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

Infodemiology and Infoveillance (1565) Misinformation and Disinformation Outbreaks and Information Prevalence Studies (95) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1926) Quality/Credibility of eHealth Information and Trust Issues (657) Bots and AI Approaches to Detect and Counter Misinformation (20)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65631, first published .
Predicting User Engagement in Health Misinformation Correction on Social Media Platforms in Taiwan: Content Analysis and Text Mining Study

Predicting User Engagement in Health Misinformation Correction on Social Media Platforms in Taiwan: Content Analysis and Text Mining Study

Predicting User Engagement in Health Misinformation Correction on Social Media Platforms in Taiwan: Content Analysis and Text Mining Study

Authors of this article:

Hsin-Yu Kuo1 Author Orcid Image ;   Su-Yen Chen2 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Elroy O, Yosipof A. The Discussions of Monkeypox Misinformation on Social Media. Data 2025;10(9):137 View
  2. Hossain B, Preum S, Rabbi M, Ara R, Ali M. Extracting Symptoms of Complex Conditions From Online Discourse (Subreddit to Symptomatology): Lexicon-Based Approach. JMIR Medical Informatics 2025;13:e70940 View
  3. Ahmed W, Hardey M, Yavetz G. Health information communication and advocacy in the haemophilia community: an X-based analysis. Journal of Documentation 2025;81(4):927 View
  4. Zhou Q, Shen J, Cao D, Yu W, Chen J. Quality and accuracy of scoliosis-related short videos on TikTok reviewed in China in August 2025. Scientific Reports 2025;15(1) View