Texas Christian University, Fort Worth, Texas, USA.
University at Albany, State University of New York, Albany, New York, USA.
Glob Health Promot. 2022 Mar;29(1):44-52. doi: 10.1177/17579759211035053. Epub 2021 Sep 11.
The prevalence of health misinformation on social media could significantly influence individuals' health behaviors. To examine the prevalent topics, propagation, and correction of coronavirus disease 2019 (COVID-19) misinformation, automated content analyses were conducted for posts on Sina Weibo, which is China's largest microblogging site. In total, 177,816 posts related to COVID-19 misinformation during the COVID-19 outbreak in China were analyzed. The structural topic modeling identified 23 valid topics regarding COVID-19 misinformation and its correction, which were further categorized into three general themes. Sentiment analysis was conducted to generate positive and negative sentiment scores for each post. The zero-inflated Poisson model indicated that only the negative sentiment was a significant predictor of the number of comments (β = 0.003, < 0.001) but not reposts. Furthermore, users are more prone to repost and comment on information regarding prevention/treatment (e.g., traditional Chinese medicine preventing COVID) as well as potential threats of COVID-19 (e.g., COVID-19 was defined as an epidemic by World Health Organization). Health education and promotion implications are discussed.
社交媒体上健康错误信息的流行可能会极大地影响个人的健康行为。为了研究 2019 冠状病毒病（COVID-19）错误信息的流行主题、传播和纠正，对中国最大的微博网站新浪微博上的帖子进行了自动化内容分析。共分析了中国 COVID-19 疫情期间与 COVID-19 错误信息相关的 177,816 篇帖子。结构主题建模确定了 23 个关于 COVID-19 错误信息及其纠正的有效主题，并进一步分为三个一般主题。进行情感分析，为每个帖子生成积极和消极的情感分数。零膨胀泊松模型表明，只有负面情绪才是评论数量的显著预测因素（β=0.003，<0.001），而不是转发。此外，用户更倾向于转发和评论有关预防/治疗的信息（例如，中药预防 COVID）以及 COVID-19 的潜在威胁（例如，世界卫生组织将 COVID-19 定义为流行病）。讨论了健康教育和促进的意义。
