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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

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
  5. Çeleğen İ, Sarıöz A. Exposure to health misinformation on social media across key health domains: a systematic review and meta-analysis of survey-based studies. BMC Public Health 2026 View

Conference Proceedings

  1. Dipali D, Deokar S, Nayak A, Shukla A, Chetia B. 2025 IEEE Pune Section International Conference (PuneCon). Predicting Online News Popularity: A Regressionbased Approach View