Published on 24.10.16 in Vol 2, No 2 (2016): Jul-Dec
Works citing "“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets"
According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.6327):
(note that this is only a small subset of citations)
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Crocamo C, Viviani M, Bartoli F, Carrà G, Pasi G. Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis. International Journal of Environmental Research and Public Health 2020;17(5):1510
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Guiñazú MF, Cortés V, Ibáñez CF, Velásquez JD. Employing online social networks in precision-medicine approach using information fusion predictive model to improve substance use surveillance: A lesson from Twitter and marijuana consumption. Information Fusion 2020;55:150
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Adams N, Artigiani EE, Wish ED. Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List. Journal of Drug Issues 2019;49(3):477
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Yao H, Rashidian S, Dong X, Duanmu H, Rosenthal RN, Wang F. Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach. Journal of Medical Internet Research 2020;22(11):e15293
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Ashford RD, Curtis BL. Commentary on Cohn and Colleagues: Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality (ACER, 2019). Alcoholism: Clinical and Experimental Research 2019;43(3):401
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Gohil S, Vuik S, Darzi A. Sentiment Analysis of Health Care Tweets: Review of the Methods Used. JMIR Public Health and Surveillance 2018;4(2):e43
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Metwally O, Blumberg S, Ladabaum U, Sinha SR. Using Social Media to Characterize Public Sentiment Toward Medical Interventions Commonly Used for Cancer Screening: An Observational Study. Journal of Medical Internet Research 2017;19(6):e200
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Young SD, Padwa H, Bonar EE. Social Big Data as a Tool for Understanding and Predicting the Impact of Cannabis Legalization. Frontiers in Public Health 2019;7
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Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023
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Mamidi R, Miller M, Banerjee T, Romine W, Sheth A. Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic. JMIR Public Health and Surveillance 2019;5(2):e11036
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Yin Z, Sulieman LM, Malin BA. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561
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Taylor J, Pagliari C. Mining social media data: How are research sponsors and researchers addressing the ethical challenges?. Research Ethics 2018;14(2):1
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O'Connor K, Sarker A, Perrone J, Gonzalez Hernandez G. Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines. Journal of Medical Internet Research 2020;22(2):e15861
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van Draanen J, Tao H, Gupta S, Liu S. Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study. JMIR Public Health and Surveillance 2020;6(4):e18540
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Lamy FR, Daniulaityte R, Zatreh M, Nahhas RW, Sheth A, Martins SS, Boyer EW, Carlson RG. "You got to love rosin: Solventless dabs, pure, clean, natural medicine." Exploring Twitter data on emerging trends in Rosin Tech marijuana concentrates. Drug and Alcohol Dependence 2018;183:248
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Kim SJ, Marsch LA, Hancock JT, Das AK. Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data. Journal of Medical Internet Research 2017;19(10):e353
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Daniulaityte R, Lamy FR, Barratt M, Nahhas RW, Martins SS, Boyer EW, Sheth A, Carlson RG. Characterizing marijuana concentrate users: A web-based survey. Drug and Alcohol Dependence 2017;178:399
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He L, Yin T, Hu Z, Chen Y, Hanauer DA, Zheng K. Developing a standardized protocol for computational sentiment analysis research using health-related social media data. Journal of the American Medical Informatics Association 2021;28(6):1125
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Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health and Surveillance 2020;6(4):e21660
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Pavan Kumar C, Dhinesh Babu L. Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks. Journal of Intelligent & Fuzzy Systems 2021;40(6):11749
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Oyebode O, Lomotey R, Orji R. “I Tried to Breastfeed but…”: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis. IEEE Access 2021;9:61074
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Tsai MH, Wang Y. Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19. International Journal of Environmental Research and Public Health 2021;18(12):6272
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Obiedat R, Al-Qaisi L, Qaddoura R, Harfoushi O, Al-Zoubi AM. An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era. Symmetry 2021;13(12):2287
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Najafizada M, Rahman A, Donnan J, Dong Z, Bishop L. Analyzing sentiments and themes on cannabis in Canada using 2018 to 2020 Twitter data. Journal of Cannabis Research 2022;4(1)
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Scaboro S, Portelli B, Chersoni E, Santus E, Serra G. Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation. Experimental Biology and Medicine 2022;247(22):2003
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Black J, Margolin ZR, Bau G, Olson R, Iwanicki JL, Dart RC. Web-Based Discussion and Illicit Street Sales of Tapentadol and Oxycodone in Australia: Epidemiological Surveillance Study. JMIR Public Health and Surveillance 2021;7(12):e29187
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Lokala U, Lamy F, Daniulaityte R, Gaur M, Gyrard A, Thirunarayan K, Kursuncu U, Sheth A. Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study. JMIR Public Health and Surveillance 2022;8(12):e24938
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Arias F, Zambrano Nunez M, Guerra-Adames A, Tejedor-Flores N, Vargas-Lombardo M. Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics. IEEE Access 2022;10:74850
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Rahim AIA, Ibrahim MI, Chua S, Musa KI. Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier. Healthcare 2021;9(12):1679
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Boukobza A, Burgun A, Roudier B, Tsopra R. Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set. JMIR Medical Informatics 2022;10(5):e34306
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A. Rahim AI, Ibrahim MI, Musa KI, Chua S, Yaacob NM. Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews. International Journal of Environmental Research and Public Health 2021;18(18):9912
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Khademi Habibabadi S, Hallinan C, Bonomo Y, Conway M. Consumer-Generated Discourse on Cannabis as a Medicine: Scoping Review of Techniques. Journal of Medical Internet Research 2022;24(11):e35974
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Tang JE, Arvind V, Dominy C, White CA, Cho SK, Kim JS. How Are Patients Reviewing Spine Surgeons Online? A Sentiment Analysis of Physician Review Website Written Comments. Global Spine Journal 2023;13(8):2107
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He L, Yin T, Zheng K. They May Not Work! An evaluation of eleven sentiment analysis tools on seven social media datasets. Journal of Biomedical Informatics 2022;132:104142
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Rahim AIA, Ibrahim MI, Musa KI, Chua S, Yaacob NM. Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook. Healthcare 2021;9(10):1369
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Walker AL, LoParco C, Rossheim ME, Livingston MD. #Delta8: a retailer-driven increase in Delta-8 THC discussions on Twitter from 2020 to 2021. The American Journal of Drug and Alcohol Abuse 2023;49(4):491
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. Reply to critique of the paper, ‘investigating the attitude and perspectives of Indian citizens toward COVID-19 vaccines: A text analytics study’. International Journal of Disaster Risk Reduction 2024;100:104105
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SV P, Gajjar P. Critique of the paper, ‘Investigating the attitude and perspectives of Indian citizens toward COVID-19 vaccines: A text analytics study’. International Journal of Disaster Risk Reduction 2024;100:104104
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Lossio-Ventura JA, Weger R, Lee AY, Guinee EP, Chung J, Atlas L, Linos E, Pereira F. A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data. JMIR Mental Health 2024;11:e50150
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Luo J, Yoo JE, Park JE. ‘
From fail to prevail
’ : How a salesperson’s communication sentiment influences consumer forgiveness in service failures focusing on the role of consumer self-construal. Journal of Global Scholars of Marketing Science 2024;34(2):231
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According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.6327):
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Natsiavas P, Maglaveras N, Koutkias V. Knowledge Representation for Health Care. 2017. Chapter 4:51
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Kursuncu U, Gaur M, Lokala U, Thirunarayan K, Sheth A, Arpinar IB. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. 2019. Chapter 4:67
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Zhang H, Wheldon C, Tao C, Dunn AG, Guo Y, Huo J, Bian J. Social Web and Health Research. 2019. Chapter 11:207
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Leightley D, Sharp M, Williamson V, Fear NT, Gribble R. Social Media and the Armed Forces. 2020. Chapter 9:145
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Portelli B, Passabì D, Lenzi E, Serra G, Santus E, Chersoni E. AI for Disease Surveillance and Pandemic Intelligence. 2022. Chapter 8:87
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Leightley D, Sharp M, Williamson V, Fear NT, Gribble R. Soziale Medien und die Streitkräfte. 2023. Chapter 9:183
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