ArticleJMIR medical informatics2020
Factors Associated With Influential Health-Promoting Messages on Social Media: Content Analysis of Sina Weibo.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Exploring cyberbullying and cybervictimization among sports fans on Sina Weibo: the roles of personality traits and fan identity.Scientific reports · 2026Article
- Improving the communication of uncertainty in food safety on social media: a content analysis of aspartame risk messages.Frontiers in public health · 2025Article
- Article
- Effectiveness of Social Video Platforms in Promoting COVID-19 Vaccination Among Youth: A Content-Specific Analysis of COVID-19 Vaccination Topic Videos on Bilibili.Risk management and healthcare policy · 2022Article
- Textual Features and Risk Preference Effects on Mental Health Education Among Teenager Students in Chongqing, China.Frontiers in psychology · 2022Article
- Quality of Internet Videos Related to Pediatric Urology in Mainland China: A Cross-Sectional Study.Frontiers in public health · 2022Article
- The Impact of Public Health Events on COVID-19 Vaccine Hesitancy on Chinese Social Media: National Infoveillance Study.JMIR public health and surveillance · 2021Article
- Unfolding the Determinants of COVID-19 Vaccine Acceptance in China.Journal of medical Internet research · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSocial media is a powerful tool for the dissemination of health messages. However, few studies have focused on the factors that improve the influence of health messages on social media.
objectiveTo explore the influence of goal-framing effects, information organizing, and the use of pictures or videos in health-promoting messages, we conducted a case study of Sina Weibo, a popular social media platform in China.
methodsLiterature review and expert discussion were used to determine the health themes of childhood obesity, smoking, and cancer. Web crawler technology was employed to capture data on health-promoting messages. We used the number of retweets, comments, and likes to evaluate the influence of a message. Statistical analysis was then conducted after manual coding. Specifically, binary logistic regression was used for the data analyses.
resultsWe crawled 20,799 Sina Weibo messages and selected 389 health-promoting messages for this study. Results indicated that the use of gain-framed messages could improve the influence of messages regarding childhood obesity (P<.001), smoking (P=.03), and cancer (P<.001). Statistical expressions could improve the influence of messages about childhood obesity (P=.02), smoking (P=.002), and cancer (P<.001). However, the use of videos significantly improved the influence of health-promoting messages only for the smoking-related messages (P=.009).
conclusionsThe findings suggested that gain-framed messages and statistical expressions can be successful strategies to improve the influence of messages. Moreover, appropriate pictures and videos should be added as much as possible when generating health-promoting messages.
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