Published on in Vol 5, No 2 (2019): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11036, first published .
Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic

Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic

Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic

Journals

  1. Saleh S, Lehmann C, McDonald S, Basit M, Medford R. Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter. Infection Control & Hospital Epidemiology 2021;42(2):131 View
  2. Li J, Xu Q, Cuomo R, Purushothaman V, Mackey T. Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study. JMIR Public Health and Surveillance 2020;6(2):e18700 View
  3. Sesagiri Raamkumar A, Tan S, Wee H. Measuring the Outreach Efforts of Public Health Authorities and the Public Response on Facebook During the COVID-19 Pandemic in Early 2020: Cross-Country Comparison. Journal of Medical Internet Research 2020;22(5):e19334 View
  4. Gesualdo F, D’Ambrosio A, Agricola E, Russo L, Campagna I, Ferretti B, Pandolfi E, Cristoforetti M, Tozzi A, Rizzo C. How do Twitter users react to TV broadcasts dedicated to vaccines in Italy?. European Journal of Public Health 2020;30(3):481 View
  5. Pfeiffer C, Hollenstein N, Zhang C, Langer N. Neural dynamics of sentiment processing during naturalistic sentence reading. NeuroImage 2020;218:116934 View
  6. 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 View
  7. He L, Yin T, Hu Z, Chen Y, Hanauer D, 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 View
  8. Dubey A. The Resurgence of Cyber Racism During the COVID-19 Pandemic and its Aftereffects: Analysis of Sentiments and Emotions in Tweets. JMIR Public Health and Surveillance 2020;6(4):e19833 View
  9. 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
  10. Boon-Itt S, Skunkan Y. Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health and Surveillance 2020;6(4):e21978 View
  11. 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
  12. Yu S, Eisenman D, Han Z. Temporal Dynamics of Public Emotions During the COVID-19 Pandemic at the Epicenter of the Outbreak: Sentiment Analysis of Weibo Posts From Wuhan. Journal of Medical Internet Research 2021;23(3):e27078 View
  13. 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
  14. Valecha R, Srinivasan S, Volety T, Kwon K, Agrawal M, Rao H. Fake News Sharing. Digital Threats: Research and Practice 2021;2(2):1 View
  15. 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
  16. Ainley E, Witwicki C, Tallett A, Graham C. Using Twitter Comments to Understand People’s Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis. Journal of Medical Internet Research 2021;23(10):e31101 View
  17. 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 View
  18. 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 View
  19. Lamsal R, Harwood A, Read M. Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey. ACM Computing Surveys 2023;55(4):1 View
  20. Burwell E, Agarwal A, Romine W. Understanding communication about the COVID-19 vaccines: analysis of emergent sentiments and topics of discussion on Twitter during the initial phase of the vaccine rollout. International Journal of Science Education, Part B 2024;14(1):18 View
  21. Zhang Q, Niu T, Yang J, Geng X, Lin Y. A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government. Frontiers in Public Health 2023;11 View
  22. Diaz M, Medford R, Lehmann C, Petersen C. The lived experience of people with disabilities during the COVID-19 pandemic on Twitter: Content analysis. DIGITAL HEALTH 2023;9 View
  23. Gupta P, Mahajan R, Badhera U, Kushwaha P. Integrating generative AI in management education: A mixed-methods study using social construction of technology theory. The International Journal of Management Education 2024;22(3):101017 View