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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1139) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1911) Infodemiology and Infoveillance (1558) Mass Media/Social Media Communication and Campaigns (574) Infectious Diseases (non-STD/STI) (1726)

Published on in Vol 4, No 4 (2018): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10827, first published .
Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic

Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic

Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic

Authors of this article:

Shi Chen1 Author Orcid Image ;   Qian Xu2 Author Orcid Image ;   John Buchenberger1 Author Orcid Image ;   Arunkumar Bagavathi3 Author Orcid Image ;   Gabriel Fair3 Author Orcid Image ;   Samira Shaikh3 Author Orcid Image ;   Siddharth Krishnan3 Author Orcid Image
Article Authors Cited by (30) Tweetations (6) Metrics

Journals

  1. Safarishahrbijari A, Osgood N. Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study. JMIR Public Health and Surveillance 2019;5(2):e11615 View
  2. 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
  3. Mavragani A. Tracking COVID-19 in Europe: Infodemiology Approach. JMIR Public Health and Surveillance 2020;6(2):e18941 View
  4. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  5. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  6. Caputi T, Ayers J, Dredze M, Suplina N, Burd-Sharps S. Collateral Crises of Gun Preparation and the COVID-19 Pandemic: Infodemiology Study. JMIR Public Health and Surveillance 2020;6(2):e19369 View
  7. ŞENOL Y, AVCI K. Salgın iletişiminde sosyal medyanın kullanımı. Journal of Health Sciences and Medicine 2020;3(3):340 View
  8. Ojo A, Guntuku S, Zheng M, Beidas R, Ranney M. How Health Care Workers Wield Influence Through Twitter Hashtags: Retrospective Cross-sectional Study of the Gun Violence and COVID-19 Public Health Crises. JMIR Public Health and Surveillance 2021;7(1):e24562 View
  9. 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
  10. Chen Q, Min C, Zhang W, Ma X, Evans R. Factors Driving Citizen Engagement With Government TikTok Accounts During the COVID-19 Pandemic: Model Development and Analysis. Journal of Medical Internet Research 2021;23(2):e21463 View
  11. Zeng R, Li M. Social Media Use for Health Communication by the CDC in Mainland China: National Survey Study 2009-2020. Journal of Medical Internet Research 2020;22(12):e19470 View
  12. 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
  13. Malik A, Khan M, Quan-Haase A. Public health agencies outreach through Instagram during the COVID-19 pandemic: Crisis and Emergency Risk Communication perspective. International Journal of Disaster Risk Reduction 2021;61:102346 View
  14. Alhassan F, AlDossary S. The Saudi Ministry of Health’s Twitter Communication Strategies and Public Engagement During the COVID-19 Pandemic: Content Analysis Study. JMIR Public Health and Surveillance 2021;7(7):e27942 View
  15. Ezike N, Ames Boykin A, Dobbs P, Mai H, Primack B. Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series. JMIR Infodemiology 2022;2(2):e37412 View
  16. Moore J, Harris J, Hutti E. ‘Falsehood flies, and the truth comes limping after it’: social media and public health. Current Opinion in Psychiatry 2021;34(5):485 View
  17. Luo C, Ji K, Tang Y, Du Z. Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach. Journal of Medical Internet Research 2021;23(8):e30715 View
  18. 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
  19. Wei H, Hai C, Shan D, Lyu B, Wang X. Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs. Multimedia Tools and Applications 2023;82(17):25811 View
  20. Hauer M, Jenkins A, MacPherson J, Sun Q, Swain M. Tweeting about the COVID-19 vaccine: A content analysis. Atlantic Journal of Communication 2024;32(4):545 View
  21. Yin M, Chen S, Pan X, Lu C, Lin X, Wang M, Ni J. Effects of Chinese provincial CDCs WeChat official account article features on user engagement during the COVID-19 pandemic. Journal of Global Health 2023;13 View
  22. Martínez‐Arias A, Valerio L, Roure‐Díez S, Fernández‐Rivas G, Rivaya B, Pérez‐Olmeda M, Soldevila‐Langa L, Parrón I, Clotet‐Sala B, Vallès X, Rodrigo C. Zika virus screening during pregnancy: Results and lessons learned from a screening program and a post‐delivery follow‐up analysis (2016–2022). Birth Defects Research 2023;115(17):1646 View
  23. Balogun B, Hogden A, Kemp N, Yang L, Agaliotis M. Public health agencies’ use of social media for communication during pandemics: a scoping review of the literature. Osong Public Health and Research Perspectives 2023;14(4):235 View
  24. Terry K, Yang F, Yao Q, Liu C. The role of social media in public health crises caused by infectious disease: a scoping review. BMJ Global Health 2023;8(12):e013515 View
  25. 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
  26. 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
  27. Choi S, Eom S. Using Twitter to fight the COVID-19 pandemic: the case of United States governors. Public Management Review 2025;27(12):3006 View
  28. Zhang J, Chen J, Geszvain Y, Qin C. Temporal analysis of users’ engagements in CDC public health information on Twitter/X. Online Information Review 2025;49(6):1101 View
  29. Song Y, Ma R, Liu J, Guo Y, An S. How can timeliness and accuracy be balanced? The surveillance information reporting mechanism for public health emergencies based on the online direct reporting system. BMC Public Health 2025;25(1) View

Books/Policy Documents

  1. Balogun B, Dhanya M, Viswanathan P. Accelerating Strategic Changes for Digital Transformation in the Healthcare Industry. View