphenidate KW - social media KW - Twitter KW - prescription drug misuse KW - drug-related side effects and adverse reactions KW - machine learning KW - support vector machine N2 - Background: Methylphenidate, a stimulant used to treat attention deficit hyperactivity disorder, has the potential to be used nonmedically, such as for studying and recreation. In an era when many people actively use social networking services, experience with the nonmedical use or side effects of methylphenidate might be shared on Twitter. Objective: The purpose of this study was to analyze tweets about the nonmedical use and side effects of methylphenidate using a machine learning approach. Methods: A total of 34,293 tweets mentioning methylphenidate from August 2018 to July 2019 were collected using searches for ?methylphenidate? and its brand names. Tweets in a randomly selected training dataset (6860/34,293, 20.00%) were annotated as positive or negative for two dependent variables: nonmedical use and side effects. Features such as personal noun, nonmedical use terms, medical use terms, side effect terms, sentiment scores, and the presence of a URL were generated for supervised learning. Using the labeled training dataset and features, support vector machine (SVM) classifiers were built and the performance was evaluated using F1 scores. The classifiers were applied to the test dataset to determine the number of tweets about nonmedical use and side effects. Results: Of the 6860 tweets in the training dataset, 5.19% (356/6860) and 5.52% (379/6860) were about nonmedical use and side effects, respectively. Performance of SVM classifiers for nonmedical use and side effects, expressed as F1 scores, were 0.547 (precision: 0.926, recall: 0.388, and accuracy: 0.967) and 0.733 (precision: 0.920, recall: 0.609, and accuracy: 0.976), respectively. In the test dataset, the SVM classifiers identified 361 tweets (1.32%) about nonmedical use and 519 tweets (1.89%) about side effects. The proportion of tweets about nonmedical use was highest in May 2019 (46/2624, 1.75%) and December 2018 (36/2041, 1.76%). Conclusions: The SVM classifiers that were built in this study were highly precise and accurate and will help to automatically identify the nonmedical use and side effects of methylphenidate using Twitter. UR - http://www.jmir.org/2020/2/e16466/ UR - http://dx.doi.org/10.2196/16466 UR - http://www.ncbi.nlm.nih.gov/pubmed/32130160 ID - info:doi/10.2196/16466 ER - TY - JOUR AU - Griffis, Heather AU - Asch, A. David AU - Schwartz, Andrew H. AU - Ungar, Lyle AU - Buttenheim, M. Alison AU - Barg, K. Frances AU - Mitra, Nandita AU - Merchant, M. Raina PY - 2020/2/11 TI - Using Social Media to Track Geographic Variability in Language About Diabetes: Infodemiology Analysis JO - JMIR Diabetes SP - e14431 VL - 5 IS - 1 KW - social media KW - epidemiology KW - infodemiology KW - diabetes KW - prevalence KW - twitter N2 - Background: Social media posts about diabetes could reveal patients? knowledge, attitudes, and beliefs as well as approaches for better targeting of public health messages and care management. Objective: This study aimed to characterize the language of Twitter users? posts regarding diabetes and describe the correlation of themes with the county-level prevalence of diabetes. Methods: A retrospective study of diabetes-related tweets identified from a random sample of approximately 37 billion tweets from the United States from 2009 to 2015 was conducted. We extracted diabetes-specific tweets and used machine learning to identify statistically significant topics of related terms. Topics were combined into themes and compared with the prevalence of diabetes by US counties and further compared with geography (US Census Divisions). Pearson correlation coefficients are reported for each topic and relationship with prevalence. Results: A total of 239,989 tweets from 121,494 unique users included the term diabetes. The themes emerging from the topics included unhealthy food and drink, treatment, symptoms/diagnoses, risk factors, research, recipes, news, health care, management, fundraising, diet, communication, and supplements/remedies. The theme of unhealthy foods most positively correlated with geographic areas with high prevalence of diabetes (r=0.088), whereas tweets related to research most negatively correlated (r=?0.162) with disease prevalence. Themes and topics about diabetes differed in overall frequency across the US geographical divisions, with the East South Central and South Atlantic states having a higher frequency of topics referencing unhealthy food (r range=0.073-0.146; P<.001). Conclusions: Diabetes-related tweets originating from counties with high prevalence of diabetes have different themes than tweets originating from counties with low prevalence of diabetes. Interventions could be informed from this variation to promote healthy behaviors. UR - http://diabetes.jmir.org/2020/1/e14431/ UR - http://dx.doi.org/10.2196/14431 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/14431 ER - TY - JOUR AU - Memon, Ali Shahan AU - Razak, Saquib AU - Weber, Ingmar PY - 2020/1/27 TI - Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study JO - J Med Internet Res SP - e13347 VL - 22 IS - 1 KW - noncommunicable diseases KW - lifestyle disease surveillance KW - infodemiology KW - infoveillance KW - Google Trends KW - Web search KW - nowcasting KW - public health KW - digital epidemiology N2 - Background: As the process of producing official health statistics for lifestyle diseases is slow, researchers have explored using Web search data as a proxy for lifestyle disease surveillance. Existing studies, however, are prone to at least one of the following issues: ad-hoc keyword selection, overfitting, insufficient predictive evaluation, lack of generalization, and failure to compare against trivial baselines. Objective: The aims of this study were to (1) employ a corrective approach improving previous methods; (2) study the key limitations in using Google Trends for lifestyle disease surveillance; and (3) test the generalizability of our methodology to other countries beyond the United States. Methods: For each of the target variables (diabetes, obesity, and exercise), prevalence rates were collected. After a rigorous keyword selection process, data from Google Trends were collected. These data were denormalized to form spatio-temporal indices. L1-regularized regression models were trained to predict prevalence rates from denormalized Google Trends indices. Models were tested on a held-out set and compared against baselines from the literature as well as a trivial last year equals this year baseline. A similar analysis was done using a multivariate spatio-temporal model where the previous year?s prevalence was included as a covariate. This model was modified to create a time-lagged regression analysis framework. Finally, a hierarchical time-lagged multivariate spatio-temporal model was created to account for subnational trends in the data. The model trained on US data was, then, applied in a transfer learning framework to Canada. Results: In the US context, our proposed models beat the performances of the prior work, as well as the trivial baselines. In terms of the mean absolute error (MAE), the best of our proposed models yields 24% improvement (0.72-0.55; P<.001) for diabetes; 18% improvement (1.20-0.99; P=.001) for obesity, and 34% improvement (2.89-1.95; P<.001) for exercise. Our proposed across-country transfer learning framework also shows promising results with an average Spearman and Pearson correlation of 0.70 for diabetes and 0.90 and 0.91 for obesity, respectively. Conclusions: Although our proposed models beat the baselines, we find the modeling of lifestyle diseases to be a challenging problem, one that requires an abundance of data as well as creative modeling strategies. In doing so, this study shows a low-to-moderate validity of Google Trends in the context of lifestyle disease surveillance, even when applying novel corrective approaches, including a proposed denormalization scheme. We envision qualitative analyses to be a more practical use of Google Trends in the context of lifestyle disease surveillance. For the quantitative analyses, the highest utility of using Google Trends is in the context of transfer learning where low-resource countries could benefit from high-resource countries by using proxy models. UR - http://www.jmir.org/2020/1/e13347/ UR - http://dx.doi.org/10.2196/13347 UR - http://www.ncbi.nlm.nih.gov/pubmed/32012050 ID - info:doi/10.2196/13347 ER - TY - JOUR AU - Kwon, Misol AU - Park, Eunhee PY - 2020/1/15 TI - Perceptions and Sentiments About Electronic Cigarettes on Social Media Platforms: Systematic Review JO - JMIR Public Health Surveill SP - e13673 VL - 6 IS - 1 KW - electronic cigarettes KW - electronic nicotine delivery systems KW - internet KW - social media KW - review N2 - Background: Electronic cigarettes (e-cigarettes) have been widely promoted on the internet, and subsequently, social media has been used as an important informative platform by e-cigarette users. Beliefs and knowledge expressed on social media platforms have largely influenced e-cigarette uptake, the decision to switch from conventional smoking to e-cigarette smoking, and positive and negative connotations associated with e-cigarettes. Despite this, there is a gap in our knowledge of people?s perceptions and sentiments on e-cigarettes as depicted on social media platforms. Objective: This study aimed to (1) provide an overview of studies examining the perceptions and sentiments associated with e-cigarettes on social media platforms and online discussion forums, (2) explore people?s perceptions of e-cigarette therein, and (3) examine the methodological limitations and gaps of the included studies. Methods: Searches in major electronic databases, including PubMed, Cumulative Index of Nursing and Allied Health Literature, EMBASE, Web of Science, and Communication and Mass Media Complete, were conducted using the following search terms: ?electronic cigarette,? ?electronic vaporizer,? ?electronic nicotine,? and ?electronic nicotine delivery systems? combined with ?internet,? ?social media,? and ?internet use.? The studies were selected if they examined participants? perceptions and sentiments of e-cigarettes on online forums or social media platforms during the 2007-2017 period. Results: A total of 21 articles were included. A total of 20 different social media platforms and online discussion forums were identified. A real-time snapshot and characteristics of sentiments, personal experience, and perceptions toward e-cigarettes on social media platforms and online forums were identified. Common topics regarding e-cigarettes included positive and negative health effects, testimony by current users, potential risks, benefits, regulations associated with e-cigarettes, and attitude toward them as smoking cessation aids. Conclusions: Although perceptions among social media users were mixed, there were more positive sentiments expressed than negative ones. This study particularly adds to our understanding of current trends in the popularity of and attitude toward e-cigarettes among social media users. In addition, this study identified conflicting perceptions about e-cigarettes among social media users. This suggests that accurate and up-to-date information on the benefits and risks of e-cigarettes needs to be disseminated to current and potential e-cigarette users via social media platforms, which can serve as important educational channels. Future research can explore the efficacy of social media?based interventions that deliver appropriate information (eg, general facts, benefits, and risks) about e-cigarettes. Trial Registration: PROSPERO CRD42019121611; https://tinyurl.com/yfr27uxs UR - http://publichealth.jmir.org/2020/1/e13673/ UR - http://dx.doi.org/10.2196/13673 UR - http://www.ncbi.nlm.nih.gov/pubmed/31939747 ID - info:doi/10.2196/13673 ER - TY - JOUR AU - Hua, My AU - Sadah, Shouq AU - Hristidis, Vagelis AU - Talbot, Prue PY - 2020/1/3 TI - Health Effects Associated With Electronic Cigarette Use: Automated Mining of Online Forums JO - J Med Internet Res SP - e15684 VL - 22 IS - 1 KW - electronic cigarettes KW - vaping epidemic KW - vaping-associated pulmonary illness KW - e-cigarettes KW - electronic nicotine delivery devices KW - health effects KW - nicotine KW - symptoms KW - disorders KW - pulmonary disease KW - pneumonia KW - headaches KW - content analysis KW - text classification KW - e-cigarette, or vaping, product use associated lung injury N2 - Background: Our previous infodemiological study was performed by manually mining health-effect data associated with electronic cigarettes (ECs) from online forums. Manual mining is time consuming and limits the number of posts that can be retrieved. Objective: Our goal in this study was to automatically extract and analyze a large number (>41,000) of online forum posts related to the health effects associated with EC use between 2008 and 2015. Methods: Data were annotated with medical concepts from the Unified Medical Language System using a modified version of the MetaMap tool. Of over 1.4 million posts, 41,216 were used to analyze symptoms (undiagnosed conditions) and disorders (physician-diagnosed terminology) associated with EC use. For each post, sentiment (positive, negative, and neutral) was also assigned. Results: Symptom and disorder data were categorized into 12 organ systems or anatomical regions. Most posts on symptoms and disorders contained negative sentiment, and affected systems were similar across all years. Health effects were reported most often in the neurological, mouth and throat, and respiratory systems. The most frequently reported symptoms and disorders were headache (n=939), coughing (n=852), malaise (n=468), asthma (n=916), dehydration (n=803), and pharyngitis (n=565). In addition, users often reported linked symptoms (eg, coughing and headache). Conclusions: Online forums are a valuable repository of data that can be used to identify positive and negative health effects associated with EC use. By automating extraction of online information, we obtained more data than in our prior study, identified new symptoms and disorders associated with EC use, determined which systems are most frequently adversely affected, identified specific symptoms and disorders most commonly reported, and tracked health effects over 7 years. UR - https://www.jmir.org/2020/1/e15684 UR - http://dx.doi.org/10.2196/15684 UR - http://www.ncbi.nlm.nih.gov/pubmed/31899452 ID - info:doi/10.2196/15684 ER - TY - JOUR AU - Melvin, Sara AU - Jamal, Amanda AU - Hill, Kaitlyn AU - Wang, Wei AU - Young, D. Sean PY - 2019/12/6 TI - Identifying Sleep-Deprived Authors of Tweets: Prospective Study JO - JMIR Ment Health SP - e13076 VL - 6 IS - 12 KW - wearable electronic devices KW - safety KW - natural language processing KW - information storage and retrieval KW - sleep deprivation KW - neural networks (computer) KW - sleep KW - social media N2 - Background: Social media data can be explored as a tool to detect sleep deprivation. First-year undergraduate students in their first quarter were invited to wear sleep-tracking devices (Basis; Intel), allow us to follow them on Twitter, and complete weekly surveys regarding their sleep. Objective: This study aimed to determine whether social media data can be used to monitor sleep deprivation. Methods: The sleep data obtained from the device were utilized to create a tiredness model that aided in labeling the tweets as sleep deprived or not at the time of posting. Labeled data were used to train and test a gated recurrent unit (GRU) neural network as to whether or not study participants were sleep deprived at the time of posting. Results: Results from the GRU neural network suggest that it is possible to classify the sleep-deprivation status of a tweet?s author with an average area under the curve of 0.68. Conclusions: It is feasible to use social media to identify students? sleep deprivation. The results add to the body of research suggesting that social media data should be further explored as a potential source for monitoring health. UR - https://mental.jmir.org/2019/12/e13076 UR - http://dx.doi.org/10.2196/13076 UR - http://www.ncbi.nlm.nih.gov/pubmed/31808747 ID - info:doi/10.2196/13076 ER - TY - JOUR AU - Timimi, Farris AU - Ray, Sara AU - Jones, Erik AU - Aase, Lee AU - Hoffman, Kathleen PY - 2019/11/28 TI - Patient-Reported Outcomes in Online Communications on Statins, Memory, and Cognition: Qualitative Analysis Using Online Communities JO - J Med Internet Res SP - e14809 VL - 21 IS - 11 KW - social media KW - hydroxymethylglutaryl-CoA reductase inhibitors KW - drug-related side effects and adverse reactions KW - memory loss KW - PROMs KW - pharmacovigilance KW - infodemiology KW - infoveillance KW - peer-support groups N2 - Background: In drug development clinical trials, there is a need for balance between restricting variables by setting eligibility criteria and representing the broader patient population that may use a product once it is approved. Similarly, although recent policy initiatives focusing on the inclusion of historically underrepresented groups are being implemented, barriers still remain. These limitations of clinical trials may mask potential product benefits and side effects. To bridge these gaps, online communication in health communities may serve as an additional population signal for drug side effects. Objective: The aim of this study was to employ a nontraditional dataset to identify drug side-effect signals. The study was designed to apply both natural language processing (NLP) technology and hands-on linguistic analysis to a set of online posts from known statin users to (1) identify any underlying crossover between the use of statins and impairment of memory or cognition and (2) obtain patient lexicon in their descriptions of experiences with statin medications and memory changes. Methods: Researchers utilized user-generated content on Inspire, looking at over 11 million posts across Inspire. Posts were written by patients and caregivers belonging to a variety of communities on Inspire. After identifying these posts, researchers used NLP and hands-on linguistic analysis to draw and expand upon correlations among statin use, memory, and cognition. Results: NLP analysis of posts identified statistical correlations between statin users and the discussion of memory impairment, which were not observed in control groups. NLP found that, out of all members on Inspire, 3.1% had posted about memory or cognition. In a control group of those who had posted about TNF inhibitors, 6.2% had also posted about memory and cognition. In comparison, of all those who had posted about a statin medication, 22.6% (P<.001) also posted about memory and cognition. Furthermore, linguistic analysis of a sample of posts provided themes and context to these statistical findings. By looking at posts from statin users about memory, four key themes were found and described in detail in the data: memory loss, aphasia, cognitive impairment, and emotional change. Conclusions: Correlations from this study point to a need for further research on the impact of statins on memory and cognition. Furthermore, when using nontraditional datasets, such as online communities, NLP and linguistic methodologies broaden the population for identifying side-effect signals. For side effects such as those on memory and cognition, where self-reporting may be unreliable, these methods can provide another avenue to inform patients, providers, and the Food and Drug Administration. UR - http://www.jmir.org/2019/11/e14809/ UR - http://dx.doi.org/10.2196/14809 UR - http://www.ncbi.nlm.nih.gov/pubmed/31778117 ID - info:doi/10.2196/14809 ER - TY - JOUR AU - Ssendikaddiwa, Joseph AU - Lavergne, Ruth PY - 2019/11/18 TI - Access to Primary Care and Internet Searches for Walk-In Clinics and Emergency Departments in Canada: Observational Study Using Google Trends and Population Health Survey Data JO - JMIR Public Health Surveill SP - e13130 VL - 5 IS - 4 KW - internet KW - ambulatory care facilities KW - emergency departments KW - primary health care KW - health services accessibility N2 - Background: Access to primary care is a challenge for many Canadians. Models of primary care vary widely among provinces, including arrangements for same-day and after-hours access. Use of walk-in clinics and emergency departments (EDs) may also vary, but data sources that allow comparison are limited. Objective: We used Google Trends to examine the relative frequency of searches for walk-in clinics and EDs across provinces and over time in Canada. We correlated provincial relative search frequencies from Google Trends with survey responses about primary care access from the Commonwealth Fund?s 2016 International Health Policy Survey of Adults in 11 Countries and the 2016 Canadian Community Health Survey. Methods: We developed search strategies to capture the range of terms used for walk-in clinics (eg, urgent care clinic and after-hours clinic) and EDs (eg, emergency room) across Canadian provinces. We used Google Trends to determine the frequencies of these terms relative to total search volume within each province from January 2011 to December 2018. We calculated correlation coefficients and 95% CIs between provincial Google Trends relative search frequencies and survey responses. Results: Relative search frequency of walk-in clinic searches increased steadily, doubling in most provinces between 2011 and 2018. Relative frequency of walk-in clinic searches was highest in the western provinces of British Columbia, Alberta, Saskatchewan, and Manitoba. At the provincial level, higher walk-in clinic relative search frequency was strongly positively correlated with the percentage of survey respondents who reported being able to get same- or next-day appointments to see a doctor or a nurse and inversely correlated with the percentage of respondents who reported going to ED for a condition that they thought could have been treated by providers at usual place of care. Relative search frequency for walk-in clinics was also inversely correlated with the percentage of respondents who reported having a regular medical provider. ED relative search frequencies were more stable over time, and we did not observe statistically significant correlation with survey data. Conclusions: Higher relative search frequency for walk-in clinics was positively correlated with the ability to get a same- or next-day appointment and inversely correlated with ED use for conditions treatable in the patient?s regular place of care and also with having a regular medical provider. Findings suggest that patient use of Web-based tools to search for more convenient or accessible care through walk-in clinics is increasing over time. Further research is needed to validate Google Trends data with administrative information on service use. UR - http://publichealth.jmir.org/2019/4/e13130/ UR - http://dx.doi.org/10.2196/13130 UR - http://www.ncbi.nlm.nih.gov/pubmed/31738175 ID - info:doi/10.2196/13130 ER - TY - JOUR AU - Nguyen, Jennifer AU - Gilbert, Lauren AU - Priede, Lianne AU - Heckman, Carolyn PY - 2019/11/13 TI - The Reach of the ?Don?t Fry Day? Twitter Campaign: Content Analysis JO - JMIR Dermatol SP - e14137 VL - 2 IS - 1 KW - social media KW - skin neoplasms KW - health communication N2 - Background: Skin cancer is the most common cancer in the United States, disproportionately affecting young women. Since many young adults use Twitter, it may be an effective channel to communicate skin cancer prevention information. Objective: The study aimed to assess the reach of the National Council on Skin Cancer Prevention (NCSCP)?s 2018 Don?t Fry Day Twitter campaign, categorize the types of individuals or tweeters who engaged in the campaign, and identify themes of the tweets. Methods: Descriptive statistics were used, and a content analysis of Twitter activity during the 2018 Don?t Fry Day campaign was conducted. The NCSCP tweeted about Don?t Fry Day and skin cancer prevention for 14 days in May 2018. Twitter contributors were categorized into groups. The number of impressions (potential views) and retweets were recorded. Content analysis was used to describe the text of the tweets. Results: A total of 1881 Twitter accounts, largely health professionals, used the Don?t Fry Day hashtag, generating over 45 million impressions. These accounts were grouped into nine categories (eg, news or media and public figures). The qualitative content analysis revealed informative, minimally informative, and self-interest campaign promotion themes. Informative tweets involved individuals and organizations who would mention and give further context and information about the #DontFryDay campaign. Subthemes of the informative theme were sun safety, contextual, and epidemiologic information. Minimally informative tweets used the hashtag (#DontFryDay) and other types of hashtags but did not give any further context or original material in the tweets. Self-interest campaign promotion involved businesses, firms, and medical practices that would utilize and promote the campaign to boost their own ventures. Conclusions: These analyses demonstrate the large potential reach of social media public health campaigns. However, limitations of such campaigns were also identified, for example, the relatively homogeneous groups actively engaged in the campaign. This study contributes to the understanding of the types of accounts and messages engaged in social media campaigns utilizing a hashtag, providing insight into the messages and participants that are effective and those that are not to achieve campaign goals. Further research on the potential impact of social media on health behaviors and outcomes is necessary to ensure wide-reaching implications. UR - http://derma.jmir.org/2019/1/e14137/ UR - http://dx.doi.org/10.2196/14137 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/14137 ER - TY - JOUR AU - Reuter, Katja AU - Zhu, Yifan AU - Angyan, Praveen AU - Le, NamQuyen AU - Merchant, A. Akil AU - Zimmer, Michael PY - 2019/10/30 TI - Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study JO - J Med Internet Res SP - e15455 VL - 21 IS - 10 KW - AIDS KW - cancer KW - clinical research KW - clinical trial KW - crowdsourcing KW - ethics KW - HIV KW - HPV KW - infoveillance KW - infodemiology KW - informed consent KW - Internet KW - research ethics KW - Mechanical Turk KW - MTurk KW - monitoring KW - obesity KW - privacy KW - public opinion KW - recruitment KW - smoking KW - social media KW - social network KW - surveillance KW - TurkPrime KW - Twitter N2 - Background: Social networks such as Twitter offer the clinical research community a novel opportunity for engaging potential study participants based on user activity data. However, the availability of public social media data has led to new ethical challenges about respecting user privacy and the appropriateness of monitoring social media for clinical trial recruitment. Researchers have voiced the need for involving users? perspectives in the development of ethical norms and regulations. Objective: This study examined the attitudes and level of concern among Twitter users and nonusers about using Twitter for monitoring social media users and their conversations to recruit potential clinical trial participants. Methods: We used two online methods for recruiting study participants: the open survey was (1) advertised on Twitter between May 23 and June 8, 2017, and (2) deployed on TurkPrime, a crowdsourcing data acquisition platform, between May 23 and June 8, 2017. Eligible participants were adults, 18 years of age or older, who lived in the United States. People with and without Twitter accounts were included in the study. Results: While nearly half the respondents?on Twitter (94/603, 15.6%) and on TurkPrime (509/603, 84.4%)?indicated agreement that social media monitoring constitutes a form of eavesdropping that invades their privacy, over one-third disagreed and nearly 1 in 5 had no opinion. A chi-square test revealed a positive relationship between respondents? general privacy concern and their average concern about Internet research (P<.005). We found associations between respondents? Twitter literacy and their concerns about the ability for researchers to monitor their Twitter activity for clinical trial recruitment (P=.001) and whether they consider Twitter monitoring for clinical trial recruitment as eavesdropping (P<.001) and an invasion of privacy (P=.003). As Twitter literacy increased, so did people?s concerns about researchers monitoring Twitter activity. Our data support the previously suggested use of the nonexceptionalist methodology for assessing social media in research, insofar as social media-based recruitment does not need to be considered exceptional and, for most, it is considered preferable to traditional in-person interventions at physical clinics. The expressed attitudes were highly contextual, depending on factors such as the type of disease or health topic (eg, HIV/AIDS vs obesity vs smoking), the entity or person monitoring users on Twitter, and the monitored information. Conclusions: The data and findings from this study contribute to the critical dialogue with the public about the use of social media in clinical research. The findings suggest that most users do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate surveillance or a violation of privacy. However, researchers should remain mindful that some participants might find social media monitoring problematic when connected with certain conditions or health topics. Further research should isolate factors that influence the level of concern among social media users across platforms and populations and inform the development of more clear and consistent guidelines. UR - http://www.jmir.org/2019/10/e15455/ UR - http://dx.doi.org/10.2196/15455 UR - http://www.ncbi.nlm.nih.gov/pubmed/31670698 ID - info:doi/10.2196/15455 ER - TY - JOUR AU - Mimura, Wataru AU - Akazawa, Manabu PY - 2019/10/8 TI - The Association Between Internet Searches and Moisturizer Prescription in Japan: Retrospective Observational Study JO - JMIR Public Health Surveill SP - e13212 VL - 5 IS - 4 KW - internet KW - moisturizer KW - heparinoid KW - Google Trends KW - time series analysis KW - infodemiology N2 - Background: Heparinoid is a medication prescribed in Japan for skin diseases, such as atopic dermatitis and dry skin. Heparinoid prescription has increased with instances of internet blogs recommending its use as a cosmetic. Objective: This study aimed to examine the prescription trends in moisturizer use and analyze their association with internet searches. Methods: We used a claims database to identify pharmacy claims of heparinoid-only prescriptions in Japan. Additionally, we used Google Trends to obtain internet search data for the period between October 1, 2007, and September 31, 2017. To analyze the association between heparinoid prescriptions and internet searches, we performed an autoregressive integrated moving average approach for each time series. Results: We identified 155,733 patients who had been prescribed heparinoid. The number of prescriptions increased from 2011 onward, and related internet searches increased from 2012 onward. Internet searches were significantly correlated with total heparinoid prescription (correlation coefficient=.25, P=.005). In addition, internet searches were significantly correlated with heparinoid prescription in those aged 20-59 years at ?1-month lag in Google Trends (correlation coefficient=.30, P=.001). Conclusions: Google searches related to heparinoid prescriptions showed a seasonal pattern and increased gradually over the preceding several years. Google searches were positively correlated with prescription trends. In addition, in a particular age group (20-59 years), prescriptions increased with the increase in internet searches. These results suggest that people obtained health-related information on the internet and that this affected their behavior and prescription requests. UR - https://publichealth.jmir.org/2019/4/e13212 UR - http://dx.doi.org/10.2196/13212 UR - http://www.ncbi.nlm.nih.gov/pubmed/31596248 ID - info:doi/10.2196/13212 ER - TY - JOUR AU - Barker, M. Kathryn AU - Subramanian, V. S. AU - Selman, Robert AU - Austin, Bryn S. PY - 2019/09/17 TI - Gender Perspectives on Social Norms Surrounding Teen Pregnancy: A Thematic Analysis of Social Media Data JO - JMIR Pediatr Parent SP - e13936 VL - 2 IS - 2 KW - teenage childbearing KW - teen pregnancy KW - adolescent sexual behavior KW - social media KW - social norms KW - gender N2 - Background: Social concern with teen pregnancy emerged in the 1970s, and today?s popular and professional health literature continues to draw on social norms that view teen pregnancy as a problem?for the teen mother, her baby, and society. It is unclear, however, how adolescents directly affected by teen pregnancy draw upon social norms against teen pregnancy in their own lives, whether the norms operate differently for girls and boys, and how these social norms affect pregnant or parenting adolescents. Objective: This research aims to examine whether and how US adolescents use, interpret, and experience social norms against teen pregnancy. Methods: Online ethnographic methods were used for the analysis of peer-to-peer exchanges from an online social network site designed for adolescents. Data were collected between March 2010 and February 2015 (n=1662). Thematic analysis was conducted using NVivo software. Results: American adolescents in this online platform draw on dominant social norms against teen pregnancy to provide rationales for why pregnancy in adolescence is wrong or should be avoided. Rationales range from potential socioeconomic harms to life-course rationales that view adolescence as a special, carefree period in life. Despite joint contributions from males and females to a pregnancy, it is primarily females who report pregnancy-related concerns, including experiences of bullying, social isolation, and fear. Conclusions: Peer exchange in this online forum indicates that American adolescents reproduce prevailing US social norms of viewing teen pregnancy as a social problem. These norms intersect with the norms of age, gender, and female sexuality. Female adolescents who transgress these norms experience bullying, shame, and stigma. Health professionals must ensure that strategies designed to prevent unintended adolescent pregnancy do not simultaneously create hardship and stigma in the lives of young women who are pregnant and parent their children. UR - http://pediatrics.jmir.org/2019/2/e13936/ UR - http://dx.doi.org/10.2196/13936 UR - http://www.ncbi.nlm.nih.gov/pubmed/31536963 ID - info:doi/10.2196/13936 ER - TY - JOUR AU - Modrek, Sepideh AU - Chakalov, Bozhidar PY - 2019/09/03 TI - The #MeToo Movement in the United States: Text Analysis of Early Twitter Conversations JO - J Med Internet Res SP - e13837 VL - 21 IS - 9 KW - social media KW - sexual abuse KW - sexual assault KW - machine learning KW - infodemiology KW - infoveillance N2 - Background: The #MeToo movement sparked an international debate on the sexual harassment, abuse, and assault and has taken many directions since its inception in October of 2017. Much of the early conversation took place on public social media sites such as Twitter, where the hashtag movement began. Objective: The aim of this study is to document, characterize, and quantify early public discourse and conversation of the #MeToo movement from Twitter data in the United States. We focus on posts with public first-person revelations of sexual assault/abuse and early life experiences of such events. Methods: We purchased full tweets and associated metadata from the Twitter Premium application programming interface between October 14 and 21, 2017 (ie, the first week of the movement). We examined the content of novel English language tweets with the phrase ?MeToo? from within the United States (N=11,935). We used machine learning methods, least absolute shrinkage and selection operator regression, and support vector machine models to summarize and classify the content of individual tweets with revelations of sexual assault and abuse and early life experiences of sexual assault and abuse. Results: We found that the most predictive words created a vivid archetype of the revelations of sexual assault and abuse. We then estimated that in the first week of the movement, 11% of novel English language tweets with the words ?MeToo? revealed details about the poster?s experience of sexual assault or abuse and 5.8% revealed early life experiences of such events. We examined the demographic composition of posters of sexual assault and abuse and found that white women aged 25-50 years were overrepresented in terms of their representation on Twitter. Furthermore, we found that the mass sharing of personal experiences of sexual assault and abuse had a large reach, where 6 to 34 million Twitter users may have seen such first-person revelations from someone they followed in the first week of the movement. Conclusions: These data illustrate that revelations shared went beyond acknowledgement of having experienced sexual harassment and often included vivid and traumatic descriptions of early life experiences of assault and abuse. These findings and methods underscore the value of content analysis, supported by novel machine learning methods, to improve our understanding of how widespread the revelations were, which likely amplified the spread and saliency of the #MeToo movement. UR - https://www.jmir.org/2019/9/e13837/ UR - http://dx.doi.org/10.2196/13837 UR - http://www.ncbi.nlm.nih.gov/pubmed/31482849 ID - info:doi/10.2196/13837 ER - TY - JOUR AU - Garcia-Rudolph, Alejandro AU - Laxe, Sara AU - Saurí, Joan AU - Bernabeu Guitart, Montserrat PY - 2019/08/26 TI - Stroke Survivors on Twitter: Sentiment and Topic Analysis From a Gender Perspective JO - J Med Internet Res SP - e14077 VL - 21 IS - 8 KW - stroke KW - emotions KW - Twitter KW - infodemiology KW - infoveillance KW - sentiment analysis KW - topic models KW - gender N2 - Background: Stroke is the worldwide leading cause of long-term disabilities. Women experience more activity limitations, worse health-related quality of life, and more poststroke depression than men. Twitter is increasingly used by individuals to broadcast their day-to-day happenings, providing unobtrusive access to samples of spontaneously expressed opinions on all types of topics and emotions. Objective: This study aimed to consider the raw frequencies of words in the collection of tweets posted by a sample of stroke survivors and to compare the posts by gender of the survivor for 8 basic emotions (anger, fear, anticipation, surprise, joy, sadness, trust and disgust); determine the proportion of each emotion in the collection of tweets and statistically compare each of them by gender of the survivor; extract the main topics (represented as sets of words) that occur in the collection of tweets, relative to each gender; and assign happiness scores to tweets and topics (using a well-established tool) and compare them by gender of the survivor. Methods: We performed sentiment analysis based on a state-of-the-art lexicon (National Research Council) with syuzhet R package. The emotion scores for men and women were first subjected to an F-test and then to a Wilcoxon rank sum test. We extended the emotional analysis, assigning happiness scores with the hedonometer (a tool specifically designed considering Twitter inputs). We calculated daily happiness average scores for all tweets. We created a term map for an exploratory clustering analysis using VosViewer software. We performed structural topic modelling with stm R package, allowing us to identify main topics by gender. We assigned happiness scores to all the words defining the main identified topics and compared them by gender. Results: We analyzed 800,424 tweets posted from August 1, 2007 to December 1, 2018, by 479 stroke survivors: Women (n=244) posted 396,898 tweets, and men (n=235) posted 403,526 tweets. The stroke survivor condition and gender as well as membership in at least 3 stroke-specific Twitter lists of active users were manually verified for all 479 participants. Their total number of tweets since 2007 was 5,257,433; therefore, we analyzed the most recent 15.2% of all their tweets. Positive emotions (anticipation, trust, and joy) were significantly higher (P<.001) in women, while negative emotions (disgust, fear, and sadness) were significantly higher (P<.001) in men in the analysis of raw frequencies and proportion of emotions. Happiness mean scores throughout the considered period show higher levels of happiness in women. We calculated the top 20 topics (with percentages and CIs) more likely addressed by gender and found that women?s topics show higher levels of happiness scores. Conclusions: We applied two different approaches?the Plutchik model and hedonometer tool?to a sample of stroke survivors? tweets. We conclude that women express positive emotions and happiness much more than men. UR - http://www.jmir.org/2019/8/e14077/ UR - http://dx.doi.org/10.2196/14077 UR - http://www.ncbi.nlm.nih.gov/pubmed/31452514 ID - info:doi/10.2196/14077 ER - TY - JOUR AU - Johnston, Jade Emily AU - Campbell, Katarzyna AU - Coleman, Tim AU - Lewis, Sarah AU - Orton, Sophie AU - Cooper, Sue PY - 2019/08/12 TI - Safety of Electronic Cigarette Use During Breastfeeding: Qualitative Study Using Online Forum Discussions JO - J Med Internet Res SP - e11506 VL - 21 IS - 8 KW - e-cigarette KW - online forum KW - postpartum relapse KW - smoking KW - breastfeeding KW - forum data N2 - Background: Electronic cigarettes (e-cigs) are an increasingly popular alternative to smoking, helping to prevent relapse in those trying to quit and with the potential to reduce harm as they are likely to be safer than standard cigarettes. Many women return to smoking in the postpartum period having stopped during pregnancy, and while this can affect their decisions about breastfeeding, little is known about women?s opinions on using e-cigs during this period. Objective: The aim of this study is to explore online forum users? current attitudes, motivations, and barriers to postpartum e-cig use, particularly as a breastfeeding mother. Methods: Data were collected via publicly accessible (identified by Google search) online forum discussions, and a priori codes identified. All transcripts were entered into NVivo for analysis, with a template approach to thematic analysis being used to code all transcripts from which themes were derived. Results: Four themes were identified: use, perceived risk, social support and evidence, with a number of subthemes identified within these. Women were using e-cigs to prevent postpartum return to smoking, but opinions on their safety were conflicting. They were concerned about possible transfer of harmful products from e-cigs via breastmilk and secondhand exposure, so they were actively seeking and sharing information on e-cigs from a variety of sources. Although some women were supportive of e-cig use, others provided harsh judgement for mothers who used them. Conclusions: E-cigs have the potential to reduce the number of women who return to smoking in the postpartum period and potentially improve breastfeeding rates, if breastfeeding mothers have access to relevant and reliable information. Health care providers should consider discussing e-cigs with mothers at risk of returning to smoking in the postpartum period. UR - https://www.jmir.org/2019/8/e11506/ UR - http://dx.doi.org/10.2196/11506 UR - http://www.ncbi.nlm.nih.gov/pubmed/31407672 ID - info:doi/10.2196/11506 ER - TY - JOUR AU - Rezaallah, Bita AU - Lewis, John David AU - Pierce, Carrie AU - Zeilhofer, Hans-Florian AU - Berg, Britt-Isabelle PY - 2019/08/07 TI - Social Media Surveillance of Multiple Sclerosis Medications Used During Pregnancy and Breastfeeding: Content Analysis JO - J Med Internet Res SP - e13003 VL - 21 IS - 8 KW - pharmacovigilance KW - machine learning KW - pregnancy outcome KW - postpartum KW - central nervous system agents KW - risk assessment KW - text mining N2 - Background: Multiple sclerosis (MS) is a chronic neurological disease occurring mostly in women of childbearing age. Pregnant women with MS are usually excluded from clinical trials; as users of the internet, however, they are actively engaged in threads and forums on social media. Social media provides the potential to explore real-world patient experiences and concerns about the use of medicinal products during pregnancy and breastfeeding. Objective: This study aimed to analyze the content of posts concerning pregnancy and use of medicines in online forums; thus, the study aimed to gain a thorough understanding of patients? experiences with MS medication. Methods: Using the names of medicinal products as search terms, we collected posts from 21 publicly available pregnancy forums, which were accessed between March 2015 and March 2018. After the identification of relevant posts, we analyzed the content of each post using a content analysis technique and categorized the main topics that users discussed most frequently. Results: We identified 6 main topics in 70 social media posts. These topics were as follows: (1) expressing personal experiences with MS medication use during the reproductive period (55/70, 80%), (2) seeking and sharing advice about the use of medicines (52/70, 74%), (3) progression of MS during and after pregnancy (35/70, 50%), (4) discussing concerns about MS medications during the reproductive period (35/70, 50%), (5) querying the possibility of breastfeeding while taking MS medications (30/70, 42%), and (6) commenting on communications with physicians (26/70, 37%). Conclusions: Overall, many pregnant women or women considering pregnancy shared profound uncertainties and specific concerns about taking medicines during the reproductive period. There is a significant need to provide advice and guidance to MS patients concerning the use of medicines in pregnancy and postpartum as well as during breastfeeding. Advice must be tailored to the circumstances of each patient and, of course, to the individual medicine. Information must be provided by a trusted source with relevant expertise and made publicly available. UR - https://www.jmir.org/2019/8/e13003/ UR - http://dx.doi.org/10.2196/13003 UR - http://www.ncbi.nlm.nih.gov/pubmed/31392963 ID - info:doi/10.2196/13003 ER - TY - JOUR AU - Tizek, Linda AU - Schielein, Maximilian AU - Rüth, Melvin AU - Ständer, Sonja AU - Pereira, Pedro Manuel AU - Eberlein, Bernadette AU - Biedermann, Tilo AU - Zink, Alexander PY - 2019/07/12 TI - Influence of Climate on Google Internet Searches for Pruritus Across 16 German Cities: Retrospective Analysis JO - J Med Internet Res SP - e13739 VL - 21 IS - 7 KW - pruritus KW - Internet KW - informatics KW - environment KW - weather KW - retrospective studies N2 - Background: The burden of pruritus is high, especially among patients with dermatologic diseases. Identifying trends in pruritus burden and people?s medical needs is challenging, since not all affected people consult a physician. Objective: The purpose of this study was to investigate pruritus search behavior trends in Germany and identify associations with weather factors. Methods: Google AdWords Keyword Planner was used to quantify pruritus-related search queries in 16 German cities from August 2014 to July 2018. All identified keywords were qualitatively categorized and pruritus-related terms were descriptively analyzed. The number of search queries per 100,000 inhabitants of each city was compared to environmental factors such as temperature, humidity, particulate matter 10 micrometers or less in diameter (PM10), and sunshine duration to investigate potential correlations. Results: We included 1150 pruritus-related keywords, which resulted in 2,851,290 queries. ?Pruritus? (n=115,680) and ?anal pruritus? (n=102,390) were the most-searched-for keywords. Nearly half of all queries were related to the category localization, with Berlin and Munich having a comparatively high proportion of people that searched for pruritus in the genital and anal areas. People searched more frequently for information on chronic compared to acute pruritus. The most populated cities had the lowest number of queries per 100,000 inhabitants (Berlin, n=13,641; Hamburg, n=18,303; and Munich, n=21,363), while smaller cities (Kiel, n=35,027; and Freiburg, n=39,501) had the highest. Temperature had a greater effect on search query number (beta -7.94, 95% CI -10.74 to -5.15) than did PM10 (beta -5.13, 95% CI -7.04 to -3.22), humidity (beta 4.73, 95% CI 2.70 to 6.75), or sunshine duration (beta 0.66, 95% CI 0.36 to 0.97). The highest relative number of search queries occurred during the winter (ie, December to February). Conclusions: By taking into account the study results, Google data analysis helps to examine people?s search frequency, behavior, and interest across cities and regions. The results indicated a general increase in search queries during the winter as well as differences across cities located in the same region; for example, there was a decline in search volume in Saarbrucken, while there were increases in Cologne, Frankfurt, and Dortmund. In addition, the detected correlation between search volume and weather data seems to be valuable in predicting an increase in pruritus burden, since a significant association with rising humidity and sunshine duration, as well as declining temperature and PM10, was found. Accordingly, this is an unconventional and inexpensive method to identify search behavior trends and respective inhabitants? needs. UR - http://www.jmir.org/2019/7/e13739/ UR - http://dx.doi.org/10.2196/13739 UR - http://www.ncbi.nlm.nih.gov/pubmed/31301128 ID - info:doi/10.2196/13739 ER - TY - JOUR AU - Chu, Kar-Hai AU - Colditz, Jason AU - Malik, Momin AU - Yates, Tabitha AU - Primack, Brian PY - 2019/07/08 TI - Identifying Key Target Audiences for Public Health Campaigns: Leveraging Machine Learning in the Case of Hookah Tobacco Smoking JO - J Med Internet Res SP - e12443 VL - 21 IS - 7 KW - smoking water pipes KW - waterpipe tobacco KW - tobacco KW - smoking KW - social media KW - public health KW - infodemiology KW - infoveillance KW - machine learning N2 - Background: Hookah tobacco smoking (HTS) is a particularly important issue for public health professionals to address owing to its prevalence and deleterious health effects. Social media sites can be a valuable tool for public health officials to conduct informational health campaigns. Current social media platforms provide researchers with opportunities to better identify and target specific audiences and even individuals. However, we are not aware of systematic research attempting to identify audiences with mixed or ambivalent views toward HTS. Objective: The objective of this study was to (1) confirm previous research showing positively skewed HTS sentiment on Twitter using a larger dataset by leveraging machine learning techniques and (2) systematically identify individuals who exhibit mixed opinions about HTS via the Twitter platform and therefore represent key audiences for intervention. Methods: We prospectively collected tweets related to HTS from January to June 2016. We double-coded sentiment for a subset of approximately 5000 randomly sampled tweets for sentiment toward HTS and used these data to train a machine learning classifier to assess the remaining approximately 556,000 HTS-related Twitter posts. Natural language processing software was used to extract linguistic features (ie, language-based covariates). The data were processed by machine learning tools and algorithms using R. Finally, we used the results to identify individuals who, because they had consistently posted both positive and negative content, might be ambivalent toward HTS and represent an ideal audience for intervention. Results: There were 561,960 HTS-related tweets: 373,911 were classified as positive and 183,139 were classified as negative. A set of 12,861 users met a priori criteria indicating that they posted both positive and negative tweets about HTS. Conclusions: Sentiment analysis can allow researchers to identify audience segments on social media that demonstrate ambiguity toward key public health issues, such as HTS, and therefore represent ideal populations for intervention. Using large social media datasets can help public health officials to preemptively identify specific audience segments that would be most receptive to targeted campaigns. UR - http://www.jmir.org/2019/7/e12443/ UR - http://dx.doi.org/10.2196/12443 UR - http://www.ncbi.nlm.nih.gov/pubmed/31287063 ID - info:doi/10.2196/12443 ER - TY - JOUR AU - Alessa, Ali AU - Faezipour, Miad PY - 2019/6/23 TI - Preliminary Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study JO - JMIR Public Health Surveill SP - e12383 VL - 5 IS - 2 KW - FastText KW - influenza KW - machine learning KW - social networking site KW - text classification N2 - Background: Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. Objective: The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately. Methods: We presented a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. Results: The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression?based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29% . Conclusions: The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs. UR - http://publichealth.jmir.org/2019/2/e12383/ UR - http://dx.doi.org/10.2196/12383 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/12383 ER - TY - JOUR AU - On, Jeongah AU - Park, Hyeoun-Ae AU - Song, Tae-Min PY - 2019/6/7 TI - Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology JO - J Med Internet Res SP - e13456 VL - 21 IS - 6 KW - social media KW - vaccination KW - health information interoperability KW - semantics N2 - Background: Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates. Objective: The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data. Methods: The domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules. Results: Our childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent?s health belief appeared in 53.21% (15,709/29,521) of posts and positive sentiments appeared in 64.08% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents? health belief, vaccination availability, and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments. Conclusions: The childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data. UR - http://www.jmir.org/2019/6/e13456/ UR - http://dx.doi.org/10.2196/13456 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199290 ID - info:doi/10.2196/13456 ER - TY - JOUR AU - Safarishahrbijari, Anahita AU - Osgood, D. Nathaniel PY - 2019/5/26 TI - Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study JO - JMIR Public Health Surveill SP - e11615 VL - 5 IS - 2 KW - machine learning KW - infectious disease transmission KW - disease models KW - system dynamics analysis KW - social media KW - outbreaks KW - infodemiology KW - infoveillance N2 - Background: Although dynamic models are increasingly used by decision makers as a source of insight to guide interventions in order to control communicable disease outbreaks, such models have long suffered from a risk of rapid obsolescence due to failure to keep updated with emerging epidemiological evidence. The application of statistical filtering algorithms to high-velocity data streams has recently demonstrated effectiveness in allowing such models to be automatically regrounded by each new set of incoming observations. The attractiveness of such techniques has been enhanced by the emergence of a new generation of geospatially specific, high-velocity data sources, including daily counts of relevant searches and social media posts. The information available in such electronic data sources complements that of traditional epidemiological data sources. Objective: This study aims to evaluate the degree to which the predictive accuracy of pandemic projection models regrounded via machine learning in daily clinical data can be enhanced by extending such methods to leverage daily search counts. Methods: We combined a previously published influenza A (H1N1) pandemic projection model with the sequential Monte Carlo technique of particle filtering, to reground the model bu using confirmed incident case counts and search volumes. The effectiveness of particle filtering was evaluated using a norm discrepancy metric via predictive and dataset-specific cross-validation. Results: Our results suggested that despite the data quality limitations of daily search volume data, the predictive accuracy of dynamic models can be strongly elevated by inclusion of such data in filtering methods. Conclusions: The predictive accuracy of dynamic models can be notably enhanced by tapping a readily accessible, publicly available, high-velocity data source. This work highlights a low-cost, low-burden avenue for strengthening model-based outbreak intervention response planning using low-cost public electronic datasets. UR - http://publichealth.jmir.org/2019/2/e11615/ UR - http://dx.doi.org/10.2196/11615 UR - http://www.ncbi.nlm.nih.gov/pubmed/31199339 ID - info:doi/10.2196/11615 ER - TY - JOUR AU - Mamidi, Ravali AU - Miller, Michele AU - Banerjee, Tanvi AU - Romine, William AU - Sheth, Amit PY - 2019/06/04 TI - Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic JO - JMIR Public Health Surveill SP - e11036 VL - 5 IS - 2 KW - social media KW - machine learning KW - natural language processing KW - epidemiology KW - Zika KW - infodemiology KW - infoveillance KW - twitter KW - sentiment analysis N2 - Background: To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies? efforts to disseminate relevant information to the public about Zika-related issues. Objective: The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed. Methods: Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into 3 sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic-modeling approach to determine the main topics for each category, with focus on the negative category. Results: A total of 5303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score=0.48-0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Overall, 10 topics for each sentiment category were identified using topic modeling, with a focus on the negative sentiment category. Conclusions: Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real time. Our negative sentiment classifier was able to identify tweets concerning Zika with 3 broad themes: neural defects,Zika abnormalities, and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as Morbidity and Mortality Weekly Report and Vaccine. As the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research. UR - http://publichealth.jmir.org/2019/2/e11036/ UR - http://dx.doi.org/10.2196/11036 UR - http://www.ncbi.nlm.nih.gov/pubmed/31165711 ID - info:doi/10.2196/11036 ER - TY - JOUR AU - Hodgson, Saldivar Nikkie AU - Yom-Tov, Elad AU - Strong, F. William AU - Flores, L. Priscilla AU - Ricoy, N. Giselle PY - 2019/06/04 TI - Concerns of Female Adolescents About Menarche and First Sexual Intercourse: Mixed Methods Analysis of Social Media Questions JO - JMIR Pediatr Parent SP - e13158 VL - 2 IS - 1 KW - menarche KW - sexual intercourse KW - social media KW - infodemiology KW - infoveillance N2 - Background: Adolescents use social media for information on medical and social aspects of maturation. Objective: The aim of this study was to investigate the concerns and information needs of adolescents regarding menarche and first sexual intercourse. Methods: Questions about menarche or first sexual intercourse were obtained from Yahoo Answers, a community-based social media question-and-answer website. A total of 1226 questions were analyzed. We focused on 123 question pairs made by users who asked questions on both topics and reported their ages at each. Quantitative and qualitative analyses were performed on these question pairs. Results: Qualitative analysis identified uncertainty as a significant theme for both menarche and first intercourse. Quantitative analysis showed that uncertainty was expressed in 26% (13/50) of menarche questions and 14% (7/50) of intercourse questions. Lack of communication was expressed in 4% (2/50) of menarche questions, compared with 8% (4/50) of intercourse questions. Ages at menarche and at first sexual intercourse were correlated, with women reporting menarche at the age of 13 years or younger being 2.6 times more likely to experience first sexual intercourse before the age of 16 years (P<.001, chi-square test). Older age at menarche was associated with greater lack of communication with parents (analysis of variance, P=.002). Conclusions: The questions of adolescents on the topics of menarche and first sexual intercourse express anxiety and uncertainty and are associated with a lack of information and deficient communication with parents. The more normative and expected a behavior, the less these factors appear. Therefore, parents and educators should, to the extent possible, improve communication around these topics, especially when they occur at less typical ages. UR - http://pediatrics.jmir.org/2019/1/e13158/ UR - http://dx.doi.org/10.2196/13158 UR - http://www.ncbi.nlm.nih.gov/pubmed/31518326 ID - info:doi/10.2196/13158 ER - TY - JOUR AU - Liu, Sam AU - Chen, Brian AU - Kuo, Alex PY - 2019/06/03 TI - Monitoring Physical Activity Levels Using Twitter Data: Infodemiology Study JO - J Med Internet Res SP - e12394 VL - 21 IS - 6 KW - physical activity KW - social media KW - internet KW - Twitter messaging KW - population surveillance KW - public health N2 - Background: Social media technology such as Twitter allows users to share their thoughts, feelings, and opinions online. The growing body of social media data is becoming a central part of infodemiology research as these data can be combined with other public health datasets (eg, physical activity levels) to provide real-time monitoring of psychological and behavior outcomes that inform health behaviors. Currently, it is unclear whether Twitter data can be used to monitor physical activity levels. Objective: The aim of this study was to establish the feasibility of using Twitter data to monitor physical activity levels by assessing whether the frequency and sentiment of physical activity?related tweets were associated with physical activity levels across the United States. Methods: Tweets were collected from Twitter?s application programming interface (API) between January 10, 2017 and January 2, 2018. We used Twitter's garden hose method of collecting tweets, which provided a random sample of approximately 1% of all tweets with location metadata falling within the United States. Geotagged tweets were filtered. A list of physical activity?related hashtags was collected and used to further classify these geolocated tweets. Twitter data were merged with physical activity data collected as part of the Behavioral Risk Factor Surveillance System. Multiple linear regression models were fit to assess the relationship between physical activity?related tweets and physical activity levels by county while controlling for population and socioeconomic status measures. Results: During the study period, 442,959,789 unique tweets were collected, of which 64,005,336 (14.44%) were geotagged with latitude and longitude coordinates. Aggregated data were obtained for a total of 3138 counties in the United States. The mean county-level percentage of physically active individuals was 74.05% (SD 5.2) and 75.30% (SD 4.96) after adjusting for age. The model showed that the percentage of physical activity?related tweets was significantly associated with physical activity levels (beta=.11; SE 0.2; P<.001) and age-adjusted physical activity (beta=.10; SE 0.20; P<.001) on a county level while adjusting for both Gini index and education level. However, the overall explained variance of the model was low (R2=.11). The sentiment of the physical activity?related tweets was not a significant predictor of physical activity level and age-adjusted physical activity on a county level after including the Gini index and education level in the model (P>.05). Conclusions: Social media data may be a valuable tool for public health organizations to monitor physical activity levels, as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (eg, surveys and observational studies). Consequently, this tool may have the potential to help public health organizations better mobilize and target physical activity interventions. UR - https://www.jmir.org/2019/6/e12394/ UR - http://dx.doi.org/10.2196/12394 UR - http://www.ncbi.nlm.nih.gov/pubmed/31162126 ID - info:doi/10.2196/12394 ER - TY - JOUR AU - Nikfarjam, Azadeh AU - Ransohoff, D. Julia AU - Callahan, Alison AU - Jones, Erik AU - Loew, Brian AU - Kwong, Y. Bernice AU - Sarin, Y. Kavita AU - Shah, H. Nigam PY - 2019/06/03 TI - Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection JO - JMIR Public Health Surveill SP - e11264 VL - 5 IS - 2 KW - natural language processing KW - signal detection KW - adverse drug reactions KW - social media KW - drug-related side effects KW - medical oncology KW - antineoplastic agents KW - machine learning N2 - Background: Adverse drug reactions (ADRs) occur in nearly all patients on chemotherapy, causing morbidity and therapy disruptions. Detection of such ADRs is limited in clinical trials, which are underpowered to detect rare events. Early recognition of ADRs in the postmarketing phase could substantially reduce morbidity and decrease societal costs. Internet community health forums provide a mechanism for individuals to discuss real-time health concerns and can enable computational detection of ADRs. Objective: The goal of this study is to identify cutaneous ADR signals in social health networks and compare the frequency and timing of these ADRs to clinical reports in the literature. Methods: We present a natural language processing-based, ADR signal-generation pipeline based on patient posts on Internet social health networks. We identified user posts from the Inspire health forums related to two chemotherapy classes: erlotinib, an epidermal growth factor receptor inhibitor, and nivolumab and pembrolizumab, immune checkpoint inhibitors. We extracted mentions of ADRs from unstructured content of patient posts. We then performed population-level association analyses and time-to-detection analyses. Results: Our system detected cutaneous ADRs from patient reports with high precision (0.90) and at frequencies comparable to those documented in the literature but an average of 7 months ahead of their literature reporting. Known ADRs were associated with higher proportional reporting ratios compared to negative controls, demonstrating the robustness of our analyses. Our named entity recognition system achieved a 0.738 microaveraged F-measure in detecting ADR entities, not limited to cutaneous ADRs, in health forum posts. Additionally, we discovered the novel ADR of hypohidrosis reported by 23 patients in erlotinib-related posts; this ADR was absent from 15 years of literature on this medication and we recently reported the finding in a clinical oncology journal. Conclusions: Several hundred million patients report health concerns in social health networks, yet this information is markedly underutilized for pharmacosurveillance. We demonstrated the ability of a natural language processing-based signal-generation pipeline to accurately detect patient reports of ADRs months in advance of literature reporting and the robustness of statistical analyses to validate system detections. Our findings suggest the important contributions that social health network data can play in contributing to more comprehensive and timely pharmacovigilance. UR - http://publichealth.jmir.org/2019/2/e11264/ UR - http://dx.doi.org/10.2196/11264 UR - http://www.ncbi.nlm.nih.gov/pubmed/31162134 ID - info:doi/10.2196/11264 ER - TY - JOUR AU - Pereira-Sanchez, Victor AU - Alvarez-Mon, Angel Miguel AU - Asunsolo del Barco, Angel AU - Alvarez-Mon, Melchor AU - Teo, Alan PY - 2019/05/29 TI - Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets JO - J Med Internet Res SP - e14167 VL - 21 IS - 5 KW - social isolation KW - loneliness KW - hikikomori KW - hidden youth KW - social media KW - Twitter KW - social withdrawal N2 - Background: Hikikomori is a severe form of social withdrawal, originally described in Japan but recently reported in other countries. Debate exists as to what extent hikikomori is viewed as a problem outside of the Japanese context. Objective: We aimed to explore perceptions about hikikomori outside Japan by analyzing Western language content from the popular social media platform, Twitter. Methods: We conducted a mixed methods analysis of all publicly available tweets using the hashtag #hikikomori between February 1 and August 16, 2018, in 5 Western languages (Catalan, English, French, Italian, and Spanish). Tweets were first classified as to whether they described hikikomori as a problem or a nonproblematic phenomenon. Tweets regarding hikikomori as a problem were then subclassified in terms of the type of problem (medical, social, or anecdotal) they referred to, and we marked if they referenced scientific publications or the presence of hikikomori in countries other than Japan. We also examined measures of interest in content related to hikikomori, including retweets, likes, and associated hashtags. Results: A total of 1042 tweets used #hikikomori, and 656 (62.3%) were included in the content analysis. Most of the included tweets were written in English (44.20%) and Italian (34.16%), and a majority (56.70%) discussed hikikomori as a problem. Tweets referencing scientific publications (3.96%) and hikikomori as present in countries other than Japan (13.57%) were less common. Tweets mentioning hikikomori outside Japan were statistically more likely to be retweeted (P=.01) and liked (P=.01) than those not mentioning it, whereas tweets with explicit scientific references were statistically more retweeted (P=.01) but not liked (P=.10) than those without that reference. Retweet and like figures were not statistically significantly different among other categories and subcategories. The most associated hashtags included references to Japan, mental health, and the youth. Conclusions: Hikikomori is a repeated word in non-Japanese Western languages on Twitter, suggesting the presence of hikikomori in countries outside Japan. Most tweets treat hikikomori as a problem, but the ways they post about it are highly heterogeneous. UR - http://www.jmir.org/2019/5/e14167/ UR - http://dx.doi.org/10.2196/14167 UR - http://www.ncbi.nlm.nih.gov/pubmed/31144665 ID - info:doi/10.2196/14167 ER - TY - JOUR AU - Mavragani, Amaryllis AU - Ochoa, Gabriela PY - 2019/05/29 TI - Google Trends in Infodemiology and Infoveillance: Methodology Framework JO - JMIR Public Health Surveill SP - e13439 VL - 5 IS - 2 KW - big data KW - health KW - infodemiology KW - infoveillance KW - internet behavior KW - Google Trends UR - http://publichealth.jmir.org/2019/2/e13439/ UR - http://dx.doi.org/10.2196/13439 UR - http://www.ncbi.nlm.nih.gov/pubmed/31144671 ID - info:doi/10.2196/13439 ER - TY - JOUR AU - Alvarez-Mon, Angel Miguel AU - Llavero-Valero, María AU - Sánchez-Bayona, Rodrigo AU - Pereira-Sanchez, Victor AU - Vallejo-Valdivielso, Maria AU - Monserrat, Jorge AU - Lahera, Guillermo AU - Asunsolo del Barco, Angel AU - Alvarez-Mon, Melchor PY - 2019/05/28 TI - Areas of Interest and Stigmatic Attitudes of the General Public in Five Relevant Medical Conditions: Thematic and Quantitative Analysis Using Twitter JO - J Med Internet Res SP - e14110 VL - 21 IS - 5 KW - social stigma KW - social media KW - psychosis KW - breast cancer KW - HIV KW - dementia KW - public opinion KW - diabetes N2 - Background: Twitter is an indicator of real-world performance, thus, is an appropriate arena to assess the social consideration and attitudes toward psychosis. Objective: The aim of this study was to perform a mixed-methods study of the content and key metrics of tweets referring to psychosis in comparison with tweets referring to control diseases (breast cancer, diabetes, Alzheimer, and human immunodeficiency virus). Methods: Each tweet?s content was rated as nonmedical (NM: testimonies, health care products, solidarity or awareness and misuse) or medical (M: included a reference to the illness?s diagnosis, treatment, prognosis, or prevention). NM tweets were classified as positive or pejorative. We assessed the appropriateness of the medical content. The number of retweets generated and the potential reach and impact of the hashtags analyzed was also investigated. Results: We analyzed a total of 15,443 tweets: 8055 classified as NM and 7287 as M. Psychosis-related tweets (PRT) had a significantly higher frequency of misuse 33.3% (212/636) vs 1.15% (853/7419; P<.001) and pejorative content 36.2% (231/636) vs 11.33% (840/7419; P<.001). The medical content of the PRT showed the highest scientific appropriateness 100% (391/391) vs 93.66% (6030/6439; P<.001) and had a higher frequency of content about disease prevention. The potential reach and impact of the tweets related to psychosis were low, but they had a high retweet-to-tweet ratio. Conclusions: We show a reduced number and a different pattern of contents in tweets about psychosis compared with control diseases. PRT showed a predominance of nonmedical content with increased frequencies of misuse and pejorative tone. However, the medical content of PRT showed high scientific appropriateness aimed toward prevention. UR - http://www.jmir.org/2019/5/e14110/ UR - http://dx.doi.org/10.2196/14110 UR - http://www.ncbi.nlm.nih.gov/pubmed/31140438 ID - info:doi/10.2196/14110 ER - TY - JOUR AU - Madden, Michael Kenneth AU - Feldman, Boris PY - 2019/05/28 TI - Weekly, Seasonal, and Geographic Patterns in Health Contemplations About Sundown Syndrome: An Ecological Correlational Study JO - JMIR Aging SP - e13302 VL - 2 IS - 1 KW - sundown syndrome KW - geriatric medicine KW - dementia KW - circadian rhythms KW - infodemiology KW - infoveillance KW - internet N2 - Background: Sundown syndrome (ie, agitation later in the day) is common in older adults with dementia. The underlying etiology for these behaviors is unclear. Possibilities include increased caregiver fatigue at the end of the day and disruption of circadian rhythms by both age and neurodegenerative illness. Objective: This study sought to examine circumseptan (weekly) patterns in search volumes related to sundown syndrome, in order to determine if such searches peaked at the end of the weekend, a time when caregiver supports are least available. We also sought to examine both seasonal differences and associations of state-by-state search activity with both state latitude and yearly sun exposure. Methods: Daily Internet search query data was obtained from Google Trends (2005-2017 inclusive). Circumseptan patterns were determined by wavelet analysis, and seasonality was determined by the difference in search volumes between winter (December, January, and February) and summer (June, July, and August) months. Geographic associations between percent sunny days and latitude were done on a state-by-state basis. Results: ?Sundowning? searches showed a significant increase at the end of the weekend with activity being 10.9% (SD 4.0) higher on Sunday as compared to the rest of the week. Search activity showed a seasonal pattern with search activity significantly highest in the winter months (36.6 [SD 0.6] vs 13.7 [SD 0.2], P<.001). State-by-state variations in ?sundowning? searches showed a significant negative association with increasing mean daily sunlight (R2=.16, ?=-.429 [SD .149], P=.006) and showed a positive association with increasing latitude (R2=.38, ?=.648 [SD .122], P<.001). Conclusions: Interest in ?sundowning? is highest after a weekend, which is a time when external caregiver support is reduced. Searches related to sundown syndrome also were highest in winter, in states with less sun, and in states at more northerly latitudes, supporting disrupted circadian rhythms as another contributing factor to these behaviors. UR - http://aging.jmir.org/2019/1/e13302/ UR - http://dx.doi.org/10.2196/13302 UR - http://www.ncbi.nlm.nih.gov/pubmed/31518264 ID - info:doi/10.2196/13302 ER - TY - JOUR AU - Litchman, L. Michelle AU - Wawrzynski, E. Sarah AU - Woodruff, S. Whitney AU - Arrington, B. Joseph AU - Nguyen, C. Quynh AU - Gee, M. Perry PY - 2019/05/24 TI - Continuous Glucose Monitoring in the Real World Using Photosurveillance of #Dexcom on Instagram: Exploratory Mixed Methods Study JO - JMIR Public Health Surveill SP - e11024 VL - 5 IS - 2 KW - diabetes KW - continuous glucose monitoring KW - off-label use KW - social media KW - Instagram KW - photosurveillance N2 - Background: Individuals with diabetes are using social media as a method to share and gather information about their health via the diabetes online community. Infoveillance is one methodological approach to examine health care trends. However, infoveillance, while very effective in identifying many real-world health trends, may miss opportunities that use photographs as primary sources for data. We propose a new methodology, photosurveillance, in which photographs are analyzed to examine real-world trends. Objective: The purpose of this research is to (1) assess the use of photosurveillance as a research method to examine real-world trends in diabetes and (2) report on real-world use of continuous glucose monitoring (CGM) on Instagram. Methods: This exploratory mixed methods study examined all photographs posted on Instagram that were identified with the hashtag #dexcom over a 3-month period?December 2016 to February 2017. Photographs were coded by CGM location on the body. Original posts and corresponding comments were textually coded for length of CGM device wear and CGM failure and were analyzed for emerging themes. Results: A total of 2923 photographs were manually screened; 12.08% (353/2923) depicted a photograph with a CGM site location. The majority (225/353, 63.7%) of the photographs showed a CGM site in an off-label location, while 26.2% (92/353) were in an FDA-approved location (ie, abdomen) and 10.2% (36/353) were in an unidentifiable location. There were no significant differences in the number of likes or comments based on US Food and Drug Administration (FDA) approval. Five themes emerged from the analysis of original posts (N=353) and corresponding comments (N=2364): (1) endorsement of CGM as providing a sense of well-being; (2) reciprocating information, encouragement, and support; (3) reciprocating CGM-related frustrations; (4) life hacks to optimize CGM use; and (5) sharing and learning about off-label CGM activity. Conclusions: Our results indicate that individuals successfully used CGM in off-label locations, posting photos of these areas with greater frequency than of the abdomen, with no indication of sensor failure. While these photographs only capture a snapshot in time, these posts can be used to inform providers and industry leaders of real-world trends in CGM use. Additionally, there were instances in which sensors were worn beyond the FDA-approved 7-day period; however, they represented the minority in this study. UR - http://publichealth.jmir.org/2019/2/e11024/ UR - http://dx.doi.org/10.2196/11024 UR - http://www.ncbi.nlm.nih.gov/pubmed/31127724 ID - info:doi/10.2196/11024 ER - TY - JOUR AU - Daughton, R. Ashlynn AU - Paul, J. Michael PY - 2019/05/13 TI - Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus JO - J Med Internet Res SP - e13090 VL - 21 IS - 5 KW - social media KW - travel KW - behavior KW - communicable diseases KW - zika virus KW - public health KW - epidemiology KW - information science KW - travel-related illness N2 - Background: An estimated 3.9 billion individuals live in a location endemic for common mosquito-borne diseases. The emergence of Zika virus in South America in 2015 marked the largest known Zika outbreak and caused hundreds of thousands of infections. Internet data have shown promise in identifying human behaviors relevant for tracking and understanding other diseases. Objective: Using Twitter posts regarding the 2015-16 Zika virus outbreak, we sought to identify and describe considerations and self-disclosures of a specific behavior change relevant to the spread of disease?travel cancellation. If this type of behavior is identifiable in Twitter, this approach may provide an additional source of data for disease modeling. Methods: We combined keyword filtering and machine learning classification to identify first-person reactions to Zika in 29,386 English-language tweets in the context of travel, including considerations and reports of travel cancellation. We further explored demographic, network, and linguistic characteristics of users who change their behavior compared with control groups. Results: We found differences in the demographics, social networks, and linguistic patterns of 1567 individuals identified as changing or considering changing travel behavior in response to Zika as compared with a control sample of Twitter users. We found significant differences between geographic areas in the United States, significantly more discussion by women than men, and some evidence of differences in levels of exposure to Zika-related information. Conclusions: Our findings have implications for informing the ways in which public health organizations communicate with the public on social media, and the findings contribute to our understanding of the ways in which the public perceives and acts on risks of emerging infectious diseases. UR - https://www.jmir.org/2019/5/e13090/ UR - http://dx.doi.org/10.2196/13090 UR - http://www.ncbi.nlm.nih.gov/pubmed/31094347 ID - info:doi/10.2196/13090 ER - TY - JOUR AU - Shah, Zubair AU - Martin, Paige AU - Coiera, Enrico AU - Mandl, D. Kenneth AU - Dunn, G. Adam PY - 2019/05/08 TI - Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations JO - J Med Internet Res SP - e12881 VL - 21 IS - 5 KW - text mining KW - social media KW - public health N2 - Background: Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications. Objective: The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter. Methods: Using a dataset of 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, we estimated the positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, city, and interaction type (conversations or broadcasting) as factors and found that all factors were independently associated with sentiment. Results: In the full multivariable model of positive (Pearson r in test data 0.236; 95% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than weather and day of week. Models that account for these confounders produce a different distribution and ranking of important events compared with models that do not account for these confounders. Conclusions: In public health applications that aim to detect localized events by aggregating sentiment across populations of Twitter users, it is worthwhile accounting for baseline differences before looking for unexpected changes. UR - https://www.jmir.org/2019/5/e12881/ UR - http://dx.doi.org/10.2196/12881 UR - http://www.ncbi.nlm.nih.gov/pubmed/31344669 ID - info:doi/10.2196/12881 ER - TY - JOUR AU - Arnoux-Guenegou, Armelle AU - Girardeau, Yannick AU - Chen, Xiaoyi AU - Deldossi, Myrtille AU - Aboukhamis, Rim AU - Faviez, Carole AU - Dahamna, Badisse AU - Karapetiantz, Pierre AU - Guillemin-Lanne, Sylvie AU - Lillo-Le Louët, Agnčs AU - Texier, Nathalie AU - Burgun, Anita AU - Katsahian, Sandrine PY - 2019/05/07 TI - The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard JO - JMIR Res Protoc SP - e11448 VL - 8 IS - 5 KW - social media KW - drug-related side effects and adverse reactions KW - natural language processing KW - data mining KW - MedDRA KW - Racine Pharma N2 - Background: Social media is a potential source of information on postmarketing drug safety surveillance that still remains unexploited nowadays. Information technology solutions aiming at extracting adverse reactions (ADRs) from posts on health forums require a rigorous evaluation methodology if their results are to be used to make decisions. First, a gold standard, consisting of manual annotations of the ADR by human experts from the corpus extracted from social media, must be implemented and its quality must be assessed. Second, as for clinical research protocols, the sample size must rely on statistical arguments. Finally, the extraction methods must target the relation between the drug and the disease (which might be either treated or caused by the drug) rather than simple co-occurrences in the posts. Objective: We propose a standardized protocol for the evaluation of a software extracting ADRs from the messages on health forums. The study is conducted as part of the Adverse Drug Reactions from Patient Reports in Social Media project. Methods: Messages from French health forums were extracted. Entity recognition was based on Racine Pharma lexicon for drugs and Medical Dictionary for Regulatory Activities terminology for potential adverse events (AEs). Natural language processing?based techniques automated the ADR information extraction (relation between the drug and AE entities). The corpus of evaluation was a random sample of the messages containing drugs and/or AE concepts corresponding to recent pharmacovigilance alerts. A total of 2 persons experienced in medical terminology manually annotated the corpus, thus creating the gold standard, according to an annotator guideline. We will evaluate our tool against the gold standard with recall, precision, and f-measure. Interannotator agreement, reflecting gold standard quality, will be evaluated with hierarchical kappa. Granularities in the terminologies will be further explored. Results: Necessary and sufficient sample size was calculated to ensure statistical confidence in the assessed results. As we expected a global recall of 0.5, we needed at least 384 identified ADR concepts to obtain a 95% CI with a total width of 0.10 around 0.5. The automated ADR information extraction in the corpus for evaluation is already finished. The 2 annotators already completed the annotation process. The analysis of the performance of the ADR information extraction module as compared with gold standard is ongoing. Conclusions: This protocol is based on the standardized statistical methods from clinical research to create the corpus, thus ensuring the necessary statistical power of the assessed results. Such evaluation methodology is required to make the ADR information extraction software useful for postmarketing drug safety surveillance. International Registered Report Identifier (IRRID): RR1-10.2196/11448 UR - http://www.researchprotocols.org/2019/5/e11448/ UR - http://dx.doi.org/10.2196/11448 UR - http://www.ncbi.nlm.nih.gov/pubmed/31066711 ID - info:doi/10.2196/11448 ER - TY - JOUR AU - Nitzburg, George AU - Weber, Ingmar AU - Yom-Tov, Elad PY - 2019/05/03 TI - Internet Searches for Medical Symptoms Before Seeking Information on 12-Step Addiction Treatment Programs: A Web-Search Log Analysis JO - J Med Internet Res SP - e10946 VL - 21 IS - 5 KW - alcohol use disorder KW - substance use disorder KW - 12-step programs KW - brief intervention KW - brief physician advice KW - anonymized internet search log data N2 - Background: Brief intervention is a critical method for identifying patients with problematic substance use in primary care settings and for motivating them to consider treatment options. However, despite considerable evidence of delay discounting in patients with substance use disorders, most brief advice by physicians focuses on the long-term negative medical consequences, which may not be the best way to motivate patients to seek treatment information. Objective: Identification of the specific symptoms that most motivate individuals to seek treatment information may offer insights for further improving brief interventions. To this end, we used anonymized internet search engine data to investigate which medical conditions and symptoms preceded searches for 12-step meeting locators and general 12-step information. Methods: We extracted all queries made by people in the United States on the Bing search engine from November 2016 to July 2017. These queries were filtered for those who mentioned seeking Alcoholics Anonymous (AA) or Narcotics Anonymous (NA); in addition, queries that contained a medical symptom or condition or a synonym thereof were analyzed. We identified medical symptoms and conditions that predicted searches for seeking treatment at different time lags. Specifically, symptom queries were first determined to be significantly predictive of subsequent 12-step queries if the probability of querying a medical symptom by those who later sought information about the 12-step program exceeded the probability of that same query being made by a comparison group of all other Bing users in the United States. Second, we examined symptom queries preceding queries on the 12-step program at time lags of 0-7 days, 7-14 days, and 14-30 days, where the probability of asking about a medical symptom was greater in the 30-day time window preceding 12-step program information-seeking as compared to all previous times that the symptom was queried. Results: In our sample of 11,784 persons, we found 10 medical symptoms that predicted AA information seeking and 9 symptoms that predicted NA information seeking. Of these symptoms, a substantial number could be categorized as nonsevere in nature. Moreover, when medical symptom persistence was examined across a 1-month time period, a substantial number of nonsevere, yet persistent, symptoms were identified. Conclusions: Our results suggest that many common or nonsevere medical symptoms and conditions motivate subsequent interest in AA and NA programs. In addition to highlighting severe long-term consequences, brief interventions could be restructured to highlight how increasing substance misuse can worsen discomfort from common medical symptoms in the short term, as well as how these worsening symptoms could exacerbate social embarrassment or decrease physical attractiveness. UR - https://www.jmir.org/2019/5/e10946/ UR - http://dx.doi.org/10.2196/10946 UR - http://www.ncbi.nlm.nih.gov/pubmed/31066685 ID - info:doi/10.2196/10946 ER - TY - JOUR AU - Soreni, Noam AU - Cameron, H. Duncan AU - Streiner, L. David AU - Rowa, Karen AU - McCabe, E. Randi PY - 2019/04/24 TI - Seasonality Patterns of Internet Searches on Mental Health: Exploratory Infodemiology Study JO - JMIR Ment Health SP - e12974 VL - 6 IS - 4 KW - anxiety KW - depression KW - OCD KW - schizophrenia KW - autism KW - suicide KW - seasonality KW - Google KW - internet KW - infodemiology KW - infoveillance KW - mental health N2 - Background: The study of seasonal patterns of public interest in psychiatric disorders has important theoretical and practical implications for service planning and delivery. The recent explosion of internet searches suggests that mining search databases yields unique information on public interest in mental health disorders, which is a significantly more affordable approach than population health studies. Objective: This study aimed to investigate seasonal patterns of internet mental health queries in Ontario, Canada. Methods: Weekly data on health queries in Ontario from Google Trends were downloaded for a 5-year period (2012-2017) for the terms ?schizophrenia,? ?autism,? ?bipolar,? ?depression,? ?anxiety,? ?OCD? (obsessive-compulsive disorder), and ?suicide.? Control terms were overall search results for the terms ?health? and ?how.? Time-series analyses using a continuous wavelet transform were performed to isolate seasonal components in the search volume for each term. Results: All mental health queries showed significant seasonal patterns with peak periodicity occurring over the winter months and troughs occurring during summer, except for ?suicide.? The comparison term ?health? also exhibited seasonal periodicity, while the term ?how? did not, indicating that general information seeking may not follow a seasonal trend in the way that mental health information seeking does. Conclusions: Seasonal patterns of internet search volume in a wide range of mental health terms were observed, with the exception of ?suicide.? Our study demonstrates that monitoring internet search trends is an affordable, instantaneous, and naturalistic method to sample public interest in large populations and inform health policy planners. UR - https://mental.jmir.org/2019/4/e12974/ UR - http://dx.doi.org/10.2196/12974 UR - http://www.ncbi.nlm.nih.gov/pubmed/31017582 ID - info:doi/10.2196/12974 ER - TY - JOUR AU - Blomberg, Karin AU - Eriksson, Mats AU - Böö, Rickard AU - Grönlund, Ĺke PY - 2019/04/16 TI - Using a Facebook Forum to Cope With Narcolepsy After Pandemrix Vaccination: Infodemiology Study JO - J Med Internet Res SP - e11419 VL - 21 IS - 4 KW - narcolepsy KW - mass vaccination KW - social media N2 - Background: In 2010, newly diagnosed narcolepsy cases among children and adolescents were seen in several European countries as a consequence of comprehensive national vaccination campaigns with Pandemrix against H1N1 influenza. Since then, a large number of people have had to live with narcolepsy and its consequences in daily life, such as effects on school life, social relationships, and activities. Initially, the adverse effects were not well understood and there was uncertainty about whether there would be any financial compensation. The situation remained unresolved until 2016, and during these years affected people sought various ways to join forces to handle the many issues involved, including setting up a social media forum. Objective: Our aim was to examine how information was shared, and how opinions and beliefs about narcolepsy as a consequence of Pandemrix vaccination were formed through discussions on social media. Methods: We used quantitative and qualitative methods to investigate a series of messages posted in a social media forum for people affected by narcolepsy after vaccination. Results: Group activity was high throughout the years 2010 to 2016, with peaks corresponding to major narcolepsy-related events, such as the appearance of the first cases in 2010, the first payment of compensation in 2011, and passage of a law on compensation in July 2016. Unusually, most (462/774, 59.7%) of the group took part in discussions and only 312 of 774 (40.3%) were lurkers (compared with the usual 90% rule of thumb for participation in an online community). The conversation in the group was largely factual and had a civil tone, even though there was a long struggle for the link between the vaccine and narcolepsy to be acknowledged and regarding the compensation issue. Radical, nonscientific views, such as those expounded by the antivaccination movement, did not shape the discussions in the group but were being actively expressed elsewhere on the internet. At the outset of the pandemic, there were 18 active Swedish discussion groups on the topic, but most dissolved quickly and only one Facebook group remained active throughout the period. Conclusions: The group studied is a good example of social media use for self-help through a difficult situation among people affected by illness and disease. This shows that social media do not by themselves induce trench warfare but, given a good group composition, can provide a necessary forum for managing an emergency situation where health care and government have failed or are mistrusted, and patients have to organize themselves so as to cope. UR - http://www.jmir.org/2019/4/e11419/ UR - http://dx.doi.org/10.2196/11419 UR - http://www.ncbi.nlm.nih.gov/pubmed/30990457 ID - info:doi/10.2196/11419 ER - TY - JOUR AU - Lebwohl, Benjamin AU - Yom-Tov, Elad PY - 2019/04/08 TI - Symptoms Prompting Interest in Celiac Disease and the Gluten-Free Diet: Analysis of Internet Search Term Data JO - J Med Internet Res SP - e13082 VL - 21 IS - 4 KW - celiac disease KW - gluten KW - epidemiology N2 - Background: Celiac disease, a common immune-based disease triggered by gluten, has diverse clinical manifestations, and the relative distribution of symptoms leading to diagnosis has not been well characterized in the population. Objective: This study aimed to use search engine data to identify a set of symptoms and conditions that would identify individuals at elevated likelihood of a subsequent celiac disease diagnosis. We also measured the relative prominence of these search terms before versus after a search related to celiac disease. Methods: We extracted English-language queries submitted to the Bing search engine in the United States and identified those who submitted a new celiac-related query during a 1-month period, without any celiac-related queries in the preceding 9 months. We compared the ratio between the number of times that each symptom or condition was asked in the 14 days preceding the first celiac-related query of each person and the number of searches for that same symptom or condition in the 14 days after the celiac-related query. Results: We identified 90,142 users who made a celiac-related query, of whom 6528 (7%) exhibited sustained interest, defined as making a query on more than 1 day. Though a variety of symptoms and associated conditions were also queried before a celiac-related query, the maximum area under the receiver operating characteristic curve was 0.53. The symptom most likely to be queried more before than after a celiac-related query was diarrhea (query ratio [QR] 1.28). Extraintestinal symptoms queried before a celiac disease query included headache (QR 1.26), anxiety (QR 1.10), depression (QR 1.03), and attention-deficit hyperactivity disorder (QR 1.64). Conclusions: We found an increase in antecedent searches for symptoms known to be associated with celiac disease, a rise in searches for depression and anxiety, and an increase in symptoms that are associated with celiac disease but may not be reported to health care providers. The protean clinical manifestations of celiac disease are reflected in the diffuse nature of antecedent internet queries of those interested in celiac disease, underscoring the challenge of effective case-finding strategies. UR - https://www.jmir.org/2019/4/e13082/ UR - http://dx.doi.org/10.2196/13082 UR - http://www.ncbi.nlm.nih.gov/pubmed/30958273 ID - info:doi/10.2196/13082 ER - TY - JOUR AU - Clemente, Leonardo AU - Lu, Fred AU - Santillana, Mauricio PY - 2019/04/04 TI - Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries JO - JMIR Public Health Surveill SP - e12214 VL - 5 IS - 2 KW - google flu trends KW - influenza monitoring KW - real-time disease surveillance KW - digital epidemiology KW - influenza, human KW - developing countries KW - machine learning N2 - Background: Novel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates. Objective: The aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America. Methods: A machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information. Results: Our results show that ARGO-like models? predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available. Conclusions: We demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates. UR - https://publichealth.jmir.org/2019/2/e12214/ UR - http://dx.doi.org/10.2196/12214 UR - http://www.ncbi.nlm.nih.gov/pubmed/30946017 ID - info:doi/10.2196/12214 ER - TY - JOUR AU - Lama, Yuki AU - Hu, Dian AU - Jamison, Amelia AU - Quinn, Crouse Sandra AU - Broniatowski, A. David PY - 2019/3/18 TI - Characterizing Trends in Human Papillomavirus Vaccine Discourse on Reddit (2007-2015): An Observational Study JO - JMIR Public Health Surveill SP - e12480 VL - 5 IS - 1 KW - papillomavirus infections KW - prevention & control KW - cancer prevention KW - cervical cancer KW - HPV KW - vaccination KW - papillomavirus vaccines KW - immunology KW - administration & dosage KW - social media KW - health communication KW - infodemiology N2 - Background: Despite the introduction of the human papillomavirus (HPV) vaccination as a preventive measure in 2006 for cervical and other cancers, uptake rates remain suboptimal, resulting in preventable cancer mortality. Social media, widely used for information seeking, can influence users? knowledge and attitudes regarding HPV vaccination. Little is known regarding attitudes related to HPV vaccination on Reddit (a popular news aggregation site and online community), particularly related to cancer risk and sexual activity. Examining HPV vaccine?related messages on Reddit may provide insight into how HPV discussions are characterized on forums online and influence decision making related to vaccination. Objective: We observed how the HPV vaccine is characterized on Reddit over time and by user gender. Specifically, this study aimed to determine (1) if Reddit messages are more related to cancer risks or sexual behavior and (2) what other HPV vaccine?related discussion topics appear on Reddit. Methods: We gathered all public Reddit comments from January 2007 to September 2015. We manually annotated 400 messages to generate keywords and identify salient themes. We then measured the similarity between each comment and lists of keywords associated with sexual behavior and cancer risk using Latent Semantic Analysis (LSA). Next, we used Latent Dirichlet Allocation (LDA) to characterize remaining topics within the Reddit data. Results: We analyzed 22,729 messages containing the strings hpv or human papillomavirus and vaccin. LSA findings show that HPV vaccine discussions are significantly more related to cancer compared with sexual behavior from 2008 to 2015 (P<.001). We did not find a significant difference between genders in discussions of cancer and sexual activity (P>.05). LDA analyses demonstrated that although topics related to cancer risk and sexual activity were both frequently discussed (16.1% and 14.5% of word tokens, respectively), the majority of online discussions featured other topics. The most frequently discussed topic was politics associated with the vaccine (17.2%). Other topics included HPV disease and/or immunity (13.5%), the HPV vaccine schedule (11.5%), HPV vaccine side effects (9.7%), hyperlinks to outside sources (9.1%), and the risks and benefit of HPV vaccination (8.5%). Conclusions: Reddit discourse on HPV vaccine encompasses a broad range of topics among men and women, with HPV political debates and cancer risk making up the plurality of the discussion. Our findings demonstrated that women and men both discussed HPV, highlighting that Reddit users do not perceive HPV as an issue that only pertains to women. Given the increasing use of social media as a source of health information, these results can inform the development of targeted online health communication strategies to promote HPV vaccination to young adult users of Reddit. Analyzing online discussions on Reddit can inform health communication efforts by identifying relevant, important HPV-related topics among online communities. UR - http://publichealth.jmir.org/2019/1/e12480/ UR - http://dx.doi.org/10.2196/12480 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/12480 ER - TY - JOUR AU - Bragazzi, Luigi Nicola AU - Mahroum, Naim PY - 2019/03/08 TI - Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study JO - JMIR Public Health Surveill SP - e13142 VL - 5 IS - 1 KW - plague KW - infodemiology KW - infoveillance KW - infectious outbreaks KW - Google Trends KW - nowcasting and forecasting models KW - digital surveillance N2 - Background: Plague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases. Objective: In this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public?s interest. Methods: We captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern. Results: Statistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100%), Toliara (68%), and Antananarivo (65%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model. Conclusions: An earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread. UR - http://publichealth.jmir.org/2019/1/e13142/ UR - http://dx.doi.org/10.2196/13142 UR - http://www.ncbi.nlm.nih.gov/pubmed/30763255 ID - info:doi/10.2196/13142 ER - TY - JOUR AU - Watad, Abdulla AU - Watad, Samaa AU - Mahroum, Naim AU - Sharif, Kassem AU - Amital, Howard AU - Bragazzi, Luigi Nicola AU - Adawi, Mohammad PY - 2019/02/28 TI - Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams?Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior JO - JMIR Public Health Surveill SP - e9176 VL - 5 IS - 1 KW - forecasting model KW - West Nile virus KW - Google Trends KW - infodemiology KW - infoveillance KW - seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) N2 - Background: West Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus. Objective: This study aimed at exploring the potential predictive power of West Nile virus?related Web searches. Methods: Different novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis. Results: In the regression analysis, an association between Web searches and ?real-world? epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus?related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus?related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus?related Wikipedia pages. Conclusions: Our study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings. UR - http://publichealth.jmir.org/2019/1/e9176/ UR - http://dx.doi.org/10.2196/publichealth.9176 UR - http://www.ncbi.nlm.nih.gov/pubmed/30601755 ID - info:doi/10.2196/publichealth.9176 ER - TY - JOUR AU - Wakamiya, Shoko AU - Morita, Mizuki AU - Kano, Yoshinobu AU - Ohkuma, Tomoko AU - Aramaki, Eiji PY - 2019/02/20 TI - Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations JO - J Med Internet Res SP - e12783 VL - 21 IS - 2 KW - text mining KW - social media KW - machine learning KW - natural language processing KW - artificial intelligence KW - surveillance KW - infodemiology KW - infoveillance N2 - Background: The amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media?based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient?s symptom and those that do not. Objective: This study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP. Methods: In summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss. Results: The best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively. Conclusions: This paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications. UR - http://www.jmir.org/2019/2/e12783/ UR - http://dx.doi.org/10.2196/12783 UR - http://www.ncbi.nlm.nih.gov/pubmed/30785407 ID - info:doi/10.2196/12783 ER - TY - JOUR AU - Wakamiya, Shoko AU - Matsune, Shoji AU - Okubo, Kimihiro AU - Aramaki, Eiji PY - 2019/02/20 TI - Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis JO - J Med Internet Res SP - e10450 VL - 21 IS - 2 KW - seasonal allergic rhinitis KW - social media KW - Twitter KW - causal relationship KW - infoveillance KW - disease surveillance N2 - Background: Health-related social media data are increasingly used in disease-surveillance studies, which have demonstrated moderately high correlations between the number of social media posts and the number of patients. However, there is a need to understand the causal relationship between the behavior of social media users and the actual number of patients in order to increase the credibility of disease surveillance based on social media data. Objective: This study aimed to clarify the causal relationships among pollen count, the posting behavior of social media users, and the number of patients with seasonal allergic rhinitis in the real world. Methods: This analysis was conducted using datasets of pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis from Kanagawa Prefecture, Japan. We examined daily pollen counts for Japanese cedar (the major cause of seasonal allergic rhinitis in Japan) and hinoki cypress (which commonly complicates seasonal allergic rhinitis) from February 1 to May 31, 2017. The daily numbers of tweets that included the keyword ?kafunsh?? (or seasonal allergic rhinitis) were calculated between January 1 and May 31, 2017. Daily numbers of patients with seasonal allergic rhinitis from January 1 to May 31, 2017, were obtained from three healthcare institutes that participated in the study. The Granger causality test was used to examine the causal relationships among pollen count, tweet numbers, and the number of patients with seasonal allergic rhinitis from February to May 2017. To determine if time-variant factors affect these causal relationships, we analyzed the main seasonal allergic rhinitis phase (February to April) when Japanese cedar trees actively produce and release pollen. Results: Increases in pollen count were found to increase the number of tweets during the overall study period (P=.04), but not the main seasonal allergic rhinitis phase (P=.05). In contrast, increases in pollen count were found to increase patient numbers in both the study period (P=.04) and the main seasonal allergic rhinitis phase (P=.01). Increases in the number of tweets increased the patient numbers during the main seasonal allergic rhinitis phase (P=.02), but not the overall study period (P=.89). Patient numbers did not affect the number of tweets in both the overall study period (P=.24) and the main seasonal allergic rhinitis phase (P=.47). Conclusions: Understanding the causal relationships among pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis is an important step to increasing the credibility of surveillance systems that use social media data. Further in-depth studies are needed to identify the determinants of social media posts described in this exploratory analysis. UR - http://www.jmir.org/2019/2/e10450/ UR - http://dx.doi.org/10.2196/10450 UR - http://www.ncbi.nlm.nih.gov/pubmed/30785411 ID - info:doi/10.2196/10450 ER - TY - JOUR AU - Hswen, Yulin AU - Gopaluni, Anuraag AU - Brownstein, S. John AU - Hawkins, B. Jared PY - 2019/02/12 TI - Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study JO - JMIR Mhealth Uhealth SP - e12264 VL - 7 IS - 2 KW - autism KW - digital data KW - emotion KW - mobile phone KW - obsessive-compulsive disorder KW - social media KW - textual analysis KW - tweets KW - Twitter KW - infodemiology N2 - Background: More than 3.5 million Americans live with autism spectrum disorder (ASD). Major challenges persist in diagnosing ASD as no medical test exists to diagnose this disorder. Digital phenotyping holds promise to guide in the clinical diagnoses and screening of ASD. Objective: This study aims to explore the feasibility of using the Web-based social media platform Twitter to detect psychological and behavioral characteristics of self-identified persons with ASD. Methods: Data from Twitter were retrieved from 152 self-identified users with ASD and 182 randomly selected control users from March 22, 2012 to July 20, 2017. We conducted a between-group comparative textual analysis of tweets about repetitive and obsessive-compulsive behavioral characteristics typically associated with ASD. In addition, common emotional characteristics of persons with ASD, such as fear, paranoia, and anxiety, were examined between groups through textual analysis. Furthermore, we compared the timing of tweets between users with ASD and control users to identify patterns in communication. Results: Users with ASD posted a significantly higher frequency of tweets related to the specific repetitive behavior of counting compared with control users (P<.001). The textual analysis of obsessive-compulsive behavioral characteristics, such as fixate, excessive, and concern, were significantly higher among users with ASD compared with the control group (P<.001). In addition, emotional terms related to fear, paranoia, and anxiety were tweeted at a significantly higher rate among users with ASD compared with control users (P<.001). Users with ASD posted a smaller proportion of tweets during time intervals of 00:00-05:59 (P<.001), 06:00-11:59 (P<.001), and 18:00-23.59 (P<.001), as well as a greater proportion of tweets from 12:00 to 17:59 (P<.001) compared with control users. Conclusions: Social media may be a valuable resource for observing unique psychological characteristics of self-identified persons with ASD. Collecting and analyzing data from these digital platforms may afford opportunities to identify the characteristics of ASD and assist in the diagnosis or verification of ASD. This study highlights the feasibility of leveraging digital data for gaining new insights into various health conditions. UR - http://mhealth.jmir.org/2019/2/e12264/ UR - http://dx.doi.org/10.2196/12264 UR - http://www.ncbi.nlm.nih.gov/pubmed/30747718 ID - info:doi/10.2196/12264 ER - TY - JOUR AU - Johnsen, K. Jan-Are AU - Eggesvik, B. Trude AU - Rřrvik, H. Thea AU - Hanssen, W. Miriam AU - Wynn, Rolf AU - Kummervold, Egil Per PY - 2019/02/06 TI - Differences in Emotional and Pain-Related Language in Tweets About Dentists and Medical Doctors: Text Analysis of Twitter Content JO - JMIR Public Health Surveill SP - e10432 VL - 5 IS - 1 KW - dental anxiety KW - dentistry KW - psychology KW - social media KW - internet KW - dental public health KW - Twitter KW - professional role KW - occupational stereotype N2 - Background: Social media provides people with easy ways to communicate their attitudes and feelings to a wide audience. Many people, unfortunately, have negative associations and feelings about dental treatment due to former painful experiences. Previous research indicates that there might be a pervasive and negative occupational stereotype related to dentists and that this stereotype is expressed in many different venues, including movies and literature. Objective: This study investigates the language used in relation to dentists and medical doctors on the social media platform Twitter. The purpose is to compare the professions in terms of the use of emotional and pain-related words, which might underlie and reflect the pervasive negative stereotype identified in relation to dentists. We hypothesized that (A) tweets about dentists will have more negative emotion-related words than those about medical doctors and (B) pain-related words occur more frequently in tweets about dentists than in those about medical doctors. Methods: Twitter content (?tweets?) about dentists and medical doctors was collected using the Twitter application program interface 140Dev over a 4-week period in 2015, scanning the search terms ?dentist? and ?doctor?. Word content of the selected tweets was analyzed using Linguistic Inquiry and Word Count software. The research hypotheses were investigated using nonparametric Wilcoxon-Mann-Whitney tests. Results: Over 2.3 million tweets were collected in total, of which about one-third contained the word ?dentist? and about two-thirds contained the word ?doctor.? Hypothesis A was supported since a higher proportion of negative words was used in tweets about dentists than in those about medical doctors (z=?10.47; P<.001). Similarly, tests showed a difference in the proportions of anger words (z=?12.54; P<.001), anxiety words (z=?6.96; P<.001), and sadness words (z=?9.58; P<.001), with higher proportions of these words in tweets about dentists than in those about doctors. Also, Hypothesis B was supported since a higher proportion of pain-related words was used in tweets about dentists than in those about doctors (z=?8.02; P<.001). Conclusions: The results from this study suggest that stereotypes regarding dentists and dental treatment are spread through social media such as Twitter and that social media also might represent an avenue for improving messaging and disseminating more positive attitudes toward dentists and dental treatment. UR - http://publichealth.jmir.org/2019/1/e10432/ UR - http://dx.doi.org/10.2196/10432 UR - http://www.ncbi.nlm.nih.gov/pubmed/30724738 ID - info:doi/10.2196/10432 ER - TY - JOUR AU - Liu, Qian AU - Chen, Qiuyi AU - Shen, Jiayi AU - Wu, Huailiang AU - Sun, Yimeng AU - Ming, Wai-Kit PY - 2019/01/29 TI - Data Analysis and Visualization of Newspaper Articles on Thirdhand Smoke: A Topic Modeling Approach JO - JMIR Med Inform SP - e12414 VL - 7 IS - 1 KW - media concerns KW - topic modeling KW - third-hand smoke KW - tobacco KW - indoor air quality N2 - Background: Thirdhand smoke has been a growing topic for years in China. Thirdhand smoke (THS) consists of residual tobacco smoke pollutants that remain on surfaces and in dust. These pollutants are re-emitted as a gas or react with oxidants and other compounds in the environment to yield secondary pollutants. Objective: Collecting media reports on THS from major media outlets and analyzing this subject using topic modeling can facilitate a better understanding of the role that the media plays in communicating this health issue to the public. Methods: The data were retrieved from the Wiser and Factiva news databases. A preliminary investigation focused on articles dated between January 1, 2013, and December 31, 2017. Use of Latent Dirichlet Allocation yielded the top 10 topics about THS. The use of the modified LDAvis tool enabled an overall view of the topic model, which visualizes different topics as circles. Multidimensional scaling was used to represent the intertopic distances on a two-dimensional plane. Results: We found 745 articles dated between January 1, 2013, and December 31, 2017. The United States ranked first in terms of publications (152 articles on THS from 2013-2017). We found 279 news reports about THS from the Chinese media over the same period and 363 news reports from the United States. Given our analysis of the percentage of news related to THS in China, Topic 1 (Cancer) was the most popular among the topics and was mentioned in 31.9% of all news stories. Topic 2 (Control of quitting smoking) was related to roughly 15% of news items on THS. Conclusions: Data analysis and the visualization of news articles can generate useful information. Our study shows that topic modeling can offer insights into understanding news reports related to THS. This analysis of media trends indicated that related diseases, air and particulate matter (PM2.5), and control and restrictions are the major concerns of the Chinese media reporting on THS. The Chinese press still needs to consider fuller reports on THS based on scientific evidence and with less focus on sensational headlines. We recommend that additional studies be conducted related to sentiment analysis of news data to verify and measure the influence of THS-related topics. UR - http://medinform.jmir.org/2019/1/e12414/ UR - http://dx.doi.org/10.2196/12414 UR - http://www.ncbi.nlm.nih.gov/pubmed/30694199 ID - info:doi/10.2196/12414 ER - TY - JOUR AU - Adler, Natalia AU - Cattuto, Ciro AU - Kalimeri, Kyriaki AU - Paolotti, Daniela AU - Tizzoni, Michele AU - Verhulst, Stefaan AU - Yom-Tov, Elad AU - Young, Andrew PY - 2019/01/04 TI - How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study JO - J Med Internet Res SP - e10179 VL - 21 IS - 1 KW - internet data KW - India KW - suicide KW - mobile phone N2 - Background: India is home to 20% of the world?s suicide deaths. Although statistics regarding suicide in India are distressingly high, data and cultural issues likely contribute to a widespread underreporting of the problem. Social stigma and only recent decriminalization of suicide are among the factors hampering official agencies? collection and reporting of suicide rates. Objective: As the product of a data collaborative, this paper leverages private-sector search engine data toward gaining a fuller, more accurate picture of the suicide issue among young people in India. By combining official statistics on suicide with data generated through search queries, this paper seeks to: add an additional layer of information to more accurately represent the magnitude of the problem, determine whether search query data can serve as an effective proxy for factors contributing to suicide that are not represented in traditional datasets, and consider how data collaboratives built on search query data could inform future suicide prevention efforts in India and beyond. Methods: We combined official statistics on demographic information with data generated through search queries from Bing to gain insight into suicide rates per state in India as reported by the National Crimes Record Bureau of India. We extracted English language queries on ?suicide,? ?depression,? ?hanging,? ?pesticide,? and ?poison?. We also collected data on demographic information at the state level in India, including urbanization, growth rate, sex ratio, internet penetration, and population. We modeled the suicide rate per state as a function of the queries on each of the 5 topics considered as linear independent variables. A second model was built by integrating the demographic information as additional linear independent variables. Results: Results of the first model fit (R2) when modeling the suicide rates from the fraction of queries in each of the 5 topics, as well as the fraction of all suicide methods, show a correlation of about 0.5. This increases significantly with the removal of 3 outliers and improves slightly when 5 outliers are removed. Results for the second model fit using both query and demographic data show that for all categories, if no outliers are removed, demographic data can model suicide rates better than query data. However, when 3 outliers are removed, query data about pesticides or poisons improves the model over using demographic data. Conclusions: In this work, we used search data and demographics to model suicide rates. In this way, search data serve as a proxy for unmeasured (hidden) factors corresponding to suicide rates. Moreover, our procedure for outlier rejection serves to single out states where the suicide rates have substantially different correlations with demographic factors and query rates. UR - https://www.jmir.org/2019/1/e10179/ UR - http://dx.doi.org/10.2196/10179 UR - http://www.ncbi.nlm.nih.gov/pubmed/30609976 ID - info:doi/10.2196/10179 ER - TY - JOUR AU - Poirier, Canelle AU - Lavenu, Audrey AU - Bertaud, Valérie AU - Campillo-Gimenez, Boris AU - Chazard, Emmanuel AU - Cuggia, Marc AU - Bouzillé, Guillaume PY - 2018/12/21 TI - Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study JO - JMIR Public Health Surveill SP - e11361 VL - 4 IS - 4 KW - electronic health records KW - big data KW - infodemiology KW - infoveillance KW - influenza KW - machine learning KW - Sentinelles network N2 - Background: Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users? activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data. Objective: Here, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods: We used Google data for internet data and the clinical data warehouse eHOP, which included all EHRs from Rennes University Hospital (France), for hospital data. We compared 3 statistical models?random forest, elastic net, and support vector machine (SVM). Results: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 obtained with hospital data and the SVM model. Conclusions: We found that EHR data together with historical epidemiological information (French Sentinelles network) allowed for accurately predicting ILI incidence rates for the entire France as well as for the Brittany region and outperformed the internet data whatever was the statistical model used. Moreover, the performance of the two statistical models, elastic net and SVM, was comparable. UR - http://publichealth.jmir.org/2018/4/e11361/ UR - http://dx.doi.org/10.2196/11361 UR - http://www.ncbi.nlm.nih.gov/pubmed/30578212 ID - info:doi/10.2196/11361 ER - TY - JOUR AU - Aoki, Tomohiro AU - Suzuki, Teppei AU - Yagahara, Ayako AU - Hasegawa, Shin AU - Tsuji, Shintaro AU - Ogasawara, Katsuhiko PY - 2018/12/18 TI - Analysis of the Regionality of the Number of Tweets Related to the 2011 Fukushima Nuclear Power Station Disaster: Content Analysis JO - JMIR Public Health Surveill SP - e70 VL - 4 IS - 4 KW - Fukushima nuclear disaster KW - Twitter messaging KW - radiation KW - radioactivity KW - radioactive hazard release KW - geographic location KW - information dissemination N2 - Background: The Great East Japan Earthquake on March 11, 2011, triggered a huge tsunami, causing the Fukushima Daiichi nuclear disaster. Radioactive substances were carried in all directions, along with the risks of radioactive contamination. Mass media companies, such as television stations and news websites, extensively reported on radiological information related to the disaster. Upon digesting the available radiological information, many citizens turned to social media, such as Twitter and Facebook, to express their opinions and feelings. Thus, the Fukushima Daiichi nuclear disaster also changed the social media landscape in Japan. However, few studies have explored how the people in Japan who received information on radiation propagated the information. Objective: This study aimed to reveal how the number of tweets by citizens containing radiological information changed regionally on Twitter. Methods: The research used about 19 million tweets that included the terms ?radiation,? ?radioactivity,? and ?radioactive substance? posted for 1 year after the Fukushima Daiichi nuclear disaster. Nearly 45,000 tweets were extracted based on their inclusion of geographic information (latitude and longitude). The number of monthly tweets in 4 districts (Fukushima Prefecture, prefectures around Fukushima Prefecture, within the Tokyo Electric Power Company area, and others) were analyzed. Results: The number of tweets containing the keywords per 100,000 people at the time of the casualty outbreak was 7.05 per month in Fukushima Prefecture, 2.07 per month in prefectures around Fukushima Prefecture, 5.23 per month in the area within Tokyo Electric Power Company, and 1.35 per month in others. The number of tweets per 100,000 people more than doubled in Fukushima Prefecture 2 months after the Fukushima Daiichi nuclear disaster, whereas the number decreased to around 0.7~0.8 tweets in other districts. Conclusions: The number of tweets per 100,000 people became half of that on March 2011 3 or 4 months after the Fukushima Daiichi Nuclear Plant disaster in 3 districts except district 1 (Fukushima Prefecture); the number became a half in Fukushima Prefecture half a year later. UR - http://publichealth.jmir.org/2018/4/e70/ UR - http://dx.doi.org/10.2196/publichealth.7496 UR - http://www.ncbi.nlm.nih.gov/pubmed/30563815 ID - info:doi/10.2196/publichealth.7496 ER - TY - JOUR AU - Hswen, Yulin AU - Naslund, A. John AU - Brownstein, S. John AU - Hawkins, B. Jared PY - 2018/12/13 TI - Monitoring Online Discussions About Suicide Among Twitter Users With Schizophrenia: Exploratory Study JO - JMIR Ment Health SP - e11483 VL - 5 IS - 4 KW - schizophrenia KW - social media KW - suicide KW - Twitter KW - digital technology KW - mental health N2 - Background: People with schizophrenia experience elevated risk of suicide. Mental health symptoms, including depression and anxiety, contribute to increased risk of suicide. Digital technology could support efforts to detect suicide risk and inform suicide prevention efforts. Objective: This exploratory study examined the feasibility of monitoring online discussions about suicide among Twitter users who self-identify as having schizophrenia. Methods: Posts containing the terms suicide or suicidal were collected from a sample of Twitter users who self-identify as having schizophrenia (N=203) and a random sample of control users (N=173) over a 200-day period. Frequency and timing of posts about suicide were compared between groups. The associations between posting about suicide and common mental health symptoms were examined. Results: Twitter users who self-identify as having schizophrenia posted more tweets about suicide (mean 7.10, SD 15.98) compared to control users (mean 1.89, SD 4.79; t374=-4.13, P<.001). Twitter users who self-identify as having schizophrenia showed greater odds of tweeting about suicide compared to control users (odds ratio 2.15, 95% CI 1.42-3.28). Among all users, tweets about suicide were associated with tweets about depression (r=0.62, P<.001) and anxiety (r=0.45, P<.001). Conclusions: Twitter users who self-identify as having schizophrenia appear to commonly discuss suicide on social media, which is associated with greater discussion about other mental health symptoms. These findings should be interpreted cautiously, as it is not possible to determine whether online discussions about suicide correlate with suicide risk. However, these patterns of online discussion may be indicative of elevated risk of suicide observed in this patient group. There may be opportunities to leverage social media for supporting suicide prevention among individuals with schizophrenia. UR - http://mental.jmir.org/2018/4/e11483/ UR - http://dx.doi.org/10.2196/11483 UR - http://www.ncbi.nlm.nih.gov/pubmed/30545811 ID - info:doi/10.2196/11483 ER - TY - JOUR AU - Cheng, Yi-mei Tiffany AU - Liu, Lisa AU - Woo, KP Benjamin PY - 2018/12/10 TI - Analyzing Twitter as a Platform for Alzheimer-Related Dementia Awareness: Thematic Analyses of Tweets JO - JMIR Aging SP - e11542 VL - 1 IS - 2 KW - social media KW - Twitter KW - dementia KW - social support N2 - Background: Dementia is a prevalent disorder among adults and often subjects an individual and his or her family. Social media websites may serve as a platform to raise awareness for dementia and allow researchers to explore health-related data. Objective: The objective of this study was to utilize Twitter, a social media website, to examine the content and location of tweets containing the keyword ?dementia? to better understand the reasons why individuals discuss dementia. We adopted an approach that analyzed user location, user category, and tweet content subcategories to classify large publicly available datasets. Methods: A total of 398 tweets were collected using the Twitter search application programming interface with the keyword ?dementia,? circulated between January and February 2018. Twitter users were categorized into 4 categories: general public, health care field, advocacy organization, and public broadcasting. Tweets posted by ?general public? users were further subcategorized into 5 categories: mental health advocate, affected persons, stigmatization, marketing, and other. Placement into the categories was done through thematic analysis. Results: A total of 398 tweets were written by 359 different screen names from 28 different countries. The largest number of Twitter users were from the United States and the United Kingdom. Within the United States, the largest number of users were from California and Texas. The majority (281/398, 70.6%) of Twitter users were categorized into the ?general public? category. Content analysis of tweets from the ?general public? category revealed stigmatization (113/281, 40.2%) and mental health advocacy (102/281, 36.3%) as the most common themes. Among tweets from California and Texas, California had more stigmatization tweets, while Texas had more mental health advocacy tweets. Conclusions: Themes from the content of tweets highlight the mixture of the political climate and the supportive network present on Twitter. The ability to use Twitter to combat stigma and raise awareness of mental health indicates the benefits that can potentially be facilitated via the platform, but negative stigmatizing tweets may interfere with the effectiveness of this social support. UR - http://aging.jmir.org/2018/2/e11542/ UR - http://dx.doi.org/10.2196/11542 UR - http://www.ncbi.nlm.nih.gov/pubmed/31518232 ID - info:doi/10.2196/11542 ER - TY - JOUR AU - Ricard, J. Benjamin AU - Marsch, A. Lisa AU - Crosier, Benjamin AU - Hassanpour, Saeed PY - 2018/12/06 TI - Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram JO - J Med Internet Res SP - e11817 VL - 20 IS - 12 KW - machine learning KW - depression KW - social media KW - mental health N2 - Background: The content produced by individuals on various social media platforms has been successfully used to identify mental illness, including depression. However, most of the previous work in this area has focused on user-generated content, that is, content created by the individual, such as an individual?s posts and pictures. In this study, we explored the predictive capability of community-generated content, that is, the data generated by a community of friends or followers, rather than by a sole individual, to identify depression among social media users. Objective: The objective of this research was to evaluate the utility of community-generated content on social media, such as comments on an individual?s posts, to predict depression as defined by the clinically validated Patient Health Questionnaire-8 (PHQ-8) assessment questionnaire. We hypothesized that the results of this research may provide new insights into next generation of population-level mental illness risk assessment and intervention delivery. Methods: We created a Web-based survey on a crowdsourcing platform through which participants granted access to their Instagram profiles as well as provided their responses to PHQ-8 as a reference standard for depression status. After data quality assurance and postprocessing, the study analyzed the data of 749 participants. To build our predictive model, linguistic features were extracted from Instagram post captions and comments, including multiple sentiment scores, emoji sentiment analysis results, and meta-variables such as the number of likes and average comment length. In this study, 10.4% (78/749) of the data were held out as a test set. The remaining 89.6% (671/749) of the data were used to train an elastic-net regularized linear regression model to predict PHQ-8 scores. We compared different versions of this model (ie, a model trained on only user-generated data, a model trained on only community-generated data, and a model trained on the combination of both types of data) on a test set to explore the utility of community-generated data in our predictive analysis. Results: The 2 models, the first trained on only community-generated data (area under curve [AUC]=0.71) and the second trained on a combination of user-generated and community-generated data (AUC=0.72), had statistically significant performances for predicting depression based on the Mann-Whitney U test (P=.03 and P=.02, respectively). The model trained on only user-generated data (AUC=0.63; P=.11) did not achieve statistically significant results. The coefficients of the models revealed that our combined data classifier effectively amalgamated both user-generated and community-generated data and that the 2 feature sets were complementary and contained nonoverlapping information in our predictive analysis. Conclusions: The results presented in this study indicate that leveraging community-generated data from social media, in addition to user-generated data, can be informative for predicting depression among social media users. UR - http://www.jmir.org/2018/12/e11817/ UR - http://dx.doi.org/10.2196/11817 UR - http://www.ncbi.nlm.nih.gov/pubmed/30522991 ID - info:doi/10.2196/11817 ER - TY - JOUR AU - Tufts, Christopher AU - Polsky, Daniel AU - Volpp, G. Kevin AU - Groeneveld, W. Peter AU - Ungar, Lyle AU - Merchant, M. Raina AU - Pelullo, P. Arthur PY - 2018/12/06 TI - Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis JO - JMIR Public Health Surveill SP - e10834 VL - 4 IS - 4 KW - Twitter messaging KW - disease KW - prevalence KW - public health surveillance KW - social media N2 - Background: Tweets can provide broad, real-time perspectives about health and medical diagnoses that can inform disease surveillance in geographic regions. Less is known, however, about how much individuals post about common health conditions or what they post about. Objective: We sought to collect and analyze tweets from 1 state about high prevalence health conditions and characterize the tweet volume and content. Methods: We collected 408,296,620 tweets originating in Pennsylvania from 2012-2015 and compared the prevalence of 14 common diseases to the frequency of disease mentions on Twitter. We identified and corrected bias induced due to variance in disease term specificity and used the machine learning approach of differential language analysis to determine the content (words and themes) most highly correlated with each disease. Results: Common disease terms were included in 226,802 tweets (174,381 tweets after disease term correction). Posts about breast cancer (39,156/174,381 messages, 22.45%; 306,127/12,702,379 prevalence, 2.41%) and diabetes (40,217/174,381 messages, 23.06%; 2,189,890/12,702,379 prevalence, 17.24%) were overrepresented on Twitter relative to disease prevalence, whereas hypertension (17,245/174,381 messages, 9.89%; 4,614,776/12,702,379 prevalence, 36.33%), chronic obstructive pulmonary disease (1648/174,381 messages, 0.95%; 1,083,627/12,702,379 prevalence, 8.53%), and heart disease (13,669/174,381 messages, 7.84%; 2,461,721/12,702,379 prevalence, 19.38%) were underrepresented. The content of messages also varied by disease. Personal experience messages accounted for 12.88% (578/4487) of prostate cancer tweets and 24.17% (4046/16,742) of asthma tweets. Awareness-themed tweets were more often about breast cancer (9139/39,156 messages, 23.34%) than asthma (1040/16,742 messages, 6.21%). Tweets about risk factors were more often about heart disease (1375/13,669 messages, 10.06%) than lymphoma (105/4927 messages, 2.13%). Conclusions: Twitter provides a window into the Web-based visibility of diseases and how the volume of Web-based content about diseases varies by condition. Further, the potential value in tweets is in the rich content they provide about individuals? perspectives about diseases (eg, personal experiences, awareness, and risk factors) that are not otherwise easily captured through traditional surveys or administrative data. UR - http://publichealth.jmir.org/2018/4/e10834/ UR - http://dx.doi.org/10.2196/10834 UR - http://www.ncbi.nlm.nih.gov/pubmed/30522989 ID - info:doi/10.2196/10834 ER - TY - JOUR AU - Jones, Josette AU - Pradhan, Meeta AU - Hosseini, Masoud AU - Kulanthaivel, Anand AU - Hosseini, Mahmood PY - 2018/11/29 TI - Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum JO - JMIR Med Inform SP - e45 VL - 6 IS - 4 KW - data interpretation KW - natural language processing KW - patient-generated information KW - social media KW - statistical analysis KW - infodemiology N2 - Background: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. Objective: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. Methods: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. Results: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ?0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics?based on the Akaike information criterion values ranging from ?642.75 to ?412.32?were statistically significant. Conclusions: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life. UR - http://medinform.jmir.org/2018/4/e45/ UR - http://dx.doi.org/10.2196/medinform.9162 UR - http://www.ncbi.nlm.nih.gov/pubmed/30497991 ID - info:doi/10.2196/medinform.9162 ER - TY - JOUR AU - Chen, Shi AU - Xu, Qian AU - Buchenberger, John AU - Bagavathi, Arunkumar AU - Fair, Gabriel AU - Shaikh, Samira AU - Krishnan, Siddharth PY - 2018/11/22 TI - Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic JO - JMIR Public Health Surveill SP - e10827 VL - 4 IS - 4 KW - Centers for Disease Control and Prevention KW - public engagement KW - Twitter KW - time series analysis KW - Zika epidemic KW - social media KW - infodemiology KW - infoveillance N2 - Background: Social media have been increasingly adopted by health agencies to disseminate information, interact with the public, and understand public opinion. Among them, the Centers for Disease Control and Prevention (CDC) is one of the first US government health agencies to adopt social media during health emergencies and crisis. It had been active on Twitter during the 2016 Zika epidemic that caused 5168 domestic noncongenital cases in the United States. Objective: The aim of this study was to quantify the temporal variabilities in CDC?s tweeting activities throughout the Zika epidemic, public engagement defined as retweeting and replying, and Zika case counts. It then compares the patterns of these 3 datasets to identify possible discrepancy among domestic Zika case counts, CDC?s response on Twitter, and public engagement in this topic. Methods: All of the CDC-initiated tweets published in 2016 with corresponding retweets and replies were collected from 67 CDC?associated Twitter accounts. Both univariate and multivariate time series analyses were performed in each quarter of 2016 for domestic Zika case counts, CDC tweeting activities, and public engagement in the CDC-initiated tweets. Results: CDC sent out >84.0% (5130/6104) of its Zika tweets in the first quarter of 2016 when Zika case counts were low in the 50 US states and territories (only 560/5168, 10.8% cases and 662/38,885, 1.70% cases, respectively). While Zika case counts increased dramatically in the second and third quarters, CDC efforts on Twitter substantially decreased. The time series of public engagement in the CDC-initiated tweets generally differed among quarters and from that of original CDC tweets based on autoregressive integrated moving average model results. Both original CDC tweets and public engagement had the highest mutual information with Zika case counts in the second quarter. Furthermore, public engagement in the original CDC tweets was substantially correlated with and preceded actual Zika case counts. Conclusions: Considerable discrepancies existed among CDC?s original tweets regarding Zika, public engagement in these tweets, and actual Zika epidemic. The patterns of these discrepancies also varied between different quarters in 2016. CDC was much more active in the early warning of Zika, especially in the first quarter of 2016. Public engagement in CDC?s original tweets served as a more prominent predictor of actual Zika epidemic than the number of CDC?s original tweets later in the year. UR - http://publichealth.jmir.org/2018/4/e10827/ UR - http://dx.doi.org/10.2196/10827 UR - http://www.ncbi.nlm.nih.gov/pubmed/30467106 ID - info:doi/10.2196/10827 ER - TY - JOUR AU - Odlum, Michelle AU - Yoon, Sunmoo AU - Broadwell, Peter AU - Brewer, Russell AU - Kuang, Da PY - 2018/11/22 TI - How Twitter Can Support the HIV/AIDS Response to Achieve the 2030 Eradication Goal: In-Depth Thematic Analysis of World AIDS Day Tweets JO - JMIR Public Health Surveill SP - e10262 VL - 4 IS - 4 KW - community KW - human rights KW - social network KW - infodemiology KW - infoveillence KW - Twitter N2 - Background: HIV/AIDS is a tremendous public health crisis, with a call for its eradication by 2030. A human rights response through civil society engagement is critical to support and sustain HIV eradication efforts. However, ongoing civil engagement is a challenge. Objective: This study aimed to demonstrate the use of Twitter data to assess public sentiment in support of civil society engagement. Methods: Tweets were collected during World AIDS Days 2014 and 2015. A total of 39,940 unique tweets (>10 billion users) in 2014 and 78,215 unique tweets (>33 billion users) in 2015 were analyzed. Response frequencies were aggregated using natural language processing. Hierarchical rank-2 nonnegative matrix factorization algorithm generated a hierarchy of tweets into binary trees. Tweet hierarchy clusters were thematically organized by the Joint United Nations Programme on HIV/AIDS core action principles and categorized under HIV/AIDS Prevention, Treatment or Care, or Support. Results: Topics tweeted 35 times or more were visualized. Results show a decrease in 2015 in the frequency of tweets associated with the fight to end HIV/AIDS, the recognition of women, and to achieve an AIDS-free generation. Moreover, an increase in tweets was associated with an integrative approach to the HIV/AIDS response. Hierarchical thematic differences in 2015 included no prevention discussion and the recognition of the pandemic?s impact and discrimination. In addition, a decrease was observed in motivation to fast track the pandemic?s end and combat HIV/AIDS. Conclusions: The human rights?based response to HIV/AIDS eradication is critical. Findings demonstrate the usefulness of Twitter as a low-cost method to assess public sentiment for enhanced knowledge, increased hope, and revitalized expectations for HIV/AIDS eradication. UR - http://publichealth.jmir.org/2018/4/e10262/ UR - http://dx.doi.org/10.2196/10262 UR - http://www.ncbi.nlm.nih.gov/pubmed/30467102 ID - info:doi/10.2196/10262 ER - TY - JOUR AU - Zhang, Youshan AU - Allem, Jon-Patrick AU - Unger, Beth Jennifer AU - Boley Cruz, Tess PY - 2018/11/21 TI - Automated Identification of Hookahs (Waterpipes) on Instagram: An Application in Feature Extraction Using Convolutional Neural Network and Support Vector Machine Classification JO - J Med Internet Res SP - e10513 VL - 20 IS - 11 KW - convolutional neural network KW - feature extraction KW - image classification KW - Instagram KW - social media KW - support vector machine N2 - Background: Instagram, with millions of posts per day, can be used to inform public health surveillance targets and policies. However, current research relying on image-based data often relies on hand coding of images, which is time-consuming and costly, ultimately limiting the scope of the study. Current best practices in automated image classification (eg, support vector machine (SVM), backpropagation neural network, and artificial neural network) are limited in their capacity to accurately distinguish between objects within images. Objective: This study aimed to demonstrate how a convolutional neural network (CNN) can be used to extract unique features within an image and how SVM can then be used to classify the image. Methods: Images of waterpipes or hookah (an emerging tobacco product possessing similar harms to that of cigarettes) were collected from Instagram and used in the analyses (N=840). A CNN was used to extract unique features from images identified to contain waterpipes. An SVM classifier was built to distinguish between images with and without waterpipes. Methods for image classification were then compared to show how a CNN+SVM classifier could improve accuracy. Results: As the number of validated training images increased, the total number of extracted features increased. In addition, as the number of features learned by the SVM classifier increased, the average level of accuracy increased. Overall, 99.5% (418/420) of images classified were correctly identified as either hookah or nonhookah images. This level of accuracy was an improvement over earlier methods that used SVM, CNN, or bag-of-features alone. Conclusions: A CNN extracts more features of images, allowing an SVM classifier to be better informed, resulting in higher accuracy compared with methods that extract fewer features. Future research can use this method to grow the scope of image-based studies. The methods presented here might help detect increases in the popularity of certain tobacco products over time on social media. By taking images of waterpipes from Instagram, we place our methods in a context that can be utilized to inform health researchers analyzing social media to understand user experience with emerging tobacco products and inform public health surveillance targets and policies. UR - http://www.jmir.org/2018/11/e10513/ UR - http://dx.doi.org/10.2196/10513 UR - http://www.ncbi.nlm.nih.gov/pubmed/30452385 ID - info:doi/10.2196/10513 ER - TY - JOUR AU - Kürzinger, Marie-Laure AU - Schück, Stéphane AU - Texier, Nathalie AU - Abdellaoui, Redhouane AU - Faviez, Carole AU - Pouget, Julie AU - Zhang, Ling AU - Tcherny-Lessenot, Stéphanie AU - Lin, Stephen AU - Juhaeri, Juhaeri PY - 2018/11/20 TI - Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis JO - J Med Internet Res SP - e10466 VL - 20 IS - 11 KW - adverse event KW - internet KW - medical forums KW - pharmacovigilance KW - signal detection KW - signals of disproportionate reporting KW - social media N2 - Background: While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs). Objective: This study aimed (1) to assess the consistency of SDRs detected from patients? medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems. Methods: Messages posted on patients? forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided. Results: The comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase. Conclusions: The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients? medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals. UR - http://www.jmir.org/2018/11/e10466/ UR - http://dx.doi.org/10.2196/10466 UR - http://www.ncbi.nlm.nih.gov/pubmed/30459145 ID - info:doi/10.2196/10466 ER - TY - JOUR AU - Allem, Jon-Patrick AU - Dharmapuri, Likhit AU - Leventhal, M. Adam AU - Unger, B. Jennifer AU - Boley Cruz, Tess PY - 2018/11/19 TI - Hookah-Related Posts to Twitter From 2017 to 2018: Thematic Analysis JO - J Med Internet Res SP - e11669 VL - 20 IS - 11 KW - hookah KW - waterpipe KW - Twitter KW - social media KW - nicotine KW - flavors KW - social smoking KW - infodemiology N2 - Background: Hookah (or tobacco waterpipe) use has recently become prevalent in the United States. The contexts and experiences associated with hookah use are unclear, yet such information is abundant via publicly available hookah users? social media postings. Objective: In this study, we utilized Twitter data to characterize Twitter users? recent experiences with hookah. Methods: Twitter posts containing the term ?hookah? were obtained from April 1, 2017 to 29 March, 2018. Text classifiers were used to identify clusters of topics that tended to co-occur in posts (n=176,706). Results: The most prevalent topic cluster was Person Tagging (use of @username to tag another Twitter account in a post) at 21.58% (38,137/176,706) followed by Promotional or Social Events (eg, mentions of ladies? nights, parties, etc) at 20.20% (35,701/176,706) and Appeal or Abuse Liability (eg, craving, enjoying hookah) at 18.12% (32,013/176,706). Additional topics included Hookah Use Behavior (eg, mentions of taking a ?hit? of hookah) at 11.67% (20,603/176,706), Polysubstance Use (eg, hookah use along with other substances) at 10.95% (19,353/176,706), Buying or Selling (eg, buy, order, purchase, sell) at 9.37% (16,552/176,706), and Flavors (eg, mint, cinnamon, watermelon) at 1.66% (2927/176,706). The topic Dislike of Hookah (eg, hate, quit, dislike) was rare at 0.59% (1043/176,706). Conclusions: Social events, appeal or abuse liability, flavors, and polysubstance use were the common contexts and experiences associated with Twitter discussions about hookah in 2017-2018. Considered in concert with traditional data sources about hookah, these results suggest that social events, appeal or abuse liability, flavors, and polysubstance use warrant consideration as targets in future surveillance, policy making, and interventions addressing hookah. UR - http://www.jmir.org/2018/11/e11669/ UR - http://dx.doi.org/10.2196/11669 UR - http://www.ncbi.nlm.nih.gov/pubmed/30455162 ID - info:doi/10.2196/11669 ER - TY - JOUR AU - DeJohn, D. Amber AU - Schulz, English Emily AU - Pearson, L. Amber AU - Lachmar, Megan E. AU - Wittenborn, K. Andrea PY - 2018/11/05 TI - Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study JO - JMIR Ment Health SP - e61 VL - 5 IS - 4 KW - depression KW - Web-based KW - social connection KW - Twitter KW - tweet KW - online communities N2 - Background: Depression is the leading cause of diseases globally and is often characterized by a lack of social connection. With the rise of social media, it is seen that Twitter users are seeking Web-based connections for depression. Objective: This study aimed to identify communities where Twitter users tweeted using the hashtag #MyDepressionLooksLike to connect about depression. Once identified, we wanted to understand which community characteristics correlated to Twitter users turning to a Web-based community to connect about depression. Methods: Tweets were collected using NCapture software from May 25 to June 1, 2016 during the Mental Health Month (n=104) in the northeastern United States and Washington DC. After mapping tweets, we used a Poisson multilevel regression model to predict tweets per community (county) offset by the population and adjusted for percent female, percent population aged 15-44 years, percent white, percent below poverty, and percent single-person households. We then compared predicted versus observed counts and calculated tweeting index values (TIVs) to represent undertweeting and overtweeting. Last, we examined trends in community characteristics by TIV using Pearson correlation. Results: We found significant associations between tweet counts and area-level proportions of females, single-person households, and population aged 15-44 years. TIVs were lower than expected (TIV 1) in eastern, seaboard areas of the study region. There were communities tweeting as expected in the western, inland areas (TIV 2). Counties tweeting more than expected were generally scattered throughout the study region with a small cluster at the base of Maine. When examining community characteristics and overtweeting and undertweeting by county, we observed a clear upward gradient in several types of nonprofits and TIV values. However, we also observed U-shaped relationships for many community factors, suggesting that the same characteristics were correlated with both overtweeting and undertweeting. Conclusions: Our findings suggest that Web-based communities, rather than replacing physical connection, may complement or serve as proxies for offline social communities, as seen through the consistent correlations between higher levels of tweeting and abundant nonprofits. Future research could expand the spatiotemporal scope to confirm these findings. UR - http://mental.jmir.org/2018/4/e61/ UR - http://dx.doi.org/10.2196/mental.9533 UR - http://www.ncbi.nlm.nih.gov/pubmed/30401662 ID - info:doi/10.2196/mental.9533 ER - TY - JOUR AU - Park, Hyun So AU - Hong, Hee Song PY - 2018/10/24 TI - Identification of Primary Medication Concerns Regarding Thyroid Hormone Replacement Therapy From Online Patient Medication Reviews: Text Mining of Social Network Data JO - J Med Internet Res SP - e11085 VL - 20 IS - 10 KW - medication counseling KW - social network data KW - primary medication concerns KW - satisfaction with levothyroxine treatment N2 - Background: Patients with hypothyroidism report poor health-related quality of life despite having undergone thyroid hormone replacement therapy (THRT). Understanding patient concerns regarding levothyroxine can help improve the treatment outcomes of THRT. Objective: This study aimed to (1) identify the distinctive themes in patient concerns regarding THRT, (2) determine whether patients have unique primary medication concerns specific to their demographics, and (3) determine the predictability of primary medication concerns on patient treatment satisfaction. Methods: We collected patient reviews from WebMD in the United States (1037 reviews about generic levothyroxine and 1075 reviews about the brand version) posted between September 1, 2007, and January 30, 2017. We used natural language processing to identify the themes of medication concerns. Multiple regression analyses were conducted in order to examine the predictability of the primary medication concerns on patient treatment satisfaction. Results: Natural language processing of the patient reviews of levothyroxine posted on a social networking site produced 6 distinctive themes of patient medication concerns related to levothyroxine treatment: how to take the drug, treatment initiation, dose adjustment, symptoms of pain, generic substitutability, and appearance. Patients had different primary medication concerns unique to their gender, age, and treatment duration. Furthermore, treatment satisfaction on levothyroxine depended on what primary medication concerns the patient had. Conclusions: Natural language processing of text content available on social media could identify different themes of patient medication concerns that can be validated in future studies to inform the design of tailored medication counseling for improved patient treatment satisfaction. UR - http://www.jmir.org/2018/10/e11085/ UR - http://dx.doi.org/10.2196/11085 UR - http://www.ncbi.nlm.nih.gov/pubmed/30355555 ID - info:doi/10.2196/11085 ER - TY - JOUR AU - Sewalk, C. Kara AU - Tuli, Gaurav AU - Hswen, Yulin AU - Brownstein, S. John AU - Hawkins, B. Jared PY - 2018/10/12 TI - Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study JO - J Med Internet Res SP - e10043 VL - 20 IS - 10 KW - health care KW - social media KW - patient experience N2 - Background: There are documented differences in access to health care across the United States. Previous research indicates that Web-based data regarding patient experiences and opinions of health care are available from Twitter. Sentiment analyses of Twitter data can be used to examine differences in patient views of health care across the United States. Objective: The objective of our study was to provide a characterization of patient experience sentiments across the United States on Twitter over a 4-year period. Methods: Using data from Twitter, we developed a set of 4 software components to automatically label and examine a database of tweets discussing patient experience. The set includes a classifier to determine patient experience tweets, a geolocation inference engine for social data, a modified sentiment classifier, and an engine to determine if the tweet is from a metropolitan or nonmetropolitan area in the United States. Using the information retrieved, we conducted spatial and temporal examinations of tweet sentiments at national and regional levels. We examined trends in the time of the day and that of the week when tweets were posted. Statistical analyses were conducted to determine if any differences existed between the discussions of patient experience in metropolitan and nonmetropolitan areas. Results: We collected 27.3 million tweets between February 1, 2013 and February 28, 2017, using a set of patient experience-related keywords; the classifier was able to identify 2,759,257 tweets labeled as patient experience. We identified the approximate location of 31.76% (876,384/2,759,257) patient experience tweets using a geolocation classifier to conduct spatial analyses. At the national level, we observed 27.83% (243,903/876,384) positive patient experience tweets, 36.22% (317,445/876,384) neutral patient experience tweets, and 35.95% (315,036/876,384) negative patient experience tweets. There were slight differences in tweet sentiments across all regions of the United States during the 4-year study period. We found the average sentiment polarity shifted toward less negative over the study period across all the regions of the United States. We observed the sentiment of tweets to have a lower negative fraction during daytime hours, whereas the sentiment of tweets posted between 8 pm and 10 am had a higher negative fraction. Nationally, sentiment scores for tweets in metropolitan areas were found to be more extremely negative and mildly positive compared with tweets in nonmetropolitan areas. This result is statistically significant (P<.001). Tweets with extremely negative sentiments had a medium effect size (d=0.34) at the national level. Conclusions: This study presents methodologies for a deeper understanding of Web-based discussion related to patient experience across space and time and demonstrates how Twitter can provide a unique and unsolicited perspective from users on the health care they receive in the United States. UR - http://www.jmir.org/2018/10/e10043/ UR - http://dx.doi.org/10.2196/10043 UR - http://www.ncbi.nlm.nih.gov/pubmed/30314959 ID - info:doi/10.2196/10043 ER - TY - JOUR AU - Wakamiya, Shoko AU - Kawai, Yukiko AU - Aramaki, Eiji PY - 2018/9/25 TI - Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study JO - JMIR Public Health Surveill SP - e65 VL - 4 IS - 3 KW - influenza surveillance KW - location mention KW - Twitter KW - social network KW - spatial analysis KW - internet KW - microblog KW - infodemiology KW - infoveillance N2 - Background: The recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor?based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results. Objective: This study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts. Methods: We presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information. Results: The estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points). Conclusions: The proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification. UR - http://publichealth.jmir.org/2018/3/e65/ UR - http://dx.doi.org/10.2196/publichealth.8627 UR - http://www.ncbi.nlm.nih.gov/pubmed/30274968 ID - info:doi/10.2196/publichealth.8627 ER - TY - JOUR AU - Pretorius, A. Kelly AU - Mackert, Michael AU - Wilcox, B. Gary PY - 2018/09/07 TI - Sudden Infant Death Syndrome and Safe Sleep on Twitter: Analysis of Influences and Themes to Guide Health Promotion Efforts JO - JMIR Pediatr Parent SP - e10435 VL - 1 IS - 2 KW - sudden infant death KW - sudden unexpected infant death KW - accidental suffocation in a sleeping environment KW - infant mortality KW - safe sleep KW - sleep environment KW - social media KW - Twitter KW - health communication KW - public health N2 - Background: In the United States, sudden infant death syndrome (SIDS) is the leading cause of death in infants aged 1 month to 1 year. Approximately 3500 infants die from SIDS and sleep-related reasons on a yearly basis. Unintentional sleep-related deaths and bed sharing, a known risk factor for SIDS, are on the rise. Furthermore, ethnic disparities exist among those most affected by SIDS. Despite public health campaigns, infant mortality persists. Given the popularity of social media, understanding social media conversations around SIDS and safe sleep may assist the medical and public health communities with information needed to spread, reinforce, or counteract false information regarding SIDS and safe sleep. Objective: The objective of our study was to investigate the social media conversation around SIDS and safe sleep to understand the possible influences and guide health promotion efforts and public health research as well as enable health professionals to engage in directed communication regarding this topic. Methods: We used textual analytics to identify topics and extract meanings contained in unstructured textual data. Twitter messages were captured during September, October, and November in 2017. Tweets and retweets were collected using NUVI software in conjunction with Twitter?s search API using the keywords: ?sids,? ?infant death syndrome,? ?sudden infant death syndrome,? and ?safe sleep.? This returned a total of 41,358 messages, which were analyzed using text mining and social media monitoring software. Results: Multiple themes were identified, including recommendations for safe sleep to prevent SIDS, safe sleep devices, the potential causes of SIDS, and how breastfeeding reduces SIDS. Compared with September and November, more personal and specific stories of infant loss were demonstrated in October (Pregnancy and Infant Loss Awareness Month). The top influencers were news organizations, universities, and health-related organizations. Conclusions: We identified valuable topics discussed and shared on Twitter regarding SIDS and safe sleep. The study results highlight the contradicting information a subset of the population is exposed to regarding SIDS and the continued controversy over vaccines. In addition, this analysis emphasizes the lack of public health organizations? presence on Twitter compared with the influence of universities and news media organizations. The results also demonstrate the prevalence of safe sleep products that are embedded in safe sleep messaging. These findings can assist providers in speaking about relevant topics when engaging in conversations about the prevention of SIDS and the promotion of safe sleep. Furthermore, public health agencies and advocates should utilize social media and Twitter to better communicate accurate health information as well as continue to combat the spread of false information. UR - http://pediatrics.jmir.org/2018/2/e10435/ UR - http://dx.doi.org/10.2196/10435 UR - http://www.ncbi.nlm.nih.gov/pubmed/31518314 ID - info:doi/10.2196/10435 ER - TY - JOUR AU - Du, Jingcheng AU - Tang, Lu AU - Xiang, Yang AU - Zhi, Degui AU - Xu, Jun AU - Song, Hsing-Yi AU - Tao, Cui PY - 2018/07/09 TI - Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models JO - J Med Internet Res SP - e236 VL - 20 IS - 7 KW - convolutional neural networks KW - social media KW - measles KW - public perception N2 - Background: Timely understanding of public perceptions allows public health agencies to provide up-to-date responses to health crises such as infectious diseases outbreaks. Social media such as Twitter provide an unprecedented way for the prompt assessment of the large-scale public response. Objective: The aims of this study were to develop a scheme for a comprehensive public perception analysis of a measles outbreak based on Twitter data and demonstrate the superiority of the convolutional neural network (CNN) models (compared with conventional machine learning methods) on measles outbreak-related tweets classification tasks with a relatively small and highly unbalanced gold standard training set. Methods: We first designed a comprehensive scheme for the analysis of public perception of measles based on tweets, including 3 dimensions: discussion themes, emotions expressed, and attitude toward vaccination. All 1,154,156 tweets containing the word ?measles? posted between December 1, 2014, and April 30, 2015, were purchased and downloaded from DiscoverText.com. Two expert annotators curated a gold standard of 1151 tweets (approximately 0.1% of all tweets) based on the 3-dimensional scheme. Next, a tweet classification system based on the CNN framework was developed. We compared the performance of the CNN models to those of 4 conventional machine learning models and another neural network model. We also compared the impact of different word embeddings configurations for the CNN models: (1) Stanford GloVe embedding trained on billions of tweets in the general domain, (2) measles-specific embedding trained on our 1 million measles related tweets, and (3) a combination of the 2 embeddings. Results: Cohen kappa intercoder reliability values for the annotation were: 0.78, 0.72, and 0.80 on the 3 dimensions, respectively. Class distributions within the gold standard were highly unbalanced for all dimensions. The CNN models performed better on all classification tasks than k-nearest neighbors, naďve Bayes, support vector machines, or random forest. Detailed comparison between support vector machines and the CNN models showed that the major contributor to the overall superiority of the CNN models is the improvement on recall, especially for classes with low occurrence. The CNN model with the 2 embedding combination led to better performance on discussion themes and emotions expressed (microaveraging F1 scores of 0.7811 and 0.8592, respectively), while the CNN model with Stanford embedding achieved best performance on attitude toward vaccination (microaveraging F1 score of 0.8642). Conclusions: The proposed scheme can successfully classify the public?s opinions and emotions in multiple dimensions, which would facilitate the timely understanding of public perceptions during the outbreak of an infectious disease. Compared with conventional machine learning methods, our CNN models showed superiority on measles-related tweet classification tasks with a relatively small and highly unbalanced gold standard. With the success of these tasks, our proposed scheme and CNN-based tweets classification system is expected to be useful for the analysis of tweets about other infectious diseases such as influenza and Ebola. UR - http://www.jmir.org/2018/7/e236/ UR - http://dx.doi.org/10.2196/jmir.9413 UR - http://www.ncbi.nlm.nih.gov/pubmed/29986843 ID - info:doi/10.2196/jmir.9413 ER - TY - JOUR AU - Oldroyd, A. Rachel AU - Morris, A. Michelle AU - Birkin, Mark PY - 2018/06/06 TI - Identifying Methods for Monitoring Foodborne Illness: Review of Existing Public Health Surveillance Techniques JO - JMIR Public Health Surveill SP - e57 VL - 4 IS - 2 KW - disease KW - review KW - social media KW - foodborne diseases KW - public health KW - infodemiology KW - infoveillance KW - digital disease detection N2 - Background: Traditional methods of monitoring foodborne illness are associated with problems of untimeliness and underreporting. In recent years, alternative data sources such as social media data have been used to monitor the incidence of disease in the population (infodemiology and infoveillance). These data sources prove timelier than traditional general practitioner data, they can help to fill the gaps in the reporting process, and they often include additional metadata that is useful for supplementary research. Objective: The aim of the study was to identify and formally analyze research papers using consumer-generated data, such as social media data or restaurant reviews, to quantify a disease or public health ailment. Studies of this nature are scarce within the food safety domain, therefore identification and understanding of transferrable methods in other health-related fields are of particular interest. Methods: Structured scoping methods were used to identify and analyze primary research papers using consumer-generated data for disease or public health surveillance. The title, abstract, and keyword fields of 5 databases were searched using predetermined search terms. A total of 5239 papers matched the search criteria, of which 145 were taken to full-text review?62 papers were deemed relevant and were subjected to data characterization and thematic analysis. Results: The majority of studies (40/62, 65%) focused on the surveillance of influenza-like illness. Only 10 studies (16%) used consumer-generated data to monitor outbreaks of foodborne illness. Twitter data (58/62, 94%) and Yelp reviews (3/62, 5%) were the most commonly used data sources. Studies reporting high correlations against baseline statistics used advanced statistical and computational approaches to calculate the incidence of disease. These include classification and regression approaches, clustering approaches, and lexicon-based approaches. Although they are computationally intensive due to the requirement of training data, studies using classification approaches reported the best performance. Conclusions: By analyzing studies in digital epidemiology, computer science, and public health, this paper has identified and analyzed methods of disease monitoring that can be transferred to foodborne disease surveillance. These methods fall into 4 main categories: basic approach, classification and regression, clustering approaches, and lexicon-based approaches. Although studies using a basic approach to calculate disease incidence generally report good performance against baseline measures, they are sensitive to chatter generated by media reports. More computationally advanced approaches are required to filter spurious messages and protect predictive systems against false alarms. Research using consumer-generated data for monitoring influenza-like illness is expansive; however, research regarding the use of restaurant reviews and social media data in the context of food safety is limited. Considering the advantages reported in this review, methods using consumer-generated data for foodborne disease surveillance warrant further investment. UR - http://publichealth.jmir.org/2018/2/e57/ UR - http://dx.doi.org/10.2196/publichealth.8218 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/publichealth.8218 ER - TY - JOUR AU - Staal, CM Yvonne AU - van de Nobelen, Suzanne AU - Havermans, Anne AU - Talhout, Reinskje PY - 2018/05/28 TI - New Tobacco and Tobacco-Related Products: Early Detection of Product Development, Marketing Strategies, and Consumer Interest JO - JMIR Public Health Surveill SP - e55 VL - 4 IS - 2 KW - noncigarette tobacco products KW - electronic nicotine delivery systems KW - public opinion KW - retrospective studies N2 - Background: A wide variety of new tobacco and tobacco-related products have emerged on the market in recent years. Objective: To understand their potential implications for public health and to guide tobacco control efforts, we have used an infoveillance approach to identify new tobacco and tobacco-related products. Methods: Our search for tobacco(-related) products consists of several tailored search profiles using combinations of keywords such as ?e-cigarette? and ?new? to extract information from almost 9000 preselected sources such as websites of online shops, tobacco manufacturers, and news sites. Results: Developments in e-cigarette design characteristics show a trend toward customization by possibilities to adjust temperature and airflow, and by the large variety of flavors of e-liquids. Additionally, more e-cigarettes are equipped with personalized accessories, such as mobile phones, applications, and Bluetooth. Waterpipe products follow the trend toward electronic vaping. Various heat-not-burn products were reintroduced to the market. Conclusions: Our search for tobacco(-related) products was specific and timely, though advances in product development require ongoing optimization of the search strategy. Our results show a trend toward products resembling tobacco cigarettes vaporizers that can be adapted to the consumers? needs. Our search for tobacco(-related) products could aid in the assessment of the likelihood of new products to gain market share, as a possible health risk or as an indicator for the need on independent and reliable information of the product to the general public. UR - http://publichealth.jmir.org/2018/2/e55/ UR - http://dx.doi.org/10.2196/publichealth.7359 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/publichealth.7359 ER - TY - JOUR AU - Alvarez-Mon, Angel Miguel AU - Asunsolo del Barco, Angel AU - Lahera, Guillermo AU - Quintero, Javier AU - Ferre, Francisco AU - Pereira-Sanchez, Victor AU - Ortuńo, Felipe AU - Alvarez-Mon, Melchor PY - 2018/05/28 TI - Increasing Interest of Mass Communication Media and the General Public in the Distribution of Tweets About Mental Disorders: Observational Study JO - J Med Internet Res SP - e205 VL - 20 IS - 5 KW - Twitter KW - social media KW - psychiatry KW - mental health N2 - Background: The contents of traditional communication media and new internet social media reflect the interests of society. However, certain barriers and a lack of attention towards mental disorders have been previously observed. Objective: The objective of this study is to measure the relevance of influential American mainstream media outlets for the distribution of psychiatric information and the interest generated in these topics among their Twitter followers. Methods: We investigated tweets generated about mental health conditions and diseases among 15 mainstream general communication media outlets in the United States of America between January 2007 and December 2016. Our study strategy focused on identifying several psychiatric terms of primary interest. The number of retweets generated from the selected tweets was also investigated. As a control, we examined tweets generated about the main causes of death in the United States of America, the main chronic neurological degenerative diseases, and HIV. Results: In total, 13,119 tweets about mental health disorders sent by the American mainstream media outlets were analyzed. The results showed a heterogeneous distribution but preferential accumulation for a select number of conditions. Suicide and gender dysphoria accounted for half of the number of tweets sent. Variability in the number of tweets related to each control disease was also found (5998). The number of tweets sent regarding each different psychiatric or organic disease analyzed was significantly correlated with the number of retweets generated by followers (1,030,974 and 424,813 responses to mental health disorders and organic diseases, respectively). However, the probability of a tweet being retweeted differed significantly among the conditions and diseases analyzed. Furthermore, the retweeted to tweet ratio was significantly higher for psychiatric diseases than for the control diseases (odds ratio 1.11, CI 1.07-1.14; P<.001). Conclusions: American mainstream media outlets and the general public demonstrate a preferential interest for psychiatric diseases on Twitter. The heterogeneous weights given by the media outlets analyzed to the different mental health disorders and conditions are reflected in the responses of Twitter followers. UR - http://www.jmir.org/2018/5/e205/ UR - http://dx.doi.org/10.2196/jmir.9582 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/jmir.9582 ER - TY - JOUR AU - Tana, Christoffer Jonas AU - Kettunen, Jyrki AU - Eirola, Emil AU - Paakkonen, Heikki PY - 2018/05/23 TI - Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data JO - JMIR Ment Health SP - e43 VL - 5 IS - 2 KW - depression KW - consumer health information KW - information seeking behavior KW - infoveillance KW - infodemiology KW - mental health KW - search engine N2 - Background: Some of the temporal variations and clock-like rhythms that govern several different health-related behaviors can be traced in near real-time with the help of search engine data. This is especially useful when studying phenomena where little or no traditional data exist. One specific area where traditional data are incomplete is the study of diurnal mood variations, or daily changes in individuals? overall mood state in relation to depression-like symptoms. Objective: The objective of this exploratory study was to analyze diurnal variations for interest in depression on the Web to discover hourly patterns of depression interest and help seeking. Methods: Hourly query volume data for 6 depression-related queries in Finland were downloaded from Google Trends in March 2017. A continuous wavelet transform (CWT) was applied to the hourly data to focus on the diurnal variation. Longer term trends and noise were also eliminated from the data to extract the diurnal variation for each query term. An analysis of variance was conducted to determine the statistical differences between the distributions of each hour. Data were also trichotomized and analyzed in 3 time blocks to make comparisons between different time periods during the day. Results: Search volumes for all depression-related query terms showed a unimodal regular pattern during the 24 hours of the day. All queries feature clear peaks during the nighttime hours around 11 PM to 4 AM and troughs between 5 AM and 10 PM. In the means of the CWT-reconstructed data, the differences in nighttime and daytime interest are evident, with a difference of 37.3 percentage points (pp) for the term ?Depression,? 33.5 pp for ?Masennustesti,? 30.6 pp for ?Masennus,? 12.8 pp for ?Depression test,? 12.0 pp for ?Masennus testi,? and 11.8 pp for ?Masennus oireet.? The trichotomization showed peaks in the first time block (00.00 AM-7.59 AM) for all 6 terms. The search volumes then decreased significantly during the second time block (8.00 AM-3.59 PM) for the terms ?Masennus oireet? (P<.001), ?Masennus? (P=.001), ?Depression? (P=.005), and ?Depression test? (P=.004). Higher search volumes for the terms ?Masennus? (P=.14), ?Masennustesti? (P=.07), and ?Depression test? (P=.10) were present between the second and third time blocks. Conclusions: Help seeking for depression has clear diurnal patterns, with significant rise in depression-related query volumes toward the evening and night. Thus, search engine query data support the notion of the evening-worse pattern in diurnal mood variation. Information on the timely nature of depression-related interest on an hourly level could improve the chances for early intervention, which is beneficial for positive health outcomes. UR - http://mental.jmir.org/2018/2/e43/ UR - http://dx.doi.org/10.2196/mental.9152 UR - http://www.ncbi.nlm.nih.gov/pubmed/29792291 ID - info:doi/10.2196/mental.9152 ER - TY - JOUR AU - Bollegala, Danushka AU - Maskell, Simon AU - Sloane, Richard AU - Hajne, Joanna AU - Pirmohamed, Munir PY - 2018/05/09 TI - Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach JO - JMIR Public Health Surveill SP - e51 VL - 4 IS - 2 KW - machine learning KW - ADR detection KW - causality KW - lexical patterns KW - causality detection KW - support vector machines N2 - Background: Detecting adverse drug reactions (ADRs) is an important task that has direct implications for the use of that drug. If we can detect previously unknown ADRs as quickly as possible, then this information can be provided to the regulators, pharmaceutical companies, and health care organizations, thereby potentially reducing drug-related morbidity and saving lives of many patients. A promising approach for detecting ADRs is to use social media platforms such as Twitter and Facebook. A high level of correlation between a drug name and an event may be an indication of a potential adverse reaction associated with that drug. Although numerous association measures have been proposed by the signal detection community for identifying ADRs, these measures are limited in that they detect correlations but often ignore causality. Objective: This study aimed to propose a causality measure that can detect an adverse reaction that is caused by a drug rather than merely being a correlated signal. Methods: To the best of our knowledge, this was the first causality-sensitive approach for detecting ADRs from social media. Specifically, the relationship between a drug and an event was represented using a set of automatically extracted lexical patterns. We then learned the weights for the extracted lexical patterns that indicate their reliability for expressing an adverse reaction of a given drug. Results: Our proposed method obtains an ADR detection accuracy of 74% on a large-scale manually annotated dataset of tweets, covering a standard set of drugs and adverse reactions. Conclusions: By using lexical patterns, we can accurately detect the causality between drugs and adverse reaction?related events. UR - http://publichealth.jmir.org/2018/2/e51/ UR - http://dx.doi.org/10.2196/publichealth.8214 UR - http://www.ncbi.nlm.nih.gov/pubmed/29743155 ID - info:doi/10.2196/publichealth.8214 ER - TY - JOUR AU - Seabrook, M. Elizabeth AU - Kern, L. Margaret AU - Fulcher, D. Ben AU - Rickard, S. Nikki PY - 2018/05/08 TI - Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates JO - J Med Internet Res SP - e168 VL - 20 IS - 5 KW - automated text analysis KW - depression KW - Facebook KW - Twitter KW - emotions KW - variability KW - instability N2 - Background: Frequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility. Objective: The aim of this study was to report on the associations between depression severity and the variability (time-unstructured) and instability (time-structured) in emotion word expression on Facebook and Twitter across status updates. Methods: Status updates and depression severity ratings of 29 Facebook users and 49 Twitter users were collected through the app MoodPrism. The average proportion of positive and negative emotion words used, within-person variability, and instability were computed. Results: Negative emotion word instability was a significant predictor of greater depression severity on Facebook (rs(29)=.44, P=.02, 95% CI 0.09-0.69), even after controlling for the average proportion of negative emotion words used (partial rs(26)=.51, P=.006) and within-person variability (partial rs(26)=.49, P=.009). A different pattern emerged on Twitter where greater negative emotion word variability indicated lower depression severity (rs(49)=?.34, P=.01, 95% CI ?0.58 to 0.09). Differences between Facebook and Twitter users in their emotion word patterns and psychological characteristics were also explored. Conclusions: The findings suggest that negative emotion word instability may be a simple yet sensitive measure of time-structured variability, useful when screening for depression through social media, though its usefulness may depend on the social media platform. UR - http://www.jmir.org/2018/5/e168/ UR - http://dx.doi.org/10.2196/jmir.9267 UR - http://www.ncbi.nlm.nih.gov/pubmed/29739736 ID - info:doi/10.2196/jmir.9267 ER - TY - JOUR AU - Berlinberg, J. Elyse AU - Deiner, S. Michael AU - Porco, C. Travis AU - Acharya, R. Nisha PY - 2018/05/02 TI - Monitoring Interest in Herpes Zoster Vaccination: Analysis of Google Search Data JO - JMIR Public Health Surveill SP - e10180 VL - 4 IS - 2 KW - herpes zoster KW - vaccination KW - Internet KW - periodicity KW - Google Trends KW - infodemiology N2 - Background: A new recombinant subunit vaccine for herpes zoster (HZ or shingles) was approved by the United States Food and Drug Administration on October 20, 2017 and is expected to replace the previous live attenuated vaccine. There have been low coverage rates with the live attenuated vaccine (Zostavax), ranging from 12-32% of eligible patients receiving the HZ vaccine. Objective: This study aimed to provide insight into trends and potential reasons for interest in HZ vaccination. Methods: Internet search data were queried from the Google Health application programming interface from 2004-2017. Seasonality of normalized search volume was analyzed using wavelets and Fisher?s g test. Results: The search terms ?shingles vaccine,? ?zoster vaccine,? and ?zostavax? all exhibited significant periodicity in the fall months (P<.001), with sharp increases after recommendations for vaccination by public health-related organizations. Although the terms ?shingles blisters,? ?shingles itch,? ?shingles rash,? ?skin rash,? and ?shingles medicine? exhibited statistically significant periodicities with a seasonal peak in the summer (P<.001), the terms ?shingles contagious,? ?shingles pain,? ?shingles treatment,? and ?shingles symptoms? did not reveal an annual trend. Conclusions: There may be increased interest in HZ vaccination during the fall and after public health organization recommendations are broadcast. This finding points to the possibility that increased awareness of the vaccine through public health announcements could be evaluated as a potential intervention for increasing vaccine coverage. UR - http://publichealth.jmir.org/2018/2/e10180/ UR - http://dx.doi.org/10.2196/10180 UR - http://www.ncbi.nlm.nih.gov/pubmed/29720364 ID - info:doi/10.2196/10180 ER - TY - JOUR AU - Seidl, Stefanie AU - Schuster, Barbara AU - Rüth, Melvin AU - Biedermann, Tilo AU - Zink, Alexander PY - 2018/05/02 TI - What Do Germans Want to Know About Skin Cancer? A Nationwide Google Search Analysis From 2013 to 2017 JO - J Med Internet Res SP - e10327 VL - 20 IS - 5 KW - skin cancer KW - melanoma KW - nonmelanoma skin cancer (NMSC) KW - Google KW - search analysis KW - population N2 - Background: Experts worldwide agree that skin cancer is a global health issue, but only a few studies have reported on world populations? interest in skin cancer. Internet search data can reflect the interest of a population in different topics and thereby identify what the population wants to know. Objective: Our aim was to assess the interest of the German population in nonmelanoma skin cancer and melanoma. Methods: Google AdWords Keyword Planner was used to identify search terms related to nonmelanoma skin cancer and melanoma in Germany from November 2013 to October 2017. The identified search terms were assessed descriptively using SPSS version 24.0. In addition, the search terms were qualitatively categorized. Results: A total of 646 skin cancer-related search terms were identified with 19,849,230 Google searches in the period under review. The search terms with the highest search volume were ?skin cancer? (n=2,388,500, 12.03%), ?white skin cancer? (n=2,056,900, 10.36%), ?basalioma? (n=907,000, 4.57%), and ?melanoma? (n=717,800, 3.62%). The most searched localizations of nonmelanoma skin cancer were ?nose? (n=93,370, 38.99%) and ?face? (n=53,270, 22.24%), and the most searched of melanoma were ?nails? (n=46,270, 70.61%) and ?eye? (n=10,480, 15.99%). The skin cancer?related category with the highest search volume was ?forms of skin cancer? (n=10,162,540, 23.28%) followed by ?skin alterations? (n=4,962,020, 11.36%). Conclusions: Our study provides insight into terms and fields of interest related to skin cancer relevant to the German population. Furthermore, temporal trends and courses are shown. This information could aid in the development and implementation of effective and sustainable awareness campaigns by developing information sources targeted to the population?s broad interest or by implementing new Internet campaigns. UR - http://www.jmir.org/2018/5/e10327/ UR - http://dx.doi.org/10.2196/10327 UR - http://www.ncbi.nlm.nih.gov/pubmed/29698213 ID - info:doi/10.2196/10327 ER - TY - JOUR AU - Young, D. Sean PY - 2018/04/30 TI - Social Media as a New Vital Sign: Commentary JO - J Med Internet Res SP - e161 VL - 20 IS - 4 KW - social media KW - big data KW - personal health records UR - http://www.jmir.org/2018/4/e161/ UR - http://dx.doi.org/10.2196/jmir.8563 UR - http://www.ncbi.nlm.nih.gov/pubmed/29712631 ID - info:doi/10.2196/jmir.8563 ER - TY - JOUR AU - Mackey, Tim AU - Kalyanam, Janani AU - Klugman, Josh AU - Kuzmenko, Ella AU - Gupta, Rashmi PY - 2018/04/27 TI - Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access JO - J Med Internet Res SP - e10029 VL - 20 IS - 4 KW - online pharmacies KW - drug abuse KW - opioid abuse KW - machine learning KW - unsupervised machine learning KW - prescription drug misuse N2 - Background: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors?an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention?participated in the Code-a-Thon as part of the prevention track. Objective: The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Methods: Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning?based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal online sellers. Results: We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique ?live? tweets, with 44% (15/34) directing consumers to illicit online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the ?no prescription? sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Conclusions: The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit online pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the online environment safer for the public. UR - http://www.jmir.org/2018/4/e10029/ UR - http://dx.doi.org/10.2196/10029 UR - http://www.ncbi.nlm.nih.gov/pubmed/29613851 ID - info:doi/10.2196/10029 ER - TY - JOUR AU - Wood, N. Lauren AU - Jamnagerwalla, Juzar AU - Markowitz, A. Melissa AU - Thum, Joseph D. AU - McCarty, Philip AU - Medendorp, R. Andrew AU - Raz, Shlomo AU - Kim, Ja-Hong PY - 2018/04/26 TI - Public Awareness of Uterine Power Morcellation Through US Food and Drug Administration Communications: Analysis of Google Trends Search Term Patterns JO - JMIR Public Health Surveill SP - e47 VL - 4 IS - 2 KW - Google KW - internet search activity KW - FDA safety communication KW - uterine morcellation N2 - Background: Uterine power morcellation, where the uterus is shred into smaller pieces, is a widely used technique for removal of uterine specimens in patients undergoing minimally invasive abdominal hysterectomy or myomectomy. Complications related to power morcellation of uterine specimens led to US Food and Drug Administration (FDA) communications in 2014 ultimately recommending against the use of power morcellation for women undergoing minimally invasive hysterectomy. Subsequently, practitioners drastically decreased the use of morcellation. Objective: We aimed to determine the effect of increased patient awareness on the decrease in use of the morcellator. Google Trends is a public tool that provides data on temporal patterns of search terms, and we correlated this data with the timing of the FDA communication. Methods: Weekly relative search volume (RSV) was obtained from Google Trends using the term ?morcellation.? Higher RSV corresponds to increases in weekly search volume. Search volumes were divided into 3 groups: the 2 years prior to the FDA communication, a 1-year period following, and thereafter, with the distribution of the weekly RSV over the 3 periods tested using 1-way analysis of variance. Additionally, we analyzed the total number of websites containing the term ?morcellation? over this time. Results: The mean RSV prior to the FDA communication was 12.0 (SD 15.8), with the RSV being 60.3 (SD 24.7) in the 1-year after and 19.3 (SD 5.2) thereafter (P<.001). The mean number of webpages containing the term ?morcellation? in 2011 was 10,800, rising to 18,800 during 2014 and 36,200 in 2017. Conclusions: Google search activity about morcellation of uterine specimens increased significantly after the FDA communications. This trend indicates an increased public awareness regarding morcellation and its complications. More extensive preoperative counseling and alteration of surgical technique and clinician practice may be necessary. UR - http://publichealth.jmir.org/2018/2/e47/ UR - http://dx.doi.org/10.2196/publichealth.9913 UR - http://www.ncbi.nlm.nih.gov/pubmed/29699965 ID - info:doi/10.2196/publichealth.9913 ER - TY - JOUR AU - Clyne, Wendy AU - Pezaro, Sally AU - Deeny, Karen AU - Kneafsey, Rosie PY - 2018/04/23 TI - Using Social Media to Generate and Collect Primary Data: The #ShowsWorkplaceCompassion Twitter Research Campaign JO - JMIR Public Health Surveill SP - e41 VL - 4 IS - 2 KW - work engagement KW - health personnel KW - empathy KW - attitude of health personnel N2 - Background: Compassion is a core value embedded in the concept of quality in healthcare. The need for compassion toward healthcare staff in the workplace, for their own health and well-being and also to enable staff to deliver compassionate care for patients, is increasingly understood. However, we do not currently know how healthcare staff understand and characterize compassion toward themselves as opposed to patients. Objective: The aim of this study was to use social media for the generation and collection of primary data to gain understanding of the concept of workplace compassion. Methods: Tweets that contained the hashtag #ShowsWorkplaceCompassion were collected from Twitter and analyzed. The study took place between April 21 and May 21, 2016. Participants were self-selecting users of the social media service Twitter. The study was promoted by a number of routes: the National Health Service (NHS) England website, the personal Twitter accounts of the research team, internal NHS England communications, and via social media sharing. Participants were asked to contribute their views about what activities, actions, policies, philosophies or approaches demonstrate workplace compassion in healthcare using the hashtag #ShowsWorkplaceCompassion. All tweets including the research hashtag #ShowsWorkplaceCompassion were extracted from Twitter and studied using content analysis. Data concerning the frequency, nature, origin, and location of Web-based engagement with the research campaign were collected using Bitly (Bitly, Inc, USA) and Symplur (Symplur LLC, USA) software. Results: A total of 260 tweets were analyzed. Of the 251 statements within the tweets that were coded, 37.8% (95/251) of the statements concerned Leadership and Management aspects of workplace compassion, 29.5% (74/251) were grouped under the theme related to Values and Culture, 17.5% (44/251) of the statements related to Personalized Policies and Procedures that support workplace compassion, and 15.2% (38/251) of the statements concerned Activities and Actions that show workplace compassion. Content analysis showed that small acts of kindness, an embedded organizational culture of caring for one another, and recognition of the emotional and physical impact of healthcare work were the most frequently mentioned characteristics of workplace compassion in healthcare. Conclusions: This study presents a new and innovative research approach using Twitter. Although previous research has analyzed the nature and pattern of tweets retrospectively, this study used Twitter to both recruit participants and collect primary data. UR - http://publichealth.jmir.org/2018/2/e41/ UR - http://dx.doi.org/10.2196/publichealth.7686 UR - http://www.ncbi.nlm.nih.gov/pubmed/29685866 ID - info:doi/10.2196/publichealth.7686 ER - TY - JOUR AU - Gohil, Sunir AU - Vuik, Sabine AU - Darzi, Ara PY - 2018/04/23 TI - Sentiment Analysis of Health Care Tweets: Review of the Methods Used JO - JMIR Public Health Surveill SP - e43 VL - 4 IS - 2 KW - Twitter KW - social media N2 - Background: Twitter is a microblogging service where users can send and read short 140-character messages called ?tweets.? There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. Objective: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. Methods: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. Results: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study?s final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. Conclusions: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting?specific corpus of manually annotated tweets first. UR - http://publichealth.jmir.org/2018/2/e43/ UR - http://dx.doi.org/10.2196/publichealth.5789 UR - http://www.ncbi.nlm.nih.gov/pubmed/29685871 ID - info:doi/10.2196/publichealth.5789 ER - TY - JOUR AU - Wang, Ho-Wei AU - Chen, Duan-Rung PY - 2018/04/06 TI - Economic Recession and Obesity-Related Internet Search Behavior in Taiwan: Analysis of Google Trends Data JO - JMIR Public Health Surveill SP - e37 VL - 4 IS - 2 KW - obesity KW - economic recession KW - Google Trends KW - fast food KW - internet search KW - health-seeking behaviors KW - infodemiology N2 - Background: Obesity is highly correlated with the development of chronic diseases and has become a critical public health issue that must be countered by aggressive action. This study determined whether data from Google Trends could provide insight into trends in obesity-related search behaviors in Taiwan. Objective: Using Google Trends, we examined how changes in economic conditions?using business cycle indicators as a proxy?were associated with people?s internet search behaviors related to obesity awareness, health behaviors, and fast food restaurants. Methods: Monthly business cycle indicators were obtained from the Taiwan National Development Council. Weekly Taiwan Stock Exchange (TWSE) weighted index data were accessed and downloaded from Yahoo Finance. The weekly relative search volumes (RSV) of obesity-related terms were downloaded from Google Trends. RSVs of obesity-related terms and the TWSE from January 2007 to December 2011 (60 months) were analyzed using correlation analysis. Results: During an economic recession, the RSV of obesity awareness and health behaviors declined (r=.441, P<.001; r=.593, P<.001, respectively); however, the RSV for fast food restaurants increased (r=?.437, P<.001). Findings indicated that when the economy was faltering, people tended to be less likely to search for information related to health behaviors and obesity awareness; moreover, they were more likely to search for fast food restaurants. Conclusions: Macroeconomic conditions can have an impact on people?s health-related internet searches. UR - http://publichealth.jmir.org/2018/2/e37/ UR - http://dx.doi.org/10.2196/publichealth.7314 UR - http://www.ncbi.nlm.nih.gov/pubmed/29625958 ID - info:doi/10.2196/publichealth.7314 ER - TY - JOUR AU - Chen, Bin AU - Shao, Jian AU - Liu, Kui AU - Cai, Gaofeng AU - Jiang, Zhenggang AU - Huang, Yuru AU - Gu, Hua AU - Jiang, Jianmin PY - 2018/03/29 TI - Does Eating Chicken Feet With Pickled Peppers Cause Avian Influenza? Observational Case Study on Chinese Social Media During the Avian Influenza A (H7N9) Outbreak JO - JMIR Public Health Surveill SP - e32 VL - 4 IS - 1 KW - social media KW - misinformation KW - infodemiology KW - avian influenza A KW - disease outbreak N2 - Background: A hot topic on the relationship between a popular avian-origin food and avian influenza occurred on social media during the outbreak of the emerging avian influenza A (H7N9). The misinformation generated from this topic had caused great confusion and public concern. Objective: Our goals were to analyze the trend and contents of the relevant posts during the outbreak. We also aimed to understand the characteristics of the misinformation and to provide suggestions to reduce public misconception on social media during the emerging disease outbreak. Methods: The original microblog posts were collected from China?s Sina Weibo and Tencent Weibo using a combination of keywords between April 1, 2013 and June 2, 2013. We analyzed the weekly and daily trend of the relevant posts. Content analyses were applied to categorize the posts into 4 types with unified sorting criteria. The posts? characteristics and geographic locations were also analyzed in each category. We conducted further analysis on the top 5 most popular misleading posts. Results: A total of 1680 original microblog posts on the topic were retrieved and 341 (20.30%) of these posts were categorized as misleading messages. The number of relevant posts had not increased much during the first 2 weeks but rose to a high level in the next 2 weeks after the sudden increase in number of reported cases at the beginning of week 3. The posts under ?misleading messages? occurred and increased from the beginning of week 3, but their daily posting number decreased when the daily number of posts under ?refuting messages? outnumbered them. The microbloggers of the misleading posts had the lowest mean rank of followers and previous posts, but their posts had a highest mean rank of posts. The proportion of ?misleading messages? in places with no reported cases was significantly higher than that in the epidemic areas (23.6% vs 13.8%). The popular misleading posts appeared to be short and consisted of personal narratives, which were easily disseminated on social media. Conclusions: Our findings suggested the importance of responding to common questions and misconceptions on social media platforms from the beginning of disease outbreaks. Authorities need to release clear and reliable information related to the popular topics early on. The microbloggers posting correct information should be empowered and their posts could be promoted to clarify false information. Equal importance should be attached to clarify misinformation in both the outbreak and nonoutbreak areas. UR - http://publichealth.jmir.org/2018/1/e32/ UR - http://dx.doi.org/10.2196/publichealth.8198 UR - http://www.ncbi.nlm.nih.gov/pubmed/29599109 ID - info:doi/10.2196/publichealth.8198 ER - TY - JOUR AU - Mejova, Yelena AU - Weber, Ingmar AU - Fernandez-Luque, Luis PY - 2018/03/28 TI - Online Health Monitoring using Facebook Advertisement Audience Estimates in the United States: Evaluation Study JO - JMIR Public Health Surveill SP - e30 VL - 4 IS - 1 KW - social media KW - public health KW - Internet KW - infodemiology N2 - Background: Facebook, the most popular social network with over one billion daily users, provides rich opportunities for its use in the health domain. Though much of Facebook?s data are not available to outsiders, the company provides a tool for estimating the audience of Facebook advertisements, which includes aggregated information on the demographics and interests, such as weight loss or dieting, of Facebook users. This paper explores the potential uses of Facebook ad audience estimates for eHealth by studying the following: (1) for what type of health conditions prevalence estimates can be obtained via social media and (2) what type of marker interests are useful in obtaining such estimates, which can then be used for recruitment within online health interventions. Objective: The objective of this study was to understand the limitations and capabilities of using Facebook ad audience estimates for public health monitoring and as a recruitment tool for eHealth interventions. Methods: We use the Facebook Marketing application programming interface to correlate estimated sizes of audiences having health-related interests with public health data. Using several study cases, we identify both potential benefits and challenges in using this tool. Results: We find several limitations in using Facebook ad audience estimates, for example, using placebo interest estimates to control for background level of user activity on the platform. Some Facebook interests such as plus-size clothing show encouraging levels of correlation (r=.74) across the 50 US states; however, we also sometimes find substantial correlations with the placebo interests such as r=.68 between interest in Technology and Obesity prevalence. Furthermore, we find demographic-specific peculiarities in the interests on health-related topics. Conclusions: Facebook?s advertising platform provides aggregate data for more than 190 million US adults. We show how disease-specific marker interests can be used to model prevalence rates in a simple and intuitive manner. However, we also illustrate that building effective marker interests involves some trial-and-error, as many details about Facebook?s black box remain opaque. UR - http://publichealth.jmir.org/2018/1/e30/ UR - http://dx.doi.org/10.2196/publichealth.7217 UR - http://www.ncbi.nlm.nih.gov/pubmed/29592849 ID - info:doi/10.2196/publichealth.7217 ER - TY - JOUR AU - Cherian, Roy AU - Westbrook, Marisa AU - Ramo, Danielle AU - Sarkar, Urmimala PY - 2018/03/20 TI - Representations of Codeine Misuse on Instagram: Content Analysis JO - JMIR Public Health Surveill SP - e22 VL - 4 IS - 1 KW - prescription opioid misuse KW - social media KW - poly-substance use KW - Instagram N2 - Background: Prescription opioid misuse has doubled over the past 10 years and is now a public health epidemic. Analysis of social media data may provide additional insights into opioid misuse to supplement the traditional approaches of data collection (eg, self-report on surveys). Objective: The aim of this study was to characterize representations of codeine misuse through analysis of public posts on Instagram to understand text phrases related to misuse. Methods: We identified hashtags and searchable text phrases associated with codeine misuse by analyzing 1156 sequential Instagram posts over the course of 2 weeks from May 2016 to July 2016. Content analysis of posts associated with these hashtags identified the most common themes arising in images, as well as culture around misuse, including how misuse is happening and being perpetuated through social media. Results: A majority of images (50/100; 50.0%) depicted codeine in its commonly misused form, combined with soda (lean). Codeine misuse was commonly represented with the ingestion of alcohol, cannabis, and benzodiazepines. Some images highlighted the previously noted affinity between codeine misuse and hip-hop culture or mainstream popular culture images. Conclusions: The prevalence of codeine misuse images, glamorizing of ingestion with soda and alcohol, and their integration with mainstream, popular culture imagery holds the potential to normalize and increase codeine misuse and overdose. To reduce harm and prevent misuse, immediate public health efforts are needed to better understand the relationship between the potential normalization, ritualization, and commercialization of codeine misuse. UR - http://publichealth.jmir.org/2018/1/e22/ UR - http://dx.doi.org/10.2196/publichealth.8144 UR - http://www.ncbi.nlm.nih.gov/pubmed/29559422 ID - info:doi/10.2196/publichealth.8144 ER - TY - JOUR AU - Yagahara, Ayako AU - Hanai, Keiri AU - Hasegawa, Shin AU - Ogasawara, Katsuhiko PY - 2018/03/15 TI - Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks JO - JMIR Public Health Surveill SP - e26 VL - 4 IS - 1 KW - Twitter KW - social media KW - public concern KW - nuclear power plants KW - morphological analysis KW - network analysis KW - radiation N2 - Background: After the Fukushima Daiichi nuclear accident on March 11, 2011, interest in, and fear of, radiation increased among citizens. When such accidents occur, appropriate risk communication must provided by the government. It is therefore necessary to understand the fears of citizens in the days after such accidents. Objective: This study aimed to identify the progression of people?s concerns, specifically fear, from a study of radiation-related tweets in the days after the Fukushima Daiichi nuclear accident. Methods: From approximately 1.5 million tweets in Japanese including any of the phrases ?radiation? (???), ?radioactivity? (???), and ?radioactive substance? (?????) sent March 11-17, 2011, we extracted tweets that expressed fear. We then performed a morphological analysis on the extracted tweets. Citizens? fears were visualized by creating co-occurrence networks using co-occurrence degrees showing relationship strength. Moreover, we calculated the Jaccard coefficient, which is one of the co-occurrence indices for expressing the strength of the relationship between morphemes when creating networks. Results: From the visualization of the co-occurrence networks, we found high citizen interest in ?nuclear power plant? on March 11 and 12, ?health? on March 12 and 13, ?medium? on March 13 and 14, and ?economy? on March 15. On March 16 and 17, citizens? interest changed to ?lack of goods in the afflicted area.? In each co-occurrence network, trending topics, citizens? fears, and opinions to the government were extracted. Conclusions: This study used Twitter to understand changes in the concerns of Japanese citizens during the week after the Fukushima Daiichi nuclear accident, with a focus specifically on citizens? fears. We found that immediately after the accident, the interest in the accident itself was high, and then interest shifted to concerns affecting life, such as health and economy, as the week progressed. Clarifying citizens? fears and the dissemination of information through mass media and social media can add to improved risk communication in the future. UR - http://publichealth.jmir.org/2018/1/e26/ UR - http://dx.doi.org/10.2196/publichealth.7598 UR - http://www.ncbi.nlm.nih.gov/pubmed/29549069 ID - info:doi/10.2196/publichealth.7598 ER - TY - JOUR AU - Abdellaoui, Redhouane AU - Foulquié, Pierre AU - Texier, Nathalie AU - Faviez, Carole AU - Burgun, Anita AU - Schück, Stéphane PY - 2018/03/14 TI - Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach JO - J Med Internet Res SP - e85 VL - 20 IS - 3 KW - medication adherence KW - compliance KW - infodemiology KW - social media KW - text mining KW - depression KW - psychosis KW - peer-to-peer support KW - virtual community N2 - Background: Medication nonadherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying noncompliance to treatment may help health professionals to address it. Patients use peer-to-peer virtual communities and social media to share their experiences regarding their treatments and diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of noncompliance. Objective: The aim of this study was to detect messages describing patients? noncompliant behaviors associated with a drug of interest. Thus, the objective was the clustering of posts featuring a homogeneous vocabulary related to nonadherent attitudes. Methods: We focused on escitalopram and aripiprazole used to treat depression and psychotic conditions, respectively. We implemented a probabilistic topic model to identify the topics that occurred in a corpus of messages mentioning these drugs, posted from 2004 to 2013 on three of the most popular French forums. Data were collected using a Web crawler designed by Kappa Santé as part of the Detec?t project to analyze social media for drug safety. Several topics were related to noncompliance to treatment. Results: Starting from a corpus of 3650 posts related to an antidepressant drug (escitalopram) and 2164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes, including interruptions of treatment and changes in dosage. The topic model approach detected cases of noncompliance behaviors with a recall of 98.5% (272/276) and a precision of 32.6% (272/844). Conclusions: Topic models enabled us to explore patients? discussions on community websites and to identify posts related with noncompliant behaviors. After a manual review of the messages in the noncompliance topics, we found that noncompliance to treatment was present in 6.17% (276/4469) of the posts. UR - http://www.jmir.org/2018/3/e85/ UR - http://dx.doi.org/10.2196/jmir.9222 UR - http://www.ncbi.nlm.nih.gov/pubmed/29540337 ID - info:doi/10.2196/jmir.9222 ER - TY - JOUR AU - Mavragani, Amaryllis AU - Sampri, Alexia AU - Sypsa, Karla AU - Tsagarakis, P. Konstantinos PY - 2018/03/12 TI - Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era JO - JMIR Public Health Surveill SP - e24 VL - 4 IS - 1 KW - asthma KW - big data KW - forecasting KW - Google trends KW - health care KW - health informatics KW - internet behavior KW - nowcasting KW - online behavior KW - smart health N2 - Background: With the internet?s penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma?a common respiratory disease?is present. Objective: This study aimed at forecasting the online behavior toward asthma and examined the correlations between queries and reported cases in order to explore the possibility of nowcasting asthma prevalence in the United States using online search traffic data. Methods: Applying Holt-Winters exponential smoothing to Google Trends time series from 2004 to 2015 for the term ?asthma,? forecasts for online queries at state and national levels are estimated from 2016 to 2020 and validated against available Google query data from January 2016 to June 2017. Correlations among yearly Google queries and between Google queries and reported asthma cases are examined. Results: Our analysis shows that search queries exhibit seasonality within each year and the relationships between each 2 years? queries are statistically significant (P<.05). Estimated forecasting models for a 5-year period (2016 through 2020) for Google queries are robust and validated against available data from January 2016 to June 2017. Significant correlations were found between (1) online queries and National Health Interview Survey lifetime asthma (r=?.82, P=.001) and current asthma (r=?.77, P=.004) rates from 2004 to 2015 and (2) between online queries and Behavioral Risk Factor Surveillance System lifetime (r=?.78, P=.003) and current asthma (r=?.79, P=.002) rates from 2004 to 2014. The correlations are negative, but lag analysis to identify the period of response cannot be employed until short-interval data on asthma prevalence are made available. Conclusions: Online behavior toward asthma can be accurately predicted, and significant correlations between online queries and reported cases exist. This method of forecasting Google queries can be used by health care officials to nowcast asthma prevalence by city, state, or nationally, subject to future availability of daily, weekly, or monthly data on reported cases. This method could therefore be used for improved monitoring and assessment of the needs surrounding the current population of patients with asthma. UR - http://publichealth.jmir.org/2018/1/e24/ UR - http://dx.doi.org/10.2196/publichealth.8726 UR - http://www.ncbi.nlm.nih.gov/pubmed/29530839 ID - info:doi/10.2196/publichealth.8726 ER - TY - JOUR AU - Farhadloo, Mohsen AU - Winneg, Kenneth AU - Chan, Sally Man-Pui AU - Hall Jamieson, Kathleen AU - Albarracin, Dolores PY - 2018/02/09 TI - Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States JO - JMIR Public Health Surveill SP - e16 VL - 4 IS - 1 KW - Zika KW - Twitter KW - topic modeling KW - public policy KW - public health N2 - Background: Recent outbreaks of Zika virus around the world led to increased discussions about this issue on social media platforms such as Twitter. These discussions may provide useful information about attitudes, knowledge, and behaviors of the population regarding issues that are important for public policy. Objective: We sought to identify the associations of the topics of discussions on Twitter and survey measures of Zika-related attitudes, knowledge, and behaviors, not solely based upon the volume of such discussions but by analyzing the content of conversations using probabilistic techniques. Methods: Using probabilistic topic modeling with US county and week as the unit of analysis, we analyzed the content of Twitter online communications to identify topics related to the reported attitudes, knowledge, and behaviors captured in a national representative survey (N=33,193) of the US adult population over 33 weeks. Results: Our analyses revealed topics related to ?congress funding for Zika,? ?microcephaly,? ?Zika-related travel discussions,? ?insect repellent,? ?blood transfusion technology,? and ?Zika in Miami? were associated with our survey measures of attitudes, knowledge, and behaviors observed over the period of the study. Conclusions: Our results demonstrated that it is possible to uncover topics of discussions from Twitter communications that are associated with the Zika-related attitudes, knowledge, and behaviors of populations over time. Social media data can be used as a complementary source of information alongside traditional data sources to gauge the patterns of attitudes, knowledge, and behaviors in a population. UR - http://publichealth.jmir.org/2018/1/e16/ UR - http://dx.doi.org/10.2196/publichealth.8186 UR - http://www.ncbi.nlm.nih.gov/pubmed/29426815 ID - info:doi/10.2196/publichealth.8186 ER - TY - JOUR AU - Pan, Chih-Long AU - Lin, Chih-Hao AU - Lin, Yan-Ren AU - Wen, Hsin-Yu AU - Wen, Jet-Chau PY - 2018/02/02 TI - The Significance of Witness Sensors for Mass Casualty Incidents and Epidemic Outbreaks JO - J Med Internet Res SP - e39 VL - 20 IS - 2 KW - social media KW - mass casualty incident KW - internet KW - sensor UR - https://www.jmir.org/2018/2/e39/ UR - http://dx.doi.org/10.2196/jmir.8249 UR - http://www.ncbi.nlm.nih.gov/pubmed/29396388 ID - info:doi/10.2196/jmir.8249 ER - TY - JOUR AU - Liu, Sam AU - Zhu, Miaoqi AU - Young, D. Sean PY - 2018/01/11 TI - Monitoring Freshman College Experience Through Content Analysis of Tweets: Observational Study JO - JMIR Public Health Surveill SP - e5 VL - 4 IS - 1 KW - social networking KW - big data KW - population surveillance KW - education KW - students KW - social media KW - Twitter N2 - Background: Freshman experiences can greatly influence students? success. Traditional methods of monitoring the freshman experience, such as conducting surveys, can be resource intensive and time consuming. Social media, such as Twitter, enable users to share their daily experiences. Thus, it may be possible to use Twitter to monitor students? postsecondary experience. Objective: Our objectives were to (1) describe the proportion of content posted on Twitter by college students relating to academic studies, personal health, and social life throughout the semester; and (2) examine whether the proportion of content differed by demographics and during nonexam versus exam periods. Methods: Between October 5 and December 11, 2015, we collected tweets from 170 freshmen attending the University of California Los Angeles, California, USA, aged 18 to 20 years. We categorized the tweets into topics related to academic, personal health, and social life using keyword searches. Mann-Whitney U and Kruskal-Wallis H tests examined whether the content posted differed by sex, ethnicity, and major. The Friedman test determined whether the total number of tweets and percentage of tweets related to academic studies, personal health, and social life differed between nonexam (weeks 1-8) and final exam (weeks 9 and 10) periods. Results: Participants posted 24,421 tweets during the fall semester. Academic-related tweets (n=3433, 14.06%) were the most prevalent during the entire semester, compared with tweets related to personal health (n=2483, 10.17%) and social life (n=1646, 6.74%). The proportion of academic-related tweets increased during final-exam compared with nonexam periods (mean rank 68.9, mean 18%, standard error (SE) 0.1% vs mean rank 80.7, mean 21%, SE 0.2%; Z=?2.1, P=.04). Meanwhile, the proportion of tweets related to social life decreased during final exams compared with nonexam periods (mean rank 70.2, mean 5.4%, SE 0.01% vs mean rank 81.8, mean 7.4%, SE 0.01%; Z=?4.8, P<.001). Women tweeted more often than men during both nonexam (mean rank 95.8 vs 76.8; U=2876, P=.02) and final-exam periods (mean rank 96.2 vs 76.2; U=2832, P=.01). The percentages of academic-related tweets were similar between ethnic groups during nonexam periods (P>.05). However, during the final-exam periods, the percentage of academic tweets was significantly lower among African Americans than whites (?24=15.1, P=.004). The percentages of tweets related to academic studies, personal health, and social life were not significantly different between areas of study during nonexam and exam periods (P>.05). Conclusions: The results suggest that the number of tweets related to academic studies and social life fluctuates to reflect real-time events. Student?s ethnicity influenced the proportion of academic-related tweets posted. The findings from this study provide valuable information on the types of information that could be extracted from social media data. This information can be valuable for school administrators and researchers to improve students? university experience. UR - http://publichealth.jmir.org/2018/1/e5/ UR - http://dx.doi.org/10.2196/publichealth.7444 UR - http://www.ncbi.nlm.nih.gov/pubmed/29326096 ID - info:doi/10.2196/publichealth.7444 ER - TY - JOUR AU - Lu, Sun Fred AU - Hou, Suqin AU - Baltrusaitis, Kristin AU - Shah, Manan AU - Leskovec, Jure AU - Sosic, Rok AU - Hawkins, Jared AU - Brownstein, John AU - Conidi, Giuseppe AU - Gunn, Julia AU - Gray, Josh AU - Zink, Anna AU - Santillana, Mauricio PY - 2018/01/09 TI - Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis JO - JMIR Public Health Surveill SP - e4 VL - 4 IS - 1 KW - epidemiology KW - public health KW - machine learning KW - regression analysis KW - influenza, human KW - communicable diseases KW - statistics KW - patient generated data N2 - Background: Influenza outbreaks pose major challenges to public health around the world, leading to thousands of deaths a year in the United States alone. Accurate systems that track influenza activity at the city level are necessary to provide actionable information that can be used for clinical, hospital, and community outbreak preparation. Objective: Although Internet-based real-time data sources such as Google searches and tweets have been successfully used to produce influenza activity estimates ahead of traditional health care?based systems at national and state levels, influenza tracking and forecasting at finer spatial resolutions, such as the city level, remain an open question. Our study aimed to present a precise, near real-time methodology capable of producing influenza estimates ahead of those collected and published by the Boston Public Health Commission (BPHC) for the Boston metropolitan area. This approach has great potential to be extended to other cities with access to similar data sources. Methods: We first tested the ability of Google searches, Twitter posts, electronic health records, and a crowd-sourced influenza reporting system to detect influenza activity in the Boston metropolis separately. We then adapted a multivariate dynamic regression method named ARGO (autoregression with general online information), designed for tracking influenza at the national level, and showed that it effectively uses the above data sources to monitor and forecast influenza at the city level 1 week ahead of the current date. Finally, we presented an ensemble-based approach capable of combining information from models based on multiple data sources to more robustly nowcast as well as forecast influenza activity in the Boston metropolitan area. The performances of our models were evaluated in an out-of-sample fashion over 4 influenza seasons within 2012-2016, as well as a holdout validation period from 2016 to 2017. Results: Our ensemble-based methods incorporating information from diverse models based on multiple data sources, including ARGO, produced the most robust and accurate results. The observed Pearson correlations between our out-of-sample flu activity estimates and those historically reported by the BPHC were 0.98 in nowcasting influenza and 0.94 in forecasting influenza 1 week ahead of the current date. Conclusions: We show that information from Internet-based data sources, when combined using an informed, robust methodology, can be effectively used as early indicators of influenza activity at fine geographic resolutions. UR - http://publichealth.jmir.org/2018/1/e4/ UR - http://dx.doi.org/10.2196/publichealth.8950 UR - http://www.ncbi.nlm.nih.gov/pubmed/29317382 ID - info:doi/10.2196/publichealth.8950 ER - TY - JOUR AU - Simpson, S. Sean AU - Adams, Nikki AU - Brugman, M. Claudia AU - Conners, J. Thomas PY - 2018/01/08 TI - Detecting Novel and Emerging Drug Terms Using Natural Language Processing: A Social Media Corpus Study JO - JMIR Public Health Surveill SP - e2 VL - 4 IS - 1 KW - natural language processing KW - street drugs KW - social media KW - vocabulary N2 - Background: With the rapid development of new psychoactive substances (NPS) and changes in the use of more traditional drugs, it is increasingly difficult for researchers and public health practitioners to keep up with emerging drugs and drug terms. Substance use surveys and diagnostic tools need to be able to ask about substances using the terms that drug users themselves are likely to be using. Analyses of social media may offer new ways for researchers to uncover and track changes in drug terms in near real time. This study describes the initial results from an innovative collaboration between substance use epidemiologists and linguistic scientists employing techniques from the field of natural language processing to examine drug-related terms in a sample of tweets from the United States. Objective: The objective of this study was to assess the feasibility of using distributed word-vector embeddings trained on social media data to uncover previously unknown (to researchers) drug terms. Methods: In this pilot study, we trained a continuous bag of words (CBOW) model of distributed word-vector embeddings on a Twitter dataset collected during July 2016 (roughly 884.2 million tokens). We queried the trained word embeddings for terms with high cosine similarity (a proxy for semantic relatedness) to well-known slang terms for marijuana to produce a list of candidate terms likely to function as slang terms for this substance. This candidate list was then compared with an expert-generated list of marijuana terms to assess the accuracy and efficacy of using word-vector embeddings to search for novel drug terminology. Results: The method described here produced a list of 200 candidate terms for the target substance (marijuana). Of these 200 candidates, 115 were determined to in fact relate to marijuana (65 terms for the substance itself, 50 terms related to paraphernalia). This included 30 terms which were used to refer to the target substance in the corpus yet did not appear on the expert-generated list and were therefore considered to be successful cases of uncovering novel drug terminology. Several of these novel terms appear to have been introduced as recently as 1 or 2 months before the corpus time slice used to train the word embeddings. Conclusions: Though the precision of the method described here is low enough as to still necessitate human review of any candidate term lists generated in such a manner, the fact that this process was able to detect 30 novel terms for the target substance based only on one month?s worth of Twitter data is highly promising. We see this pilot study as an important proof of concept and a first step toward producing a fully automated drug term discovery system capable of tracking emerging NPS terms in real time. UR - http://publichealth.jmir.org/2018/1/e2/ UR - http://dx.doi.org/10.2196/publichealth.7726 UR - http://www.ncbi.nlm.nih.gov/pubmed/29311050 ID - info:doi/10.2196/publichealth.7726 ER - TY - JOUR AU - Phillips, A. Charles AU - Barz Leahy, Allison AU - Li, Yimei AU - Schapira, M. Marilyn AU - Bailey, Charles L. AU - Merchant, M. Raina PY - 2018/01/08 TI - Relationship Between State-Level Google Online Search Volume and Cancer Incidence in the United States: Retrospective Study JO - J Med Internet Res SP - e6 VL - 20 IS - 1 KW - Google KW - cancer KW - incidence KW - Internet KW - infodemiology N2 - Background: In the United States, cancer is common, with high morbidity and mortality; cancer incidence varies between states. Online searches reflect public awareness, which could be driven by the underlying regional cancer epidemiology. Objective: The objective of our study was to characterize the relationship between cancer incidence and online Google search volumes in the United States for 6 common cancers. A secondary objective was to evaluate the association of search activity with cancer-related public events and celebrity news coverage. Methods: We performed a population-based, retrospective study of state-level cancer incidence from 2004 through 2013 reported by the Centers for Disease Control and Prevention for breast, prostate, colon, lung, and uterine cancers and leukemia compared to Google Trends (GT) relative search volume (RSV), a metric designed by Google to allow interest in search topics to be compared between regions. Participants included persons in the United States who searched for cancer terms on Google. The primary measures were the correlation between annual state-level cancer incidence and RSV as determined by Spearman correlation and linear regression with RSV and year as independent variables and cancer incidence as the dependent variable. Temporal associations between search activity and events raising public awareness such as cancer awareness months and cancer-related celebrity news were described. Results: At the state level, RSV was significantly correlated to incidence for breast (r=.18, P=.001), prostate (r=?.27, P<.001), lung (r=.33, P<.001), and uterine cancers (r=.39, P<.001) and leukemia (r=.13, P=.003) but not colon cancer (r=?.02, P=.66). After adjusting for time, state-level RSV was positively correlated to cancer incidence for all cancers: breast (P<.001, 95% CI 0.06 to 0.19), prostate (P=.38, 95% CI ?0.08 to 0.22), lung (P<.001, 95% CI 0.33 to 0.46), colon (P<.001, 95% CI 0.11 to 0.17), and uterine cancers (P<.001, 95% CI 0.07 to 0.12) and leukemia (P<.001, 95% CI 0.01 to 0.03). Temporal associations in GT were noted with breast cancer awareness month but not with other cancer awareness months and celebrity events. Conclusions: Cancer incidence is correlated with online search volume at the state level. Search patterns were temporally associated with cancer awareness months and celebrity announcements. Online searches reflect public awareness. Advancing understanding of online search patterns could augment traditional epidemiologic surveillance, provide opportunities for targeted patient engagement, and allow public information campaigns to be evaluated in ways previously unable to be measured. UR - http://www.jmir.org/2018/1/e6/ UR - http://dx.doi.org/10.2196/jmir.8870 UR - http://www.ncbi.nlm.nih.gov/pubmed/29311051 ID - info:doi/10.2196/jmir.8870 ER - TY - JOUR AU - Giat, Eitan AU - Yom-Tov, Elad PY - 2018/01/05 TI - Evidence From Web-Based Dietary Search Patterns to the Role of B12 Deficiency in Non-Specific Chronic Pain: A Large-Scale Observational Study JO - J Med Internet Res SP - e4 VL - 20 IS - 1 KW - B12 deficiency KW - diet KW - Internet searches KW - neuropsychiatric symptoms KW - neuropathy N2 - Background: Profound vitamin B12 deficiency is a known cause of disease, but the role of low or intermediate levels of B12 in the development of neuropathy and other neuropsychiatric symptoms, as well as the relationship between eating meat and B12 levels, is unclear. Objective: The objective of our study was to investigate the role of low or intermediate levels of B12 in the development of neuropathy and other neuropsychiatric symptoms. Methods: We used food-related Internet search patterns from a sample of 8.5 million people based in the US as a proxy for B12 intake and correlated these searches with Internet searches related to possible effects of B12 deficiency. Results: Food-related search patterns were highly correlated with known consumption and food-related searches (?=.69). Awareness of B12 deficiency was associated with a higher consumption of B12-rich foods and with queries for B12 supplements. Searches for terms related to neurological disorders were correlated with searches for B12-poor foods, in contrast with control terms. Popular medicines, those having fewer indications, and those which are predominantly used to treat pain, were more strongly correlated with the ability to predict neuropathic pain queries using the B12 contents of food. Conclusions: Our findings show that Internet search patterns are a useful way of investigating health questions in large populations, and suggest that low B12 intake may be associated with a broader spectrum of neurological disorders than previously thought. UR - http://www.jmir.org/2018/1/e4/ UR - http://dx.doi.org/10.2196/jmir.8667 UR - http://www.ncbi.nlm.nih.gov/pubmed/29305340 ID - info:doi/10.2196/jmir.8667 ER - TY - JOUR AU - Sinha, S. Michael AU - Freifeld, C. Clark AU - Brownstein, S. John AU - Donneyong, M. Macarius AU - Rausch, Paula AU - Lappin, M. Brian AU - Zhou, H. Esther AU - Dal Pan, J. Gerald AU - Pawar, M. Ajinkya AU - Hwang, J. Thomas AU - Avorn, Jerry AU - Kesselheim, S. Aaron PY - 2018/01/05 TI - Social Media Impact of the Food and Drug Administration's Drug Safety Communication Messaging About Zolpidem: Mixed-Methods Analysis JO - JMIR Public Health Surveill SP - e1 VL - 4 IS - 1 KW - Food and Drug Administration KW - drug safety communications KW - surveillance KW - epidemiology KW - social media KW - Twitter KW - Facebook KW - Google Trends N2 - Background: The Food and Drug Administration (FDA) issues drug safety communications (DSCs) to health care professionals, patients, and the public when safety issues emerge related to FDA-approved drug products. These safety messages are disseminated through social media to ensure broad uptake. Objective: The objective of this study was to assess the social media dissemination of 2 DSCs released in 2013 for the sleep aid zolpidem. Methods: We used the MedWatcher Social program and the DataSift historic query tool to aggregate Twitter and Facebook posts from October 1, 2012 through August 31, 2013, a period beginning approximately 3 months before the first DSC and ending 3 months after the second. Posts were categorized as (1) junk, (2) mention, and (3) adverse event (AE) based on a score between ?0.2 (completely unrelated) to 1 (perfectly related). We also looked at Google Trends data and Wikipedia edits for the same time period. Google Trends search volume is scaled on a range of 0 to 100 and includes ?Related queries? during the relevant time periods. An interrupted time series (ITS) analysis assessed the impact of DSCs on the counts of posts with specific mention of zolpidem-containing products. Chow tests for known structural breaks were conducted on data from Twitter, Facebook, and Google Trends. Finally, Wikipedia edits were pulled from the website?s editorial history, which lists all revisions to a given page and the editor?s identity. Results: In total, 174,286 Twitter posts and 59,641 Facebook posts met entry criteria. Of those, 16.63% (28,989/174,286) of Twitter posts and 25.91% (15,453/59,641) of Facebook posts were labeled as junk and excluded. AEs and mentions represented 9.21% (16,051/174,286) and 74.16% (129,246/174,286) of Twitter posts and 5.11% (3,050/59,641) and 68.98% (41,138/59,641) of Facebook posts, respectively. Total daily counts of posts about zolpidem-containing products increased on Twitter and Facebook on the day of the first DSC; Google searches increased on the week of the first DSC. ITS analyses demonstrated variability but pointed to an increase in interest around the first DSC. Chow tests were significant (P<.0001) for both DSCs on Facebook and Twitter, but only the first DSC on Google Trends. Wikipedia edits occurred soon after each DSC release, citing news articles rather than the DSC itself and presenting content that needed subsequent revisions for accuracy. Conclusions: Social media offers challenges and opportunities for dissemination of the DSC messages. The FDA could consider strategies for more actively disseminating DSC safety information through social media platforms, particularly when announcements require updating. The FDA may also benefit from directly contributing content to websites like Wikipedia that are frequently accessed for drug-related information. UR - http://publichealth.jmir.org/2018/1/e1/ UR - http://dx.doi.org/10.2196/publichealth.7823 UR - http://www.ncbi.nlm.nih.gov/pubmed/29305342 ID - info:doi/10.2196/publichealth.7823 ER - TY - JOUR AU - Wagner, Moritz AU - Lampos, Vasileios AU - Yom-Tov, Elad AU - Pebody, Richard AU - Cox, J. Ingemar PY - 2017/12/21 TI - Estimating the Population Impact of a New Pediatric Influenza Vaccination Program in England Using Social Media Content JO - J Med Internet Res SP - e416 VL - 19 IS - 12 KW - health intervention KW - influenza KW - vaccination KW - social media KW - Twitter N2 - Background: The rollout of a new childhood live attenuated influenza vaccine program was launched in England in 2013, which consisted of a national campaign for all 2 and 3 year olds and several pilot locations offering the vaccine to primary school-age children (4-11 years of age) during the influenza season. The 2014/2015 influenza season saw the national program extended to include additional pilot regions, some of which offered the vaccine to secondary school children (11-13 years of age) as well. Objective: We utilized social media content to obtain a complementary assessment of the population impact of the programs that were launched in England during the 2013/2014 and 2014/2015 flu seasons. The overall community-wide impact on transmission in pilot areas was estimated for the different age groups that were targeted for vaccination. Methods: A previously developed statistical framework was applied, which consisted of a nonlinear regression model that was trained to infer influenza-like illness (ILI) rates from Twitter posts originating in pilot (school-age vaccinated) and control (unvaccinated) areas. The control areas were then used to estimate ILI rates in pilot areas, had the intervention not taken place. These predictions were compared with their corresponding Twitter-based ILI estimates. Results: Results suggest a reduction in ILI rates of 14% (1-25%) and 17% (2-30%) across all ages in only the primary school-age vaccine pilot areas during the 2013/2014 and 2014/2015 influenza seasons, respectively. No significant impact was observed in areas where two age cohorts of secondary school children were vaccinated. Conclusions: These findings corroborate independent assessments from traditional surveillance data, thereby supporting the ongoing rollout of the program to primary school-age children and providing evidence of the value of social media content as an additional syndromic surveillance tool. UR - http://www.jmir.org/2017/12/e416/ UR - http://dx.doi.org/10.2196/jmir.8184 UR - http://www.ncbi.nlm.nih.gov/pubmed/29269339 ID - info:doi/10.2196/jmir.8184 ER - TY - JOUR AU - Allem, Jon-Patrick AU - Ferrara, Emilio AU - Uppu, Priyanka Sree AU - Cruz, Boley Tess AU - Unger, B. Jennifer PY - 2017/12/20 TI - E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends JO - JMIR Public Health Surveill SP - e98 VL - 3 IS - 4 KW - electronic cigarettes KW - vaping KW - Twitter KW - social media KW - social bots KW - electronic nicotine delivery system KW - infoveillance N2 - Background: As e-cigarette use rapidly increases in popularity, data from online social systems (Twitter, Instagram, Google Web Search) can be used to capture and describe the social and environmental context in which individuals use, perceive, and are marketed this tobacco product. Social media data may serve as a massive focus group where people organically discuss e-cigarettes unprimed by a researcher, without instrument bias, captured in near real time and at low costs. Objective: This study documents e-cigarette?related discussions on Twitter, describing themes of conversations and locations where Twitter users often discuss e-cigarettes, to identify priority areas for e-cigarette education campaigns. Additionally, this study demonstrates the importance of distinguishing between social bots and human users when attempting to understand public health?related behaviors and attitudes. Methods: E-cigarette?related posts on Twitter (N=6,185,153) were collected from December 24, 2016, to April 21, 2017. Techniques drawn from network science were used to determine discussions of e-cigarettes by describing which hashtags co-occur (concept clusters) in a Twitter network. Posts and metadata were used to describe where geographically e-cigarette?related discussions in the United States occurred. Machine learning models were used to distinguish between Twitter posts reflecting attitudes and behaviors of genuine human users from those of social bots. Odds ratios were computed from 2x2 contingency tables to detect if hashtags varied by source (social bot vs human user) using the Fisher exact test to determine statistical significance. Results: Clusters found in the corpus of hashtags from human users included behaviors (eg, #vaping), vaping identity (eg, #vapelife), and vaping community (eg, #vapenation). Additional clusters included products (eg, #eliquids), dual tobacco use (eg, #hookah), and polysubstance use (eg, #marijuana). Clusters found in the corpus of hashtags from social bots included health (eg, #health), smoking cessation (eg, #quitsmoking), and new products (eg, #ismog). Social bots were significantly more likely to post hashtags that referenced smoking cessation and new products compared to human users. The volume of tweets was highest in the Mid-Atlantic (eg, Pennsylvania, New Jersey, Maryland, and New York), followed by the West Coast and Southwest (eg, California, Arizona and Nevada). Conclusions: Social media data may be used to complement and extend the surveillance of health behaviors including tobacco product use. Public health researchers could harness these data and methods to identify new products or devices. Furthermore, findings from this study demonstrate the importance of distinguishing between Twitter posts from social bots and humans when attempting to understand attitudes and behaviors. Social bots may be used to perpetuate the idea that e-cigarettes are helpful in cessation and to promote new products as they enter the marketplace. UR - http://publichealth.jmir.org/2017/4/e98/ UR - http://dx.doi.org/10.2196/publichealth.8641 UR - http://www.ncbi.nlm.nih.gov/pubmed/29263018 ID - info:doi/10.2196/publichealth.8641 ER - TY - JOUR AU - Madden, Michael Kenneth PY - 2017/12/13 TI - The Seasonal Periodicity of Healthy Contemplations About Exercise and Weight Loss: Ecological Correlational Study JO - JMIR Public Health Surveill SP - e92 VL - 3 IS - 4 KW - healthy lifestyle KW - weight loss KW - exercise KW - Internet KW - motivation N2 - Background: Lack of physical activity and weight gain are two of the biggest drivers of health care costs in the United States. Healthy contemplations are required before any changes in behavior, and a recent study has shown that they have underlying periodicities. Objective: The aim of this study was to examine seasonal variations in state-by-state interest in both weight loss and increasing physical activity, and how these variations were associated with geographic latitude using Google Trends search data for the United States. Methods: Internet search query data were obtained from Google Trends (2004-2016). Time series analysis (every 2 weeks) was performed to determine search volume (normalized to overall search intensity). Seasonality was determined both by the difference in search volumes between winter (December, January, and February) and summer (June, July, and August) months and by the amplitude of cosinor analysis. Results: Exercise-related searches were highest during the winter months, whereas weight loss contemplations showed a biphasic pattern (peaking in the summer and winter months). The magnitude of the seasonal difference increased with increasing latitude for both exercise (R2=.45, F1,49=40.09, beta=?.671, standard deviation [SD]=0.106, P<.001) and weight loss (R2=.24, F1,49=15.79, beta=?.494, SD=0.124, P<.001) searches. Conclusions: Healthy contemplations follow specific seasonal patterns, with the highest contemplations surrounding exercise during the winter months, and weight loss contemplations peaking during both winter and summer seasons. Knowledge of seasonal variations in passive contemplations may potentially allow for more efficient use of public health campaign resources. UR - http://publichealth.jmir.org/2017/4/e92/ UR - http://dx.doi.org/10.2196/publichealth.7794 UR - http://www.ncbi.nlm.nih.gov/pubmed/29237582 ID - info:doi/10.2196/publichealth.7794 ER - TY - JOUR AU - Bian, Jiang AU - Zhao, Yunpeng AU - Salloum, G. Ramzi AU - Guo, Yi AU - Wang, Mo AU - Prosperi, Mattia AU - Zhang, Hansi AU - Du, Xinsong AU - Ramirez-Diaz, J. Laura AU - He, Zhe AU - Sun, Yuan PY - 2017/12/13 TI - Using Social Media Data to Understand the Impact of Promotional Information on Laypeople?s Discussions: A Case Study of Lynch Syndrome JO - J Med Internet Res SP - e414 VL - 19 IS - 12 KW - social media KW - Lynch syndrome KW - public health surveillance KW - sentiment analysis N2 - Background: Social media is being used by various stakeholders among pharmaceutical companies, government agencies, health care organizations, professionals, and news media as a way of engaging audiences to raise disease awareness and ultimately to improve public health. Nevertheless, it is unclear what effects this health information has on laypeople. Objective: This study aimed to provide a detailed examination of how promotional health information related to Lynch syndrome impacts laypeople?s discussions on a social media platform (Twitter) in terms of topic awareness and attitudes. Methods: We used topic modeling and sentiment analysis techniques on Lynch syndrome?related tweets to answer the following research questions (RQs): (1) what are the most discussed topics in Lynch syndrome?related tweets?; (2) how promotional Lynch syndrome?related information on Twitter affects laypeople?s discussions?; and (3) what impact do the Lynch syndrome awareness activities in the Colon Cancer Awareness Month and Lynch Syndrome Awareness Day have on laypeople?s discussions and their attitudes? In particular, we used a set of keywords to collect Lynch syndrome?related tweets from October 26, 2016 to August 11, 2017 (289 days) through the Twitter public search application programming interface (API). We experimented with two different classification methods to categorize tweets into the following three classes: (1) irrelevant, (2) promotional health information, and (3) laypeople?s discussions. We applied a topic modeling method to discover the themes in these Lynch syndrome?related tweets and conducted sentiment analysis on each layperson?s tweet to gauge the writer?s attitude (ie, positive, negative, and neutral) toward Lynch syndrome. The topic modeling and sentiment analysis results were elaborated to answer the three RQs. Results: Of all tweets (N=16,667), 87.38% (14,564/16,667) were related to Lynch syndrome. Of the Lynch syndrome?related tweets, 81.43% (11,860/14,564) were classified as promotional and 18.57% (2704/14,564) were classified as laypeople?s discussions. The most discussed themes were treatment (n=4080) and genetic testing (n=3073). We found that the topic distributions in laypeople?s discussions were similar to the distributions in promotional Lynch syndrome?related information. Furthermore, most people had a positive attitude when discussing Lynch syndrome. The proportion of negative tweets was 3.51%. Within each topic, treatment (16.67%) and genetic testing (5.60%) had more negative tweets compared with other topics. When comparing monthly trends, laypeople?s discussions had a strong correlation with promotional Lynch syndrome?related information on awareness (r=.98, P<.001), while there were moderate correlations on screening (r=.602, P=.05), genetic testing (r=.624, P=.04), treatment (r=.69, P=.02), and risk (r=.66, P=.03). We also discovered that the Colon Cancer Awareness Month (March 2017) and the Lynch Syndrome Awareness Day (March 22, 2017) had significant positive impacts on laypeople?s discussions and their attitudes. Conclusions: There is evidence that participative social media platforms, namely Twitter, offer unique opportunities to inform cancer communication surveillance and to explore the mechanisms by which these new communication media affect individual health behavior and population health. UR - http://www.jmir.org/2017/12/e414/ UR - http://dx.doi.org/10.2196/jmir.9266 UR - http://www.ncbi.nlm.nih.gov/pubmed/29237586 ID - info:doi/10.2196/jmir.9266 ER - TY - JOUR AU - P Tafti, Ahmad AU - Badger, Jonathan AU - LaRose, Eric AU - Shirzadi, Ehsan AU - Mahnke, Andrea AU - Mayer, John AU - Ye, Zhan AU - Page, David AU - Peissig, Peggy PY - 2017/12/08 TI - Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure JO - JMIR Med Inform SP - e51 VL - 5 IS - 4 KW - adverse drug event KW - adverse drug reaction KW - drug side effects KW - machine learning KW - text mining N2 - Background: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. Objective: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. Methods: We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. Results: The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. Conclusions: To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis. UR - http://medinform.jmir.org/2017/4/e51/ UR - http://dx.doi.org/10.2196/medinform.9170 UR - http://www.ncbi.nlm.nih.gov/pubmed/29222076 ID - info:doi/10.2196/medinform.9170 ER - TY - JOUR AU - Adawi, Mohammad AU - Bragazzi, Luigi Nicola AU - Watad, Abdulla AU - Sharif, Kassem AU - Amital, Howard AU - Mahroum, Naim PY - 2017/12/01 TI - Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends JO - JMIR Public Health Surveill SP - e93 VL - 3 IS - 4 KW - digital health KW - digital epidemiology KW - emerging viruses KW - Mayaro virus KW - arboviruses KW - epidemiology KW - epidemiological monitoring N2 - Background: Mayaro virus (MAYV), first discovered in Trinidad in 1954, is spread by the Haemagogus mosquito. Small outbreaks have been described in the past in the Amazon jungles of Brazil and other parts of South America. Recently, a case was reported in rural Haiti. Objective: Given the emerging importance of MAYV, we aimed to explore the feasibility of exploiting a Web-based tool for monitoring and tracking MAYV cases. Methods: Google Trends is an online tracking system. A Google-based approach is particularly useful to monitor especially infectious diseases epidemics. We searched Google Trends from its inception (from January 2004 through to May 2017) for MAYV-related Web searches worldwide. Results: We noted a burst in search volumes in the period from July 2016 (relative search volume [RSV]=13%) to December 2016 (RSV=18%), with a peak in September 2016 (RSV=100%). Before this burst, the average search activity related to MAYV was very low (median 1%). MAYV-related queries were concentrated in the Caribbean. Scientific interest from the research community and media coverage affected digital seeking behavior. Conclusions: MAYV has always circulated in South America. Its recent appearance in the Caribbean has been a source of concern, which resulted in a burst of Internet queries. While Google Trends cannot be used to perform real-time epidemiological surveillance of MAYV, it can be exploited to capture the public?s reaction to outbreaks. Public health workers should be aware of this, in that information and communication technologies could be used to communicate with users, reassure them about their concerns, and to empower them in making decisions affecting their health. UR - http://publichealth.jmir.org/2017/4/e93/ UR - http://dx.doi.org/10.2196/publichealth.9136 UR - http://www.ncbi.nlm.nih.gov/pubmed/29196278 ID - info:doi/10.2196/publichealth.9136 ER - TY - JOUR AU - Samaras, Loukas AU - García-Barriocanal, Elena AU - Sicilia, Miguel-Angel PY - 2017/11/20 TI - Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends JO - JMIR Public Health Surveill SP - e90 VL - 3 IS - 4 KW - Google Trends KW - influenza KW - Web, syndromic surveillance KW - statistical correlation KW - forecast KW - ARIMA N2 - Background: An extended discussion and research has been performed in recent years using data collected through search queries submitted via the Internet. It has been shown that the overall activity on the Internet is related to the number of cases of an infectious disease outbreak. Objective: The aim of the study was to define a similar correlation between data from Google Trends and data collected by the official authorities of Greece and Europe by examining the development and the spread of seasonal influenza in Greece and Italy. Methods: We used multiple regressions of the terms submitted in the Google search engine related to influenza for the period from 2011 to 2012 in Greece and Italy (sample data for 104 weeks for each country). We then used the autoregressive integrated moving average statistical model to determine the correlation between the Google search data and the real influenza cases confirmed by the aforementioned authorities. Two methods were used: (1) a flu score was created for the case of Greece and (2) comparison of data from a neighboring country of Greece, which is Italy. Results: The results showed that there is a significant correlation that can help the prediction of the spread and the peak of the seasonal influenza using data from Google searches. The correlation for Greece for 2011 and 2012 was .909 and .831, respectively, and correlation for Italy for 2011 and 2012 was .979 and .933, respectively. The prediction of the peak was quite precise, providing a forecast before it arrives to population. Conclusions: We can create an Internet surveillance system based on Google searches to track influenza in Greece and Italy. UR - http://publichealth.jmir.org/2017/4/e90/ UR - http://dx.doi.org/10.2196/publichealth.8015 UR - http://www.ncbi.nlm.nih.gov/pubmed/29158208 ID - info:doi/10.2196/publichealth.8015 ER - TY - JOUR AU - Pesälä, Samuli AU - Virtanen, J. Mikko AU - Sane, Jussi AU - Mustonen, Pekka AU - Kaila, Minna AU - Helve, Otto PY - 2017/11/06 TI - Health Information?Seeking Patterns of the General Public and Indications for Disease Surveillance: Register-Based Study Using Lyme Disease JO - JMIR Public Health Surveill SP - e86 VL - 3 IS - 4 KW - search engine KW - evidence-based medicine KW - medical informatics KW - information systems KW - communications media KW - Lyme disease KW - infodemiology KW - infoveillance KW - surveillance N2 - Background: People using the Internet to find information on health issues, such as specific diseases, usually start their search from a general search engine, for example, Google. Internet searches such as these may yield results and data of questionable quality and reliability. Health Library is a free-of-charge medical portal on the Internet providing medical information for the general public. Physician?s Databases, an Internet evidence-based medicine source, provides medical information for health care professionals (HCPs) to support their clinical practice. Both databases are available throughout Finland, but the latter is used only by health professionals and pharmacies. Little is known about how the general public seeks medical information from medical sources on the Internet, how this behavior differs from HCPs? queries, and what causes possible differences in behavior. Objective: The aim of our study was to evaluate how the general public?s and HCPs? information-seeking trends from Internet medical databases differ seasonally and temporally. In addition, we aimed to evaluate whether the general public?s information-seeking trends could be utilized for disease surveillance and whether media coverage could affect these seeking trends. Methods: Lyme disease, serving as a well-defined disease model with distinct seasonal variation, was chosen as a case study. Two Internet medical databases, Health Library and Physician?s Databases, were used. We compared the general public?s article openings on Lyme disease from Health Library to HCPs? article openings on Lyme disease from Physician?s Databases seasonally across Finland from 2011 to 2015. Additionally, media publications related to Lyme disease were searched from the largest and most popular media websites in Finland. Results: Both databases, Health Library and Physician?s Databases, show visually similar patterns in temporal variations of article openings on Lyme disease in Finland from 2011 to 2015. However, Health Library openings show not only an increasing trend over time but also greater fluctuations, especially during peak opening seasons. Outside these seasons, publications in the media coincide with Health Library article openings only occasionally. Conclusions: Lyme disease?related information-seeking behaviors between the general public and HCPs from Internet medical portals share similar temporal variations, which is consistent with the trend seen in epidemiological data. Therefore, the general public?s article openings could be used as a supplementary source of information for disease surveillance. The fluctuations in article openings appeared stronger among the general public, thus, suggesting that different factors such as media coverage, affect the information-seeking behaviors of the public versus professionals. However, media coverage may also have an influence on HCPs. Not every publication was associated with an increase in openings, but the higher the media coverage by some publications, the higher the general public?s access to Health Library. UR - http://publichealth.jmir.org/2017/4/e86/ UR - http://dx.doi.org/10.2196/publichealth.8306 UR - http://www.ncbi.nlm.nih.gov/pubmed/29109071 ID - info:doi/10.2196/publichealth.8306 ER - TY - JOUR AU - Kandula, Sasikiran AU - Hsu, Daniel AU - Shaman, Jeffrey PY - 2017/11/06 TI - Subregional Nowcasts of Seasonal Influenza Using Search Trends JO - J Med Internet Res SP - e370 VL - 19 IS - 11 KW - human influenza KW - classification and regression trees KW - nowcasts KW - infodemiology KW - infoveillance KW - surveillance N2 - Background: Limiting the adverse effects of seasonal influenza outbreaks at state or city level requires close monitoring of localized outbreaks and reliable forecasts of their progression. Whereas forecasting models for influenza or influenza-like illness (ILI) are becoming increasingly available, their applicability to localized outbreaks is limited by the nonavailability of real-time observations of the current outbreak state at local scales. Surveillance data collected by various health departments are widely accepted as the reference standard for estimating the state of outbreaks, and in the absence of surveillance data, nowcast proxies built using Web-based activities such as search engine queries, tweets, and access of health-related webpages can be useful. Nowcast estimates of state and municipal ILI were previously published by Google Flu Trends (GFT); however, validations of these estimates were seldom reported. Objective: The aim of this study was to develop and validate models to nowcast ILI at subregional geographic scales. Methods: We built nowcast models based on autoregressive (autoregressive integrated moving average; ARIMA) and supervised regression methods (Random forests) at the US state level using regional weighted ILI and Web-based search activity derived from Google's Extended Trends application programming interface. We validated the performance of these methods using actual surveillance data for the 50 states across six seasons. We also built state-level nowcast models using state-level estimates of ILI and compared the accuracy of these estimates with the estimates of the regional models extrapolated to the state level and with the nowcast estimates published by GFT. Results: Models built using regional ILI extrapolated to state level had a median correlation of 0.84 (interquartile range: 0.74-0.91) and a median root mean square error (RMSE) of 1.01 (IQR: 0.74-1.50), with noticeable variability across seasons and by state population size. Model forms that hypothesize the availability of timely state-level surveillance data show significantly lower errors of 0.83 (0.55-0.23). Compared with GFT, the latter model forms have lower errors but also lower correlation. Conclusions: These results suggest that the proposed methods may be an alternative to the discontinued GFT and that further improvements in the quality of subregional nowcasts may require increased access to more finely resolved surveillance data. UR - http://www.jmir.org/2017/11/e370/ UR - http://dx.doi.org/10.2196/jmir.7486 UR - http://www.ncbi.nlm.nih.gov/pubmed/29109069 ID - info:doi/10.2196/jmir.7486 ER - TY - JOUR AU - Vickey, Theodore AU - Breslin, G. John PY - 2017/10/31 TI - Online Influence and Sentiment of Fitness Tweets: Analysis of Two Million Fitness Tweets JO - JMIR Public Health Surveill SP - e82 VL - 3 IS - 4 KW - Twitter KW - physical activity KW - mobile fitness apps KW - fitness tweet classification KW - sentiment N2 - Background: Publicly available fitness tweets may provide useful and in-depth insights into the real-time sentiment of a person?s physical activity and provide motivation to others through online influence. Objective: The goal of this experimental approach using the fitness Twitter dataset is two-fold: (1) to determine if there is a correlation between the type of activity tweet (either workout or workout+, which contains the same information as a workout tweet but has additional user-generated information), gender, and one?s online influence as measured by Klout Score and (2) to examine the sentiment of the activity-coded fitness tweets by looking at real-time shared thoughts via Twitter regarding their experiences with physical activity and the associated mobile fitness app. Methods: The fitness tweet dataset includes demographic and activity data points, including minutes of activity, Klout Score, classification of each fitness tweet, the first name of each fitness tweet user, and the tweet itself. Gender for each fitness tweet user was determined by a first name comparison with the US Social Security Administration database of first names and gender. Results: Over 184 days, 2,856,534 tweets were collected in 23 different languages. However, for the purposes of this study, only the English-language tweets were analyzed from the activity tweets, resulting in a total of 583,252 tweets. After assigning gender to Twitter usernames based on the Social Security Administration database of first names, analysis of minutes of activity by both gender and Klout influence was determined. The mean Klout Score for those who shared their workout data from within four mobile apps was 20.50 (13.78 SD), less than the general Klout Score mean of 40, as was the Klout Score at the 95th percentile (40 vs 63). As Klout Score increased, there was a decrease in the number of overall workout+ tweets. With regards to sentiment, fitness-related tweets identified as workout+ reflected a positive sentiment toward physical activity by a ratio of 4 to 1. Conclusions: The results of this research suggest that the users of mobile fitness apps who share their workouts via Twitter have a lower Klout Score than the general Twitter user and that users who chose to share additional insights into their workouts are more positive in sentiment than negative. We present a novel perspective into the physical activity messaging from within mobile fitness apps that are then shared over Twitter. By moving beyond the numbers and evaluating both the Twitter user and the emotions tied to physical activity, future research could analyze additional relationships between the user?s online influence, the enjoyment of the physical activity, and with additional analysis a long-term retention strategy for the use of a fitness app. UR - http://publichealth.jmir.org/2017/4/e82/ UR - http://dx.doi.org/10.2196/publichealth.8507 UR - http://www.ncbi.nlm.nih.gov/pubmed/29089294 ID - info:doi/10.2196/publichealth.8507 ER - TY - JOUR AU - Kim, Jung Sunny AU - Marsch, A. Lisa AU - Hancock, T. Jeffrey AU - Das, K. Amarendra PY - 2017/10/31 TI - Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data JO - J Med Internet Res SP - e353 VL - 19 IS - 10 KW - opioid epidemic KW - opioid crisis KW - opioid-related disorders KW - substance use KW - substance-related disorders KW - prescription drug misuse KW - addiction KW - Facebook KW - Twitter KW - Instagram KW - big data KW - ethics N2 - Background: Substance use?related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the promising potential to utilize social media big data to monitor and treat substance use problems, the characteristics, mechanisms, and outcomes of substance use?related communications on social media are largely unknown. Understanding these aspects can help researchers effectively leverage social media big data and platforms for observation and health communication outreach for people with substance use problems. Objective: The objective of this critical review was to determine how social media big data can be used to understand communication and behavioral patterns of problematic use of prescription drugs. We elaborate on theoretical applications, ethical challenges and methodological considerations when using social media big data for research on drug abuse and addiction. Based on a critical review process, we propose a typology with key initiatives to address the knowledge gap in the use of social media for research on prescription drug abuse and addiction. Methods: First, we provided a narrative summary of the literature on drug use?related communication on social media. We also examined ethical considerations in the research processes of (1) social media big data mining, (2) subgroup or follow-up investigation, and (3) dissemination of social media data-driven findings. To develop a critical review-based typology, we searched the PubMed database and the entire e-collection theme of ?infodemiology and infoveillance? in the Journal of Medical Internet Research / JMIR Publications. Studies that met our inclusion criteria (eg, use of social media data concerning non-medical use of prescription drugs, data informatics-driven findings) were reviewed for knowledge synthesis. User characteristics, communication characteristics, mechanisms and predictors of such communications, and the psychological and behavioral outcomes of social media use for problematic drug use?related communications are the dimensions of our typology. In addition to ethical practices and considerations, we also reviewed the methodological and computational approaches used in each study to develop our typology. Results: We developed a typology to better understand non-medical, problematic use of prescription drugs through the lens of social media big data. Highly relevant studies that met our inclusion criteria were reviewed for knowledge synthesis. The characteristics of users who shared problematic substance use?related communications on social media were reported by general group terms, such as adolescents, Twitter users, and Instagram users. All reviewed studies examined the communication characteristics, such as linguistic properties, and social networks of problematic drug use?related communications on social media. The mechanisms and predictors of such social media communications were not directly examined or empirically identified in the reviewed studies. The psychological or behavioral consequence (eg, increased behavioral intention for mimicking risky health behaviors) of engaging with and being exposed to social media communications regarding problematic drug use was another area of research that has been understudied. Conclusions: We offer theoretical applications, ethical considerations, and empirical evidence within the scope of social media communication and prescription drug abuse and addiction. Our critical review suggests that social media big data can be a tremendous resource to understand, monitor and intervene on drug abuse and addiction problems. UR - http://www.jmir.org/2017/10/e353/ UR - http://dx.doi.org/10.2196/jmir.6426 UR - http://www.ncbi.nlm.nih.gov/pubmed/29089287 ID - info:doi/10.2196/jmir.6426 ER - TY - JOUR AU - Sarker, Abeed AU - Chandrashekar, Pramod AU - Magge, Arjun AU - Cai, Haitao AU - Klein, Ari AU - Gonzalez, Graciela PY - 2017/10/30 TI - Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis JO - J Med Internet Res SP - e361 VL - 19 IS - 10 KW - natural language processing KW - machine learning KW - text mining KW - social media KW - pregnancy KW - cohort studies KW - data analysis N2 - Background: Pregnancy exposure registries are the primary sources of information about the safety of maternal usage of medications during pregnancy. Such registries enroll pregnant women in a voluntary fashion early on in pregnancy and follow them until the end of pregnancy or longer to systematically collect information regarding specific pregnancy outcomes. Although the model of pregnancy registries has distinct advantages over other study designs, they are faced with numerous challenges and limitations such as low enrollment rate, high cost, and selection bias. Objective: The primary objectives of this study were to systematically assess whether social media (Twitter) can be used to discover cohorts of pregnant women and to develop and deploy a natural language processing and machine learning pipeline for the automatic collection of cohort information. In addition, we also attempted to ascertain, in a preliminary fashion, what types of longitudinal information may potentially be mined from the collected cohort information. Methods: Our discovery of pregnant women relies on detecting pregnancy-indicating tweets (PITs), which are statements posted by pregnant women regarding their pregnancies. We used a set of 14 patterns to first detect potential PITs. We manually annotated a sample of 14,156 of the retrieved user posts to distinguish real PITs from false positives and trained a supervised classification system to detect real PITs. We optimized the classification system via cross validation, with features and settings targeted toward optimizing precision for the positive class. For users identified to be posting real PITs via automatic classification, our pipeline collected all their available past and future posts from which other information (eg, medication usage and fetal outcomes) may be mined. Results: Our rule-based PIT detection approach retrieved over 200,000 posts over a period of 18 months. Manual annotation agreement for three annotators was very high at kappa (?)=.79. On a blind test set, the implemented classifier obtained an overall F1 score of 0.84 (0.88 for the pregnancy class and 0.68 for the nonpregnancy class). Precision for the pregnancy class was 0.93, and recall was 0.84. Feature analysis showed that the combination of dense and sparse vectors for classification achieved optimal performance. Employing the trained classifier resulted in the identification of 71,954 users from the collected posts. Over 250 million posts were retrieved for these users, which provided a multitude of longitudinal information about them. Conclusions: Social media sources such as Twitter can be used to identify large cohorts of pregnant women and to gather longitudinal information via automated processing of their postings. Considering the many drawbacks and limitations of pregnancy registries, social media mining may provide beneficial complementary information. Although the cohort sizes identified over social media are large, future research will have to assess the completeness of the information available through them. UR - http://www.jmir.org/2017/10/e361/ UR - http://dx.doi.org/10.2196/jmir.8164 UR - http://www.ncbi.nlm.nih.gov/pubmed/29084707 ID - info:doi/10.2196/jmir.8164 ER - TY - JOUR AU - Yom-Tov, Elad AU - Lev-Ran, Shaul PY - 2017/10/26 TI - Adverse Reactions Associated With Cannabis Consumption as Evident From Search Engine Queries JO - JMIR Public Health Surveill SP - e77 VL - 3 IS - 4 KW - cannabis KW - search engines KW - pharmacovigilance N2 - Background: Cannabis is one of the most widely used psychoactive substances worldwide, but adverse drug reactions (ADRs) associated with its use are difficult to study because of its prohibited status in many countries. Objective: Internet search engine queries have been used to investigate ADRs in pharmaceutical drugs. In this proof-of-concept study, we tested whether these queries can be used to detect the adverse reactions of cannabis use. Methods: We analyzed anonymized queries from US-based users of Bing, a widely used search engine, made over a period of 6 months and compared the results with the prevalence of cannabis use as reported in the US National Survey on Drug Use in the Household (NSDUH) and with ADRs reported in the Food and Drug Administration?s Adverse Drug Reporting System. Predicted prevalence of cannabis use was estimated from the fraction of people making queries about cannabis, marijuana, and 121 additional synonyms. Predicted ADRs were estimated from queries containing layperson descriptions to 195 ICD-10 symptoms list. Results: Our results indicated that the predicted prevalence of cannabis use at the US census regional level reaches an R2 of .71 NSDUH data. Queries for ADRs made by people who also searched for cannabis reveal many of the known adverse effects of cannabis (eg, cough and psychotic symptoms), as well as plausible unknown reactions (eg, pyrexia). Conclusions: These results indicate that search engine queries can serve as an important tool for the study of adverse reactions of illicit drugs, which are difficult to study in other settings. UR - http://publichealth.jmir.org/2017/4/e77/ UR - http://dx.doi.org/10.2196/publichealth.8391 UR - http://www.ncbi.nlm.nih.gov/pubmed/29074469 ID - info:doi/10.2196/publichealth.8391 ER - TY - JOUR AU - Oser, K. Tamara AU - Oser, M. Sean AU - McGinley, L. Erin AU - Stuckey, L. Heather PY - 2017/10/26 TI - A Novel Approach to Identifying Barriers and Facilitators in Raising a Child With Type 1 Diabetes: Qualitative Analysis of Caregiver Blogs JO - JMIR Diabetes SP - e27 VL - 2 IS - 2 KW - type 1 diabetes KW - blogs KW - caregiver KW - self-management KW - social media KW - peer support KW - Internet N2 - Background: With rising incidence of type 1 diabetes (T1D) diagnoses among children and the high levels of distress experienced by the caregivers of these children, caregiver support is becoming increasingly important. Historically, relatively few support resources have existed. Increasing use of the Internet, and blogs in particular, has seen a growth of peer support between caregivers of children with T1D. However, little is known about the type and quality of information shared on T1D caregiver blogs. At the same time, the information on such blogs offers a new window into what challenges and successes caregivers experience in helping to manage their children?s T1D. Objective: The purpose of this study was to (1) analyze blogs of caregivers to children with T1D to better understand the challenges and successes they face in raising a child with T1D, and (2) assess the blogs for the presence of unsafe or inaccurate clinical information or advice. Methods: An inductive thematic qualitative study was conducted of three blogs authored by caregivers of children living with T1D, which included 140 unique blog posts and 663 associated comments. Two physician investigators evaluated the blogs for presence of clinical or medical misinformation. Results: Five major themes emerged: (1) the impact of the child?s diagnosis, (2) the burden of intense self-management experienced in caring for a child with T1D, (3) caregivers? use of technology to ease their fear of hypoglycemia and impacts that device alarms associated with this technology have on caregiver burden, (4) caregivers? perceptions of frequently missed or delayed diagnosis of T1D and the frustration this causes, and (5) the resilience that caregivers develop despite the burdens they experience. Misinformation was exceedingly rare and benign when it did occur. Conclusions: Blog analysis represents a novel approach to understand the T1D caregiver?s experience. This qualitative study found many challenges that caregivers face in raising a child with T1D. Despite the many barriers caregivers face in managing their children?s T1D, they find support through advocacy efforts and peer-to-peer blogging. Blogs provide a unique avenue for support, with only rare and benign findings of medical misinformation, and may be a resource that diabetes care providers can consider offering to families for support. UR - http://diabetes.jmir.org/2017/2/e27/ UR - http://dx.doi.org/10.2196/diabetes.8966 UR - http://www.ncbi.nlm.nih.gov/pubmed/30291073 ID - info:doi/10.2196/diabetes.8966 ER - TY - JOUR AU - Abbe, Adeline AU - Falissard, Bruno PY - 2017/10/23 TI - Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach JO - JMIR Ment Health SP - e48 VL - 4 IS - 4 KW - social media KW - antidepressant KW - anxiolytic KW - text mining KW - data mining N2 - Background: Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help. Objective: The aim of this study was to use text mining on material from an online forum exploring patients? concerns about treatment (antidepressants and anxiolytics). Methods: Concerns about treatment were collected from discussion titles in patients? online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum. Results: The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients? concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs. Conclusions: Patients? expression on the Internet is a potential additional resource in addressing patients? concerns about treatment. Patient profiles are close to that of patients treated in psychiatry. UR - http://mental.jmir.org/2017/4/e48/ UR - http://dx.doi.org/10.2196/mental.7797 UR - http://www.ncbi.nlm.nih.gov/pubmed/29061554 ID - info:doi/10.2196/mental.7797 ER - TY - JOUR AU - Lachmar, Megan E. AU - Wittenborn, K. Andrea AU - Bogen, W. Katherine AU - McCauley, L. Heather PY - 2017/10/18 TI - #MyDepressionLooksLike: Examining Public Discourse About Depression on Twitter JO - JMIR Ment Health SP - e43 VL - 4 IS - 4 KW - social media KW - depression KW - community networks KW - social stigma N2 - Background: Social media provides a context for billions of users to connect, express sentiments, and provide in-the-moment status updates. Because Twitter users tend to tweet emotional updates from daily life, the platform provides unique insights into experiences of mental health problems. Depression is not only one of the most prevalent health conditions but also carries a social stigma. Yet, opening up about one?s depression and seeking social support may provide relief from symptoms. Objective: The aim of this study was to examine the public discourse of the trending hashtag #MyDepressionLooksLike to look more closely at how users talk about their depressive symptoms on Twitter. Methods: We captured 3225 original content tweets for the hashtag #MyDepressionLooksLike that circulated in May of 2016. Eliminating public service announcements, spam, and tweets with links to pictures or videos resulted in a total of 1978 tweets. Using qualitative content analysis, we coded the tweets to detect themes. Results: The content analysis revealed seven themes: dysfunctional thoughts, lifestyle challenges, social struggles, hiding behind a mask, apathy and sadness, suicidal thoughts and behaviors, and seeking relief. Conclusions: The themes revealed important information about the content of the public messages that people share about depression on Twitter. More research is needed to understand the effects of the hashtag on increasing social support for users and reducing social stigma related to depression. UR - http://mental.jmir.org/2017/4/e43/ UR - http://dx.doi.org/10.2196/mental.8141 UR - http://www.ncbi.nlm.nih.gov/pubmed/29046270 ID - info:doi/10.2196/mental.8141 ER - TY - JOUR AU - Allem, Jon-Patrick AU - Ramanujam, Jagannathan AU - Lerman, Kristina AU - Chu, Kar-Hai AU - Boley Cruz, Tess AU - Unger, B. Jennifer PY - 2017/10/18 TI - Identifying Sentiment of Hookah-Related Posts on Twitter JO - JMIR Public Health Surveill SP - e74 VL - 3 IS - 4 KW - hookah KW - waterpipe KW - Twitter KW - social media KW - bots KW - big data KW - sentiment N2 - Background: The increasing popularity of hookah (or waterpipe) use in the United States and elsewhere has consequences for public health because it has similar health risks to that of combustible cigarettes. While hookah use rapidly increases in popularity, social media data (Twitter, Instagram) can be used to capture and describe the social and environmental contexts in which individuals use, perceive, discuss, and are marketed this tobacco product. These data may allow people to organically report on their sentiment toward tobacco products like hookah unprimed by a researcher, without instrument bias, and at low costs. Objective: This study describes the sentiment of hookah-related posts on Twitter and describes the importance of debiasing Twitter data when attempting to understand attitudes. Methods: Hookah-related posts on Twitter (N=986,320) were collected from March 24, 2015, to December 2, 2016. Machine learning models were used to describe sentiment on 20 different emotions and to debias the data so that Twitter posts reflected sentiment of legitimate human users and not of social bots or marketing-oriented accounts that would possibly provide overly positive or overly negative sentiment of hookah. Results: From the analytical sample, 352,116 tweets (59.50%) were classified as positive while 177,537 (30.00%) were classified as negative, and 62,139 (10.50%) neutral. Among all positive tweets, 218,312 (62.00%) were classified as highly positive emotions (eg, active, alert, excited, elated, happy, and pleasant), while 133,804 (38.00%) positive tweets were classified as passive positive emotions (eg, contented, serene, calm, relaxed, and subdued). Among all negative tweets, 95,870 (54.00%) were classified as subdued negative emotions (eg, sad, unhappy, depressed, and bored) while the remaining 81,667 (46.00%) negative tweets were classified as highly negative emotions (eg, tense, nervous, stressed, upset, and unpleasant). Sentiment changed drastically when comparing a corpus of tweets with social bots to one without. For example, the probability of any one tweet reflecting joy was 61.30% from the debiased (or bot free) corpus of tweets. In contrast, the probability of any one tweet reflecting joy was 16.40% from the biased corpus. Conclusions: Social media data provide researchers the ability to understand public sentiment and attitudes by listening to what people are saying in their own words. Tobacco control programmers in charge of risk communication may consider targeting individuals posting positive messages about hookah on Twitter or designing messages that amplify the negative sentiments. Posts on Twitter communicating positive sentiment toward hookah could add to the normalization of hookah use and is an area of future research. Findings from this study demonstrated the importance of debiasing data when attempting to understand attitudes from Twitter data. UR - http://publichealth.jmir.org/2017/4/e74/ UR - http://dx.doi.org/10.2196/publichealth.8133 UR - http://www.ncbi.nlm.nih.gov/pubmed/29046267 ID - info:doi/10.2196/publichealth.8133 ER - TY - JOUR AU - Lenoir, Philippe AU - Moulahi, Bilel AU - Azé, Jérôme AU - Bringay, Sandra AU - Mercier, Gregoire AU - Carbonnel, François PY - 2017/10/16 TI - Raising Awareness About Cervical Cancer Using Twitter: Content Analysis of the 2015 #SmearForSmear Campaign JO - J Med Internet Res SP - e344 VL - 19 IS - 10 KW - uterine cervical neoplasms KW - Papanicolaou test KW - social media KW - early detection of cancer KW - health promotion KW - Twitter N2 - Background: Cervical cancer is the second most common cancer among women under 45 years of age. To deal with the decrease of smear test coverage in the United Kingdom, a Twitter campaign called #SmearForSmear has been launched in 2015 for the European Cervical Cancer Prevention Week. Its aim was to encourage women to take a selfie showing their lipstick going over the edge and post it on Twitter with a raising awareness message promoting cervical cancer screening. The estimated audience was 500 million people. Other public health campaigns have been launched on social media such as Movember to encourage participation and self-engagement. Their result was unsatisfactory as their aim had been diluted to become mainly a social buzz. Objective: The objectives of this study were to identify the tweets delivering a raising awareness message promoting cervical cancer screening (sensitizing tweets) and to understand the characteristics of Twitter users posting about this campaign. Methods: We conducted a 3-step content analysis of the English tweets tagged #SmearForSmear posted on Twitter for the 2015 European Cervical Cancer Prevention Week. Data were collected using the Twitter application programming interface. Their extraction was based on an analysis grid generated by 2 independent researchers using a thematic analysis, validated by a strong Cohen kappa coefficient. A total of 7 themes were coded for sensitizing tweets and 14 for Twitter users? status. Verbatims were thematically and then statistically analyzed. Results: A total of 3019 tweets were collected and 1881 were analyzed. Moreover, 69.96% of tweets had been posted by people living in the United Kingdom. A total of 57.36% of users were women, and sex was unknown in 35.99% of cases. In addition, 54.44% of the users had posted at least one selfie with smeared lipstick. Furthermore, 32.32% of tweets were sensitizing. Independent factors associated with posting sensitizing tweets were women who experienced an abnormal smear test (OR [odds ratio] 13.456, 95% CI 3.101-58.378, P<.001), female gender (OR 3.752, 95% CI 2.133-6.598, P<.001), and people who live in the United Kingdom (OR 2.097, 95% CI 1.447-3.038, P<.001). Nonsensitizing tweets were statistically more posted by a nonhealth or nonmedia company (OR 0.558, 95% CI 0.383-0.814, P<.001). Conclusions: This study demonstrates that the success of a public health campaign using a social media platform depends on its ability to get its targets involved. It also suggests the need to use social marketing to help its dissemination. The clinical impact of this Twitter campaign to increase cervical cancer screening is yet to be evaluated. UR - http://www.jmir.org/2017/10/e344/ UR - http://dx.doi.org/10.2196/jmir.8421 UR - http://www.ncbi.nlm.nih.gov/pubmed/29038096 ID - info:doi/10.2196/jmir.8421 ER - TY - JOUR AU - Yang, Hongxi AU - Li, Shu AU - Sun, Li AU - Zhang, Xinyu AU - Hou, Jie AU - Wang, Yaogang PY - 2017/10/03 TI - Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform JO - JMIR Public Health Surveill SP - e64 VL - 3 IS - 4 KW - lung cancer KW - risk factors KW - particulate matter KW - PM2.5 KW - Baidu Index KW - information seeking behavior KW - public awareness KW - China N2 - Background: In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM2.5) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public?s awareness and knowledge of modifiable risk factors of lung cancer for prevention. Objective: The objective of our study was to explore the public?s awareness of the association of PM2.5 with lung cancer risk in China by analyzing the relationship between the daily PM2.5 concentration and searches for the term ?lung cancer? on an Internet big data platform, Baidu. Methods: We collected daily PM2.5 concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables. Results: In 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration was poor (all r2s<.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM2.5 concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5% level for each city. Conclusions: The daily average PM2.5 concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public?s awareness of the association of PM2.5 with lung cancer risk, to lead the public to seek more information about PM2.5 and its hazards, and to cope with their environment and its risks appropriately. UR - https://publichealth.jmir.org/2017/4/e64/ UR - http://dx.doi.org/10.2196/publichealth.8078 UR - http://www.ncbi.nlm.nih.gov/pubmed/28974484 ID - info:doi/10.2196/publichealth.8078 ER - TY - JOUR AU - Kim, Annice AU - Miano, Thomas AU - Chew, Robert AU - Eggers, Matthew AU - Nonnemaker, James PY - 2017/09/26 TI - Classification of Twitter Users Who Tweet About E-Cigarettes JO - JMIR Public Health Surveill SP - e63 VL - 3 IS - 3 KW - electronic cigarettes KW - social media KW - machine learning N2 - Background: Despite concerns about their health risks, e?cigarettes have gained popularity in recent years. Concurrent with the recent increase in e?cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e?cigarettes. Monitoring the trends in e?cigarette?related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e?cigarette?related content on Twitter. Objective: The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e?cigarette?related topics into distinct categories. Methods: We collected approximately 11.5 million e?cigarette?related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e?cigarettes. Trained human coders examined the handles? profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users? metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types. Results: Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e?cigarettes (average F1 score=83.3%). Accuracy varied by user type, with F1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance?an overall F1 score gain of 10.6%?beyond metadata features alone. Conclusions: This study provides a method for classifying five different types of users who tweet about e?cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e?cigarettes online to help inform public health surveillance, education, and regulatory efforts. UR - http://publichealth.jmir.org/2017/3/e63/ UR - http://dx.doi.org/10.2196/publichealth.8060 UR - http://www.ncbi.nlm.nih.gov/pubmed/28951381 ID - info:doi/10.2196/publichealth.8060 ER - TY - JOUR AU - Bousquet, Cedric AU - Dahamna, Badisse AU - Guillemin-Lanne, Sylvie AU - Darmoni, J. Stefan AU - Faviez, Carole AU - Huot, Charles AU - Katsahian, Sandrine AU - Leroux, Vincent AU - Pereira, Suzanne AU - Richard, Christophe AU - Schück, Stéphane AU - Souvignet, Julien AU - Lillo-Le Louët, Agnčs AU - Texier, Nathalie PY - 2017/09/21 TI - The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process JO - JMIR Res Protoc SP - e179 VL - 6 IS - 9 KW - pharmacovigilance KW - social media KW - big data KW - natural language processing KW - medical terminology N2 - Background: Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. Objective: This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. Methods: In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Results: Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. Conclusions: We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. UR - http://www.researchprotocols.org/2017/9/e179/ UR - http://dx.doi.org/10.2196/resprot.6463 UR - http://www.ncbi.nlm.nih.gov/pubmed/28935617 ID - info:doi/10.2196/resprot.6463 ER - TY - JOUR AU - Kagashe, Ireneus AU - Yan, Zhijun AU - Suheryani, Imran PY - 2017/09/12 TI - Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data JO - J Med Internet Res SP - e315 VL - 19 IS - 9 KW - machine learning KW - Twitter messaging KW - social media KW - disease outbreaks KW - influenza KW - public health surveillance KW - natural language processing KW - influenza vaccines N2 - Background: Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques. Objective: Using Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance. Methods: From tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs? tweets using latent Dirichlet allocation (LDA). Results: Our proposed classifier obtained an F1 score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks. Conclusions: The study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases. UR - http://www.jmir.org/2017/9/e315/ UR - http://dx.doi.org/10.2196/jmir.7393 UR - http://www.ncbi.nlm.nih.gov/pubmed/28899847 ID - info:doi/10.2196/jmir.7393 ER - TY - JOUR AU - Mukhija, Dhruvika AU - Venkatraman, Anand AU - Nagpal, Singh Sajan Jiv PY - 2017/08/21 TI - Effectivity of Awareness Months in Increasing Internet Search Activity for Top Malignancies Among Women JO - JMIR Public Health Surveill SP - e55 VL - 3 IS - 3 KW - colorectal cancer, lung cancer, breast cancer, cancer awareness month, infoveillance UR - http://publichealth.jmir.org/2017/3/e55/ UR - http://dx.doi.org/10.2196/publichealth.7714 UR - http://www.ncbi.nlm.nih.gov/pubmed/28827213 ID - info:doi/10.2196/publichealth.7714 ER - TY - JOUR AU - Birnbaum, L. Michael AU - Ernala, Kiranmai Sindhu AU - Rizvi, F. Asra AU - De Choudhury, Munmun AU - Kane, M. John PY - 2017/08/14 TI - A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals JO - J Med Internet Res SP - e289 VL - 19 IS - 8 KW - schizophrenia KW - psychotic disorders KW - online social networks KW - machine learning KW - linguistic analysis KW - Twitter N2 - Background: Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as having schizophrenia from healthy controls. To date, limited efforts have included expert input to evaluate the authenticity of diagnostic self-disclosures. Objective: This study aims to move from noisy self-reports of schizophrenia on social media to more accurate identification of diagnoses by exploring a human-machine partnered approach, wherein computational linguistic analysis of shared content is combined with clinical appraisals. Methods: Twitter timeline data, extracted from 671 users with self-disclosed diagnoses of schizophrenia, was appraised for authenticity by expert clinicians. Data from disclosures deemed true were used to build a classifier aiming to distinguish users with schizophrenia from healthy controls. Results from the classifier were compared to expert appraisals on new, unseen Twitter users. Results: Significant linguistic differences were identified in the schizophrenia group including greater use of interpersonal pronouns (P<.001), decreased emphasis on friendship (P<.001), and greater emphasis on biological processes (P<.001). The resulting classifier distinguished users with disclosures of schizophrenia deemed genuine from control users with a mean accuracy of 88% using linguistic data alone. Compared to clinicians on new, unseen users, the classifier?s precision, recall, and accuracy measures were 0.27, 0.77, and 0.59, respectively. Conclusions: These data reinforce the need for ongoing collaborations integrating expertise from multiple fields to strengthen our ability to accurately identify and effectively engage individuals with mental illness online. These collaborations are crucial to overcome some of mental illnesses? biggest challenges by using digital technology. UR - http://www.jmir.org/2017/8/e289/ UR - http://dx.doi.org/10.2196/jmir.7956 UR - http://www.ncbi.nlm.nih.gov/pubmed/28807891 ID - info:doi/10.2196/jmir.7956 ER - TY - JOUR AU - Roccetti, Marco AU - Marfia, Gustavo AU - Salomoni, Paola AU - Prandi, Catia AU - Zagari, Maurizio Rocco AU - Gningaye Kengni, Linda Faustine AU - Bazzoli, Franco AU - Montagnani, Marco PY - 2017/08/09 TI - Attitudes of Crohn?s Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts JO - JMIR Public Health Surveill SP - e51 VL - 3 IS - 3 KW - health information systems KW - public health informatics KW - consumer health information KW - social networking N2 - Background: Data concerning patients originates from a variety of sources on social media. Objective: The aim of this study was to show how methodologies borrowed from different areas including computer science, econometrics, statistics, data mining, and sociology may be used to analyze Facebook data to investigate the patients? perspectives on a given medical prescription. Methods: To shed light on patients? behavior and concerns, we focused on Crohn?s disease, a chronic inflammatory bowel disease, and the specific therapy with the biological drug Infliximab. To gain information from the basin of big data, we analyzed Facebook posts in the time frame from October 2011 to August 2015. We selected posts from patients affected by Crohn?s disease who were experiencing or had previously been treated with the monoclonal antibody drug Infliximab. The selected posts underwent further characterization and sentiment analysis. Finally, an ethnographic review was carried out by experts from different scientific research fields (eg, computer science vs gastroenterology) and by a software system running a sentiment analysis tool. The patient feeling toward the Infliximab treatment was classified as positive, neutral, or negative, and the results from computer science, gastroenterologist, and software tool were compared using the square weighted Cohen?s kappa coefficient method. Results: The first automatic selection process returned 56,000 Facebook posts, 261 of which exhibited a patient opinion concerning Infliximab. The ethnographic analysis of these 261 selected posts gave similar results, with an interrater agreement between the computer science and gastroenterology experts amounting to 87.3% (228/261), a substantial agreement according to the square weighted Cohen?s kappa coefficient method (w2K=0.6470). A positive, neutral, and negative feeling was attributed to 36%, 27%, and 37% of posts by the computer science expert and 38%, 30%, and 32% by the gastroenterologist, respectively. Only a slight agreement was found between the experts? opinion and the software tool. Conclusions: We show how data posted on Facebook by Crohn?s disease patients are a useful dataset to understand the patient?s perspective on the specific treatment with Infliximab. The genuine, nonmedically influenced patients? opinion obtained from Facebook pages can be easily reviewed by experts from different research backgrounds, with a substantial agreement on the classification of patients? sentiment. The described method allows a fast collection of big amounts of data, which can be easily analyzed to gain insight into the patients? perspective on a specific medical therapy. UR - http://publichealth.jmir.org/2017/3/e51/ UR - http://dx.doi.org/10.2196/publichealth.7004 UR - http://www.ncbi.nlm.nih.gov/pubmed/28793981 ID - info:doi/10.2196/publichealth.7004 ER - TY - JOUR AU - Liu, Kui AU - Huang, Sichao AU - Miao, Zi-Ping AU - Chen, Bin AU - Jiang, Tao AU - Cai, Gaofeng AU - Jiang, Zhenggang AU - Chen, Yongdi AU - Wang, Zhengting AU - Gu, Hua AU - Chai, Chengliang AU - Jiang, Jianmin PY - 2017/08/08 TI - Identifying Potential Norovirus Epidemics in China via Internet Surveillance JO - J Med Internet Res SP - e282 VL - 19 IS - 8 KW - norovirus KW - Internet surveillance KW - disease prediction N2 - Background: Norovirus is a common virus that causes acute gastroenteritis worldwide, but a monitoring system for norovirus is unavailable in China. Objective: We aimed to identify norovirus epidemics through Internet surveillance and construct an appropriate model to predict potential norovirus infections. Methods: The norovirus-related data of a selected outbreak in Jiaxing Municipality, Zhejiang Province of China, in 2014 were collected from immediate epidemiological investigation, and the Internet search volume, as indicated by the Baidu Index, was acquired from the Baidu search engine. All correlated search keywords in relation to norovirus were captured, screened, and composited to establish the composite Baidu Index at different time lags by Spearman rank correlation. The optimal model was chosen and possibly predicted maps in Zhejiang Province were presented by ArcGIS software. Results: The combination of two vital keywords at a time lag of 1 day was ultimately identified as optimal (?=.924, P<.001). The exponential curve model was constructed to fit the trend of this epidemic, suggesting that a one-unit increase in the mean composite Baidu Index contributed to an increase of norovirus infections by 2.15 times during the outbreak. In addition to Jiaxing Municipality, Hangzhou Municipality might have had some potential epidemics in the study time from the predicted model. Conclusions: Although there are limitations with early warning and unavoidable biases, Internet surveillance may be still useful for the monitoring of norovirus epidemics when a monitoring system is unavailable. UR - http://www.jmir.org/2017/8/e282/ UR - http://dx.doi.org/10.2196/jmir.7855 UR - http://www.ncbi.nlm.nih.gov/pubmed/28790023 ID - info:doi/10.2196/jmir.7855 ER - TY - JOUR AU - Tapi Nzali, Donald Mike AU - Bringay, Sandra AU - Lavergne, Christian AU - Mollevi, Caroline AU - Opitz, Thomas PY - 2017/07/31 TI - What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer JO - JMIR Med Inform SP - e23 VL - 5 IS - 3 KW - breast cancer KW - text mining KW - social media KW - unsupervised learning N2 - Background: Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer. Objective: Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]). Methods: First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life. Results: Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients? concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus. Conclusions: We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life. UR - http://medinform.jmir.org/2017/3/e23/ UR - http://dx.doi.org/10.2196/medinform.7779 UR - http://www.ncbi.nlm.nih.gov/pubmed/28760725 ID - info:doi/10.2196/medinform.7779 ER - TY - JOUR AU - Jung, Hyesil AU - Park, Hyeoun-Ae AU - Song, Tae-Min PY - 2017/07/24 TI - Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals JO - J Med Internet Res SP - e259 VL - 19 IS - 7 KW - ontology KW - adolescent KW - depression KW - data mining KW - social media data N2 - Background: Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. Objective: The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. Methods: The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. Results: We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, ?academic stresses? and ?suicide? contributed negatively to the sentiment of adolescent depression. Conclusions: The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology. UR - http://www.jmir.org/2017/7/e259/ UR - http://dx.doi.org/10.2196/jmir.7452 UR - http://www.ncbi.nlm.nih.gov/pubmed/28739560 ID - info:doi/10.2196/jmir.7452 ER - TY - JOUR AU - Cheng, Qijin AU - Li, MH Tim AU - Kwok, Chi-Leung AU - Zhu, Tingshao AU - Yip, SF Paul PY - 2017/07/10 TI - Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study JO - J Med Internet Res SP - e243 VL - 19 IS - 7 KW - suicide KW - psychological stress KW - social media KW - Chinese KW - natural language KW - machine learning N2 - Background: Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention. Objective: The aim of this study was to explore whether computerized language analysis methods can be utilized to assess one?s suicide risk and emotional distress in Chinese social media. Methods: A Web-based survey of Chinese social media (ie, Weibo) users was conducted to measure their suicide risk factors including suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress levels. Participants? Weibo posts published in the public domain were also downloaded with their consent. The Weibo posts were parsed and fitted into Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) categories. The associations between SC-LIWC features and the 5 suicide risk factors were examined by logistic regression. Furthermore, the support vector machine (SVM) model was applied based on the language features to automatically classify whether a Weibo user exhibited any of the 5 risk factors. Results: A total of 974 Weibo users participated in the survey. Those with high suicide probability were marked by a higher usage of pronoun (odds ratio, OR=1.18, P=.001), prepend words (OR=1.49, P=.02), multifunction words (OR=1.12, P=.04), a lower usage of verb (OR=0.78, P<.001), and a greater total word count (OR=1.007, P=.008). Second-person plural was positively associated with severe depression (OR=8.36, P=.01) and stress (OR=11, P=.005), whereas work-related words were negatively associated with WSC (OR=0.71, P=.008), severe depression (OR=0.56, P=.005), and anxiety (OR=0.77, P=.02). Inconsistently, third-person plural was found to be negatively associated with WSC (OR=0.02, P=.047) but positively with severe stress (OR=41.3, P=.04). Achievement-related words were positively associated with depression (OR=1.68, P=.003), whereas health- (OR=2.36, P=.004) and death-related (OR=2.60, P=.01) words positively associated with stress. The machine classifiers did not achieve satisfying performance in the full sample set but could classify high suicide probability (area under the curve, AUC=0.61, P=.04) and severe anxiety (AUC=0.75, P<.001) among those who have exhibited WSC. Conclusions: SC-LIWC is useful to examine language markers of suicide risk and emotional distress in Chinese social media and can identify characteristics different from previous findings in the English literature. Some findings are leading to new hypotheses for future verification. Machine classifiers based on SC-LIWC features are promising but still require further optimization for application in real life. UR - http://www.jmir.org/2017/7/e243/ UR - http://dx.doi.org/10.2196/jmir.7276 UR - http://www.ncbi.nlm.nih.gov/pubmed/28694239 ID - info:doi/10.2196/jmir.7276 ER - TY - JOUR AU - Peiper, C. Nicholas AU - Baumgartner, M. Peter AU - Chew, F. Robert AU - Hsieh, P. Yuli AU - Bieler, S. Gayle AU - Bobashev, V. Georgiy AU - Siege, Christopher AU - Zarkin, A. Gary PY - 2017/07/04 TI - Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California JO - J Med Internet Res SP - e236 VL - 19 IS - 7 KW - cannabis KW - marijuana KW - social networking KW - social media KW - Internet N2 - Background: Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. Objectives: This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. Methods: From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. Results: The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. Conclusions: The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time. UR - http://www.jmir.org/2017/7/e236/ UR - http://dx.doi.org/10.2196/jmir.7137 UR - http://www.ncbi.nlm.nih.gov/pubmed/28676471 ID - info:doi/10.2196/jmir.7137 ER - TY - JOUR AU - Wongkoblap, Akkapon AU - Vadillo, A. Miguel AU - Curcin, Vasa PY - 2017/06/29 TI - Researching Mental Health Disorders in the Era of Social Media: Systematic Review JO - J Med Internet Res SP - e228 VL - 19 IS - 6 KW - mental health KW - mental disorders KW - social networking KW - artificial intelligence KW - machine learning KW - public health informatics KW - depression KW - anxiety KW - infodemiology N2 - Background: Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. Objective: The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. Methods: We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. Results: The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Conclusions: Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. UR - http://www.jmir.org/2017/6/e228/ UR - http://dx.doi.org/10.2196/jmir.7215 UR - http://www.ncbi.nlm.nih.gov/pubmed/28663166 ID - info:doi/10.2196/jmir.7215 ER - TY - JOUR AU - Abdellaoui, Redhouane AU - Schück, Stéphane AU - Texier, Nathalie AU - Burgun, Anita PY - 2017/06/22 TI - Filtering Entities to Optimize Identification of Adverse Drug Reaction From Social Media: How Can the Number of Words Between Entities in the Messages Help? JO - JMIR Public Health Surveill SP - e36 VL - 3 IS - 2 KW - pharmacovigilance KW - social media KW - text mining KW - Gaussian mixture model KW - EM algorithm KW - clustering KW - density estimation N2 - Background: With the increasing popularity of Web 2.0 applications, social media has made it possible for individuals to post messages on adverse drug reactions. In such online conversations, patients discuss their symptoms, medical history, and diseases. These disorders may correspond to adverse drug reactions (ADRs) or any other medical condition. Therefore, methods must be developed to distinguish between false positives and true ADR declarations. Objective: The aim of this study was to investigate a method for filtering out disorder terms that did not correspond to adverse events by using the distance (as number of words) between the drug term and the disorder or symptom term in the post. We hypothesized that the shorter the distance between the disorder name and the drug, the higher the probability to be an ADR. Methods: We analyzed a corpus of 648 messages corresponding to a total of 1654 (drug and disorder) pairs from 5 French forums using Gaussian mixture models and an expectation-maximization (EM) algorithm . Results: The distribution of the distances between the drug term and the disorder term enabled the filtering of 50.03% (733/1465) of the disorders that were not ADRs. Our filtering strategy achieved a precision of 95.8% and a recall of 50.0%. Conclusions: This study suggests that such distance between terms can be used for identifying false positives, thereby improving ADR detection in social media. UR - http://publichealth.jmir.org/2017/2/e36/ UR - http://dx.doi.org/10.2196/publichealth.6577 UR - http://www.ncbi.nlm.nih.gov/pubmed/28642212 ID - info:doi/10.2196/publichealth.6577 ER - TY - JOUR AU - Miller, Michele AU - Banerjee, Tanvi AU - Muppalla, Roopteja AU - Romine, William AU - Sheth, Amit PY - 2017/06/19 TI - What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention JO - JMIR Public Health Surveill SP - e38 VL - 3 IS - 2 KW - viruses KW - epidemiology KW - social media KW - machine learning N2 - Background: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus. Objective: The purpose of this study was to determine the relevancy of the tweets and what people were tweeting about the 4 disease characteristics of Zika: symptoms, transmission, prevention, and treatment. Methods: A combination of natural language processing and machine learning techniques was used to determine what people were tweeting about Zika. Specifically, a two-stage classifier system was built to find relevant tweets about Zika, and then the tweets were categorized into 4 disease categories. Tweets in each disease category were then examined using latent Dirichlet allocation (LDA) to determine the 5 main tweet topics for each disease characteristic. Results: Over 4 months, 1,234,605 tweets were collected. The number of tweets by males and females was similar (28.47% [351,453/1,234,605] and 23.02% [284,207/1,234,605], respectively). The classifier performed well on the training and test data for relevancy (F1 score=0.87 and 0.99, respectively) and disease characteristics (F1 score=0.79 and 0.90, respectively). Five topics for each category were found and discussed, with a focus on the symptoms category. Conclusions: We demonstrate how categories of discussion on Twitter about an epidemic can be discovered so that public health officials can understand specific societal concerns within the disease-specific categories. Our two-stage classifier was able to identify relevant tweets to enable more specific analysis, including the specific aspects of Zika that were being discussed as well as misinformation being expressed. Future studies can capture sentiments and opinions on epidemic outbreaks like Zika virus in real time, which will likely inform efforts to educate the public at large. UR - http://publichealth.jmir.org/2017/2/e38/ UR - http://dx.doi.org/10.2196/publichealth.7157 UR - http://www.ncbi.nlm.nih.gov/pubmed/28630032 ID - info:doi/10.2196/publichealth.7157 ER - TY - JOUR AU - Doan, Son AU - Ritchart, Amanda AU - Perry, Nicholas AU - Chaparro, D. Juan AU - Conway, Mike PY - 2017/06/13 TI - How Do You #relax When You?re #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets JO - JMIR Public Health Surveill SP - e35 VL - 3 IS - 2 KW - social media KW - Twitter KW - stress KW - relaxation KW - natural language processing KW - machine learning N2 - Background: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases. Relaxation is often recommended in mental health treatment as a frontline strategy to reduce stress, thereby improving health conditions. Twitter is a microblog platform that allows users to post their own personal messages (tweets), including their expressions about feelings and actions related to stress and stress management (eg, relaxing). While Twitter is increasingly used as a source of data for understanding mental health from a population perspective, the specific issue of stress?as manifested on Twitter?has not yet been the focus of any systematic study. Objective: The objective of our study was to understand how people express their feelings of stress and relaxation through Twitter messages. In addition, we aimed at investigating automated natural language processing methods to (1) classify stress versus nonstress and relaxation versus nonrelaxation tweets, and (2) identify first-hand experience?that is, who is the experiencer?in stress and relaxation tweets. Methods: We first performed a qualitative content analysis of 1326 and 781 tweets containing the keywords ?stress? and ?relax,? respectively. We then investigated the use of machine learning algorithms?in particular naive Bayes and support vector machines?to automatically classify tweets as stress versus nonstress and relaxation versus nonrelaxation. Finally, we applied these classifiers to sample datasets drawn from 4 cities in the United States (Los Angeles, New York, San Diego, and San Francisco) obtained from Twitter?s streaming application programming interface, with the goal of evaluating the extent of any correlation between our automatic classification of tweets and results from public stress surveys. Results: Content analysis showed that the most frequent topic of stress tweets was education, followed by work and social relationships. The most frequent topic of relaxation tweets was rest & vacation, followed by nature and water. When we applied the classifiers to the cities dataset, the proportion of stress tweets in New York and San Diego was substantially higher than that in Los Angeles and San Francisco. In addition, we found that characteristic expressions of stress and relaxation varied for each city based on its geolocation. Conclusions: This content analysis and infodemiology study revealed that Twitter, when used in conjunction with natural language processing techniques, is a useful data source for understanding stress and stress management strategies, and can potentially supplement infrequently collected survey-based stress data. UR - http://publichealth.jmir.org/2017/2/e35/ UR - http://dx.doi.org/10.2196/publichealth.5939 UR - http://www.ncbi.nlm.nih.gov/pubmed/28611016 ID - info:doi/10.2196/publichealth.5939 ER - TY - JOUR AU - van Lent, GG Liza AU - Sungur, Hande AU - Kunneman, A. Florian AU - van de Velde, Bob AU - Das, Enny PY - 2017/06/13 TI - Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter JO - J Med Internet Res SP - e193 VL - 19 IS - 6 KW - psychological theory KW - epidemics KW - fear KW - distance perception KW - social media N2 - Background: In 2014, the world was startled by a sudden outbreak of Ebola. Although Ebola infections and deaths occurred almost exclusively in Guinea, Sierra Leone, and Liberia, few potential Western cases, in particular, caused a great stir among the public in Western countries. Objective: This study builds on the construal level theory to examine the relationship between psychological distance to an epidemic and public attention and sentiment expressed on Twitter. Whereas previous research has shown the potential of social media to assess real-time public opinion and sentiment, generalizable insights that further the theory development lack. Methods: Epidemiological data (number of Ebola infections and fatalities) and media data (tweet volume and key events reported in the media) were collected for the 2014 Ebola outbreak, and Twitter content from the Netherlands was coded for (1) expressions of fear for self or fear for others and (2) psychological distance of the outbreak to the tweet source. Longitudinal relations were compared using vector error correction model (VECM) methodology. Results: Analyses based on 4500 tweets revealed that increases in public attention to Ebola co-occurred with severe world events related to the epidemic, but not all severe events evoked fear. As hypothesized, Web-based public attention and expressions of fear responded mainly to the psychological distance of the epidemic. A chi-square test showed a significant positive relation between proximity and fear: ?22=103.2 (P<.001). Public attention and fear for self in the Netherlands showed peaks when Ebola became spatially closer by crossing the Mediterranean Sea and Atlantic Ocean. Fear for others was mostly predicted by the social distance to the affected parties. Conclusions: Spatial and social distance are important predictors of public attention to worldwide crisis such as epidemics. These factors need to be taken into account when communicating about human tragedies. UR - http://www.jmir.org/2017/6/e193/ UR - http://dx.doi.org/10.2196/jmir.7219 UR - http://www.ncbi.nlm.nih.gov/pubmed/28611015 ID - info:doi/10.2196/jmir.7219 ER - TY - JOUR AU - Huesch, Marco AU - Chetlen, Alison AU - Segel, Joel AU - Schetter, Susann PY - 2017/06/09 TI - Frequencies of Private Mentions and Sharing of Mammography and Breast Cancer Terms on Facebook: A Pilot Study JO - J Med Internet Res SP - e201 VL - 19 IS - 6 KW - Facebook KW - online social network KW - social media KW - breast cancer screening KW - mammography KW - user comments KW - websites KW - links N2 - Background: The most popular social networking site in the United States is Facebook, an online forum where circles of friends create, share, and interact with each other?s content in a nonpublic way. Objective: Our objectives were to understand (1) the most commonly used terms and phrases relating to breast cancer screening, (2) the most commonly shared website links that other women interacted with, and (3) the most commonly shared website links, by age groups. Methods: We used a novel proprietary tool from Facebook to analyze all of the more than 1.7 million unique interactions (comments on stories, reshares, and emoji reactions) and stories associated with breast cancer screening keywords that were generated by more than 1.1 million unique female Facebook users over the 1 month between November 15 and December 15, 2016. We report frequency distributions of the most popular shared Web content by age group and keywords. Results: On average, each of 59,000 unique stories during the month was reshared 1.5 times, commented on nearly 8 times, and reacted to more than 20 times by other users. Posted stories were most often authored by women aged 45-54 years. Users shared, reshared, commented on, and reacted to website links predominantly to e-commerce sites (12,200/1.7 million, 36% of all the most popular links), celebrity news (n=8800, 26%), and major advocacy organizations (n=4900, 15%; almost all accounted for by the American Cancer Society breast cancer site). Conclusions: On Facebook, women shared and reacted to links to commercial and informative websites regarding breast cancer and screening. This information could inform patient outreach regarding breast cancer screening, indirectly through better understanding of key issues, and directly through understanding avenues for paid messaging to women authoring and reacting to content in this space. UR - http://www.jmir.org/2017/6/e201/ UR - http://dx.doi.org/10.2196/jmir.7508 UR - http://www.ncbi.nlm.nih.gov/pubmed/28600279 ID - info:doi/10.2196/jmir.7508 ER - TY - JOUR AU - Davis, A. Matthew AU - Zheng, Kai AU - Liu, Yang AU - Levy, Helen PY - 2017/05/26 TI - Public Response to Obamacare on Twitter JO - J Med Internet Res SP - e167 VL - 19 IS - 5 KW - Patient Protection and Affordable Care Act KW - health care reform KW - social media KW - data collection N2 - Background: The Affordable Care Act (ACA), often called ?Obamacare,? is a controversial law that has been implemented gradually since its enactment in 2010. Polls have consistently shown that public opinion of the ACA is quite negative. Objective: The aim of our study was to examine the extent to which Twitter data can be used to measure public opinion of the ACA over time. Methods: We prospectively collected a 10% random sample of daily tweets (approximately 52 million since July 2011) using Twitter?s streaming application programming interface (API) from July 10, 2011 to July 31, 2015. Using a list of key terms and ACA-specific hashtags, we identified tweets about the ACA and examined the overall volume of tweets about the ACA in relation to key ACA events. We applied standard text sentiment analysis to assign each ACA tweet a measure of positivity or negativity and compared overall sentiment from Twitter with results from the Kaiser Family Foundation health tracking poll. Results: Public opinion on Twitter (measured via sentiment analysis) was slightly more favorable than public opinion measured by the Kaiser poll (approximately 50% vs 40%, respectively) but trends over time in both favorable and unfavorable views were similar in both sources. The Twitter-based measures of opinion as well as the Kaiser poll changed very little over time: correlation coefficients for favorable and unfavorable public opinion were .43 and .37, respectively. However, we found substantial spikes in the volume of ACA-related tweets in response to key events in the law?s implementation, such as the first open enrollment period in October 2013 and the Supreme Court decision in June 2012. Conclusions: Twitter may be useful for tracking public opinion of health care reform as it appears to be comparable with conventional polling results. Moreover, in contrast with conventional polling, the overall amount of tweets also provides a potential indication of public interest of a particular issue at any point in time. UR - http://www.jmir.org/2017/5/e167/ UR - http://dx.doi.org/10.2196/jmir.6946 UR - http://www.ncbi.nlm.nih.gov/pubmed/28550002 ID - info:doi/10.2196/jmir.6946 ER - TY - JOUR AU - Leal Neto, Onicio AU - Dimech, Santiago George AU - Libel, Marlo AU - de Souza, Vieira Wayner AU - Cesse, Eduarda AU - Smolinski, Mark AU - Oliveira, Wanderson AU - Albuquerque, Jones PY - 2017/05/04 TI - Saúde na Copa: The World?s First Application of Participatory Surveillance for a Mass Gathering at FIFA World Cup 2014, Brazil JO - JMIR Public Health Surveill SP - e26 VL - 3 IS - 2 KW - mass gatherings KW - participatory surveillance KW - public health KW - epidemiology N2 - Background: The 2005 International Health Regulations (IHRs) established parameters for event assessments and notifications that may constitute public health emergencies of international concern. These requirements and parameters opened up space for the use of nonofficial mechanisms (such as websites, blogs, and social networks) and technological improvements of communication that can streamline the detection, monitoring, and response to health problems, and thus reduce damage caused by these problems. Specifically, the revised IHR created space for participatory surveillance to function, in addition to the traditional surveillance mechanisms of detection, monitoring, and response. Participatory surveillance is based on crowdsourcing methods that collect information from society and then return the collective knowledge gained from that information back to society. The spread of digital social networks and wiki-style knowledge platforms has created a very favorable environment for this model of production and social control of information. Objective: The aim of this study was to describe the use of a participatory surveillance app, Healthy Cup, for the early detection of acute disease outbreaks during the Fédération Internationale de Football Association (FIFA) World Cup 2014. Our focus was on three specific syndromes (respiratory, diarrheal, and rash) related to six diseases that were considered important in a mass gathering context (influenza, measles, rubella, cholera, acute diarrhea, and dengue fever). Methods: From May 12 to July 13, 2014, users from anywhere in the world were able to download the Healthy Cup app and record their health condition, reporting whether they were good, very good, ill, or very ill. For users that reported being ill or very ill, a screen with a list of 10 symptoms was displayed. Participatory surveillance allows for the real-time identification of aggregates of symptoms that indicate possible cases of infectious diseases. Results: From May 12 through July 13, 2014, there were 9434 downloads of the Healthy Cup app and 7155 (75.84%) registered users. Among the registered users, 4706 (4706/7155, 65.77%) were active users who posted a total of 47,879 times during the study period. The maximum number of users that signed up in one day occurred on May 30, 2014, the day that the app was officially launched by the Minister of Health during a press conference. During this event, the Minister of Health announced the special government program Health in the World Cup on national television media. On that date, 3633 logins were recorded, which accounted for more than half of all sign-ups across the entire duration of the study (50.78%, 3633/7155). Conclusions: Participatory surveillance through community engagement is an innovative way to conduct epidemiological surveillance. Compared to traditional epidemiological surveillance, advantages include lower costs of data acquisition, timeliness of information collected and shared, platform scalability, and capacity for integration between the population being served and public health services. UR - http://publichealth.jmir.org/2017/2/e26/ UR - http://dx.doi.org/10.2196/publichealth.7313 UR - http://www.ncbi.nlm.nih.gov/pubmed/28473308 ID - info:doi/10.2196/publichealth.7313 ER - TY - JOUR AU - Alvaro, Nestor AU - Miyao, Yusuke AU - Collier, Nigel PY - 2017/05/03 TI - TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations JO - JMIR Public Health Surveill SP - e24 VL - 3 IS - 2 KW - Twitter KW - PubMed KW - corpus KW - pharmacovigilance KW - natural language processing KW - text mining KW - annotation N2 - Background: Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner. Objective: This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed. Methods: We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines. Results: The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes. Conclusions: We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP. UR - http://publichealth.jmir.org/2017/2/e24/ UR - http://dx.doi.org/10.2196/publichealth.6396 UR - http://www.ncbi.nlm.nih.gov/pubmed/28468748 ID - info:doi/10.2196/publichealth.6396 ER - TY - JOUR AU - Rosenblum, Sara AU - Yom-Tov, Elad PY - 2017/04/21 TI - Seeking Web-Based Information About Attention Deficit Hyperactivity Disorder: Where, What, and When JO - J Med Internet Res SP - e126 VL - 19 IS - 4 KW - attention deficit hyperactivity disorder KW - Internet KW - search engine KW - coping behavior KW - parents N2 - Background: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder, prevalent among 2-10% of the population. Objective: The objective of this study was to describe where, what, and when people search online for topics related to ADHD. Methods: Data were collected from Microsoft?s Bing search engine and from the community question and answer site, Yahoo Answers. The questions were analyzed based on keywords and using further statistical methods. Results: Our results revealed that the Internet indeed constitutes a source of information for people searching the topic of ADHD, and that they search for information mostly about ADHD symptoms. Furthermore, individuals personally affected by the disorder made 2.0 more questions about ADHD compared with others. Questions begin when children reach 2 years of age, with an average age of 5.1 years. Most of the websites searched were not specifically related to ADHD and the timing of searches as well as the query content were different among those prediagnosis compared with postdiagnosis. Conclusions: The study results shed light on the features of ADHD-related searches. Thus, they may help improve the Internet as a source of reliable information, and promote improved awareness and knowledge about ADHD as well as quality of life for populations dealing with the complex phenomena of ADHD. UR - http://www.jmir.org/2017/4/e126/ UR - http://dx.doi.org/10.2196/jmir.6579 UR - http://www.ncbi.nlm.nih.gov/pubmed/28432038 ID - info:doi/10.2196/jmir.6579 ER - TY - JOUR AU - Stefanidis, Anthony AU - Vraga, Emily AU - Lamprianidis, Georgios AU - Radzikowski, Jacek AU - Delamater, L. Paul AU - Jacobsen, H. Kathryn AU - Pfoser, Dieter AU - Croitoru, Arie AU - Crooks, Andrew PY - 2017/04/20 TI - Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts JO - JMIR Public Health Surveill SP - e22 VL - 3 IS - 2 KW - Zika virus KW - social media KW - Twitter messaging KW - geographic information systems N2 - Background: The recent Zika outbreak witnessed the disease evolving from a regional health concern to a global epidemic. During this process, different communities across the globe became involved in Twitter, discussing the disease and key issues associated with it. This paper presents a study of this discussion in Twitter, at the nexus of location, actors, and concepts. Objective: Our objective in this study was to demonstrate the significance of 3 types of events: location related, actor related, and concept related, for understanding how a public health emergency of international concern plays out in social media, and Twitter in particular. Accordingly, the study contributes to research efforts toward gaining insights on the mechanisms that drive participation, contributions, and interaction in this social media platform during a disease outbreak. Methods: We collected 6,249,626 tweets referring to the Zika outbreak over a period of 12 weeks early in the outbreak (December 2015 through March 2016). We analyzed this data corpus in terms of its geographical footprint, the actors participating in the discourse, and emerging concepts associated with the issue. Data were visualized and evaluated with spatiotemporal and network analysis tools to capture the evolution of interest on the topic and to reveal connections between locations, actors, and concepts in the form of interaction networks. Results: The spatiotemporal analysis of Twitter contributions reflects the spread of interest in Zika from its original hotspot in South America to North America and then across the globe. The Centers for Disease Control and World Health Organization had a prominent presence in social media discussions. Tweets about pregnancy and abortion increased as more information about this emerging infectious disease was presented to the public and public figures became involved in this. Conclusions: The results of this study show the utility of analyzing temporal variations in the analytic triad of locations, actors, and concepts. This contributes to advancing our understanding of social media discourse during a public health emergency of international concern. UR - http://publichealth.jmir.org/2017/2/e22/ UR - http://dx.doi.org/10.2196/publichealth.6925 UR - http://www.ncbi.nlm.nih.gov/pubmed/28428164 ID - info:doi/10.2196/publichealth.6925 ER - TY - JOUR AU - Gayle, Alberto AU - Shimaoka, Motomu PY - 2017/04/20 TI - Public Response to Scientific Misconduct: Assessing Changes in Public Sentiment Toward the Stimulus-Triggered Acquisition of Pluripotency (STAP) Cell Case via Twitter JO - JMIR Public Health Surveill SP - e21 VL - 3 IS - 2 KW - scientific misconduct KW - retraction of publication as a topic KW - mass media KW - social media KW - public opinion KW - public policy KW - data mining KW - publication KW - stem cells KW - Japan N2 - Background: In this age of social media, any news?good or bad?has the potential to spread in unpredictable ways. Changes in public sentiment have the potential to either drive or limit investment in publicly funded activities, such as scientific research. As a result, understanding the ways in which reported cases of scientific misconduct shape public sentiment is becoming increasingly essential?for researchers and institutions, as well as for policy makers and funders. In this study, we thus set out to assess and define the patterns according to which public sentiment may change in response to reported cases of scientific misconduct. This study focuses on the public response to the events involved in a recent case of major scientific misconduct that occurred in 2014 in Japan?stimulus-triggered acquisition of pluripotency (STAP) cell case. Objectives: The aims of this study were to determine (1) the patterns according to which public sentiment changes in response to scientific misconduct; (2) whether such measures vary significantly, coincident with major timeline events; and (3) whether the changes observed mirror the response patterns reported in the literature with respect to other classes of events, such as entertainment news and disaster reports. Methods: The recent STAP cell scandal is used as a test case. Changes in the volume and polarity of discussion were assessed using a sampling of case-related Twitter data, published between January 28, 2014 and March 15, 2015. Rapidminer was used for text processing and the popular bag-of-words algorithm, SentiWordNet, was used in Rapidminer to calculate sentiment for each sample Tweet. Relative volume and sentiment was then assessed overall, month-to-month, and with respect to individual entities. Results: Despite the ostensibly negative subject, average sentiment over the observed period tended to be neutral (?0.04); however, a notable downward trend (y=?0.01 x +0.09; R ˛=.45) was observed month-to-month. Notably polarized tweets accounted for less than one-third of sampled discussion: 17.49% (1656/9467) negative and 12.59% positive (1192/9467). Significant polarization was found in only 4 out of the 15 months covered, with significant variation month-to-month (P<.001). Significant increases in polarization tended to coincide with increased discussion volume surrounding major events (P<.001). Conclusions: These results suggest that public opinion toward scientific research may be subject to the same sensationalist dynamics driving public opinion in other, consumer-oriented topics. The patterns in public response observed here, with respect to the STAP cell case, were found to be consistent with those observed in the literature with respect to other classes of news-worthy events on Twitter. Discussion was found to become strongly polarized only during times of increased public attention, and such increases tended to be driven primarily by negative reporting and reactionary commentary. UR - http://publichealth.jmir.org/2017/2/e21/ UR - http://dx.doi.org/10.2196/publichealth.5980 UR - http://www.ncbi.nlm.nih.gov/pubmed/28428163 ID - info:doi/10.2196/publichealth.5980 ER - TY - JOUR AU - Pesälä, Samuli AU - Virtanen, J. Mikko AU - Sane, Jussi AU - Jousimaa, Jukkapekka AU - Lyytikäinen, Outi AU - Murtopuro, Satu AU - Mustonen, Pekka AU - Kaila, Minna AU - Helve, Otto PY - 2017/04/11 TI - Health Care Professionals? Evidence-Based Medicine Internet Searches Closely Mimic the Known Seasonal Variation of Lyme Borreliosis: A Register-Based Study JO - JMIR Public Health Surveill SP - e19 VL - 3 IS - 2 KW - search engine KW - evidence-based medicine KW - information systems KW - public health surveillance KW - Lyme borreliosis N2 - Background: Both health care professionals and nonprofessionals seek medical information on the Internet. Using Web-based search engine searches to detect epidemic diseases has, however, been problematic. Physician?s databases (PD) is a chargeable evidence-based medicine (EBM) portal on the Internet for health care professionals and is available throughout the entire health care system in Finland. Lyme borreliosis (LB), a well-defined disease model, shows temporal and regional variation in Finland. Little data exist on health care professionals? searches from Internet-based EBM databases in public health surveillance. Objective: The aim of this study was to assess whether health care professionals? use of Internet EBM databases could describe seasonal increases of the disease and supplement routine public health surveillance. Methods: Two registers, PD and the register of primary health care diagnoses (Avohilmo), were used to compare health care professionals? Internet searches on LB from EBM databases and national register-based LB diagnoses in order to evaluate annual and regional variations of LB in the whole country and in three selected high-incidence LB regions in Finland during 2011-2015. Results: Both registers, PD and Avohilmo, show visually similar patterns in annual and regional variation of LB in Finland and in the three high-incidence LB regions during 2011-2015. Conclusions: Health care professionals? Internet searches from EBM databases coincide with national register diagnoses of LB. PD searches showed a clear seasonal variation. In addition, notable regional differences were present in both registers. However, physicians? Internet medical searches should be considered as a supplementary source of information for disease surveillance. UR - http://publichealth.jmir.org/2017/2/e19/ UR - http://dx.doi.org/10.2196/publichealth.6764 UR - http://www.ncbi.nlm.nih.gov/pubmed/28400357 ID - info:doi/10.2196/publichealth.6764 ER - TY - JOUR AU - Baltrusaitis, Kristin AU - Santillana, Mauricio AU - Crawley, W. Adam AU - Chunara, Rumi AU - Smolinski, Mark AU - Brownstein, S. John PY - 2017/04/07 TI - Determinants of Participants? Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System JO - JMIR Public Health Surveill SP - e18 VL - 3 IS - 2 KW - public health surveillance KW - influenza, human KW - community-based participatory research KW - crowdsourcing KW - public health informatics KW - digital disease detection N2 - Background: Flu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report. Objective: Our objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY?s high-participation users, and summarize results from a user survey of a cohort of FNY participants. Methods: We compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY?s 2015 and 2016 end-of-season user surveys were calculated. Results: During the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2% vs 51.1%, P<.001). Although each age group was represented in the FNY population, the age distribution was significantly different from that of the US population (P<.001). Compared with the US population, FNY had a greater proportion of individuals with HDI >5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first symptom report, older age, and reporting for household members (all differences between high- and low-participation users P<.001). Approximately 10% of FNY users completed an additional survey at the end of the flu season that assessed detailed user characteristics (3217/33,324 in 2015; 4850/44,313 in 2016). Of these users, most identified as being either retired or employed in the health, education, and social services sectors and indicated that they achieved a bachelor?s degree or higher. Conclusions: The representativeness of the FNY population and characteristics of its high-participation users are consistent with what has been observed in other Internet-based influenza surveillance systems. With targeted recruitment of underrepresented populations, FNY may improve as a complementary system to timely tracking of flu activity, especially in populations that do not seek medical attention and in areas with poor official surveillance data. UR - http://publichealth.jmir.org/2017/2/e18/ UR - http://dx.doi.org/10.2196/publichealth.7304 UR - http://www.ncbi.nlm.nih.gov/pubmed/28389417 ID - info:doi/10.2196/publichealth.7304 ER - TY - JOUR AU - Vasconcellos-Silva, Roberto Paulo AU - Carvalho, Feres Dárlinton Barbosa AU - Trajano, Valéria AU - de La Rocque, Rodriguez Lucia AU - Sawada, Braz Anunciata Cristina Marins AU - Juvanhol, Lopes Leidjaira PY - 2017/04/06 TI - Using Google Trends Data to Study Public Interest in Breast Cancer Screening in Brazil: Why Not a Pink February? JO - JMIR Public Health Surveill SP - e17 VL - 3 IS - 2 KW - Internet KW - cancer information seeking KW - breast cancer KW - mass screening KW - health communication KW - early detection of cancer KW - infoveillance KW - infodemiology N2 - Background: One of the major challenges of the Brazilian Ministry of Health is to foster interest in breast cancer screening (BCS), especially among women at high risk. Strategies have been developed to promote the early identification of breast cancer mainly by Pink October campaigns. The massive number of queries conducted through Google creates traffic data that can be analyzed to show unrevealed interest cycles and their seasonalities. Objectives: Using Google Trends, we studied cycles of public interest in queries toward mammography and breast cancer along the last 5 years. We hypothesize that these data may be correlated with collective interest cycles leveraged by national BCS campaigns such as Pink October. Methods: Google Trends was employed to normalize traffic data on a scale from 0 (<1% of the peak volume) to 100 (peak of traffic) presented as weekly relative search volume (RSV) concerning mammography and breast cancer as search terms. A time series covered the last 261 weeks (November 2011 to October 2016), and RSV of both terms were compared with their respective annual means. Polynomial trendlines (second order) were employed to estimate overall trends. Results: We found an upward trend for both terms over the 5 years, with almost parallel trendlines. Remarkable peaks were found along Pink October months? mammography and breast cancer searches were leveraged up reaching, respectively, 119.1% (2016) and 196.8% (2015) above annual means. Short downward RSVs along December-January months were also noteworthy along all the studied period. These trends traced an N-shaped pattern with higher peaks in Pink October months and sharp falls along subsequent December and January. Conclusions: Considering these findings, it would be reasonable to bring Pink October to the beginning of each year, thereby extending the beneficial effect of the campaigns. It would be more appropriate to start screening campaigns at the beginning of the year, when new resolutions are taken and new projects are added to everyday routines. Our work raises attention to the study of traffic data to encourage health campaign analysts to undertake better analysis based on marketing practices. UR - http://publichealth.jmir.org/2017/2/e17/ UR - http://dx.doi.org/10.2196/publichealth.7015 UR - http://www.ncbi.nlm.nih.gov/pubmed/28385679 ID - info:doi/10.2196/publichealth.7015 ER - TY - JOUR AU - Noll-Hussong, Michael PY - 2017/03/27 TI - Whiplash Syndrome Reloaded: Digital Echoes of Whiplash Syndrome in the European Internet Search Engine Context JO - JMIR Public Health Surveill SP - e15 VL - 3 IS - 1 KW - search engine KW - whiplash injuries KW - legislation & jurisprudence KW - medicolegal aspects KW - compensation and redress KW - compensation KW - accidents, traffic KW - adult KW - female KW - humans KW - incidence KW - insurance claim reporting KW - male KW - neck pain KW - prognosis KW - search engine analytics KW - whiplash syndrome KW - Google Trends N2 - Background: In many Western countries, after a motor vehicle collision, those involved seek health care for the assessment of injuries and for insurance documentation purposes. In contrast, in many less wealthy countries, there may be limited access to care and no insurance or compensation system. Objective: The purpose of this infodemiology study was to investigate the global pattern of evolving Internet usage in countries with and without insurance and the corresponding compensation systems for whiplash injury. Methods: We used the Internet search engine analytics via Google Trends to study the health information-seeking behavior concerning whiplash injury at national population levels in Europe. Results: We found that the search for ?whiplash? is strikingly and consistently often associated with the search for ?compensation? in countries or cultures with a tort system. Frequent or traumatic painful injuries; diseases or disorders such as arthritis, headache, radius, and hip fracture; depressive disorders; and fibromyalgia were not associated similarly with searches on ?compensation.? Conclusions: In this study, we present evidence from the evolving viewpoint of naturalistic Internet search engine analytics that the expectations for receiving compensation may influence Internet search behavior in relation to whiplash injury. UR - http://publichealth.jmir.org/2017/1/e15/ UR - http://dx.doi.org/10.2196/publichealth.7054 UR - http://www.ncbi.nlm.nih.gov/pubmed/28347974 ID - info:doi/10.2196/publichealth.7054 ER - TY - JOUR AU - Menachemi, Nir AU - Rahurkar, Saurabh AU - Rahurkar, Mandar PY - 2017/03/23 TI - Using Web-Based Search Data to Study the Public?s Reactions to Societal Events: The Case of the Sandy Hook Shooting JO - JMIR Public Health Surveill SP - e12 VL - 3 IS - 1 KW - Internet KW - search engine KW - firearms KW - health policy KW - information seeking behavior KW - public health informatics KW - gun control debate N2 - Background: Internet search is the most common activity on the World Wide Web and generates a vast amount of user-reported data regarding their information-seeking preferences and behavior. Although this data has been successfully used to examine outbreaks, health care utilization, and outcomes related to quality of care, its value in informing public health policy remains unclear. Objective: The aim of this study was to evaluate the role of Internet search query data in health policy development. To do so, we studied the public?s reaction to a major societal event in the context of the 2012 Sandy Hook School shooting incident. Methods: Query data from the Yahoo! search engine regarding firearm-related searches was analyzed to examine changes in user-selected search terms and subsequent websites visited for a period of 14 days before and after the shooting incident. Results: A total of 5,653,588 firearm-related search queries were analyzed. In the after period, queries increased for search terms related to ?guns? (+50.06%), ?shooting incident? (+333.71%), ?ammunition? (+155.14%), and ?gun-related laws? (+535.47%). The highest increase (+1054.37%) in Web traffic was seen by news websites following ?shooting incident? queries whereas searches for ?guns? (+61.02%) and ?ammunition? (+173.15%) resulted in notable increases in visits to retail websites. Firearm-related queries generally returned to baseline levels after approximately 10 days. Conclusions: Search engine queries present a viable infodemiology metric on public reactions and subsequent behaviors to major societal events and could be used by policymakers to inform policy development. UR - http://publichealth.jmir.org/2017/1/e12/ UR - http://dx.doi.org/10.2196/publichealth.6033 UR - http://www.ncbi.nlm.nih.gov/pubmed/28336508 ID - info:doi/10.2196/publichealth.6033 ER - TY - JOUR AU - Mowery, Danielle AU - Smith, Hilary AU - Cheney, Tyler AU - Stoddard, Greg AU - Coppersmith, Glen AU - Bryan, Craig AU - Conway, Mike PY - 2017/02/28 TI - Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study JO - J Med Internet Res SP - e48 VL - 19 IS - 2 KW - social media KW - Twitter messaging KW - natural language processing KW - major depressive disorder KW - data annotation KW - machine learning N2 - Background: With a lifetime prevalence of 16.2%, major depressive disorder is the fifth biggest contributor to the disease burden in the United States. Objective: The aim of this study, building on previous work qualitatively analyzing depression-related Twitter data, was to describe the development of a comprehensive annotation scheme (ie, coding scheme) for manually annotating Twitter data with Diagnostic and Statistical Manual of Mental Disorders, Edition 5 (DSM 5) major depressive symptoms (eg, depressed mood, weight change, psychomotor agitation, or retardation) and Diagnostic and Statistical Manual of Mental Disorders, Edition IV (DSM-IV) psychosocial stressors (eg, educational problems, problems with primary support group, housing problems). Methods: Using this annotation scheme, we developed an annotated corpus, Depressive Symptom and Psychosocial Stressors Acquired Depression, the SAD corpus, consisting of 9300 tweets randomly sampled from the Twitter application programming interface (API) using depression-related keywords (eg, depressed, gloomy, grief). An analysis of our annotated corpus yielded several key results. Results: First, 72.09% (6829/9473) of tweets containing relevant keywords were nonindicative of depressive symptoms (eg, ?we?re in for a new economic depression?). Second, the most prevalent symptoms in our dataset were depressed mood and fatigue or loss of energy. Third, less than 2% of tweets contained more than one depression related category (eg, diminished ability to think or concentrate, depressed mood). Finally, we found very high positive correlations between some depression-related symptoms in our annotated dataset (eg, fatigue or loss of energy and educational problems; educational problems and diminished ability to think). Conclusions: We successfully developed an annotation scheme and an annotated corpus, the SAD corpus, consisting of 9300 tweets randomly-selected from the Twitter application programming interface using depression-related keywords. Our analyses suggest that keyword queries alone might not be suitable for public health monitoring because context can change the meaning of keyword in a statement. However, postprocessing approaches could be useful for reducing the noise and improving the signal needed to detect depression symptoms using social media. UR - http://www.jmir.org/2017/2/e48/ UR - http://dx.doi.org/10.2196/jmir.6895 UR - http://www.ncbi.nlm.nih.gov/pubmed/28246066 ID - info:doi/10.2196/jmir.6895 ER - TY - JOUR AU - Rose, W. Shyanika AU - Jo, L. Catherine AU - Binns, Steven AU - Buenger, Melissa AU - Emery, Sherry AU - Ribisl, M. Kurt PY - 2017/02/27 TI - Perceptions of Menthol Cigarettes Among Twitter Users: Content and Sentiment Analysis JO - J Med Internet Res SP - e56 VL - 19 IS - 2 KW - tobacco products KW - menthol KW - smoking KW - social media KW - Twitter messaging KW - policy KW - public opinion N2 - Background: Menthol cigarettes are used disproportionately by African American, female, and adolescent smokers. Twitter is also used disproportionately by minority and younger populations, providing a unique window into conversations reflecting social norms, behavioral intentions, and sentiment toward menthol cigarettes. Objective: Our purpose was to identify the content and frequency of conversations about menthol cigarettes, including themes, populations, user smoking status, other tobacco or substances, tweet characteristics, and sentiment. We also examined differences in menthol cigarette sentiment by prevalent categories, which allowed us to assess potential perceptions, misperceptions, and social norms about menthol cigarettes on Twitter. This approach can inform communication about these products, particularly to subgroups who are at risk for menthol cigarette use. Methods: Through a combination of human and machine classification, we identified 94,627 menthol cigarette-relevant tweets from February 1, 2012 to January 31, 2013 (1 year) from over 47 million tobacco-related messages gathered prospectively from the Twitter Firehose of all public tweets and metadata. Then, 4 human coders evaluated a random sample of 7000 tweets for categories, including sentiment toward menthol cigarettes. Results: We found that 47.98% (3194/6657) of tweets expressed positive sentiment, while 40.26% (2680/6657) were negative toward menthol cigarettes. The majority of tweets by likely smokers (2653/4038, 65.70%) expressed positive sentiment, while 91.2% (320/351) of nonsmokers and 71.7% (91/127) of former smokers indicated negative views. Positive views toward menthol cigarettes were predominant in tweets that discussed addiction or craving, marijuana, smoking, taste or sensation, song lyrics, and tobacco industry or marketing or tweets that were commercial in nature. Negative views toward menthol were more common in tweets about smoking cessation, health, African Americans, women, and children and adolescents?largely due to expression of negative stereotypes associated with these groups? use of menthol cigarettes. Conclusions: Examinations of public opinions toward menthol cigarettes through social media can help to inform the framing of public communication about menthol cigarettes, particularly in light of potential regulation by the European Union, US Food and Drug Administration, other jurisdictions, and localities. UR - http://www.jmir.org/2017/2/e56/ UR - http://dx.doi.org/10.2196/jmir.5694 UR - http://www.ncbi.nlm.nih.gov/pubmed/28242592 ID - info:doi/10.2196/jmir.5694 ER - TY - JOUR AU - Matsuda, Shinichi AU - Aoki, Kotonari AU - Tomizawa, Shiho AU - Sone, Masayoshi AU - Tanaka, Riwa AU - Kuriki, Hiroshi AU - Takahashi, Yoichiro PY - 2017/02/24 TI - Analysis of Patient Narratives in Disease Blogs on the Internet: An Exploratory Study of Social Pharmacovigilance JO - JMIR Public Health Surveill SP - e10 VL - 3 IS - 1 KW - Internet KW - social media KW - adverse drug reaction KW - pharmacovigilance KW - text mining N2 - Background: Although several reports have suggested that patient-generated data from Internet sources could be used to improve drug safety and pharmacovigilance, few studies have identified such data sources in Japan. We introduce a unique Japanese data source: t?by?ki, which translates literally as ?an account of a struggle with disease.? Objective: The objective of this study was to evaluate the basic characteristics of the TOBYO database, a collection of t?by?ki blogs on the Internet, and discuss potential applications for pharmacovigilance. Methods: We analyzed the overall gender and age distribution of the patient-generated TOBYO database and compared this with other external databases generated by health care professionals. For detailed analysis, we prepared separate datasets for blogs written by patients with depression and blogs written by patients with rheumatoid arthritis (RA), because these conditions were expected to entail subjective patient symptoms such as discomfort, insomnia, and pain. Frequently appearing medical terms were counted, and their variations were compared with those in an external adverse drug reaction (ADR) reporting database. Frequently appearing words regarding patients with depression and patients with RA were visualized using word clouds and word cooccurrence networks. Results: As of June 4, 2016, the TOBYO database comprised 54,010 blogs representing 1405 disorders. Overall, more entries were written by female bloggers (68.8%) than by male bloggers (30.8%). The most frequently observed disorders were breast cancer (4983 blogs), depression (3556), infertility (2430), RA (1118), and panic disorder (1090). Comparison of medical terms observed in t?by?ki blogs with those in an external ADR reporting database showed that subjective and symptomatic events and general terms tended to be frequently observed in t?by?ki blogs (eg, anxiety, headache, and pain), whereas events using more technical medical terms (eg, syndrome and abnormal laboratory test result) tended to be observed frequently in the ADR database. We also confirmed the feasibility of using visualization techniques to obtain insights from unstructured text-based t?by?ki blog data. Word clouds described the characteristics of each disorder, such as ?sleeping? and ?anxiety? in depression and ?pain? and ?painful? in RA. Conclusions: Pharmacovigilance should maintain a strong focus on patients? actual experiences, concerns, and outcomes, and this approach can be expected to uncover hidden adverse event signals earlier and to help us understand adverse events in a patient-centered way. Patient-generated t?by?ki blogs in the TOBYO database showed unique characteristics that were different from the data in existing sources generated by health care professionals. Analysis of t?by?ki blogs would add value to the assessment of disorders with a high prevalence in women, psychiatric disorders in which subjective symptoms have important clinical meaning, refractory disorders, and other chronic disorders. UR - http://publichealth.jmir.org/2017/1/e10/ UR - http://dx.doi.org/10.2196/publichealth.6872 UR - http://www.ncbi.nlm.nih.gov/pubmed/28235749 ID - info:doi/10.2196/publichealth.6872 ER - TY - JOUR AU - Anderson, S. Laurie AU - Bell, G. Heidi AU - Gilbert, Michael AU - Davidson, E. Julie AU - Winter, Christina AU - Barratt, J. Monica AU - Win, Beta AU - Painter, L. Jeffery AU - Menone, Christopher AU - Sayegh, Jonathan AU - Dasgupta, Nabarun PY - 2017/2/1 TI - Using Social Listening Data to Monitor Misuse and Nonmedical Use of Bupropion: A Content Analysis JO - JMIR Public Health Surveill SP - e6 VL - 3 IS - 1 KW - social media KW - Internet KW - prescription drug misuse KW - substance-related disorders KW - pharmacovigilance KW - harm reduction KW - community-based participatory research KW - bupropion KW - amitriptyline KW - venlafaxine hydrochloride N2 - Background: The nonmedical use of pharmaceutical products has become a significant public health concern. Traditionally, the evaluation of nonmedical use has focused on controlled substances with addiction risk. Currently, there is no effective means of evaluating the nonmedical use of noncontrolled antidepressants. Objective: Social listening, in the context of public health sometimes called infodemiology or infoveillance, is the process of identifying and assessing what is being said about a company, product, brand, or individual, within forms of electronic interactive media. The objectives of this study were (1) to determine whether content analysis of social listening data could be utilized to identify posts discussing potential misuse or nonmedical use of bupropion and two comparators, amitriptyline and venlafaxine, and (2) to describe and characterize these posts. Methods: Social listening was performed on all publicly available posts cumulative through July 29, 2015, from two harm-reduction Web forums, Bluelight and Opiophile, which mentioned the study drugs. The acquired data were stripped of personally identifiable identification (PII). A set of generic, brand, and vernacular product names was used to identify product references in posts. Posts were obtained using natural language processing tools to identify vernacular references to drug misuse-related Preferred Terms from the English Medical Dictionary for Regulatory Activities (MedDRA) version 18 terminology. Posts were reviewed manually by coders, who extracted relevant details. Results: A total of 7756 references to at least one of the study antidepressants were identified within posts gathered for this study. Of these posts, 668 (8.61%, 668/7756) referenced misuse or nonmedical use of the drug, with bupropion accounting for 438 (65.6%, 438/668). Of the 668 posts, nonmedical use was discouraged by 40.6% (178/438), 22% (22/100), and 18.5% (24/130) and encouraged by 12.3% (54/438), 10% (10/100), and 10.8% (14/130) for bupropion, amitriptyline, and venlafaxine, respectively. The most commonly reported desired effects were similar to stimulants with bupropion, sedatives with amitriptyline, and dissociatives with venlafaxine. The nasal route of administration was most frequently reported for bupropion, whereas the oral route was most frequently reported for amitriptyline and venlafaxine. Bupropion and venlafaxine were most commonly procured from health care providers, whereas amitriptyline was most commonly obtained or stolen from a third party. The Fleiss kappa for interrater agreement among 20 items with 7 categorical response options evaluated by all 11 raters was 0.448 (95% CI 0.421-0.457). Conclusions: Social listening, conducted in collaboration with harm-reduction Web forums, offers a valuable new data source that can be used for monitoring nonmedical use of antidepressants. Additional work on the capabilities of social listening will help further delineate the benefits and limitations of this rapidly evolving data source. UR - http://publichealth.jmir.org/2017/1/e6/ UR - http://dx.doi.org/10.2196/publichealth.6174 UR - http://www.ncbi.nlm.nih.gov/pubmed/28148472 ID - info:doi/10.2196/publichealth.6174 ER - TY - JOUR AU - Zhan, Yongcheng AU - Liu, Ruoran AU - Li, Qiudan AU - Leischow, James Scott AU - Zeng, Dajun Daniel PY - 2017/01/20 TI - Identifying Topics for E-Cigarette User-Generated Contents: A Case Study From Multiple Social Media Platforms JO - J Med Internet Res SP - e24 VL - 19 IS - 1 KW - electronic cigarettes KW - topic modeling KW - Latent Dirichlet Allocation KW - social media KW - infodemiology N2 - Background: Electronic cigarette (e-cigarette) is an emerging product with a rapid-growth market in recent years. Social media has become an important platform for information seeking and sharing. We aim to mine hidden topics from e-cigarette datasets collected from different social media platforms. Objective: This paper aims to gain a systematic understanding of the characteristics of various types of social media, which will provide deep insights into how consumers and policy makers effectively use social media to track e-cigarette-related content and adjust their decisions and policies. Methods: We collected data from Reddit (27,638 e-cigarette flavor-related posts from January 1, 2011, to June 30, 2015), JuiceDB (14,433 e-juice reviews from June 26, 2013 to November 12, 2015), and Twitter (13,356 ?e-cig ban?-related tweets from January, 1, 2010 to June 30, 2015). Latent Dirichlet Allocation, a generative model for topic modeling, was used to analyze the topics from these data. Results: We found four types of topics across the platforms: (1) promotions, (2) flavor discussions, (3) experience sharing, and (4) regulation debates. Promotions included sales from vendors to users, as well as trades among users. A total of 10.72% (2,962/27,638) of the posts from Reddit were related to trading. Promotion links were found between social media platforms. Most of the links (87.30%) in JuiceDB were related to Reddit posts. JuiceDB and Reddit identified consistent flavor categories. E-cigarette vaping methods and features such as steeping, throat hit, and vapor production were broadly discussed both on Reddit and on JuiceDB. Reddit provided space for policy discussions and majority of the posts (60.7%) holding a negative attitude toward regulations, whereas Twitter was used to launch campaigns using certain hashtags. Our findings are based on data across different platforms. The topic distribution between Reddit and JuiceDB was significantly different (P<.001), which indicated that the user discussions focused on different perspectives across the platforms. Conclusions: This study examined Reddit, JuiceDB, and Twitter as social media data sources for e-cigarette research. These mined findings could be further used by other researchers and policy makers. By utilizing the automatic topic-modeling method, the proposed unified feedback model could be a useful tool for policy makers to comprehensively consider how to collect valuable feedback from social media. UR - http://www.jmir.org/2017/1/e24/ UR - http://dx.doi.org/10.2196/jmir.5780 UR - http://www.ncbi.nlm.nih.gov/pubmed/28108428 ID - info:doi/10.2196/jmir.5780 ER - TY - JOUR AU - Delir Haghighi, Pari AU - Kang, Yong-Bin AU - Buchbinder, Rachelle AU - Burstein, Frada AU - Whittle, Samuel PY - 2017/01/19 TI - Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter JO - JMIR Public Health Surveill SP - e4 VL - 3 IS - 1 KW - fibromyalgia KW - Twitter messaging KW - social networks KW - pain KW - weather KW - sentiment analysis KW - infodemiology N2 - Background: Little is understood about the determinants of symptom expression in individuals with fibromyalgia syndrome (FMS). While individuals with FMS often report environmental influences, including weather events, on their symptom severity, a consistent effect of specific weather conditions on FMS symptoms has yet to be demonstrated. Content analysis of a large number of messages by individuals with FMS on Twitter can provide valuable insights into variation in the fibromyalgia experience from a first-person perspective. Objective: The objective of our study was to use content analysis of tweets to investigate the association between weather conditions and fibromyalgia symptoms among individuals who tweet about fibromyalgia. Our second objective was to gain insight into how Twitter is used as a form of communication and expression by individuals with fibromyalgia and to explore and uncover thematic clusters and communities related to weather. Methods: Computerized sentiment analysis was performed to measure the association between negative sentiment scores (indicative of severe symptoms such as pain) and coincident environmental variables. Date, time, and location data for each individual tweet were used to identify corresponding climate data (such as temperature). We used graph analysis to investigate the frequency and distribution of domain-related terms exchanged in Twitter and their association strengths. A community detection algorithm was applied to partition the graph and detect different communities. Results: We analyzed 140,432 tweets related to fibromyalgia from 2008 to 2014. There was a very weak positive correlation between humidity and negative sentiment scores (r=.009, P=.001). There was no significant correlation between other environmental variables and negative sentiment scores. The graph analysis showed that ?pain? and ?chronicpain? were the most frequently used terms. The Louvain method identified 6 communities. Community 1 was related to feelings and symptoms at the time (subjective experience). It also included a list of weather-related terms such as ?weather,? ?cold,? and ?rain.? Conclusions: According to our results, a uniform causal effect of weather variation on fibromyalgia symptoms at the group level remains unlikely. Any impact of weather on fibromyalgia symptoms may vary geographically or at an individual level. Future work will further explore geographic variation and interactions focusing on individual pain trajectories over time. UR - http://publichealth.jmir.org/2017/1/e4/ UR - http://dx.doi.org/10.2196/publichealth.6344 UR - http://www.ncbi.nlm.nih.gov/pubmed/28104577 ID - info:doi/10.2196/publichealth.6344 ER - TY - JOUR AU - Liu, Sam AU - Zhu, Miaoqi AU - Yu, Jin Dong AU - Rasin, Alexander AU - Young, D. Sean PY - 2017/01/10 TI - Using Real-Time Social Media Technologies to Monitor Levels of Perceived Stress and Emotional State in College Students: A Web-Based Questionnaire Study JO - JMIR Ment Health SP - e2 VL - 4 IS - 1 KW - social media KW - twitter messaging, stress KW - monitoring N2 - Background: College can be stressful for many freshmen as they cope with a variety of stressors. Excess stress can negatively affect both psychological and physical health. Thus, there is a need to find innovative and cost-effective strategies to help identify students experiencing high levels of stress to receive appropriate treatment. Social media use has been rapidly growing, and recent studies have reported that data from these technologies can be used for public health surveillance. Currently, no studies have examined whether Twitter data can be used to monitor stress level and emotional state among college students. Objective: The primary objective of our study was to investigate whether students? perceived levels of stress were associated with the sentiment and emotions of their tweets. The secondary objective was to explore whether students? emotional state was associated with the sentiment and emotions of their tweets. Methods: We recruited 181 first-year freshman students aged 18-20 years at University of California, Los Angeles. All participants were asked to complete a questionnaire that assessed their demographic characteristics, levels of stress, and emotional state for the last 7 days. All questionnaires were completed within a 48-hour period. All tweets posted by the participants from that week (November 2 to 8, 2015) were mined and manually categorized based on their sentiment (positive, negative, neutral) and emotion (anger, fear, love, happiness) expressed. Ordinal regressions were used to assess whether weekly levels of stress and emotional states were associated with the percentage of positive, neutral, negative, anger, fear, love, or happiness tweets. Results: A total of 121 participants completed the survey and were included in our analysis. A total of 1879 tweets were analyzed. A higher level of weekly stress was significantly associated with a greater percentage of negative sentiment tweets (beta=1.7, SE 0.7; P=.02) and tweets containing emotions of fear (beta=2.4, SE 0.9; P=.01) and love (beta=3.6, SE 1.4; P=.01). A greater level of anger was negatively associated with the percentage of positive sentiment (beta=?1.6, SE 0.8; P=.05) and tweets related to the emotions of happiness (beta=?2.2, SE 0.9; P=.02). A greater level of fear was positively associated with the percentage of negative sentiment (beta=1.67, SE 0.7; P=.01), particularly a greater proportion of tweets related to the emotion of fear (beta=2.4, SE 0.8; P=.01). Participants who reported a greater level of love showed a smaller percentage of negative sentiment tweets (beta=?1.3, SE 0.7; P=0.05). Emotions of happiness were positively associated with the percentage of tweets related to the emotion of happiness (beta=?1.8, SE 0.8; P=.02) and negatively associated with percentage of negative sentiment tweets (beta=?1.7, SE 0.7; P=.02) and tweets related to the emotion of fear (beta=?2.8, SE 0.8; P=.01). Conclusions: Sentiment and emotions expressed in the tweets have the potential to provide real-time monitoring of stress level and emotional well-being in college students. UR - http://mental.jmir.org/2017/1/e2/ UR - http://dx.doi.org/10.2196/mental.5626 UR - http://www.ncbi.nlm.nih.gov/pubmed/28073737 ID - info:doi/10.2196/mental.5626 ER - TY - JOUR AU - Lazard, J. Allison AU - Saffer, J. Adam AU - Wilcox, B. Gary AU - Chung, DongWoo Arnold AU - Mackert, S. Michael AU - Bernhardt, M. Jay PY - 2016/12/12 TI - E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter JO - JMIR Public Health Surveill SP - e171 VL - 2 IS - 2 KW - e-cigarettes KW - social media KW - tweet KW - Internet N2 - Background: As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. Objective: This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Methods: Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter?s streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation. Results: While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices. Conclusions: These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes. UR - http://publichealth.jmir.org/2016/2/e171/ UR - http://dx.doi.org/10.2196/publichealth.6551 UR - http://www.ncbi.nlm.nih.gov/pubmed/27956376 ID - info:doi/10.2196/publichealth.6551 ER - TY - JOUR AU - Massey, M. Philip AU - Leader, Amy AU - Yom-Tov, Elad AU - Budenz, Alexandra AU - Fisher, Kara AU - Klassen, C. Ann PY - 2016/12/05 TI - Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter JO - J Med Internet Res SP - e318 VL - 18 IS - 12 KW - HPV vaccine KW - Twitter KW - communication methods KW - content analysis KW - data mining N2 - Background: Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. There are several vaccines that protect against strains of HPV most associated with cervical and other cancers. Thus, HPV vaccination has become an important component of adolescent preventive health care. As media evolves, more information about HPV vaccination is shifting to social media platforms such as Twitter. Health information consumed on social media may be especially influential for segments of society such as younger populations, as well as ethnic and racial minorities. Objective: The objectives of our study were to quantify HPV vaccine communication on Twitter, and to develop a novel methodology to improve the collection and analysis of Twitter data. Methods: We collected Twitter data using 10 keywords related to HPV vaccination from August 1, 2014 to July 31, 2015. Prospective data collection used the Twitter Search API and retrospective data collection used Twitter Firehose. Using a codebook to characterize tweet sentiment and content, we coded a subsample of tweets by hand to develop classification models to code the entire sample using machine learning procedures. We also documented the words in the 140-character tweet text most associated with each keyword. We used chi-square tests, analysis of variance, and nonparametric equality of medians to test for significant differences in tweet characteristic by sentiment. Results: A total of 193,379 English-language tweets were collected, classified, and analyzed. Associated words varied with each keyword, with more positive and preventive words associated with ?HPV vaccine? and more negative words associated with name-brand vaccines. Positive sentiment was the largest type of sentiment in the sample, with 75,393 positive tweets (38.99% of the sample), followed by negative sentiment with 48,940 tweets (25.31% of the sample). Positive and neutral tweets constituted the largest percentage of tweets mentioning prevention or protection (20,425/75,393, 27.09% and 6477/25,110, 25.79%, respectively), compared with only 11.5% of negative tweets (5647/48,940; P<.001). Nearly one-half (22,726/48,940, 46.44%) of negative tweets mentioned side effects, compared with only 17.14% (12,921/75,393) of positive tweets and 15.08% of neutral tweets (3787/25,110; P<.001). Conclusions: Examining social media to detect health trends, as well as to communicate important health information, is a growing area of research in public health. Understanding the content and implications of conversations that form around HPV vaccination on social media can aid health organizations and health-focused Twitter users in creating a meaningful exchange of ideas and in having a significant impact on vaccine uptake. This area of research is inherently interdisciplinary, and this study supports this movement by applying public health, health communication, and data science approaches to extend methodologies across fields. UR - http://www.jmir.org/2016/12/e318/ UR - http://dx.doi.org/10.2196/jmir.6670 UR - http://www.ncbi.nlm.nih.gov/pubmed/27919863 ID - info:doi/10.2196/jmir.6670 ER - TY - JOUR AU - Nishimoto, Naoki AU - Ota, Mizuki AU - Yagahara, Ayako AU - Ogasawara, Katsuhiko PY - 2016/11/25 TI - Estimating the Duration of Public Concern After the Fukushima Dai-ichi Nuclear Power Station Accident From the Occurrence of Radiation Exposure-Related Terms on Twitter: A Retrospective Data Analysis JO - JMIR Public Health Surveill SP - e168 VL - 2 IS - 2 KW - Twitter KW - social media KW - public concern KW - nuclear power plants KW - survival analysis KW - Kaplan-Meier estimate KW - infodemiology KW - radiation N2 - Background: After the Fukushima Dai-ichi Nuclear Power Station accident in Japan on March 11, 2011, a large number of comments, both positive and negative, were posted on social media. Objective: The objective of this study was to clarify the characteristics of the trend in the number of tweets posted on Twitter, and to estimate how long public concern regarding the accident continued. We surveyed the attenuation period of the first term occurrence related to radiation exposure as a surrogate endpoint for the duration of concern. Methods: We retrieved 18,891,284 tweets from Twitter data between March 11, 2011 and March 10, 2012, containing 143 variables in Japanese. We selected radiation, radioactive, Sievert (Sv), Becquerel (Bq), and gray (Gy) as keywords to estimate the attenuation period of public concern regarding radiation exposure. These data, formatted as comma-separated values, were transferred into a Statistical Analysis System (SAS) dataset for analysis, and survival analysis methodology was followed using the SAS LIFETEST procedure. This study was approved by the institutional review board of Hokkaido University and informed consent was waived. Results: A Kaplan-Meier curve was used to show the rate of Twitter users posting a message after the accident that included one or more of the keywords. The term Sv occurred in tweets up to one year after the first tweet. Among the Twitter users studied, 75.32% (880,108/1,168,542) tweeted the word radioactive and 9.20% (107,522/1,168,542) tweeted the term Sv. The first reduction was observed within the first 7 days after March 11, 2011. The means and standard errors (SEs) of the duration from the first tweet on March 11, 2011 were 31.9 days (SE 0.096) for radioactive and 300.6 days (SE 0.181) for Sv. These keywords were still being used at the end of the study period. The mean attenuation period for radioactive was one month, and approximately one year for radiation and radiation units. The difference in mean duration between the keywords was attributed to the effect of mass media. Regularly posted messages, such as daily radiation dose reports, were relatively easy to detect from their time and formatted contents. The survival estimation indicated that public concern about the nuclear power plant accident remained after one year. Conclusions: Although the simple plot of the number of tweets did not show clear results, we estimated the mean attenuation period as approximately one month for the keyword radioactive, and found that the keywords were still being used in posts at the end of the study period. Further research is required to quantify the effect of other phrases in social media data. The results of this exploratory study should advance progress in influencing and quantifying the communication of risk. UR - http://publichealth.jmir.org/2016/2/e168/ UR - http://dx.doi.org/10.2196/publichealth.5384 UR - http://www.ncbi.nlm.nih.gov/pubmed/27888168 ID - info:doi/10.2196/publichealth.5384 ER - TY - JOUR AU - Tangherlini, R. Timothy AU - Roychowdhury, Vwani AU - Glenn, Beth AU - Crespi, M. Catherine AU - Bandari, Roja AU - Wadia, Akshay AU - Falahi, Misagh AU - Ebrahimzadeh, Ehsan AU - Bastani, Roshan PY - 2016/11/22 TI - ?Mommy Blogs? and the Vaccination Exemption Narrative: Results From A Machine-Learning Approach for Story Aggregation on Parenting Social Media Sites JO - JMIR Public Health Surveill SP - e166 VL - 2 IS - 2 KW - vaccination KW - social media KW - machine learning KW - personal narratives KW - Internet KW - health knowledge KW - attitudes KW - practice N2 - Background: Social media offer an unprecedented opportunity to explore how people talk about health care at a very large scale. Numerous studies have shown the importance of websites with user forums for people seeking information related to health. Parents turn to some of these sites, colloquially referred to as ?mommy blogs,? to share concerns about children?s health care, including vaccination. Although substantial work has considered the role of social media, particularly Twitter, in discussions of vaccination and other health care?related issues, there has been little work on describing the underlying structure of these discussions and the role of persuasive storytelling, particularly on sites with no limits on post length. Understanding the role of persuasive storytelling at Internet scale provides useful insight into how people discuss vaccinations, including exemption-seeking behavior, which has been tied to a recent diminution of herd immunity in some communities. Objective: To develop an automated and scalable machine-learning method for story aggregation on social media sites dedicated to discussions of parenting. We wanted to discover the aggregate narrative frameworks to which individuals, through their exchange of experiences and commentary, contribute over time in a particular topic domain. We also wanted to characterize temporal trends in these narrative frameworks on the sites over the study period. Methods: To ensure that our data capture long-term discussions and not short-term reactions to recent events, we developed a dataset of 1.99 million posts contributed by 40,056 users and viewed 20.12 million times indexed from 2 parenting sites over a period of 105 months. Using probabilistic methods, we determined the topics of discussion on these parenting sites. We developed a generative statistical-mechanical narrative model to automatically extract the underlying stories and story fragments from millions of posts. We aggregated the stories into an overarching narrative framework graph. In our model, stories were represented as network graphs with actants as nodes and their various relationships as edges. We estimated the latent stories circulating on these sites by modeling the posts as a sampling of the hidden narrative framework graph. Temporal trends were examined based on monthly user-poststatistics. Results: We discovered that discussions of exemption from vaccination requirements are highly represented. We found a strong narrative framework related to exemption seeking and a culture of distrust of government and medical institutions. Various posts reinforced part of the narrative framework graph in which parents, medical professionals, and religious institutions emerged as key nodes, and exemption seeking emerged as an important edge. In the aggregate story, parents used religion or belief to acquire exemptions to protect their children from vaccines that are required by schools or government institutions, but (allegedly) cause adverse reactions such as autism, pain, compromised immunity, and even death. Although parents joined and left the discussion forums over time, discussions and stories about exemptions were persistent and robust to these membership changes. Conclusions: Analyzing parent forums about health care using an automated analytic approach, such as the one presented here, allows the detection of widespread narrative frameworks that structure and inform discussions. In most vaccination stories from the sites we analyzed, it is taken for granted that vaccines and not vaccine preventable diseases (VPDs) pose a threat to children. Because vaccines are seen as a threat, parents focus on sharing successful strategies for avoiding them, with exemption being the foremost among these strategies. When new parents join such sites, they may be exposed to this endemic narrative framework in the threads they read and to which they contribute, which may influence their health care decision making. UR - http://publichealth.jmir.org/2016/2/e166/ UR - http://dx.doi.org/10.2196/publichealth.6586 UR - http://www.ncbi.nlm.nih.gov/pubmed/27876690 ID - info:doi/10.2196/publichealth.6586 ER - TY - JOUR AU - Ben-Sasson, Ayelet AU - Yom-Tov, Elad PY - 2016/11/22 TI - Online Concerns of Parents Suspecting Autism Spectrum Disorder in Their Child: Content Analysis of Signs and Automated Prediction of Risk JO - J Med Internet Res SP - e300 VL - 18 IS - 11 KW - online queries KW - autistic disorders KW - parents KW - machine learning KW - early detection N2 - Background: Online communities are used as platforms by parents to verify developmental and health concerns related to their child. The increasing public awareness of autism spectrum disorders (ASD) leads more parents to suspect ASD in their child. Early identification of ASD is important for early intervention. Objective: To characterize the symptoms mentioned in online queries posed by parents who suspect that their child might have ASD and determine whether they are age-specific. To test the efficacy of machine learning tools in classifying the child?s risk of ASD based on the parent?s narrative. Methods: To this end, we analyzed online queries posed by parents who were concerned that their child might have ASD and categorized the warning signs they mentioned according to ASD-specific and non-ASD?specific domains. We then used the data to test the efficacy with which a trained machine learning tool classified the degree of ASD risk. Yahoo Answers, a social site for posting queries and finding answers, was mined for queries of parents asking the community whether their child has ASD. A total of 195 queries were sampled for this study (mean child age=38.0 months; 84.7% [160/189] boys). Content text analysis of the queries aimed to categorize the types of symptoms described and obtain clinical judgment of the child?s ASD-risk level. Results: Concerns related to repetitive and restricted behaviors and interests (RRBI) were the most prevalent (75.4%, 147/195), followed by concerns related to language (61.5%, 120/195) and emotional markers (50.3%, 98/195). Of the 195 queries, 18.5% (36/195) were rated by clinical experts as low-risk, 30.8% (60/195) as medium-risk, and 50.8% (99/195) as high-risk. Risk groups differed significantly (P<.001) in the rate of concerns in the language, social, communication, and RRBI domains. When testing whether an automatic classifier (decision tree) could predict if a query was medium- or high-risk based on the text of the query and the coded symptoms, performance reached an area under the receiver operating curve (ROC) curve of 0.67 (CI 95% 0.50-0.78), whereas predicting from the text and the coded signs resulted in an area under the curve of 0.82 (0.80-0.86). Conclusions: Findings call for health care providers to closely listen to parental ASD-related concerns, as recommended by screening guidelines. They also demonstrate the need for Internet-based screening systems that utilize parents? narratives using a decision tree questioning method. UR - http://www.jmir.org/2016/11/e300/ UR - http://dx.doi.org/10.2196/jmir.5439 UR - http://www.ncbi.nlm.nih.gov/pubmed/27876688 ID - info:doi/10.2196/jmir.5439 ER - TY - JOUR AU - Hand, K. Rosa AU - Kenne, Deric AU - Wolfram, M. Taylor AU - Abram, K. Jenica AU - Fleming, Michael PY - 2016/11/15 TI - Assessing the Viability of Social Media for Disseminating Evidence-Based Nutrition Practice Guideline Through Content Analysis of Twitter Messages and Health Professional Interviews: An Observational Study JO - J Med Internet Res SP - e295 VL - 18 IS - 11 KW - social media KW - information dissemination KW - medical nutrition therapy KW - evidence-based medicine KW - heart failure N2 - Background: Given the high penetration of social media use, social media has been proposed as a method for the dissemination of information to health professionals and patients. This study explored the potential for social media dissemination of the Academy of Nutrition and Dietetics Evidence-Based Nutrition Practice Guideline (EBNPG) for Heart Failure (HF). Objectives: The objectives were to (1) describe the existing social media content on HF, including message content, source, and target audience, and (2) describe the attitude of physicians and registered dietitian nutritionists (RDNs) who care for outpatient HF patients toward the use of social media as a method to obtain information for themselves and to share this information with patients. Methods: The methods were divided into 2 parts. Part 1 involved conducting a content analysis of tweets related to HF, which were downloaded from Twitonomy and assigned codes for message content (19 codes), source (9 codes), and target audience (9 codes); code frequency was described. A comparison in the popularity of tweets (those marked as favorites or retweeted) based on applied codes was made using t tests. Part 2 involved conducting phone interviews with RDNs and physicians to describe health professionals? attitude toward the use of social media to communicate general health information and information specifically related to the HF EBNPG. Interviews were transcribed and coded; exemplar quotes representing frequent themes are presented. Results: The sample included 294 original tweets with the hashtag ?#heartfailure.? The most frequent message content codes were ?HF awareness? (166/294, 56.5%) and ?patient support? (97/294, 33.0%). The most frequent source codes were ?professional, government, patient advocacy organization, or charity? (112/277, 40.4%) and ?patient or family? (105/277, 37.9%). The most frequent target audience codes were ?unable to identify? (111/277, 40.1%) and ?other? (55/277, 19.9%). Significant differences were found in the popularity of tweets with (mean 1, SD 1.3 favorites) or without (mean 0.7, SD 1.3 favorites), the content code being ?HF research? (P=.049). Tweets with the source code ?professional, government, patient advocacy organizations, or charities? were significantly more likely to be marked as a favorite and retweeted than those without this source code (mean 1.2, SD 1.4 vs mean 0.8, SD 1.2, P=.03) and (mean 1.5, SD 1.8 vs mean 0.9, SD 2.0, P=.03). Interview participants believed that social media was a useful way to gather professional information. They did not believe that social media was useful for communicating with patients due to privacy concerns and the fact that the information had to be kept general rather than be tailored for a specific patient and the belief that their patients did not use social media or technology. Conclusions: Existing Twitter content related to HF comes from a combination of patients and evidence-based organizations; however, there is little nutrition content. That gap may present an opportunity for EBNPG dissemination. Health professionals use social media to gather information for themselves but are skeptical of its value when communicating with patients, particularly due to privacy concerns and misconceptions about the characteristics of social media users. UR - http://www.jmir.org/2016/11/e295/ UR - http://dx.doi.org/10.2196/jmir.5811 UR - http://www.ncbi.nlm.nih.gov/pubmed/27847349 ID - info:doi/10.2196/jmir.5811 ER - TY - JOUR AU - Liu, Yang AU - Mei, Qiaozhu AU - Hanauer, A. David AU - Zheng, Kai AU - Lee, M. Joyce PY - 2016/11/07 TI - Use of Social Media in the Diabetes Community: An Exploratory Analysis of Diabetes-Related Tweets JO - JMIR Diabetes SP - e4 VL - 1 IS - 2 KW - social media KW - Twitter, DSMA KW - diabetes community KW - spatiotemporal analysis KW - content analysis N2 - Background: Use of social media is becoming ubiquitous, and disease-related communities are forming online, including communities of interest around diabetes. Objective: Our objective was to examine diabetes-related participation on Twitter by describing the frequency and timing of diabetes-related tweets, the geography of tweets, and the types of participants over a 2-year sample of 10% of all tweets. Methods: We identified tweets with diabetes-related search terms and hashtags in a dataset of 29.6 billion tweets for the years 2013 and 2014 and extracted the text, time, location, retweet, and user information. We assessed the frequencies of tweets used across different search terms and hashtags by month and day of week and, for tweets that provided location information, by country. We also performed these analyses for a subset of tweets that used the hashtag #dsma, a social media advocacy community focused on diabetes. Random samples of user profiles in the 2 groups were also drawn and reviewed to understand the types of stakeholders participating online. Results: We found 1,368,575 diabetes-related tweets based on diabetes-related terms and hashtags. There was a seasonality to tweets; a higher proportion occurred during the month of November, which is when World Diabetes Day occurs. The subset of tweets with the #dsma were most frequent on Thursdays (coordinated universal time), which is consistent with the timing of a weekly chat organized by this online community. Approximately 2% of tweets carried geolocation information and were most prominent in the United States (on the east and west coasts), followed by Indonesia and the United Kingdom. For the user profiles randomly selected among overall tweets, we could not identify a relationship to diabetes for the majority of users; for the profiles using the #dsma hashtag, we found that patients with type 1 diabetes and their caregivers represented the largest proportion of individuals. Conclusions: Twitter is increasingly becoming a space for online conversations about diabetes. Further qualitative and quantitative content analysis is needed to understand the nature and purpose of these conversations. UR - http://diabetes.jmir.org/2016/2/e4/ UR - http://dx.doi.org/10.2196/diabetes.6256 UR - http://www.ncbi.nlm.nih.gov/pubmed/30291053 ID - info:doi/10.2196/diabetes.6256 ER - TY - JOUR AU - Daniulaityte, Raminta AU - Chen, Lu AU - Lamy, R. Francois AU - Carlson, G. Robert AU - Thirunarayan, Krishnaprasad AU - Sheth, Amit PY - 2016/10/24 TI - ?When ?Bad? is ?Good??: Identifying Personal Communication and Sentiment in Drug-Related Tweets JO - JMIR Public Health Surveill SP - e162 VL - 2 IS - 2 KW - social media KW - Twitter KW - cannabis KW - synthetic cannabinoids KW - machine learning KW - sentiment analysis KW - eDrugTrends N2 - Background: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. Objectives: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid?related tweets. Methods: Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was used to determine if differences in F-scores were statistically significant. Results: In multiclass source classification, the use of expanded URLs did not contribute to significant improvement in classifier performance (0.7972 vs 0.8102 for SVM, P=.19). In binary classification, the identification of all source categories improved significantly when unshortened URLs were used, with personal communication tweets benefiting the most (0.8736 vs 0.8200, P<.001). In multiclass sentiment classification Approach 1, SVM (0.6723) performed similarly to NB (0.6683) and LR (0.6703). In Approach 2, SVM (0.7062) did not differ from NB (0.6980, P=.13) or LR (F=0.6931, P=.05), but it was over 40% more accurate than VADER (F=0.5030, P<.001). In multiclass task, improvements in sentiment classification (Approach 2 vs Approach 1) did not reach statistical significance (eg, SVM: 0.7062 vs 0.6723, P=.052). In binary sentiment classification (positive vs negative), Approach 2 (focus on personal communication tweets only) improved classification results, compared with Approach 1, for LR (0.8752 vs 0.8516, P=.04) and SVM (0.8800 vs 0.8557, P=.045). Conclusions: The study provides an example of the use of supervised machine learning methods to categorize cannabis- and synthetic cannabinoid?related tweets with fairly high accuracy. Use of these content analysis tools along with geographic identification capabilities developed by the eDrugTrends platform will provide powerful methods for tracking regional changes in user opinions related to cannabis and synthetic cannabinoids use over time and across different regions. UR - http://publichealth.jmir.org/2016/2/e162/ UR - http://dx.doi.org/10.2196/publichealth.6327 UR - http://www.ncbi.nlm.nih.gov/pubmed/27777215 ID - info:doi/10.2196/publichealth.6327 ER - TY - JOUR AU - Sharpe, Danielle J. AU - Hopkins, S. Richard AU - Cook, L. Robert AU - Striley, W. Catherine PY - 2016/10/20 TI - Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis JO - JMIR Public Health Surveill SP - e161 VL - 2 IS - 2 KW - Internet KW - social media KW - Bayes theorem KW - public health surveillance KW - influenza, human N2 - Background: Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods. Objective: The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter. Methods: Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC?s change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package ?bcp? version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. Results: During the 2012-2015 influenza seasons, a high sensitivity of 92% was found for Google, whereas the PPV for Google was 85%. A low sensitivity of 50% was calculated for Twitter; a low PPV of 43% was found for Twitter also. Wikipedia had the lowest sensitivity of 33% and lowest PPV of 40%. Conclusions: Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed. UR - http://publichealth.jmir.org/2016/2/e161/ UR - http://dx.doi.org/10.2196/publichealth.5901 UR - http://www.ncbi.nlm.nih.gov/pubmed/27765731 ID - info:doi/10.2196/publichealth.5901 ER - TY - JOUR AU - Nguyen, C. Quynh AU - Li, Dapeng AU - Meng, Hsien-Wen AU - Kath, Suraj AU - Nsoesie, Elaine AU - Li, Feifei AU - Wen, Ming PY - 2016/10/17 TI - Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity JO - JMIR Public Health Surveill SP - e158 VL - 2 IS - 2 KW - social media KW - Twitter messaging KW - health behavior KW - happiness KW - food KW - physical activity N2 - Background: Studies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research. Objective: The aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors. Methods: We utilized Twitter?s streaming application programming interface to continuously collect a random 1% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics. Results: Machine labeled and manually labeled tweets had a high level of accuracy: 78% for happiness, 83% for food, and 85% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast food mentions. Surprisingly, fitness centers and nature parks were only modestly associated with higher frequency of physical activity tweets. Greater state-level happiness, positivity toward physical activity, and positivity toward healthy foods, assessed via tweets, were associated with lower all-cause mortality and prevalence of chronic conditions such as obesity and diabetes and lower physical inactivity and smoking, controlling for state median income, median age, and percentage white non-Hispanic. Conclusions: Machine learning algorithms can be built with relatively high accuracy to characterize sentiment, food, and physical activity mentions on social media. Such data can be utilized to construct neighborhood indicators consistently and cost effectively. Access to neighborhood data, in turn, can be leveraged to better understand neighborhood effects and address social determinants of health. We found that neighborhoods with social and economic disadvantage, high urbanicity, and more fast food restaurants may exhibit lower happiness and fewer healthy behaviors. UR - http://publichealth.jmir.org/2016/2/e158/ UR - http://dx.doi.org/10.2196/publichealth.5869 UR - http://www.ncbi.nlm.nih.gov/pubmed/27751984 ID - info:doi/10.2196/publichealth.5869 ER - TY - JOUR AU - Ling, Rebecca AU - Lee, Joon PY - 2016/10/12 TI - Disease Monitoring and Health Campaign Evaluation Using Google Search Activities for HIV and AIDS, Stroke, Colorectal Cancer, and Marijuana Use in Canada: A Retrospective Observational Study JO - JMIR Public Health Surveill SP - e156 VL - 2 IS - 2 KW - public health informatics KW - Internet KW - information seeking behavior N2 - Background: Infodemiology can offer practical and feasible health research applications through the practice of studying information available on the Web. Google Trends provides publicly accessible information regarding search behaviors in a population, which may be studied and used for health campaign evaluation and disease monitoring. Additional studies examining the use and effectiveness of Google Trends for these purposes remain warranted. Objective: The objective of our study was to explore the use of infodemiology in the context of health campaign evaluation and chronic disease monitoring. It was hypothesized that following a launch of a campaign, there would be an increase in information seeking behavior on the Web. Second, increasing and decreasing disease patterns in a population would be associated with search activity patterns. This study examined 4 different diseases: human immunodeficiency virus (HIV) infection, stroke, colorectal cancer, and marijuana use. Methods: Using Google Trends, relative search volume data were collected throughout the period of February 2004 to January 2015. Campaign information and disease statistics were obtained from governmental publications. Search activity trends were graphed and assessed with disease trends and the campaign interval. Pearson product correlation statistics and joinpoint methodology analyses were used to determine significance. Results: Disease patterns and online activity across all 4 diseases were significantly correlated: HIV infection (r=.36, P<.001), stroke (r=.40, P<.001), colorectal cancer (r= ?.41, P<.001), and substance use (r=.64, P<.001). Visual inspection and the joinpoint analysis showed significant correlations for the campaigns on colorectal cancer and marijuana use in stimulating search activity. No significant correlations were observed for the campaigns on stroke and HIV regarding search activity. Conclusions: The use of infoveillance shows promise as an alternative and inexpensive solution to disease surveillance and health campaign evaluation. Further research is needed to understand Google Trends as a valid and reliable tool for health research. UR - http://publichealth.jmir.org/2016/2/e156/ UR - http://dx.doi.org/10.2196/publichealth.6504 UR - http://www.ncbi.nlm.nih.gov/pubmed/27733330 ID - info:doi/10.2196/publichealth.6504 ER - TY - JOUR AU - Agarwal, Vibhu AU - Zhang, Liangliang AU - Zhu, Josh AU - Fang, Shiyuan AU - Cheng, Tim AU - Hong, Chloe AU - Shah, H. Nigam PY - 2016/09/21 TI - Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis JO - J Med Internet Res SP - e251 VL - 18 IS - 9 KW - search behavior KW - geotagged search logs KW - health care utilization KW - utility KW - health care costs KW - Internet N2 - Background: By recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear. Objective: The aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of user?s future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015. Methods: We inferred presence within the geofence of a medical facility from location and duration information from users? search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show conversion rate and predicted utilization score, served as a surrogate measure of the model?s utility. Results: We obtained the highest area under the curve (0.796) in medical visit prediction with our random forests model and daywise features. Ablating feature categories one at a time showed that the model performance worsened the most when location features were dropped. An online evaluation in which advertisements were served to users who had a high predicted probability of a future medical visit showed a 3.96% increase in the show conversion rate. Conclusions: Results from our experiments done in a research setting suggest that it is possible to accurately predict future patient visits from geotagged mobile search logs. Results from the offline and online experiments on the utility of health utilization predictions suggest that such prediction can have utility for health care providers. UR - http://www.jmir.org/2016/9/e251/ UR - http://dx.doi.org/10.2196/jmir.6240 UR - http://www.ncbi.nlm.nih.gov/pubmed/27655225 ID - info:doi/10.2196/jmir.6240 ER - TY - JOUR AU - Marcon, R. Alessandro AU - Klostermann, Philip AU - Caulfield, Timothy PY - 2016/09/16 TI - Chiropractic and Spinal Manipulation Therapy on Twitter: Case Study Examining the Presence of Critiques and Debates JO - JMIR Public Health Surveill SP - e153 VL - 2 IS - 2 KW - spinal manipulation KW - manipulation therapy KW - chiropractic KW - alternative medicine KW - Twitter KW - social media KW - infodemiology N2 - Background: Spinal manipulation therapy (SMT) is a popular though controversial practice. The debates surrounding efficacy and risk of SMT are only partially evident in popular discourse. Objective: This study aims to investigate the presence of critiques and debates surrounding efficacy and risk of SMT on the social media platform Twitter. The study examines whether there is presence of debate and whether critical information is being widely disseminated. Methods: An initial corpus of 31,339 tweets was compiled through Twitter?s Search Application Programming Interface using the query terms ?chiropractic,? ?chiropractor,? and ?spinal manipulation therapy.? Tweets were collected for the month of December 2015. Post removal of tweets made by bots and spam, the corpus totaled 20,695 tweets, of which a sample (n=1267) was analyzed for skeptical or critical tweets. Additional criteria were also assessed. Results: There were 34 tweets explicitly containing skepticism or critique of SMT, representing 2.68% of the sample (n=1267). As such, there is a presence of 2.68% of tweets in the total corpus, 95% CI 0-6.58% displaying explicitly skeptical or critical perspectives of SMT. In addition, there are numerous tweets highlighting the health benefits of SMT for health issues such as attention deficit hyperactivity disorder (ADHD), immune system, and blood pressure that receive scant critical attention. The presence of tweets in the corpus highlighting the risks of ?stroke? and ?vertebral artery dissection? is also minute (0.1%). Conclusions: In the abundance of tweets substantiating and promoting chiropractic and SMT as sound health practices and valuable business endeavors, the debates surrounding the efficacy and risks of SMT on Twitter are almost completely absent. Although there are some critical voices of SMT proving to be influential, issues persist regarding how widely this information is being disseminated. UR - http://publichealth.jmir.org/2016/2/e153/ UR - http://dx.doi.org/10.2196/publichealth.5739 UR - http://www.ncbi.nlm.nih.gov/pubmed/27637456 ID - info:doi/10.2196/publichealth.5739 ER - TY - JOUR AU - Zhang, Zhu AU - Zheng, Xiaolong AU - Zeng, Dajun Daniel AU - Leischow, J. Scott PY - 2016/09/16 TI - Tracking Dabbing Using Search Query Surveillance: A Case Study in the United States JO - J Med Internet Res SP - e252 VL - 18 IS - 9 KW - marijuana KW - information seeking behavior KW - surveillance KW - search engine KW - time series analysis KW - spatial analysis N2 - Background: Dabbing is an emerging method of marijuana ingestion. However, little is known about dabbing owing to limited surveillance data on dabbing. Objective: The aim of the study was to analyze Google search data to assess the scope and breadth of information seeking on dabbing. Methods: Google Trends data about dabbing and related topics (eg, electronic nicotine delivery system [ENDS], also known as e-cigarettes) in the United States between January 2004 and December 2015 were collected by using relevant search terms such as ?dab rig.? The correlation between dabbing (including topics: dab and hash oil) and ENDS (including topics: vaping and e-cigarette) searches, the regional distribution of dabbing searches, and the impact of cannabis legalization policies on geographical location in 2015 were analyzed. Results: Searches regarding dabbing increased in the United States over time, with 1,526,280 estimated searches during 2015. Searches for dab and vaping have very similar temporal patterns, where the Pearson correlation coefficient (PCC) is .992 (P<.001). Similar phenomena were also obtained in searches for hash oil and e-cigarette, in which the corresponding PCC is .931 (P<.001). Dabbing information was searched more in some western states than other regions. The average dabbing searches were significantly higher in the states with medical and recreational marijuana legalization than in the states with only medical marijuana legalization (P=.02) or the states without medical and recreational marijuana legalization (P=.01). Conclusions: Public interest in dabbing is increasing in the United States. There are close associations between dabbing and ENDS searches. The findings suggest greater popularity of dabs in the states that legalized medical and recreational marijuana use. This study proposes a novel and timely way of cannabis surveillance, and these findings can help enhance the understanding of the popularity of dabbing and provide insights for future research and informed policy making on dabbing. UR - http://www.jmir.org/2016/9/e252/ UR - http://dx.doi.org/10.2196/jmir.5802 UR - http://www.ncbi.nlm.nih.gov/pubmed/27637361 ID - info:doi/10.2196/jmir.5802 ER - TY - JOUR AU - Surian, Didi AU - Nguyen, Quoc Dat AU - Kennedy, Georgina AU - Johnson, Mark AU - Coiera, Enrico AU - Dunn, G. Adam PY - 2016/08/29 TI - Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection JO - J Med Internet Res SP - e232 VL - 18 IS - 8 KW - topic modelling KW - graph algorithms analysis KW - social media KW - public health surveillance N2 - Background: In public health surveillance, measuring how information enters and spreads through online communities may help us understand geographical variation in decision making associated with poor health outcomes. Objective: Our aim was to evaluate the use of community structure and topic modeling methods as a process for characterizing the clustering of opinions about human papillomavirus (HPV) vaccines on Twitter. Methods: The study examined Twitter posts (tweets) collected between October 2013 and October 2015 about HPV vaccines. We tested Latent Dirichlet Allocation and Dirichlet Multinomial Mixture (DMM) models for inferring topics associated with tweets, and community agglomeration (Louvain) and the encoding of random walks (Infomap) methods to detect community structure of the users from their social connections. We examined the alignment between community structure and topics using several common clustering alignment measures and introduced a statistical measure of alignment based on the concentration of specific topics within a small number of communities. Visualizations of the topics and the alignment between topics and communities are presented to support the interpretation of the results in context of public health communication and identification of communities at risk of rejecting the safety and efficacy of HPV vaccines. Results: We analyzed 285,417 Twitter posts (tweets) about HPV vaccines from 101,519 users connected by 4,387,524 social connections. Examining the alignment between the community structure and the topics of tweets, the results indicated that the Louvain community detection algorithm together with DMM produced consistently higher alignment values and that alignments were generally higher when the number of topics was lower. After applying the Louvain method and DMM with 30 topics and grouping semantically similar topics in a hierarchy, we characterized 163,148 (57.16%) tweets as evidence and advocacy, and 6244 (2.19%) tweets describing personal experiences. Among the 4548 users who posted experiential tweets, 3449 users (75.84%) were found in communities where the majority of tweets were about evidence and advocacy. Conclusions: The use of community detection in concert with topic modeling appears to be a useful way to characterize Twitter communities for the purpose of opinion surveillance in public health applications. Our approach may help identify online communities at risk of being influenced by negative opinions about public health interventions such as HPV vaccines. UR - http://www.jmir.org/2016/8/e232/ UR - http://dx.doi.org/10.2196/jmir.6045 UR - http://www.ncbi.nlm.nih.gov/pubmed/27573910 ID - info:doi/10.2196/jmir.6045 ER - TY - JOUR AU - Meaney, Sarah AU - Cussen, Leanne AU - Greene, A. Richard AU - O'Donoghue, Keelin PY - 2016/07/27 TI - Reaction on Twitter to a Cluster of Perinatal Deaths: A Mixed Method Study JO - JMIR Public Health Surveill SP - e36 VL - 2 IS - 2 KW - social media KW - health care services KW - maternity KW - perinatal death KW - Twitter KW - infodemiology KW - infoveillance N2 - Background: Participation in social networking sites is commonplace and the micro-blogging site Twitter can be considered a platform for the rapid broadcasting of news stories. Objective: The aim of this study was to explore the Twitter status updates and subsequent responses relating to a number of perinatal deaths which occurred in a small maternity unit in Ireland. Methods: An analysis of Twitter status updates, over a two month period from January to March 2014, was undertaken to identify the key themes arising in relation to the perinatal deaths. Results: Our search identified 3577 tweets relating to the reported perinatal deaths. At the height of the controversy, Twitter updates generated skepticism in relation to the management of not only of the unit in question, which was branded as unsafe, but also the governance of the entire Irish maternity service. Themes of concern and uncertainty arose whereby the professional motives of the obstetric community and staffing levels in the maternity services were called into question. Conclusions: Twitter activity provides a useful insight into attitudes towards health-related events. The role of the media in influencing opinion is well-documented and this study underscores the challenges that clinicians face in light of an obstetric media scandal. Further study to identify how the obstetric community could develop tools to utilize Twitter to disseminate valid health information could be beneficial. UR - http://publichealth.jmir.org/2016/2/e36/ UR - http://dx.doi.org/10.2196/publichealth.5333 UR - http://www.ncbi.nlm.nih.gov/pubmed/27466002 ID - info:doi/10.2196/publichealth.5333 ER - TY - JOUR AU - Woo, Hyekyung AU - Cho, Youngtae AU - Shim, Eunyoung AU - Lee, Jong-Koo AU - Lee, Chang-Gun AU - Kim, Hwan Seong PY - 2016/07/04 TI - Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea JO - J Med Internet Res SP - e177 VL - 18 IS - 7 KW - influenza KW - surveillance KW - population surveillance KW - infodemiology KW - infoveillance KW - Internet search KW - query KW - social media KW - big data KW - forecasting KW - epidemiology KW - early response N2 - Background: As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. Objective: In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. Methods: Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. Results: In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001). Conclusions: These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data. UR - http://www.jmir.org/2016/7/e177/ UR - http://dx.doi.org/10.2196/jmir.4955 UR - http://www.ncbi.nlm.nih.gov/pubmed/27377323 ID - info:doi/10.2196/jmir.4955 ER - TY - JOUR AU - Klembczyk, Jeffrey Joseph AU - Jalalpour, Mehdi AU - Levin, Scott AU - Washington, E. Raynard AU - Pines, M. Jesse AU - Rothman, E. Richard AU - Dugas, Freyer Andrea PY - 2016/06/28 TI - Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits JO - J Med Internet Res SP - e175 VL - 18 IS - 6 KW - influenza KW - surveillance KW - emergency department KW - google flu trends KW - infoveillance N2 - Background: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. Objective: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. Methods: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. Results: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P<.10) with improved GFT surveillance include higher proportion of female population, higher proportion with Medicare coverage, higher ED visits per capita, and lower socioeconomic status. Conclusions: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness. UR - http://www.jmir.org/2016/6/e175/ UR - http://dx.doi.org/10.2196/jmir.5585 UR - http://www.ncbi.nlm.nih.gov/pubmed/27354313 ID - info:doi/10.2196/jmir.5585 ER - TY - JOUR AU - Lyles, Rees Courtney AU - Godbehere, Andrew AU - Le, Gem AU - El Ghaoui, Laurent AU - Sarkar, Urmimala PY - 2016/06/10 TI - Applying Sparse Machine Learning Methods to Twitter: Analysis of the 2012 Change in Pap Smear Guidelines. A Sequential Mixed-Methods Study JO - JMIR Public Health Surveill SP - e21 VL - 2 IS - 1 KW - Twitter KW - machine learning KW - social media KW - cervical cancer KW - qualitative research N2 - Background: It is difficult to synthesize the vast amount of textual data available from social media websites. Capturing real-world discussions via social media could provide insights into individuals? opinions and the decision-making process. Objective: We conducted a sequential mixed methods study to determine the utility of sparse machine learning techniques in summarizing Twitter dialogues. We chose a narrowly defined topic for this approach: cervical cancer discussions over a 6-month time period surrounding a change in Pap smear screening guidelines. Methods: We applied statistical methodologies, known as sparse machine learning algorithms, to summarize Twitter messages about cervical cancer before and after the 2012 change in Pap smear screening guidelines by the US Preventive Services Task Force (USPSTF). All messages containing the search terms ?cervical cancer,? ?Pap smear,? and ?Pap test? were analyzed during: (1) January 1?March 13, 2012, and (2) March 14?June 30, 2012. Topic modeling was used to discern the most common topics from each time period, and determine the singular value criterion for each topic. The results were then qualitatively coded from top 10 relevant topics to determine the efficiency of clustering method in grouping distinct ideas, and how the discussion differed before vs. after the change in guidelines . Results: This machine learning method was effective in grouping the relevant discussion topics about cervical cancer during the respective time periods (~20% overall irrelevant content in both time periods). Qualitative analysis determined that a significant portion of the top discussion topics in the second time period directly reflected the USPSTF guideline change (eg, ?New Screening Guidelines for Cervical Cancer?), and many topics in both time periods were addressing basic screening promotion and education (eg, ?It is Cervical Cancer Awareness Month! Click the link to see where you can receive a free or low cost Pap test.?) Conclusions: It was demonstrated that machine learning tools can be useful in cervical cancer prevention and screening discussions on Twitter. This method allowed us to prove that there is publicly available significant information about cervical cancer screening on social media sites. Moreover, we observed a direct impact of the guideline change within the Twitter messages. UR - http://publichealth.jmir.org/2016/1/e21/ UR - http://dx.doi.org/10.2196/publichealth.5308 UR - http://www.ncbi.nlm.nih.gov/pubmed/27288093 ID - info:doi/10.2196/publichealth.5308 ER - TY - JOUR AU - Majumder, S. Maimuna AU - Santillana, Mauricio AU - Mekaru, R. Sumiko AU - McGinnis, P. Denise AU - Khan, Kamran AU - Brownstein, S. John PY - 2016/06/01 TI - Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak JO - JMIR Public Health Surveill SP - e30 VL - 2 IS - 1 KW - Zika virus disease KW - digital disease surveillance KW - mathematical modeling KW - reproductive number KW - transmission dynamics N2 - Background: Approximately 40 countries in Central and South America have experienced local vector-born transmission of Zika virus, resulting in nearly 300,000 total reported cases of Zika virus disease to date. Of the cases that have sought care thus far in the region, more than 70,000 have been reported out of Colombia. Objective: In this paper, we use nontraditional digital disease surveillance data via HealthMap and Google Trends to develop near real-time estimates for the basic (R0) and observed (Robs) reproductive numbers associated with Zika virus disease in Colombia. We then validate our results against traditional health care-based disease surveillance data. Methods: Cumulative reported case counts of Zika virus disease in Colombia were acquired via the HealthMap digital disease surveillance system. Linear smoothing was conducted to adjust the shape of the HealthMap cumulative case curve using Google search data. Traditional surveillance data on Zika virus disease were obtained from weekly Instituto Nacional de Salud (INS) epidemiological bulletin publications. The Incidence Decay and Exponential Adjustment (IDEA) model was used to estimate R0 and Robs for both data sources. Results: Using the digital (smoothed HealthMap) data, we estimated a mean R0 of 2.56 (range 1.42-3.83) and a mean Robs of 1.80 (range 1.42-2.30). The traditional (INS) data yielded a mean R0 of 4.82 (range 2.34-8.32) and a mean Robs of 2.34 (range 1.60-3.31). Conclusions: Although modeling using the traditional (INS) data yielded higher R0 estimates than the digital (smoothed HealthMap) data, modeled ranges for Robs were comparable across both data sources. As a result, the narrow range of possible case projections generated by the traditional (INS) data was largely encompassed by the wider range produced by the digital (smoothed HealthMap) data. Thus, in the absence of traditional surveillance data, digital surveillance data can yield similar estimates for key transmission parameters and should be utilized in other Zika virus-affected countries to assess outbreak dynamics in near real time. UR - http://publichealth.jmir.org/2016/1/e30/ UR - http://dx.doi.org/10.2196/publichealth.5814 UR - http://www.ncbi.nlm.nih.gov/pubmed/27251981 ID - info:doi/10.2196/publichealth.5814 ER - TY - JOUR AU - Braithwaite, R. Scott AU - Giraud-Carrier, Christophe AU - West, Josh AU - Barnes, D. Michael AU - Hanson, Lee Carl PY - 2016/05/16 TI - Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality JO - JMIR Mental Health SP - e21 VL - 3 IS - 2 KW - suicide KW - social media KW - twitter KW - machine learning N2 - Background: One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective: Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods: Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results: Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions: Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. UR - http://mental.jmir.org/2016/2/e21/ UR - http://dx.doi.org/10.2196/mental.4822 UR - http://www.ncbi.nlm.nih.gov/pubmed/27185366 ID - info:doi/10.2196/mental.4822 ER - TY - JOUR AU - Foroughi, Forough AU - Lam, K-Y Alfred AU - Lim, S.C Megan AU - Saremi, Nassim AU - Ahmadvand, Alireza PY - 2016/05/04 TI - ?Googling? for Cancer: An Infodemiological Assessment of Online Search Interests in Australia, Canada, New Zealand, the United Kingdom, and the United States JO - JMIR Cancer SP - e5 VL - 2 IS - 1 KW - cancer KW - neoplasms KW - infodemiology KW - epidemiology KW - geographic mapping KW - Google Trends KW - Internet KW - consumer health information N2 - Background: The infodemiological analysis of queries from search engines to shed light on the status of various noncommunicable diseases has gained increasing popularity in recent years. Objective: The aim of the study was to determine the international perspective on the distribution of information seeking in Google regarding ?cancer? in major English-speaking countries. Methods: We used Google Trends service to assess people?s interest in searching about ?Cancer? classified as ?Disease,? from January 2004 to December 2015 in Australia, Canada, New Zealand, the United Kingdom, and the United States. Then, we evaluated top cities and their relative search volumes (SVs) and country-specific ?Top searches? and ?Rising searches.? We also evaluated the cross-country correlations of SVs for cancer, as well as rank correlations of SVs from 2010 to 2014 with the incidence of cancer in 2012 in the abovementioned countries. Results: From 2004 to 2015, the United States (relative SV [from 100]: 63), Canada (62), and Australia (61) were the top countries searching for cancer in Google, followed by New Zealand (54) and the United Kingdom (48). There was a consistent seasonality pattern in searching for cancer in the United States, Canada, Australia, and New Zealand. Baltimore (United States), St John?s (Canada), Sydney (Australia), Otaika (New Zealand), and Saint Albans (United Kingdom) had the highest search interest in their corresponding countries. ?Breast cancer? was the cancer entity that consistently appeared high in the list of top searches in all 5 countries. The ?Rising searches? were ?pancreatic cancer? in Canada and ?ovarian cancer? in New Zealand. Cross-correlation of SVs was strong between the United States, Canada, and Australia (>.70, P<.01). Conclusions: Cancer maintained its popularity as a search term for people in the United States, Canada, and Australia, comparably higher than New Zealand and the United Kingdom. The increased interest in searching for keywords related to cancer shows the possible effectiveness of awareness campaigns in increasing societal demand for health information on the Web, to be met in community-wide communication or awareness interventions. UR - http://cancer.jmir.org/2016/1/e5/ UR - http://dx.doi.org/10.2196/cancer.5212 UR - http://www.ncbi.nlm.nih.gov/pubmed/28410185 ID - info:doi/10.2196/cancer.5212 ER - TY - JOUR AU - Xu, Songhua AU - Markson, Christopher AU - Costello, L. Kaitlin AU - Xing, Y. Cathleen AU - Demissie, Kitaw AU - Llanos, AM Adana PY - 2016/04/28 TI - Leveraging Social Media to Promote Public Health Knowledge: Example of Cancer Awareness via Twitter JO - JMIR Public Health Surveill SP - e17 VL - 2 IS - 1 KW - awareness KW - breast cancer KW - colorectal cancer KW - disparities KW - lung cancer KW - prostate cancer KW - social media KW - Twitter N2 - Background: As social media becomes increasingly popular online venues for engaging in communication about public health issues, it is important to understand how users promote knowledge and awareness about specific topics. Objective: The aim of this study is to examine the frequency of discussion and differences by race and ethnicity of cancer-related topics among unique users via Twitter. Methods: Tweets were collected from April 1, 2014 through January 21, 2015 using the Twitter public streaming Application Programming Interface (API) to collect 1% of public tweets. Twitter users were classified into racial and ethnic groups using a new text mining approach applied to English-only tweets. Each ethnic group was then analyzed for frequency in cancer-related terms within user timelines, investigated for changes over time and across groups, and measured for statistical significance. Results: Observable usage patterns of the terms "cancer", "breast cancer", "prostate cancer", and "lung cancer" between Caucasian and African American groups were evident across the study period. We observed some variation in the frequency of term usage during months known to be labeled as cancer awareness months, particularly September, October, and November. Interestingly, we found that of the terms studied, "colorectal cancer" received the least Twitter attention. Conclusions: The findings of the study provide evidence that social media can serve as a very powerful and important tool in implementing and disseminating critical prevention, screening, and treatment messages to the community in real-time. The study also introduced and tested a new methodology of identifying race and ethnicity among users of the social media. Study findings highlight the potential benefits of social media as a tool in reducing racial and ethnic disparities. UR - http://publichealth.jmir.org/2016/1/e17/ UR - http://dx.doi.org/10.2196/publichealth.5205 UR - http://www.ncbi.nlm.nih.gov/pubmed/27227152 ID - info:doi/10.2196/publichealth.5205 ER - TY - JOUR AU - Ayers, W. John AU - Westmaas, Lee J. AU - Leas, C. Eric AU - Benton, Adrian AU - Chen, Yunqi AU - Dredze, Mark AU - Althouse, M. Benjamin PY - 2016/03/31 TI - Leveraging Big Data to Improve Health Awareness Campaigns: A Novel Evaluation of the Great American Smokeout JO - JMIR Public Health Surveill SP - e16 VL - 2 IS - 1 KW - big data KW - evaluation KW - health communication KW - mass media KW - social media KW - tobacco control KW - infodemiology KW - infoveillence KW - twitter KW - smoking cessation N2 - Background: Awareness campaigns are ubiquitous, but little is known about their potential effectiveness because traditional evaluations are often unfeasible. For 40 years, the ?Great American Smokeout? (GASO) has encouraged media coverage and popular engagement with smoking cessation on the third Thursday of November as the nation?s longest running awareness campaign. Objective: We proposed a novel evaluation framework for assessing awareness campaigns using the GASO as a case study by observing cessation-related news reports and Twitter postings, and cessation-related help seeking via Google, Wikipedia, and government-sponsored quitlines. Methods: Time trends (2009-2014) were analyzed using a quasi-experimental design to isolate spikes during the GASO by comparing observed outcomes on the GASO day with the simulated counterfactual had the GASO not occurred. Results: Cessation-related news typically increased by 61% (95% CI 35-87) and tweets by 13% (95% CI ?21 to 48) during the GASO compared with what was expected had the GASO not occurred. Cessation-related Google searches increased by 25% (95% CI 10-40), Wikipedia page visits by 22% (95% CI ?26 to 67), and quitline calls by 42% (95% CI 19-64). Cessation-related news media positively coincided with cessation tweets, Internet searches, and Wikipedia visits; for example, a 50% increase in news for any year predicted a 28% (95% CI ?2 to 59) increase in tweets for the same year. Increases on the day of the GASO rivaled about two-thirds of a typical New Year?s Day?the day that is assumed to see the greatest increases in cessation-related activity. In practical terms, there were about 61,000 more instances of help seeking on Google, Wikipedia, or quitlines on GASO each year than would normally be expected. Conclusions: These findings provide actionable intelligence to improve the GASO and model how to rapidly, cost-effectively, and efficiently evaluate hundreds of awareness campaigns, nearly all for the first time. UR - http://publichealth.jmir.org/2016/1/e16/ UR - http://dx.doi.org/10.2196/publichealth.5304 UR - http://www.ncbi.nlm.nih.gov/pubmed/27227151 ID - info:doi/10.2196/publichealth.5304 ER - TY - JOUR AU - Risson, Valéry AU - Saini, Deepanshu AU - Bonzani, Ian AU - Huisman, Alice AU - Olson, Melvin PY - 2016/03/17 TI - Patterns of Treatment Switching in Multiple Sclerosis Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research JO - J Med Internet Res SP - e62 VL - 18 IS - 3 KW - Internet KW - multiple sclerosis KW - outcomes assessment KW - drug switching N2 - Background: Social media analysis has rarely been applied to the study of specific questions in outcomes research. Objective: The aim was to test the applicability of social media analysis to outcomes research using automated listening combined with filtering and analysis of data by specialists. After validation, the process was applied to the study of patterns of treatment switching in multiple sclerosis (MS). Methods: A comprehensive listening and analysis process was developed that blended automated listening with filtering and analysis of data by life sciences-qualified analysts and physicians. The population was patients with MS from the United States. Data sources were Facebook, Twitter, blogs, and online forums. Sources were searched for mention of specific oral, injectable, and intravenous (IV) infusion treatments. The representativeness of the social media population was validated by comparison with community survey data and with data from three large US administrative claims databases: MarketScan, PharMetrics Plus, and Department of Defense. Results: A total of 10,260 data points were sampled for manual review: 3025 from Twitter, 3771 from Facebook, 2773 from Internet forums, and 691 from blogs. The demographics of the social media population were similar to those reported from community surveys and claims databases. Mean age was 39 (SD 11) years and 14.56% (326/2239) of the population was older than 50 years. Women, patients aged 30 to 49 years, and those diagnosed for more than 10 years were represented by more data points than other patients were. Women also accounted for a large majority (82.6%, 819/991) of reported switches. Two-fifths of switching patients had lived with their disease for more than 10 years since diagnosis. Most reported switches (55.05%, 927/1684) were from injectable to oral drugs with switches from IV therapies to orals the second largest switch (15.38%, 259/1684). Switches to oral drugs accounted for more than 80% (927/1114) of the switches away from injectable therapies. Four reasons accounted for more than 90% of all switches: severe side effects, lack of efficacy, physicians? advice, and greater ease of use. Side effects were the main reason for switches to oral or to injectable therapies and search for greater efficacy was the most important factor in switches to IV therapies. Cost of medication was the reason for switching in less than 0.5% of patients. Conclusions: Social intelligence can be applied to outcomes research with power to analyze MS patients? personal experiences of treatments and to chart the most common reasons for switching between therapies. UR - http://www.jmir.org/2016/3/e62/ UR - http://dx.doi.org/10.2196/jmir.5409 UR - http://www.ncbi.nlm.nih.gov/pubmed/26987964 ID - info:doi/10.2196/jmir.5409 ER - TY - JOUR AU - Du, Li AU - Rachul, Christen AU - Guo, Zhaochen AU - Caulfield, Timothy PY - 2016/03/09 TI - Gordie Howe?s ?Miraculous Treatment?: Case Study of Twitter Users? Reactions to a Sport Celebrity?s Stem Cell Treatment JO - JMIR Public Health Surveill SP - e8 VL - 2 IS - 1 KW - Gordie Howe KW - stem cell treatment KW - stem cell tourism KW - social network KW - Twitter KW - infodemiology KW - infoveillance N2 - Background: Former Detroit Red Wing Gordie Howe received stem cell (SC) treatment in Mexico in December 2014 for a stroke he suffered in October 2014. The news about his positive response to the SC treatment prompted discussion on social networks like Twitter. Objective: This study aims to provide information about discussions that took place on Twitter regarding Howe?s SC treatment and SC treatment in general. In particular, this study examines whether tweets portrayed a positive or negative attitude towards Howe?s SC treatment, whether or not tweets mention that the treatment is unproven, and whether the tweets mention risks associated with the SC treatment. Methods: This is an infodemiology study, harnessing big data published on the Internet for public health research and analysis of public engagement. A corpus of 2783 tweets about Howe?s SC treatment was compiled using a program that collected English-language tweets from December 19, 2014 at 00:00 to February 7, 2015 at 00:00. A content analysis of the corpus was conducted using a coding framework developed through a two-stage process. Results: 78.87% (2195/2783) of tweets mentioned improvements to Howe?s health. Only one tweet explicitly mentioned that Howe?s SC treatment was unproven, and 3 tweets warned that direct-to-consumer SC treatments lacked scientific evidence. In addition, 10.31% (287/2783) of tweets mentioned challenges with SC treatment that have been raised by scientists and researchers, and 3.70% (103/2783) of tweets either defined Howe as a ?stem cell tourist? or claimed that his treatment was part of ?stem cell tourism?. In general, 71.79% (1998/2783) of tweets portrayed a positive attitude towards Howe?s SC treatment. Conclusions: Our study found the responses to Howe?s treatment on Twitter to be overwhelmingly positive. There was far less attention paid to the lack of scientific evidence regarding the efficacy of the treatment. Unbalanced and uncritical discussion on Twitter regarding SC treatments is another example of inaccurate representations of SC treatments that may create unrealistic expectations that will facilitate the market for unproven stem cell therapies. UR - http://publichealth.jmir.org/2016/1/e8/ UR - http://dx.doi.org/10.2196/publichealth.5264 UR - http://www.ncbi.nlm.nih.gov/pubmed/27227162 ID - info:doi/10.2196/publichealth.5264 ER - TY - JOUR AU - Priest, Chad AU - Knopf, Amelia AU - Groves, Doyle AU - Carpenter, S. Janet AU - Furrey, Christopher AU - Krishnan, Anand AU - Miller, R. Wendy AU - Otte, L. Julie AU - Palakal, Mathew AU - Wiehe, Sarah AU - Wilson, Jeffrey PY - 2016/03/09 TI - Finding the Patient?s Voice Using Big Data: Analysis of Users? Health-Related Concerns in the ChaCha Question-and-Answer Service (2009?2012) JO - J Med Internet Res SP - e44 VL - 18 IS - 3 KW - social meda KW - health information seeking KW - adolescent KW - sexual health KW - patient engagement KW - ChaCha KW - big data KW - question-and-answer service KW - infodemiology KW - infoveillance N2 - Background: The development of effective health care and public health interventions requires a comprehensive understanding of the perceptions, concerns, and stated needs of health care consumers and the public at large. Big datasets from social media and question-and-answer services provide insight into the public?s health concerns and priorities without the financial, temporal, and spatial encumbrances of more traditional community-engagement methods and may prove a useful starting point for public-engagement health research (infodemiology). Objective: The objective of our study was to describe user characteristics and health-related queries of the ChaCha question-and-answer platform, and discuss how these data may be used to better understand the perceptions, concerns, and stated needs of health care consumers and the public at large. Methods: We conducted a retrospective automated textual analysis of anonymous user-generated queries submitted to ChaCha between January 2009 and November 2012. A total of 2.004 billion queries were read, of which 3.50% (70,083,796/2,004,243,249) were missing 1 or more data fields, leaving 1.934 billion complete lines of data for these analyses. Results: Males and females submitted roughly equal numbers of health queries, but content differed by sex. Questions from females predominantly focused on pregnancy, menstruation, and vaginal health. Questions from males predominantly focused on body image, drug use, and sexuality. Adolescents aged 12?19 years submitted more queries than any other age group. Their queries were largely centered on sexual and reproductive health, and pregnancy in particular. Conclusions: The private nature of the ChaCha service provided a perfect environment for maximum frankness among users, especially among adolescents posing sensitive health questions. Adolescents? sexual health queries reveal knowledge gaps with serious, lifelong consequences. The nature of questions to the service provides opportunities for rapid understanding of health concerns and may lead to development of more effective tailored interventions. UR - http://www.jmir.org/2016/3/e44/ UR - http://dx.doi.org/10.2196/jmir.5033 UR - http://www.ncbi.nlm.nih.gov/pubmed/26960745 ID - info:doi/10.2196/jmir.5033 ER - TY - JOUR AU - Hamad, O. Eradah AU - Savundranayagam, Y. Marie AU - Holmes, D. Jeffrey AU - Kinsella, Anne Elizabeth AU - Johnson, M. Andrew PY - 2016/03/08 TI - Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds JO - J Med Internet Res SP - e60 VL - 18 IS - 3 KW - health care social media KW - Twitter feeds KW - health care tweets KW - mixed methods research KW - content analysis KW - coding KW - computer-aided content analysis KW - infodemiology KW - infoveillance KW - digital disease detection N2 - Background: Twitter?s 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content. Objective: The aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content. Methods: We conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18. Results: Results suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA. Conclusions: We propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts. UR - http://www.jmir.org/2016/3/e60/ UR - http://dx.doi.org/10.2196/jmir.5391 UR - http://www.ncbi.nlm.nih.gov/pubmed/26957477 ID - info:doi/10.2196/jmir.5391 ER - TY - JOUR AU - Kim, Yoonsang AU - Huang, Jidong AU - Emery, Sherry PY - 2016/02/26 TI - Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection JO - J Med Internet Res SP - e41 VL - 18 IS - 2 KW - social media KW - precision and recall KW - sensitivity and specificity KW - search filter KW - Twitter KW - standard reporting KW - infodemiology KW - infoveillance KW - digital disease detection N2 - Background: Social media have transformed the communications landscape. People increasingly obtain news and health information online and via social media. Social media platforms also serve as novel sources of rich observational data for health research (including infodemiology, infoveillance, and digital disease detection detection). While the number of studies using social data is growing rapidly, very few of these studies transparently outline their methods for collecting, filtering, and reporting those data. Keywords and search filters applied to social data form the lens through which researchers may observe what and how people communicate about a given topic. Without a properly focused lens, research conclusions may be biased or misleading. Standards of reporting data sources and quality are needed so that data scientists and consumers of social media research can evaluate and compare methods and findings across studies. Objective: We aimed to develop and apply a framework of social media data collection and quality assessment and to propose a reporting standard, which researchers and reviewers may use to evaluate and compare the quality of social data across studies. Methods: We propose a conceptual framework consisting of three major steps in collecting social media data: develop, apply, and validate search filters. This framework is based on two criteria: retrieval precision (how much of retrieved data is relevant) and retrieval recall (how much of the relevant data is retrieved). We then discuss two conditions that estimation of retrieval precision and recall rely on?accurate human coding and full data collection?and how to calculate these statistics in cases that deviate from the two ideal conditions. We then apply the framework on a real-world example using approximately 4 million tobacco-related tweets collected from the Twitter firehose. Results: We developed and applied a search filter to retrieve e-cigarette?related tweets from the archive based on three keyword categories: devices, brands, and behavior. The search filter retrieved 82,205 e-cigarette?related tweets from the archive and was validated. Retrieval precision was calculated above 95% in all cases. Retrieval recall was 86% assuming ideal conditions (no human coding errors and full data collection), 75% when unretrieved messages could not be archived, 86% assuming no false negative errors by coders, and 93% allowing both false negative and false positive errors by human coders. Conclusions: This paper sets forth a conceptual framework for the filtering and quality evaluation of social data that addresses several common challenges and moves toward establishing a standard of reporting social data. Researchers should clearly delineate data sources, how data were accessed and collected, and the search filter building process and how retrieval precision and recall were calculated. The proposed framework can be adapted to other public social media platforms. UR - http://www.jmir.org/2016/2/e41/ UR - http://dx.doi.org/10.2196/jmir.4738 UR - http://www.ncbi.nlm.nih.gov/pubmed/26920122 ID - info:doi/10.2196/jmir.4738 ER - TY - JOUR AU - Jankowski, Wojciech AU - Hoffmann, Marcin PY - 2016/02/25 TI - Can Google Searches Predict the Popularity and Harm of Psychoactive Agents? JO - J Med Internet Res SP - e38 VL - 18 IS - 2 KW - drugs KW - narcotics KW - Internet KW - psychoactive agents KW - forecasting KW - trends N2 - Background: Predicting the popularity of and harm caused by psychoactive agents is a serious problem that would be difficult to do by a single simple method. However, because of the growing number of drugs it is very important to provide a simple and fast tool for predicting some characteristics of these substances. We were inspired by the Google Flu Trends study on the activity of the influenza virus, which showed that influenza virus activity worldwide can be monitored based on queries entered into the Google search engine. Objective: Our aim was to propose a fast method for ranking the most popular and most harmful drugs based on easily available data gathered from the Internet. Methods: We used the Google search engine to acquire data for the ranking lists. Subsequently, using the resulting list and the frequency of hits for the respective psychoactive drugs combined with the word ?harm? or ?harmful?, we estimated quickly how much harm is associated with each drug. Results: We ranked the most popular and harmful psychoactive drugs. As we conducted the research over a period of several months, we noted that the relative popularity indexes tended to change depending on when we obtained them. This suggests that the data may be useful in monitoring changes over time in the use of each of these psychoactive agents. Conclusions: Our data correlate well with the results from a multicriteria decision analysis of drug harms in the United Kingdom. We showed that Google search data can be a valuable source of information to assess the popularity of and harm caused by psychoactive agents and may help in monitoring drug use trends. UR - http://www.jmir.org/2016/2/e38/ UR - http://dx.doi.org/10.2196/jmir.4033 UR - http://www.ncbi.nlm.nih.gov/pubmed/26916984 ID - info:doi/10.2196/jmir.4033 ER - TY - JOUR AU - Lee, Donghyun AU - Lee, Hojun AU - Choi, Munkee PY - 2016/02/11 TI - Examining the Relationship Between Past Orientation and US Suicide Rates: An Analysis Using Big Data-Driven Google Search Queries JO - J Med Internet Res SP - e35 VL - 18 IS - 2 KW - attitude KW - big data KW - Google search query KW - Internet search KW - past orientation KW - suicide N2 - Background: Internet search query data reflect the attitudes of the users, using which we can measure the past orientation to commit suicide. Examinations of past orientation often highlight certain predispositions of attitude, many of which can be suicide risk factors. Objective: To investigate the relationship between past orientation and suicide rate by examining Google search queries. Methods: We measured the past orientation using Google search query data by comparing the search volumes of the past year and those of the future year, across the 50 US states and the District of Columbia during the period from 2004 to 2012. We constructed a panel dataset with independent variables as control variables; we then undertook an analysis using multiple ordinary least squares regression and methods that leverage the Akaike information criterion and the Bayesian information criterion. Results: It was found that past orientation had a positive relationship with the suicide rate (P?.001) and that it improves the goodness-of-fit of the model regarding the suicide rate. Unemployment rate (P?.001 in Models 3 and 4), Gini coefficient (P?.001), and population growth rate (P?.001) had a positive relationship with the suicide rate, whereas the gross state product (P?.001) showed a negative relationship with the suicide rate. Conclusions: We empirically identified the positive relationship between the suicide rate and past orientation, which was measured by big data-driven Google search query. UR - http://www.jmir.org/2016/2/e35/ UR - http://dx.doi.org/10.2196/jmir.4981 UR - http://www.ncbi.nlm.nih.gov/pubmed/26868917 ID - info:doi/10.2196/jmir.4981 ER - TY - JOUR AU - Radzikowski, Jacek AU - Stefanidis, Anthony AU - Jacobsen, H. Kathryn AU - Croitoru, Arie AU - Crooks, Andrew AU - Delamater, L. Paul PY - 2016/01/04 TI - The Measles Vaccination Narrative in Twitter: A Quantitative Analysis JO - JMIR Public Health Surveill SP - e1 VL - 2 IS - 1 KW - social media KW - health narrative KW - geographic characteristics KW - data analysis KW - health informatics KW - GIS (geographic information systems) N2 - Background: The emergence of social media is providing an alternative avenue for information exchange and opinion formation on health-related issues. Collective discourse in such media leads to the formation of a complex narrative, conveying public views and perceptions. Objective: This paper presents a study of Twitter narrative regarding vaccination in the aftermath of the 2015 measles outbreak, both in terms of its cyber and physical characteristics. We aimed to contribute to the analysis of the data, as well as presenting a quantitative interdisciplinary approach to analyze such open-source data in the context of health narratives. Methods: We collected 669,136 tweets referring to vaccination from February 1 to March 9, 2015. These tweets were analyzed to identify key terms, connections among such terms, retweet patterns, the structure of the narrative, and connections to the geographical space. Results: The data analysis captures the anatomy of the themes and relations that make up the discussion about vaccination in Twitter. The results highlight the higher impact of stories contributed by news organizations compared to direct tweets by health organizations in communicating health-related information. They also capture the structure of the antivaccination narrative and its terms of reference. Analysis also revealed the relationship between community engagement in Twitter and state policies regarding child vaccination. Residents of Vermont and Oregon, the two states with the highest rates of non-medical exemption from school-entry vaccines nationwide, are leading the social media discussion in terms of participation. Conclusions: The interdisciplinary study of health-related debates in social media across the cyber-physical debate nexus leads to a greater understanding of public concerns, views, and responses to health-related issues. Further coalescing such capabilities shows promise towards advancing health communication, thus supporting the design of more effective strategies that take into account the complex and evolving public views of health issues. UR - http://publichealth.jmir.org/2016/1/e1/ UR - http://dx.doi.org/10.2196/publichealth.5059 UR - http://www.ncbi.nlm.nih.gov/pubmed/27227144 ID - info:doi/10.2196/publichealth.5059 ER - TY - JOUR AU - Katsuki, Takeo AU - Mackey, Ken Tim AU - Cuomo, Raphael PY - 2015/12/16 TI - Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data JO - J Med Internet Res SP - e280 VL - 17 IS - 12 KW - social media KW - surveillance KW - prescription drug abuse KW - twitter KW - eHealth KW - illicit Internet pharmacies KW - cyberpharmacies KW - infodemiology KW - infoveillance N2 - Background: Youth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens. Objective: In order to better assess NUPM behavior online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via online pharmacies. Methods: Tweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal online access to prescription drugs using a protocol of content coding and supervised machine learning. Results: A total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72% of the generic and 12.28% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72% of the tweets with URLs included a hyperlink to an online marketing affiliate that directly linked to an illicit online pharmacy advertising the sale of Valium without a prescription. Conclusions: This study examined the association between Twitter content, NUPM behavior promotion, and online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, online health promotion activities, and public policy to combat NUPM online. UR - http://www.jmir.org/2015/12/e280/ UR - http://dx.doi.org/10.2196/jmir.5144 UR - http://www.ncbi.nlm.nih.gov/pubmed/26677966 ID - info:doi/10.2196/jmir.5144 ER - TY - JOUR AU - Albalawi, Yousef AU - Sixsmith, Jane PY - 2015/11/25 TI - Agenda Setting for Health Promotion: Exploring an Adapted Model for the Social Media Era JO - JMIR Public Health Surveill SP - e21 VL - 1 IS - 2 KW - agenda setting, health promotion, social media, Twitter, health communication, Saudi Arabia, road traffic accidents N2 - Background: The foundation of best practice in health promotion is a robust theoretical base that informs design, implementation, and evaluation of interventions that promote the public?s health. This study provides a novel contribution to health promotion through the adaptation of the agenda-setting approach in response to the contribution of social media. This exploration and proposed adaptation is derived from a study that examined the effectiveness of Twitter in influencing agenda setting among users in relation to road traffic accidents in Saudi Arabia. Objective: The proposed adaptations to the agenda-setting model to be explored reflect two levels of engagement: agenda setting within the social media sphere and the position of social media within classic agenda setting. This exploratory research aims to assess the veracity of the proposed adaptations on the basis of the hypotheses developed to test these two levels of engagement. Methods: To validate the hypotheses, we collected and analyzed data from two primary sources: Twitter activities and Saudi national newspapers. Keyword mentions served as indicators of agenda promotion; for Twitter, interactions were used to measure the process of agenda setting within the platform. The Twitter final dataset comprised 59,046 tweets and 38,066 users who contributed by tweeting, replying, or retweeting. Variables were collected for each tweet and user. In addition, 518 keyword mentions were recorded from six popular Saudi national newspapers. Results: The results showed significant ratification of the study hypotheses at both levels of engagement that framed the proposed adaptions. The results indicate that social media facilitates the contribution of individuals in influencing agendas (individual users accounted for 76.29%, 67.79%, and 96.16% of retweet impressions, total impressions, and amplification multipliers, respectively), a component missing from traditional constructions of agenda-setting models. The influence of organizations on agenda setting is also highlighted (in the data of user interactions, organizational accounts registered 17% and 14.74% as source and target of interactions, respectively). In addition, 13 striking similarities showed the relationship between newspapers and Twitter on the mentions trends line. Conclusions: The effective use of social media platforms in health promotion intervention programs requires new strategies that consider the limitations of traditional communication channels. Conducting research is vital to establishing a strong basis for modifying, designing, and developing new health promotion strategies and approaches. UR - http://publichealth.jmir.org/2015/2/e21/ UR - http://dx.doi.org/10.2196/publichealth.5014 UR - http://www.ncbi.nlm.nih.gov/pubmed/27227139 ID - info:doi/10.2196/publichealth.5014 ER - TY - JOUR AU - Wang, Ho-Wei AU - Chen, Duan-Rung AU - Yu, Hsiao-Wei AU - Chen, Ya-Mei PY - 2015/11/19 TI - Forecasting the Incidence of Dementia and Dementia-Related Outpatient Visits With Google Trends: Evidence From Taiwan JO - J Med Internet Res SP - e264 VL - 17 IS - 11 KW - dementia KW - Alzheimer?s disease KW - Google Trends KW - big data KW - incidence KW - early detection KW - self-diagnosis KW - Internet search KW - health-seeking behaviors N2 - Background: Google Trends has demonstrated the capability to both monitor and predict epidemic outbreaks. The connection between Internet searches for dementia information and dementia incidence and dementia-related outpatient visits remains unknown. Objective: This study aimed to determine whether Google Trends could provide insight into trends in dementia incidence and related outpatient visits in Taiwan. We investigated and validated the local search terms that would be the best predictors of new dementia cases and outpatient visits. We further evaluated the nowcasting (ie, forecasting the present) and forecasting effects of Google Trends search trends for new dementia cases and outpatient visits. The long-term goal is to develop a surveillance system to help early detection and interventions for dementia in Taiwan. Methods: This study collected (1) dementia data from Taiwan?s National Health Insurance Research Database and (2) local Internet search data from Google Trends, both from January 2009 to December 2011. We investigated and validated search terms that would be the best predictors of new dementia cases and outpatient visits. We then evaluated both the nowcasting and the forecasting effects of Google Trends search trends through cross-correlation analysis of the dementia incidence and outpatient visit data with the Google Trends data. Results: The search term ?dementia + Alzheimer?s disease? demonstrated a 3-month lead effect for new dementia cases and a 6-month lead effect for outpatient visits (r=.503, P=.002; r=.431, P=.009, respectively). When gender was included in the analysis, the search term ?dementia? showed 6-month predictive power for new female dementia cases (r=.520, P=.001), but only a nowcasting effect for male cases (r=.430, P=.009). The search term ?neurology? demonstrated a 3-month leading effect for new dementia cases (r=.433, P=.008), for new male dementia cases (r=.434, P=.008), and for outpatient visits (r=.613, P<.001). Conclusions: Google Trends established a plausible relationship between search terms and new dementia cases and dementia-related outpatient visits in Taiwan. This data may allow the health care system in Taiwan to prepare for upcoming outpatient and dementia screening visits. In addition, the validated search term results can be used to provide caregivers with caregiving-related health, skills, and social welfare information by embedding dementia-related search keywords in relevant online articles. UR - http://www.jmir.org/2015/11/e264/ UR - http://dx.doi.org/10.2196/jmir.4516 UR - http://www.ncbi.nlm.nih.gov/pubmed/26586281 ID - info:doi/10.2196/jmir.4516 ER - TY - JOUR AU - Kim, E. Annice AU - Hopper, Timothy AU - Simpson, Sean AU - Nonnemaker, James AU - Lieberman, J. Alicea AU - Hansen, Heather AU - Guillory, Jamie AU - Porter, Lauren PY - 2015/11/06 TI - Using Twitter Data to Gain Insights into E-cigarette Marketing and Locations of Use: An Infoveillance Study JO - J Med Internet Res SP - e251 VL - 17 IS - 11 KW - electronic cigarettes KW - social media KW - tobacco KW - marketing KW - natural language processing N2 - Background: Marketing and use of electronic cigarettes (e-cigarettes) and other electronic nicotine delivery devices have increased exponentially in recent years fueled, in part, by marketing and word-of-mouth communications via social media platforms, such as Twitter. Objective: This study examines Twitter posts about e-cigarettes between 2008 and 2013 to gain insights into (1) marketing trends for selling and promoting e-cigarettes and (2) locations where people use e-cigarettes. Methods: We used keywords to gather tweets about e-cigarettes between July 1, 2008 and February 28, 2013. A randomly selected subset of tweets was manually coded as advertising (eg, marketing, advertising, sales, promotion) or nonadvertising (eg, individual users, consumers), and classification algorithms were trained to code the remaining data into these 2 categories. A combination of manual coding and natural language processing methods was used to indicate locations where people used e-cigarettes. Additional metadata were used to generate insights about users who tweeted most frequently about e-cigarettes. Results: We identified approximately 1.7 million tweets about e-cigarettes between 2008 and 2013, with the majority of these tweets being advertising (93.43%, 1,559,508/1,669,123). Tweets about e-cigarettes increased more than tenfold between 2009 and 2010, suggesting a rapid increase in the popularity of e-cigarettes and marketing efforts. The Twitter handles tweeting most frequently about e-cigarettes were a mixture of e-cigarette brands, affiliate marketers, and resellers of e-cigarette products. Of the 471 e-cigarette tweets mentioning a specific place, most mentioned e-cigarette use in class (39.1%, 184/471) followed by home/room/bed (12.5%, 59/471), school (12.1%, 57/471), in public (8.7%, 41/471), the bathroom (5.7%, 27/471), and at work (4.5%, 21/471). Conclusions: Twitter is being used to promote e-cigarettes by different types of entities and the online marketplace is more diverse than offline product offerings and advertising strategies. E-cigarettes are also being used in public places, such as schools, underscoring the need for education and enforcement of policies banning e-cigarette use in public places. Twitter data can provide new insights on e-cigarettes to help inform future research, regulations, surveillance, and enforcement efforts. UR - http://www.jmir.org/2015/11/e251/ UR - http://dx.doi.org/10.2196/jmir.4466 UR - http://www.ncbi.nlm.nih.gov/pubmed/26545927 ID - info:doi/10.2196/jmir.4466 ER - TY - JOUR AU - Cole-Lewis, Heather AU - Pugatch, Jillian AU - Sanders, Amy AU - Varghese, Arun AU - Posada, Susana AU - Yun, Christopher AU - Schwarz, Mary AU - Augustson, Erik PY - 2015/10/27 TI - Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter JO - J Med Internet Res SP - e243 VL - 17 IS - 10 KW - social media KW - Twitter KW - e-cigarettes KW - content analysis N2 - Background: Electronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter. Objective: The objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data. Methods: A 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends. Results: The analysis revealed an increase in e-cigarette?related tweets from May 2013 through April 2014, with tweets generally being positive; 71% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65%) and individuals who are part of the e-cigarette community movement (16%). These two user groups were responsible for a majority of informational (79%) and news tweets (75%), compared to reputable news sources and foundations or organizations, which combined provided 5% of informational tweets and 12% of news tweets. Personal opinion (28%), marketing (21%), and first person e-cigarette use or intent (20%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26%), and policy and government was the second most common theme (20%), with 86% of these tweets coming from everyday people and the e-cigarette community movement combined, compared to 5% of policy and government tweets coming from government, reputable news sources, and foundations or organizations combined. Conclusions: Everyday people and the e-cigarette community are dominant forces across several genres and themes, warranting continued monitoring to understand trends and their implications regarding public opinion, e-cigarette use, and smoking cessation. Analyzing social media trends is a meaningful way to inform public health practitioners of current sentiments regarding e-cigarettes, and this study contributes a replicable methodology. UR - http://www.jmir.org/2015/10/e243/ UR - http://dx.doi.org/10.2196/jmir.4969 UR - http://www.ncbi.nlm.nih.gov/pubmed/26508089 ID - info:doi/10.2196/jmir.4969 ER - TY - JOUR AU - Cole-Lewis, Heather AU - Varghese, Arun AU - Sanders, Amy AU - Schwarz, Mary AU - Pugatch, Jillian AU - Augustson, Erik PY - 2015/08/25 TI - Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning JO - J Med Internet Res SP - e208 VL - 17 IS - 8 KW - social media KW - Twitter KW - e-cigarette KW - machine learning N2 - Background: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public?s knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Objective: Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Methods: Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Results: Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Conclusions: Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics. UR - http://www.jmir.org/2015/8/e208/ UR - http://dx.doi.org/10.2196/jmir.4392 UR - http://www.ncbi.nlm.nih.gov/pubmed/26307512 ID - info:doi/10.2196/jmir.4392 ER - TY - JOUR AU - Adrover, Cosme AU - Bodnar, Todd AU - Huang, Zhuojie AU - Telenti, Amalio AU - Salathé, Marcel PY - 2015/07/27 TI - Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter JO - JMIR Public Health Surveill SP - e7 VL - 1 IS - 2 KW - Twitter KW - HIV KW - AIDS KW - pharmacovigilance KW - mTurk KW - mechanical Turk N2 - Background: Social media platforms are increasingly seen as a source of data on a wide range of health issues. Twitter is of particular interest for public health surveillance because of its public nature. However, the very public nature of social media platforms such as Twitter may act as a barrier to public health surveillance, as people may be reluctant to publicly disclose information about their health. This is of particular concern in the context of diseases that are associated with a certain degree of stigma, such as HIV/AIDS. Objective: The objective of the study is to assess whether adverse effects of HIV drug treatment and associated sentiments can be determined using publicly available data from social media. Methods: We describe a combined approach of machine learning and crowdsourced human assessment to identify adverse effects of HIV drug treatment solely on individual reports posted publicly on Twitter. Starting from a large dataset of 40 million tweets collected over three years, we identify a very small subset (1642; 0.004%) of individual reports describing personal experiences with HIV drug treatment. Results: Despite the small size of the extracted final dataset, the summary representation of adverse effects attributed to specific drugs, or drug combinations, accurately captures well-recognized toxicities. In addition, the data allowed us to discriminate across specific drug compounds, to identify preferred drugs over time, and to capture novel events such as the availability of preexposure prophylaxis. Conclusions: The effect of limited data sharing due to the public nature of the data can be partially offset by the large number of people sharing data in the first place, an observation that may play a key role in digital epidemiology in general. UR - http://publichealth.jmir.org/2015/2/e7/ UR - http://dx.doi.org/10.2196/publichealth.4488 UR - http://www.ncbi.nlm.nih.gov/pubmed/27227141 ID - info:doi/10.2196/publichealth.4488 ER - TY - JOUR AU - Koschack, Janka AU - Weibezahl, Lara AU - Friede, Tim AU - Himmel, Wolfgang AU - Makedonski, Philip AU - Grabowski, Jens PY - 2015/07/01 TI - Scientific Versus Experiential Evidence: Discourse Analysis of the Chronic Cerebrospinal Venous Insufficiency Debate in a Multiple Sclerosis Forum JO - J Med Internet Res SP - e159 VL - 17 IS - 7 KW - multiple sclerosis KW - venous insufficiency KW - Internet KW - social media KW - cognitive dissonance KW - qualitative research N2 - Background: The vascular hypothesis of multiple sclerosis (MS), called chronic cerebrospinal venous insufficiency (CCSVI), and its treatment (known as liberation therapy) was immediately rejected by experts but enthusiastically gripped by patients who shared their experiences with other patients worldwide by use of social media, such as patient online forums. Contradictions between scientific information and lay experiences may be a source of distress for MS patients, but we do not know how patients perceive and deal with these contradictions. Objective: We aimed to understand whether scientific and experiential knowledge were experienced as contradictory in MS patient online forums and, if so, how these contradictions were resolved and how patients tried to reconcile the CCSVI debate with their own illness history and experience. Methods: By using critical discourse analysis, we studied CCSVI-related posts in the patient online forum of the German MS Society in a chronological order from the first post mentioning CCSVI to the time point when saturation was reached. For that time period, a total of 117 CCSVI-related threads containing 1907 posts were identified. We analyzed the interaction and communication practices of and between individuals, looked for the relation between concrete subtopics to identify more abstract discourse strands, and tried to reveal discourse positions explaining how users took part in the CCSVI discussion. Results: There was an emotionally charged debate about CCSVI which could be generalized to 2 discourse strands: (1) the ?downfall of the professional knowledge providers? and (2) the ?rise of the nonprofessional treasure trove of experience.? The discourse strands indicated that the discussion moved away from the question whether scientific or experiential knowledge had more evidentiary value. Rather, the question whom to trust (ie, scientists, fellow sufferers, or no one at all) was of fundamental significance. Four discourse positions could be identified by arranging them into the dimensions ?trust in evidence-based knowledge,? ?trust in experience-based knowledge,? and ?subjectivity? (ie, the emotional character of contributions manifested by the use of popular rhetoric that seemed to mask a deep personal involvement). Conclusions: By critical discourse analysis of the CCSVI discussion in a patient online forum, we reconstruct a lay discourse about the evidentiary value of knowledge. We detected evidence criteria in this lay discourse that are different from those in the expert discourse. But we should be cautious to interpret this dissociation as a sign of an intellectual incapability to understand scientific evidence or a naďve trust in experiential knowledge. Instead, it might be an indication of cognitive dissonance reduction to protect oneself against contradictory information. UR - http://www.jmir.org/2015/7/e159/ UR - http://dx.doi.org/10.2196/jmir.4103 UR - http://www.ncbi.nlm.nih.gov/pubmed/26133525 ID - info:doi/10.2196/jmir.4103 ER - TY - JOUR AU - Weeg, Christopher AU - Schwartz, Andrew H. AU - Hill, Shawndra AU - Merchant, M. Raina AU - Arango, Catalina AU - Ungar, Lyle PY - 2015/06/26 TI - Using Twitter to Measure Public Discussion of Diseases: A Case Study JO - JMIR Public Health Surveill SP - e6 VL - 1 IS - 1 KW - bias KW - data mining KW - demographics KW - disease KW - epidemiology KW - prevalence KW - public health KW - social media N2 - Background: Twitter is increasingly used to estimate disease prevalence, but such measurements can be biased, due to both biased sampling and inherent ambiguity of natural language. Objective: We characterized the extent of these biases and how they vary with disease. Methods: We correlated self-reported prevalence rates for 22 diseases from Experian?s Simmons National Consumer Study (n=12,305) with the number of times these diseases were mentioned on Twitter during the same period (2012). We also identified and corrected for two types of bias present in Twitter data: (1) demographic variance between US Twitter users and the general US population; and (2) natural language ambiguity, which creates the possibility that mention of a disease name may not actually refer to the disease (eg, ?heart attack? on Twitter often does not refer to myocardial infarction). We measured the correlation between disease prevalence and Twitter disease mentions both with and without bias correction. This allowed us to quantify each disease?s overrepresentation or underrepresentation on Twitter, relative to its prevalence. Results: Our sample included 80,680,449 tweets. Adjusting disease prevalence to correct for Twitter demographics more than doubles the correlation between Twitter disease mentions and disease prevalence in the general population (from .113 to .258, P <.001). In addition, diseases varied widely in how often mentions of their names on Twitter actually referred to the diseases, from 14.89% (3827/25,704) of instances (for stroke) to 99.92% (5044/5048) of instances (for arthritis). Applying ambiguity correction to our Twitter corpus achieves a correlation between disease mentions and prevalence of .208 ( P <.001). Simultaneously applying correction for both demographics and ambiguity more than triples the baseline correlation to .366 ( P <.001). Compared with prevalence rates, cancer appeared most overrepresented in Twitter, whereas high cholesterol appeared most underrepresented. Conclusions: Twitter is a potentially useful tool to measure public interest in and concerns about different diseases, but when comparing diseases, improvements can be made by adjusting for population demographics and word ambiguity. UR - http://publichealth.jmir.org/2015/1/e6/ UR - http://dx.doi.org/10.2196/publichealth.3953 UR - http://www.ncbi.nlm.nih.gov/pubmed/26925459 ID - info:doi/10.2196/publichealth.3953 ER - TY - JOUR AU - Kendra, Lynn Rachel AU - Karki, Suman AU - Eickholt, Lee Jesse AU - Gandy, Lisa PY - 2015/06/19 TI - Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture? JO - J Med Internet Res SP - e154 VL - 17 IS - 6 KW - Twitter messaging KW - social media KW - Internet KW - Web mining KW - semi-supervised learning KW - neural network N2 - Background: User content posted through Twitter has been used for biosurveillance, to characterize public perception of health-related topics, and as a means of distributing information to the general public. Most of the existing work surrounding Twitter and health care has shown Twitter to be an effective medium for these problems but more could be done to provide finer and more efficient access to all pertinent data. Given the diversity of user-generated content, small samples or summary presentations of the data arguably omit a large part of the virtual discussion taking place in the Twittersphere. Still, managing, processing, and querying large amounts of Twitter data is not a trivial task. This work describes tools and techniques capable of handling larger sets of Twitter data and demonstrates their use with the issue of antibiotics. Objective: This work has two principle objectives: (1) to provide an open-source means to efficiently explore all collected tweets and query health-related topics on Twitter, specifically, questions such as what users are saying and how messages are spread, and (2) to characterize the larger discourse taking place on Twitter with respect to antibiotics. Methods: Open-source software suites Hadoop, Flume, and Hive were used to collect and query a large number of Twitter posts. To classify tweets by topic, a deep network classifier was trained using a limited number of manually classified tweets. The particular machine learning approach used also allowed the use of a large number of unclassified tweets to increase performance. Results: Query-based analysis of the collected tweets revealed that a large number of users contributed to the online discussion and that a frequent topic mentioned was resistance. A number of prominent events related to antibiotics led to a number of spikes in activity but these were short in duration. The category-based classifier developed was able to correctly classify 70% of manually labeled tweets (using a 10-fold cross validation procedure and 9 classes). The classifier also performed well when evaluated on a per category basis. Conclusions: Using existing tools such as Hive, Flume, Hadoop, and machine learning techniques, it is possible to construct tools and workflows to collect and query large amounts of Twitter data to characterize the larger discussion taking place on Twitter with respect to a particular health-related topic. Furthermore, using newer machine learning techniques and a limited number of manually labeled tweets, an entire body of collected tweets can be classified to indicate what topics are driving the virtual, online discussion. The resulting classifier can also be used to efficiently explore collected tweets by category and search for messages of interest or exemplary content. UR - http://www.jmir.org/2015/6/e154/ UR - http://dx.doi.org/10.2196/jmir.4220 UR - http://www.ncbi.nlm.nih.gov/pubmed/26091775 ID - info:doi/10.2196/jmir.4220 ER - TY - JOUR AU - Dunn, G. Adam AU - Leask, Julie AU - Zhou, Xujuan AU - Mandl, D. Kenneth AU - Coiera, Enrico PY - 2015/06/10 TI - Associations Between Exposure to and Expression of Negative Opinions About Human Papillomavirus Vaccines on Social Media: An Observational Study JO - J Med Internet Res SP - e144 VL - 17 IS - 6 KW - HPV vaccines KW - Twitter messaging KW - social media KW - public health surveillance KW - social networks N2 - Background: Groups and individuals that seek to negatively influence public opinion about the safety and value of vaccination are active in online and social media and may influence decision making within some communities. Objective: We sought to measure whether exposure to negative opinions about human papillomavirus (HPV) vaccines in Twitter communities is associated with the subsequent expression of negative opinions by explicitly measuring potential information exposure over the social structure of Twitter communities. Methods: We hypothesized that prior exposure to opinions rejecting the safety or value of HPV vaccines would be associated with an increased risk of posting similar opinions and tested this hypothesis by analyzing temporal sequences of messages posted on Twitter (tweets). The study design was a retrospective analysis of tweets related to HPV vaccines and the social connections between users. Between October 2013 and April 2014, we collected 83,551 English-language tweets that included terms related to HPV vaccines and the 957,865 social connections among 30,621 users posting or reposting the tweets. Tweets were classified as expressing negative or neutral/positive opinions using a machine learning classifier previously trained on a manually labeled sample. Results: During the 6-month period, 25.13% (20,994/83,551) of tweets were classified as negative; among the 30,621 users that tweeted about HPV vaccines, 9046 (29.54%) were exposed to a majority of negative tweets. The likelihood of a user posting a negative tweet after exposure to a majority of negative opinions was 37.78% (2780/7361) compared to 10.92% (1234/11,296) for users who were exposed to a majority of positive and neutral tweets corresponding to a relative risk of 3.46 (95% CI 3.25-3.67, P<.001). Conclusions: The heterogeneous community structure on Twitter appears to skew the information to which users are exposed in relation to HPV vaccines. We found that among users that tweeted about HPV vaccines, those who were more often exposed to negative opinions were more likely to subsequently post negative opinions. Although this research may be useful for identifying individuals and groups currently at risk of disproportionate exposure to misinformation about HPV vaccines, there is a clear need for studies capable of determining the factors that affect the formation and adoption of beliefs about public health interventions. UR - http://www.jmir.org/2015/6/e144/ UR - http://dx.doi.org/10.2196/jmir.4343 UR - http://www.ncbi.nlm.nih.gov/pubmed/26063290 ID - info:doi/10.2196/jmir.4343 ER - TY - JOUR AU - McIver, J. David AU - Hawkins, B. Jared AU - Chunara, Rumi AU - Chatterjee, K. Arnaub AU - Bhandari, Aman AU - Fitzgerald, P. Timothy AU - Jain, H. Sachin AU - Brownstein, S. John PY - 2015/06/08 TI - Characterizing Sleep Issues Using Twitter JO - J Med Internet Res SP - e140 VL - 17 IS - 6 KW - sleep issues KW - social media KW - insomnia KW - novel methods KW - sentiment KW - depression N2 - Background: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. Methods: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, ?can?t sleep?, Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. Results: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues. Conclusions: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered. UR - http://www.jmir.org/2015/6/e140/ UR - http://dx.doi.org/10.2196/jmir.4476 UR - http://www.ncbi.nlm.nih.gov/pubmed/26054530 ID - info:doi/10.2196/jmir.4476 ER - TY - JOUR AU - Yin, Zhijun AU - Fabbri, Daniel AU - Rosenbloom, Trent S. AU - Malin, Bradley PY - 2015/06/05 TI - A Scalable Framework to Detect Personal Health Mentions on Twitter JO - J Med Internet Res SP - e138 VL - 17 IS - 6 KW - consumer health KW - information retrieval KW - machine learning KW - social media KW - twitter KW - infodemiology N2 - Background: Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual?s health. Objective: The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed. Methods: We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status. Results: Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50% of the time for 11 out of 34 (33%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people?s health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69% (69/100) of the insomnia tweets disclosed the author?s status, while only 1% (1/100) disclosed another person?s status. By contrast, 1% (1/100) of the Down syndrome tweets disclosed the author?s status, while 21% (21/100) disclosed another person?s status. Conclusions: It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes. UR - http://www.jmir.org/2015/6/e138/ UR - http://dx.doi.org/10.2196/jmir.4305 UR - http://www.ncbi.nlm.nih.gov/pubmed/26048075 ID - info:doi/10.2196/jmir.4305 ER - TY - JOUR AU - Broniatowski, Andre David AU - Dredze, Mark AU - Paul, J. Michael AU - Dugas, Andrea PY - 2015/05/29 TI - Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study JO - JMIR Public Health Surveill SP - e5 VL - 1 IS - 1 KW - Web mining KW - social computing KW - time series analysis N2 - Background: Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. These individuals rely on influenza infection rate information to make important decisions during the course of an influenza season driving vaccination campaigns, clinical guidelines, and medical staffing. Web and social media data sources have emerged as attractive alternatives to supplement existing practices. While traditional surveillance methods take 1-2 weeks, and significant labor, to produce an infection estimate in each locale, web and social media data are available in near real-time for a broad range of locations. Objective: The objective of this study was to analyze the efficacy of flu surveillance from combining data from the websites Google Flu Trends and HealthTweets at the local level. We considered both emergency department influenza-like illness cases and laboratory-confirmed influenza cases for a single hospital in the City of Baltimore. Methods: This was a retrospective observational study comparing estimates of influenza activity of Google Flu Trends and Twitter to actual counts of individuals with laboratory-confirmed influenza, and counts of individuals presenting to the emergency department with influenza-like illness cases. Data were collected from November 20, 2011 through March 16, 2014. Each parameter was evaluated on the municipal, regional, and national scale. We examined the utility of social media data for tracking actual influenza infection at the municipal, state, and national levels. Specifically, we compared the efficacy of Twitter and Google Flu Trends data. Results: We found that municipal-level Twitter data was more effective than regional and national data when tracking actual influenza infection rates in a Baltimore inner-city hospital. When combined, national-level Twitter and Google Flu Trends data outperformed each data source individually. In addition, influenza-like illness data at all levels of geographic granularity were best predicted by national Google Flu Trends data. Conclusions: In order to overcome sensitivity to transient events, such as the news cycle, the best-fitting Google Flu Trends model relies on a 4-week moving average, suggesting that it may also be sacrificing sensitivity to transient fluctuations in influenza infection to achieve predictive power. Implications for influenza forecasting are discussed in this report. UR - http://publichealth.jmir.org/2015/1/e5/ UR - http://dx.doi.org/10.2196/publichealth.4472 UR - http://www.ncbi.nlm.nih.gov/pubmed/27014744 ID - info:doi/10.2196/publichealth.4472 ER - TY - JOUR AU - Mollema, Liesbeth AU - Harmsen, Anhai Irene AU - Broekhuizen, Emma AU - Clijnk, Rutger AU - De Melker, Hester AU - Paulussen, Theo AU - Kok, Gerjo AU - Ruiter, Robert AU - Das, Enny PY - 2015/05/26 TI - Disease Detection or Public Opinion Reflection? Content Analysis of Tweets, Other Social Media, and Online Newspapers During the Measles Outbreak in the Netherlands in 2013 JO - J Med Internet Res SP - e128 VL - 17 IS - 5 KW - Internet KW - Web 2.0 KW - measles KW - infectious disease outbreak KW - Netherlands KW - vaccination N2 - Background: In May 2013, a measles outbreak began in the Netherlands among Orthodox Protestants who often refuse vaccination for religious reasons. Objective: Our aim was to compare the number of messages expressed on Twitter and other social media during the measles outbreak with the number of online news articles and the number of reported measles cases to answer the question if and when social media reflect public opinion patterns versus disease patterns. Methods: We analyzed measles-related tweets, other social media messages, and online newspaper articles over a 7-month period (April 15 to November 11, 2013) with regard to topic and sentiment. Thematic analysis was used to structure and analyze the topics. Results: There was a stronger correlation between the weekly number of social media messages and the weekly number of online news articles (P<.001 for both tweets and other social media messages) than between the weekly number of social media messages and the weekly number of reported measles cases (P=.003 and P=.048 for tweets and other social media messages, respectively), especially after the summer break. All data sources showed 3 large peaks, possibly triggered by announcements about the measles outbreak by the Dutch National Institute for Public Health and the Environment and statements made by well-known politicians. Most messages informed the public about the measles outbreak (ie, about the number of measles cases) (93/165, 56.4%) followed by messages about preventive measures taken to control the measles spread (47/132, 35.6%). The leading opinion expressed was frustration regarding people who do not vaccinate because of religious reasons (42/88, 48%). Conclusions: The monitoring of online (social) media might be useful for improving communication policies aiming to preserve vaccination acceptability among the general public. Data extracted from online (social) media provide insight into the opinions that are at a certain moment salient among the public, which enables public health institutes to respond immediately and appropriately to those public concerns. More research is required to develop an automatic coding system that captures content and user?s characteristics that are most relevant to the diseases within the National Immunization Program and related public health events and can inform official responses. UR - http://www.jmir.org/2015/5/e128/ UR - http://dx.doi.org/10.2196/jmir.3863 UR - http://www.ncbi.nlm.nih.gov/pubmed/26013683 ID - info:doi/10.2196/jmir.3863 ER - TY - JOUR AU - Krueger, A. Evan AU - Young, D. Sean PY - 2015/05/07 TI - Twitter: A Novel Tool for Studying the Health and Social Needs of Transgender Communities JO - JMIR Mental Health SP - e16 VL - 2 IS - 2 KW - Twitter KW - social media KW - transgender KW - health N2 - Background: Limited research has examined the health and social needs of transgender and gender nonconforming populations. Due to high levels of stigma, transgender individuals may avoid disclosing their identities to researchers, hindering this type of work. Further, researchers have traditionally relied on clinic-based sampling methods, which may mask the true heterogeneity of transgender and gender nonconforming communities. Online social networking websites present a novel platform for studying this diverse, difficult-to-reach population. Objective: The objective of this study was to attempt to examine the perceived health and social needs of transgender and gender nonconforming communities by examining messages posted to the popular microblogging platform, Twitter. Methods: Tweets were collected from 13 transgender-related hashtags on July 11, 2014. They were read and coded according to general themes addressed, and a content analysis was performed. Qualitative and descriptive statistics are presented. Results: There were 1135 tweets that were collected in total. Both ?positive? and ?negative? events were discussed, in both personal and social contexts. Violence, discrimination, suicide, and sexual risk behavior were discussed. There were 34.36% (390/1135) of tweets that addressed transgender-relevant current events, and 60.79% (690/1135) provided a link to a relevant news article or resource. Conclusions: This study found that transgender individuals and allies use Twitter to discuss health and social needs relevant to the population. Real-time social media sites like Twitter can be used to study issues relevant to transgender communities. UR - http://mental.jmir.org/2015/2/e16/ UR - http://dx.doi.org/10.2196/mental.4113 UR - http://www.ncbi.nlm.nih.gov/pubmed/26082941 ID - info:doi/10.2196/mental.4113 ER - TY - JOUR AU - Gittelman, Steven AU - Lange, Victor AU - Gotway Crawford, A. Carol AU - Okoro, A. Catherine AU - Lieb, Eugene AU - Dhingra, S. Satvinder AU - Trimarchi, Elaine PY - 2015/04/20 TI - A New Source of Data for Public Health Surveillance: Facebook Likes JO - J Med Internet Res SP - e98 VL - 17 IS - 4 KW - big data KW - social networks KW - surveillance KW - chronic illness N2 - Background: Investigation into personal health has become focused on conditions at an increasingly local level, while response rates have declined and complicated the process of collecting data at an individual level. Simultaneously, social media data have exploded in availability and have been shown to correlate with the prevalence of certain health conditions. Objective: Facebook likes may be a source of digital data that can complement traditional public health surveillance systems and provide data at a local level. We explored the use of Facebook likes as potential predictors of health outcomes and their behavioral determinants. Methods: We performed principal components and regression analyses to examine the predictive qualities of Facebook likes with regard to mortality, diseases, and lifestyle behaviors in 214 counties across the United States and 61 of 67 counties in Florida. These results were compared with those obtainable from a demographic model. Health data were obtained from both the 2010 and 2011 Behavioral Risk Factor Surveillance System (BRFSS) and mortality data were obtained from the National Vital Statistics System. Results: Facebook likes added significant value in predicting most examined health outcomes and behaviors even when controlling for age, race, and socioeconomic status, with model fit improvements (adjusted R2) of an average of 58% across models for 13 different health-related metrics over basic sociodemographic models. Small area data were not available in sufficient abundance to test the accuracy of the model in estimating health conditions in less populated markets, but initial analysis using data from Florida showed a strong model fit for obesity data (adjusted R2=.77). Conclusions: Facebook likes provide estimates for examined health outcomes and health behaviors that are comparable to those obtained from the BRFSS. Online sources may provide more reliable, timely, and cost-effective county-level data than that obtainable from traditional public health surveillance systems as well as serve as an adjunct to those systems. UR - http://www.jmir.org/2015/4/e98/ UR - http://dx.doi.org/10.2196/jmir.3970 UR - http://www.ncbi.nlm.nih.gov/pubmed/25895907 ID - info:doi/10.2196/jmir.3970 ER - TY - JOUR AU - Tighe, J. Patrick AU - Goldsmith, C. Ryan AU - Gravenstein, Michael AU - Bernard, Russell H. AU - Fillingim, B. Roger PY - 2015/04/02 TI - The Painful Tweet: Text, Sentiment, and Community Structure Analyses of Tweets Pertaining to Pain JO - J Med Internet Res SP - e84 VL - 17 IS - 4 KW - Twitter messaging KW - emotions KW - text mining KW - social networks N2 - Background: Despite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media. Objective: The aim was to examine the type, context, and dissemination of pain-related tweets. Methods: We used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks. Results: The most common terms published in conjunction with the term ?pain? included feel (n=1504), don?t (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13% in Manila to 56% in Los Angeles, CA, with a median of 29% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24% at 1600 to 38% at 2400, with a median of 32%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25). Conclusions: Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters? perceptions of pain and how such perceptions may affect therapies for pain. UR - http://www.jmir.org/2015/4/e84/ UR - http://dx.doi.org/10.2196/jmir.3769 UR - http://www.ncbi.nlm.nih.gov/pubmed/25843553 ID - info:doi/10.2196/jmir.3769 ER - TY - JOUR AU - Mahoney, Meghan L. AU - Tang, Tang AU - Ji, Kai AU - Ulrich-Schad, Jessica PY - 2015/03/18 TI - The Digital Distribution of Public Health News Surrounding the Human Papillomavirus Vaccination: A Longitudinal Infodemiology Study JO - JMIR Public Health Surveill SP - e2 VL - 1 IS - 1 KW - new media KW - public health dissemination KW - health communication KW - social media KW - HPV vaccination KW - infodemiology KW - infoveillance N2 - Background: New media changes the dissemination of public health information and misinformation. During a guest appearance on the Today Show, US Representative Michele Bachmann claimed that human papillomavirus (HPV) vaccines could cause ?mental retardation?. Objective: The purpose of this study is to explore how new media influences the type of public health information users access, as well as the impact to these platforms after a major controversy. Specifically, this study aims to examine the similarities and differences in the dissemination of news articles related to the HPV vaccination between Google News and Twitter, as well as how the content of news changed after Michele Bachmann?s controversial comment. Methods: This study used a purposive sampling to draw the first 100 news articles that appeared on Google News and the first 100 articles that appeared on Twitter from August 1-October 31, 2011. Article tone, source, topics, concerns, references, publication date, and interactive features were coded. The intercoder reliability had a total agreement of .90. Results: Results indicate that 44.0% of the articles (88/200) about the HPV vaccination had a positive tone, 32.5% (65/200) maintained a neutral tone, while 23.5% (47/200) presented a negative tone. Protection against diseases 82.0% (164/200), vaccine eligibility for females 75.5% (151/200), and side effects 59.0% (118/200) were the top three topics covered by these articles. Google News and Twitter articles significantly differed in article tone, source, topics, concerns covered, types of sources referenced in the article, and uses of interactive features. Most notably, topic focus changed from public health information towards political conversation after Bachmann?s comment. Before the comment, the HPV vaccine news talked more often about vaccine dosing (P<.001), duration (P=.005), vaccine eligibility for females (P=.03), and protection against diseases (P=.04) than did the later pieces. After the controversy, the news topic shifted towards politics (P=.01) and talked more about HPV vaccine eligibility for males (P=.01). Conclusions: This longitudinal infodemiology study suggests that new media influences public health communication, knowledge transaction, and poses potential problems in the amount of misinformation disseminated during public health campaigns. In addition, the study calls for more research to adopt an infodemiology approach to explore relationships between online information supply and public health decisions. UR - http://publichealth.jmir.org/2015/1/e2/ UR - http://dx.doi.org/10.2196/publichealth.3310 UR - http://www.ncbi.nlm.nih.gov/pubmed/27227125 ID - info:doi/10.2196/publichealth.3310 ER - TY - JOUR AU - Hanson, L. Carl AU - Burton, H. Scott AU - Giraud-Carrier, Christophe AU - West, H. Josh AU - Barnes, D. Michael AU - Hansen, Bret PY - 2013/04/17 TI - Tweaking and Tweeting: Exploring Twitter for Nonmedical Use of a Psychostimulant Drug (Adderall) Among College Students JO - J Med Internet Res SP - e62 VL - 15 IS - 4 KW - Adderall KW - Twitter KW - social media KW - prescription drug abuse N2 - Background: Adderall is the most commonly abused prescription stimulant among college students. Social media provides a real-time avenue for monitoring public health, specifically for this population. Objective: This study explores discussion of Adderall on Twitter to identify variations in volume around college exam periods, differences across sets of colleges and universities, and commonly mentioned side effects and co-ingested substances. Methods: Public-facing Twitter status messages containing the term ?Adderall? were monitored from November 2011 to May 2012. Tweets were examined for mention of side effects and other commonly abused substances. Tweets from likely students containing GPS data were identified with clusters of nearby colleges and universities for regional comparison. Results: 213,633 tweets from 132,099 unique user accounts mentioned ?Adderall.? The number of Adderall tweets peaked during traditional college and university final exam periods. Rates of Adderall tweeters were highest among college and university clusters in the northeast and south regions of the United States. 27,473 (12.9%) mentioned an alternative motive (eg, study aid) in the same tweet. The most common substances mentioned with Adderall were alcohol (4.8%) and stimulants (4.7%), and the most common side effects were sleep deprivation (5.0%) and loss of appetite (2.6%). Conclusions: Twitter posts confirm the use of Adderall as a study aid among college students. Adderall discussions through social media such as Twitter may contribute to normative behavior regarding its abuse. UR - http://www.jmir.org/2013/4/e62/ UR - http://dx.doi.org/10.2196/jmir.2503 UR - http://www.ncbi.nlm.nih.gov/pubmed/23594933 ID - info:doi/10.2196/jmir.2503 ER -