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

Infodemic Management (54) JMIR Theme Issue: COVID-19 Special Issue (2491) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1530) Outbreak and Pandemic Preparedness and Management (1779) Infectious Diseases (non-STD/STI) (1654) Infodemiology and Infoveillance (1445) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1085) Mass Media/Social Media Communication and Campaigns (503) Misinformation and Disinformation Outbreaks and Information Prevalence Studies (61)

Published on in Vol 2, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33827, first published .
Identifying Frames of the COVID-19 Infodemic: Thematic Analysis of Misinformation Stories Across Media

Identifying Frames of the COVID-19 Infodemic: Thematic Analysis of Misinformation Stories Across Media

Identifying Frames of the COVID-19 Infodemic: Thematic Analysis of Misinformation Stories Across Media

Authors of this article:

Ehsan Mohammadi1 Author Orcid Image ;   Iman Tahamtan2 Author Orcid Image ;   Yazdan Mansourian3 Author Orcid Image ;   Holly Overton4 Author Orcid Image
Article Authors Cited by (11) Tweetations (7) Metrics

Journals

  1. Tahamtan I, Potnis D, Mohammadi E, Singh V, Miller L. The Mutual Influence of the World Health Organization (WHO) and Twitter Users During COVID-19: Network Agenda-Setting Analysis. Journal of Medical Internet Research 2022;24(4):e34321 View
  2. Su Y, Li S, Huang F, Xue J, Zhu T. Exploring the Influencing Factors of COVID-19 Vaccination Willingness among Young Adults in China. International Journal of Environmental Research and Public Health 2023;20(5):3960 View
  3. Myneni S, Cuccaro P, Montgomery S, Pakanati V, Tang J, Singh T, Dominguez O, Cohen T, Reininger B, Savas L, Fernandez M. Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science. JMIR Infodemiology 2023;3:e40156 View
  4. Isip Tan I, Cleofas J, Solano G, Pillejera J, Catapang J. Interdisciplinary Approach to Identify and Characterize COVID-19 Misinformation on Twitter: Mixed Methods Study (Preprint). JMIR Formative Research 2022 View
  5. Jiang C, Yu Y, Zhang X. Modelling and analysis of misinformation diffusion based on the double intervention mechanism. Journal of Information Science 2023 View
  6. Ghosh S, Thajudeen J. Voices of the Stacks: A Multifaceted Inquiry into Academic Librarians' Tweets. Proceedings of the Association for Information Science and Technology 2023;60(1):166 View
  7. Kuo W. Infodemic in the era of the pandemic. International Review of Pragmatics 2023;15(2):189 View
  8. Ndu M, Teachman G, Martin J, Nouvet E. “We Live Our Life Normal”: A Qualitative Analysis of Nigerian Women’s Health-Seeking Behavior during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2024;21(3):265 View
  9. Watson S, Benning T, Marcon A, Zhu X, Caulfield T, Sharp R, Master Z. Descriptions of Scientific Evidence and Uncertainty of Unproven COVID-19 Therapies in US News: Content Analysis Study. JMIR Infodemiology 2024;4:e51328 View
  10. Weerasinghe S, Oyebode O, Orji R. “No claims, no trials, no compensation. Just roll up your sleeve”: social media communication on vaccine uptake sentiments in Canada. Health & New Media Research 2024;8(2):33 View
  11. Huai P, Zhang B, Zhang L, Hou Y, Zhang L, Yang H, Guo J. Based on the multi-theory model perspective, what are the influencing factors of health behavior change among community-dwelling elderly patients with type 2 diabetes in China? A qualitative study. Diabetes Research and Clinical Practice 2025;222:112096 View