This paper is in the following e-collection/theme issue:

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infodemiology and Infoveillance (1349) Behavioural Surveillance for Public Health (229) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (111)

Published on in Vol 3, No 2 (2017): Apr-Jun

Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts

Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts

Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts

Authors of this article:

Anthony Stefanidis1 Author Orcid Image ;   Emily Vraga2 Author Orcid Image ;   Georgios Lamprianidis1 Author Orcid Image ;   Jacek Radzikowski1 Author Orcid Image ;   Paul L Delamater1 Author Orcid Image ;   Kathryn H Jacobsen3 Author Orcid Image ;   Dieter Pfoser1 Author Orcid Image ;   Arie Croitoru1 Author Orcid Image ;   Andrew Crooks4 Author Orcid Image
Article Authors Cited by (79) Tweetations (34) Metrics

Journals

  1. Ahmed W, Marin-Gomez X, Vidal-Alaball J. Contextualising the 2019 E-Cigarette Health Scare: Insights from Twitter. International Journal of Environmental Research and Public Health 2020;17(7):2236 View
  2. Fagherazzi G, Goetzinger C, Rashid M, Aguayo G, Huiart L. Digital Health Strategies to Fight COVID-19 Worldwide: Challenges, Recommendations, and a Call for Papers. Journal of Medical Internet Research 2020;22(6):e19284 View
  3. Gupta A, Katarya R. Social media based surveillance systems for healthcare using machine learning: A systematic review. Journal of Biomedical Informatics 2020;108:103500 View
  4. Seo H, Blomberg M, Altschwager D, Vu H. Vulnerable populations and misinformation: A mixed-methods approach to underserved older adults’ online information assessment. New Media & Society 2021;23(7):2012 View
  5. Aschwanden A, Demir C, Hinselmann R, Kasser S, Rohrer A. Zika and travel: Public health implications and communications for blood donors, sperm donors and pregnant women. Travel Medicine and Infectious Disease 2018;21:77 View
  6. Roy M, Moreau N, Rousseau C, Mercier A, Wilson A, Atlani-Duault L. Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014–2015 Ebola Epidemic. Culture, Medicine, and Psychiatry 2020;44(1):56 View
  7. Motti V, Kalantari N, Neris V. Understanding how social media imagery empowers caregivers: an analysis of microcephaly in Latin America. Personal and Ubiquitous Computing 2021;25(2):321 View
  8. Vijaykumar S, Nowak G, Himelboim I, Jin Y. Virtual Zika transmission after the first U.S. case: who said what and how it spread on Twitter. American Journal of Infection Control 2018;46(5):549 View
  9. Xu X, Litchman M, Gee P, Whatcott W, Chacon L, Holmes J, Srinivasan S. Predicting Prediabetes Through Facebook Postings: Protocol for a Mixed-Methods Study. JMIR Research Protocols 2018;7(12):e10720 View
  10. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  11. Vraga E, Bode L. Using Expert Sources to Correct Health Misinformation in Social Media. Science Communication 2017;39(5):621 View
  12. Owuor I, Hochmair H. An Overview of Social Media Apps and their Potential Role in Geospatial Research. ISPRS International Journal of Geo-Information 2020;9(9):526 View
  13. Chen S, Xu Q, Buchenberger J, Bagavathi A, Fair G, Shaikh S, Krishnan S. Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic. JMIR Public Health and Surveillance 2018;4(4):e10827 View
  14. Eke R, Li T, Bond K, Ho A, Graves L. Viewing Trends and Users’ Perceptions of the Effect of Sleep-Aiding Music on YouTube: Quantification and Thematic Content Analysis. Journal of Medical Internet Research 2020;22(8):e15697 View
  15. Lwin M, Lu J, Sheldenkar A, Cayabyab Y, Yee A, Smith H. Temporal and textual analysis of social media on collective discourses during the Zika virus pandemic. BMC Public Health 2020;20(1) View
  16. Gianfredi V, Bragazzi N, Nucci D, Martini M, Rosselli R, Minelli L, Moretti M. Harnessing Big Data for Communicable Tropical and Sub-Tropical Disorders: Implications From a Systematic Review of the Literature. Frontiers in Public Health 2018;6 View
  17. Hu Y. Geo‐text data and data‐driven geospatial semantics. Geography Compass 2018;12(11) View
  18. Leis A, Ronzano F, Mayer M, Furlong L, Sanz F. Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis. Journal of Medical Internet Research 2019;21(6):e14199 View
  19. Burger A, Oz T, Kennedy W, Crooks A. Computational Social Science of Disasters: Opportunities and Challenges. Future Internet 2019;11(5):103 View
  20. Curry T, Croitoru A, Crooks A, Stefanidis A. Exodus 2.0: crowdsourcing geographical and social trails of mass migration. Journal of Geographical Systems 2019;21(1):161 View
  21. Barata G, Shores K, Alperin J. Local Chatter or International Buzz? Language Differences on Posts About Zika Research on Twitter and Facebook. SSRN Electronic Journal 2017 View
  22. Vraga E, Jacobsen K. Strategies for Effective Health Communication during the Coronavirus Pandemic and Future Emerging Infectious Disease Events. World Medical & Health Policy 2020;12(3):233 View
  23. Anderson E, Ernst K, Martins F, Martins C, Koss M. Women’s Health Perceptions and Beliefs Related to Zika Virus Exposure during the 2016 Outbreak in Northern Brazil. The American Journal of Tropical Medicine and Hygiene 2020;102(3):629 View
  24. Barata G, Shores K, Alperin J, Emmert-Streib F. Local chatter or international buzz? Language differences on posts about Zika research on Twitter and Facebook. PLOS ONE 2018;13(1):e0190482 View
  25. Medford R, Saleh S, Sumarsono A, Perl T, Lehmann C. An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak. Open Forum Infectious Diseases 2020;7(7) View
  26. Doan S, Yang E, Tilak S, Li P, Zisook D, Torii M. Extracting health-related causality from twitter messages using natural language processing. BMC Medical Informatics and Decision Making 2019;19(S3) View
  27. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  28. Litchman M, Wawrzynski S, Woodruff W, Arrington J, Nguyen Q, Gee P. Continuous Glucose Monitoring in the Real World Using Photosurveillance of #Dexcom on Instagram: Exploratory Mixed Methods Study. JMIR Public Health and Surveillance 2019;5(2):e11024 View
  29. Safarnejad L, Xu Q, Ge Y, Bagavathi A, Krishnan S, Chen S. Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study. JMIR Public Health and Surveillance 2020;6(3):e17175 View
  30. Valente P, Morin C, Roy M, Mercier A, Atlani-Duault L. Sexual transmission of Zika virus on Twitter: A depoliticised epidemic. Global Public Health 2020;15(11):1689 View
  31. Yasri S, Wiwanitkit V. Virtual Zika transmission and spread on Twitter. American Journal of Infection Control 2018;46(7):850 View
  32. Essam B, Abdo M. How Do Arab Tweeters Perceive the COVID-19 Pandemic?. Journal of Psycholinguistic Research 2021;50(3):507 View
  33. Lopez B, Magliocca N, Crooks A. Challenges and Opportunities of Social Media Data for Socio-Environmental Systems Research. Land 2019;8(7):107 View
  34. Plaster A, Painter J, Tjersland D, Jacobsen K. University Students’ Knowledge, Attitudes, and Sources of Information About Zika Virus. Journal of Community Health 2018;43(4):647 View
  35. Masri S, Jia J, Li C, Zhou G, Lee M, Yan G, Wu J. Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic. BMC Public Health 2019;19(1) View
  36. Pruss D, Fujinuma Y, Daughton A, Paul M, Arnot B, Albers Szafir D, Boyd-Graber J, Xia F. Zika discourse in the Americas: A multilingual topic analysis of Twitter. PLOS ONE 2019;14(5):e0216922 View
  37. Daughton A, Paul M. Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus. Journal of Medical Internet Research 2019;21(5):e13090 View
  38. Fung I, Jackson A, Mullican L, Blankenship E, Goff M, Guinn A, Saroha N, Tse Z. Contents, Followers, and Retweets of the Centers for Disease Control and Prevention’s Office of Advanced Molecular Detection (@CDC_AMD) Twitter Profile: Cross-Sectional Study. JMIR Public Health and Surveillance 2018;4(2):e33 View
  39. Yuan X, Schuchard R, Crooks A. Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter. Social Media + Society 2019;5(3) View
  40. Zheng H, Goh D, Lee C, Lee E, Theng Y. Uncovering temporal differences in COVID‐19 tweets. Proceedings of the Association for Information Science and Technology 2020;57(1) View
  41. Wang B, Liu B, Zhang Q. An empirical study on Twitter’s use and crisis retweeting dynamics amid Covid-19. Natural Hazards 2021;107(3):2319 View
  42. Garcia K, Berton L. Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing 2021;101:107057 View
  43. AGRAWAL A, GUPTA A. The Utility of Social Media during an Emerging Infectious Diseases Crisis: A Systematic Review of Literature. Journal of Microbiology and Infectious Diseases 2020:188 View
  44. Thelwall M, Thelwall S. A thematic analysis of highly retweeted early COVID-19 tweets: consensus, information, dissent and lockdown life. Aslib Journal of Information Management 2020;72(6):945 View
  45. Lyu J, Luli G. Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study. Journal of Medical Internet Research 2021;23(2):e25108 View
  46. Chandrasekaran R, Mehta V, Valkunde T, Moustakas E. Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study. Journal of Medical Internet Research 2020;22(10):e22624 View
  47. Alnazzawi N, Fiorini N. Building a semantically annotated corpus for chronic disease complications using two document types. PLOS ONE 2021;16(3):e0247319 View
  48. Alvarez-Galvez J, Suarez-Lledo V, Rojas-Garcia A. Determinants of Infodemics During Disease Outbreaks: A Systematic Review. Frontiers in Public Health 2021;9 View
  49. Jiang Y, Huang X, Li Z. Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City. ISPRS International Journal of Geo-Information 2021;10(5):344 View
  50. Kharlamov A, Raskhodchikov A, Pilgun M. Smart City Data Sensing during COVID-19: Public Reaction to Accelerating Digital Transformation. Sensors 2021;21(12):3965 View
  51. Malik A, Antonino A, Khan M, Nieminen M. Characterizing HIV discussions and engagement on Twitter. Health and Technology 2021;11(6):1237 View
  52. Osakwe Z, Cortés Y. Impact of COVID-19: A Text Mining Analysis of Twitter Data in Spanish Language. Hispanic Health Care International 2021;19(4):239 View
  53. Bali A, Halbusi H, Ahmad A, Lee K, Lavorgna L. Public engagement in government officials’ posts on social media during coronavirus lockdown. PLOS ONE 2023;18(1):e0280889 View
  54. Gour A, Aggarwal S, Kumar S. Lending ears to unheard voices: An empirical analysis of user‐generated content on social media. Production and Operations Management 2022;31(6):2457 View
  55. Joshi A, Miller C. Review of machine learning techniques for mosquito control in urban environments. Ecological Informatics 2021;61:101241 View
  56. Xu W, Tshimula J, Dubé È, Graham J, Greyson D, MacDonald N, Meyer S. Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster–Based BERT Topic Modeling Approach. JMIR Infodemiology 2022;2(2):e41198 View
  57. ELİAÇIK B. Covid-19 Pandemisinin İlk Aylarında Twitter Gönderilerinin Metinsel Analizi.. Medical Research Reports 2022;5(3):136 View
  58. Imran M, Qazi U, Ofli F. TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels. Data 2022;7(1):8 View
  59. Ninkov A, Sedig K. A Framework for Online Public Health Debates: Some Design Elements for Visual Analytics Systems. Information 2022;13(4):201 View
  60. Durazzi F, Müller M, Salathé M, Remondini D. Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic. Scientific Reports 2021;11(1) View
  61. Abdo M, Alghonaim A, Essam B. Public perception of COVID-19’s global health crisis on Twitter until 14 weeks after the outbreak. Digital Scholarship in the Humanities 2021;36(3):509 View
  62. Pilgun M, Koreneva Antonova O. Información implícita y explícita en la percepción del covid-19 en los medios de comunicación social en español, alemán y ruso. Palabra Clave 2022;25(1):1 View
  63. Sano Y, Hori A, Kolahi J. 12-year observation of tweets about rubella in Japan: A retrospective infodemiology study. PLOS ONE 2023;18(5):e0285101 View
  64. Finnegan A, Subburaj S, Hunter K, Parkash P, Shulman E, Ramkalawan J, Huchko M. Big Data, Machine Learning and Contraceptive Use: A Scoping Review. Oxford Open Digital Health 2023;1 View
  65. Aizaz M, Khan F, Ali B, Ahmad S, Naseem K, Mishra S, Abbas F, Yang G. Significance of Digital Health Technologies (DHTs) to manage communicable and non-communicable diseases in Low and Middle-Income Countries (LMICs). Health and Technology 2023;13(6):883 View
  66. Pelletier A, Kaewkitipong L, Guitton M. Using technology to study refugee, conflict-affected, and hard-to-reach populations: Methodological and ethical considerations. Computers in Human Behavior 2024;152:108053 View
  67. Ashayeri M, Abbasabadi N. Unraveling energy justice in NYC urban buildings through social media sentiment analysis and transformer deep learning. Energy and Buildings 2024;306:113914 View

Books/Policy Documents

  1. Ahmed W, Bath P, Sbaffi L, Demartini G. Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing. View
  2. Sedkaoui S, Khelfaoui M, Keltoum O. International Conference on Managing Business Through Web Analytics. View
  3. Gilbert J, Niu J, de Montigny S, Ng V, Rees E. AI for Disease Surveillance and Pandemic Intelligence. View
  4. Ashayeri M. Artificial Intelligence in Performance‐Driven Design. View
  5. Kumar Varshney P, Sharma N, Bharara V, Kumar S, Gupta A. Exploration of Artificial Intelligence and Blockchain Technology in Smart and Secure Healthcare. View
  6. Moghaddam S, Cao H. Artificial Intelligence-Driven Geographies. View