社交网络已成为获取健康信息的主要渠道之一。然而，它们也成为健康相关错误信息的来源，严重威胁着公众的身体和心理健康。通过社交网络上的谣言主题识别可以实施健康相关错误信息的治理。然而，对于互联网上健康信息的类型和传播途径，尤其是在中国社交媒体上关于健康信息的谣言，一直没有得到足够关注。本研究旨在探讨微信公众平台用户偏爱的健康相关错误信息类型及其流行趋势，通过使用潜在狄利克雷分配模型对文本进行建模分析结果。我们使用网络爬虫工具捕获了微信辟谣公众号上的健康辟谣文章。我们收集了2016年1月1日至2022年8月31日期间发布的健康辟谣文章的信息。在对收集到的文本进行分词后，采用了一种叫做潜在狄利克雷分配的文档主题生成模型，用于识别和概括最常见的主题。计算各主题比例分布，并分析不同时期不同健康谣言的负面影响。此外，通过每个时间点生成的健康谣言数量分析健康谣言的流行程度。我们在2016年1月1日至2022年8月31日期间从微信官方账号收集了9366篇辟谣文章。通过主题建模，我们将健康谣言分为了8个主题，即传染病的预防和治疗（1284/9366，13.71%），疾病治疗及其效果（1037/9366，11.07%），食品安全（1243/9366，13.27%），癌症及其原因（946/9366，10.10%），养生与疾病（1540/9366，16.44%），传播（914/9366，9.76%），健康饮食（1068/9366，11.40%）和营养与健康（1334/9366，14.24%）。此外，我们将这8个主题总结为4个主题，即公共卫生、疾病、饮食与健康以及谣言传播。我们的研究表明，主题建模可以提供对健康谣言治理的分析和见解。谣言发展趋势显示，大多数谣言涉及公共卫生、疾病和饮食健康问题。政府仍然需要根据本国流行的谣言实施相关和综合的谣言管理策略，并制定相应的政策。除了规范社交媒体平台上传播的内容外，还应加强国家健康教育的质量。应明确实施社交网络的治理，因为这些快速发展的平台存在隐私问题。信息的发布者和接收者都应确保持有现实态度，并正确传播健康信息。此外，我们建议开展与情感分析相关的研究，以验证健康谣言相关主题的影响。©Ziyu Li, Xiaoqian Wu, Lin Xu, Ming Liu, Cheng Huang. 原文发表于医学互联网研究杂志 (https://www.jmir.org)，2023年9月21日。

Social networks have become one of the main channels for obtaining health information. However, they have also become a source of health-related misinformation, which seriously threatens the public's physical and mental health. Governance of health-related misinformation can be implemented through topic identification of rumors on social networks. However, little attention has been paid to studying the types and routes of dissemination of health rumors on the internet, especially rumors regarding health-related information in Chinese social media.This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends and to analyze the modeling results of the text by using the Latent Dirichlet Allocation model.We used a web crawler tool to capture health rumor-dispelling articles on WeChat rumor-dispelling public accounts. We collected information from health-debunking articles posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model called Latent Dirichlet Allocation was used to identify and generalize the most common topics. The proportion distribution of the themes was calculated, and the negative impact of various health rumors in different periods was analyzed. Additionally, the prevalence of health rumors was analyzed by the number of health rumors generated at each time point.We collected 9366 rumor-refuting articles from January 1, 2016, to August 31, 2022, from WeChat official accounts. Through topic modeling, we divided the health rumors into 8 topics, that is, rumors on prevention and treatment of infectious diseases (1284/9366, 13.71%), disease therapy and its effects (1037/9366, 11.07%), food safety (1243/9366, 13.27%), cancer and its causes (946/9366, 10.10%), regimen and disease (1540/9366, 16.44%), transmission (914/9366, 9.76%), healthy diet (1068/9366, 11.40%), and nutrition and health (1334/9366, 14.24%). Furthermore, we summarized the 8 topics under 4 themes, that is, public health, disease, diet and health, and spread of rumors.Our study shows that topic modeling can provide analysis and insights into health rumor governance. The rumor development trends showed that most rumors were on public health, disease, and diet and health problems. Governments still need to implement relevant and comprehensive rumor management strategies based on the rumors prevalent in their countries and formulate appropriate policies. Apart from regulating the content disseminated on social media platforms, the national quality of health education should also be improved. Governance of social networks should be clearly implemented, as these rapidly developed platforms come with privacy issues. Both disseminators and receivers of information should ensure a realistic attitude and disseminate health information correctly. In addition, we recommend that sentiment analysis-related studies be conducted to verify the impact of health rumor-related topics.©Ziyu Li, Xiaoqian Wu, Lin Xu, Ming Liu, Cheng Huang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.09.2023.