%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e65631
%T Predicting User Engagement in Health Misinformation Correction on Social Media Platforms in Taiwan: Content Analysis and Text Mining Study
%A Kuo,Hsin-Yu
%A Chen,Su-Yen
%+ Department of Educational Psychology and Counseling, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 300044, Taiwan, 886 3 571 5131 ext 34510, kuohy@mx.nthu.edu.tw
%K health misinformation
%K misinformation correction
%K fact-checking
%K content analysis
%K text mining
%K fuzzy-trace theory
%K social media
%K large language models
%K user engagement
%K health communication
%D 2025
%7 23.1.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Health misinformation undermines responses to health crises, with social media amplifying the issue. Although organizations work to correct misinformation, challenges persist due to reasons such as the difficulty of effectively sharing corrections and information being overwhelming. At the same time, social media offers valuable interactive data, enabling researchers to analyze user engagement with health misinformation corrections and refine content design strategies. Objective: This study aimed to identify the attributes of correction posts and user engagement and investigate (1) the trend of user engagement with health misinformation correction during 3 years of the COVID-19 pandemic; (2) the relationship between post attributes and user engagement in sharing and reactions; and (3) the content generated by user comments serving as additional information attached to the post, affecting user engagement in sharing and reactions. Methods: Data were collected from the Facebook pages of a fact-checking organization and a health agency from January 2020 to December 2022. A total of 1424 posts and 67,378 corresponding comments were analyzed. The posts were manually annotated by developing a research framework based on the fuzzy-trace theory, categorizing information into “gist” and “verbatim” representations. Three types of gist representations were examined: risk (risks associated with misinformation), awareness (awareness of misinformation), and value (value in health promotion). Furthermore, 3 types of verbatim representations were identified: numeric (numeric and statistical bases for correction), authority (authority from experts, scholars, or institutions), and facts (facts with varying levels of detail). The basic metrics of user engagement included shares, reactions, and comments as the primary dependent variables. Moreover, this study examined user comments and classified engagement as cognitive (knowledge-based, critical, and bias-based) or emotional (positive, negative, and neutral). Statistical analyses were performed to explore the impact of post attributes on user engagement. Results: On the basis of the results of the regression analysis, risk (β=.07; P=.001), awareness (β=.09; P<.001), and facts (β=.14; P<.001) predicted higher shares; awareness (β=.07; P=.001) and facts (β=.24; P<.001) increased reactions; and awareness (β=.06; P=.005), numeric representations (β=.06; P=.02), and facts (β=.19; P<.001) increased comments. All 3 gist representations significantly predicted shares (risk: β=.08; P<.001, awareness: β=.08; P<.001, and value: β=.06; P<.001) and reactions (risk: β=.04; P=.007, awareness: β=.06; P<.001, and value: β=.05; P<.001) when considering comment content. In addition, comments with bias-based engagement (β=–.11; P=.001) negatively predicted shares. Generally, posts providing gist attributes, especially awareness of misinformation, were beneficial for user engagement in misinformation correction. Conclusions: This study enriches the theoretical understanding of the relationship between post attributes and user engagement within web-based communication efforts to correct health misinformation. These findings provide a foundation for designing more effective content approaches to combat misinformation and strengthen public health communication. 
%M 39847418
%R 10.2196/65631
%U https://www.jmir.org/2025/1/e65631
%U https://doi.org/10.2196/65631
%U http://www.ncbi.nlm.nih.gov/pubmed/39847418
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