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Published on in Vol 6, No 3 (2020): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17175, first published .
Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study

Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study

Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study

Journals

  1. Chen S, Zhou L, Song Y, Xu Q, Wang P, Wang K, Ge Y, Janies D. A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis. Journal of Medical Internet Research 2021;23(1):e24889 View
  2. Pian W, Chi J, Ma F. The causes, impacts and countermeasures of COVID-19 “Infodemic”: A systematic review using narrative synthesis. Information Processing & Management 2021;58(6):102713 View
  3. Wu Y, Li N, Li S, Song S. Lung transplantation in a woman with paraquat poisoning that led to pulmonary fibrosis—Widely reported by the media: A case report. Medicine 2022;101(49):e32263 View
  4. Beliga S, Martinčić-Ipšić S, Matešić M, Petrijevčanin Vuksanović I, Meštrović A. Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing. JMIR Public Health and Surveillance 2021;7(12):e31540 View
  5. Safarnejad L, Xu Q, Ge Y, Chen S. A Multiple Feature Category Data Mining and Machine Learning Approach to Characterize and Detect Health Misinformation on Social Media. IEEE Internet Computing 2021;25(5):43 View
  6. Heyerdahl L, Lana B, Giles-Vernick T. The Impact of the Online COVID-19 Infodemic on French Red Cross Actors’ Field Engagement and Protective Behaviors: Mixed Methods Study. JMIR Infodemiology 2021;1(1):e27472 View
  7. Chen S, Yin S, Guo Y, Ge Y, Janies D, Dulin M, Brown C, Robinson P, Zhang D. Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics. Frontiers in Public Health 2023;11 View
  8. Yin S, Chen S, Ge Y. Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study. JMIR Infodemiology 2024;4:e49756 View
  9. Khemani B, Patil S, Kotecha K, Vora D. Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks. MethodsX 2024;12:102737 View
  10. Wang S, Zhang L, Liu Y, Feng X, Ren S. Bibliometric Insights Into the Infodemic: Global Research Trends and Policy Responses: Quantitative Research. JMIR Medical Informatics 2025;13:e76378 View
  11. Kresovich A, Emery S, Borowiecki M, Ngobo-Ekamby M, Shi H, Lamuda P, Taylor B, Schneider J, Pollack H. Refracting Blame in the Fentanyl Era: The Narrative Battleground on Facebook and Instagram. Substance Use & Misuse 2026;61(1):120 View

Books/Policy Documents

  1. Heyerdahl L, Lana B, Giles-Vernick T. Socio-Life Science and the COVID-19 Outbreak. View

Conference Proceedings

  1. Chuan C, Zhang G, Wu C. 2025 IEEE International Conference on Big Data (BigData). Exploring Barriers and Misconceptions Toward Clinical Trials on Social Media Using Large Language Models View