ArticlePsychology research and behavior management2023
Predicting Fact-Checking Health Information Before Sharing Among People with Different Levels of Altruism: Based on the Influence of Presumed Media Influence.
Article in Psychology research and behavior management, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Does fact-checking COVID-19 fake news on social media correct readers' false beliefs? A study of Chinese youths.Frontiers in psychology · 2026Article
- How the General Public Navigates Health Misinformation on Social Media: Qualitative Study of Identification and Response Approaches.JMIR infodemiology · 2025Article
- Unpacking Young Adults' Fact-Checking Intent on Oral Health Misinformation: Parallel Mediating Roles of Need for Cognition and Perceived Seriousness-A Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2025Article
- Examining Women's support for birth encouragement policies in China: an extension of the influence of presumed media influence model.Frontiers in psychology · 2024Article
- Social Media Fact-Checking: The Effects of News Literacy and News Trust on the Intent to Verify Health-Related Information.Healthcare (Basel, Switzerland) · 2023Article
- Article
- Fact-checking of health information on social networking sites: The moderating role of gender.Digital healthArticle
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1 author.
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Abstract
Background: Pervasive health misinformation on social media affects people's health. Fact-checking health information before it is shared is an altruistic behavior that effectively addresses health misinformation on social media. Purpose: Based on the influence of presumed media influence (IPMI), this study serves two purposes: The first is to investigate factors that influence social media users' decisions to fact-check health information before sharing it in accordance with the IPMI model. The second is to explore different predictive powers of the IPMI model for individuals with different levels of altruism. Methods: This study conducted a questionnaire survey of 1045 Chinese adults. Participants were divided into either a low-altruism group (n = 545) or a high-altruism group (n = 500) at the median value of altruism. A multigroup analysis was conducted with R Lavaan package (Version 0.6-15). Results: All of the hypotheses were supported, which confirms the applicability of the IPMI model in the context of fact-checking health information on social media before sharing. Notably, the IPMI model yielded different results for the low- and high-altruism groups. Conclusion: This study confirmed the IPMI model can be employed in the context of fact-checking health information. Paying attention to health misinformation can indirectly affect an individual's intention to fact-check health information before they share it on social media. Furthermore, this study demonstrated the IPMI model's varying predictive powers for individuals with different altruism levels and recommended specific strategies health-promotion officials can take to encourage others to fact-check health information.
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