Chongqing Medical University, College of Medical Informatics, Chongqing, China.
Department of Quality Management, Daping Hospital, Army Medical University (The Third Military Medical University), Chongqing, China.
J Med Internet Res. 2023 Sep 21;25:e45019. doi: 10.2196/45019.
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.
社交网络已成为获取健康信息的主要渠道之一。然而，它们也成为了健康相关错误信息的来源，严重威胁着公众的身心健康。可以通过对社交网络上的谣言进行主题识别来对健康相关错误信息进行治理。然而，对于互联网上健康谣言的传播类型和途径，特别是中国社交媒体中与健康相关信息的谣言，关注甚少。
本研究旨在探讨微信公众平台用户偏好的健康相关错误信息类型及其流行趋势，并通过潜在狄利克雷分配模型（Latent Dirichlet Allocation，LDA）分析文本的建模结果。
我们使用网络爬虫工具捕获微信辟谣公众号上的健康谣言辟谣文章。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。在对收集的文本进行分词后，使用名为潜在狄利克雷分配的文档主题生成模型来识别和概括最常见的主题。计算了主题的比例分布，并分析了不同时期各种健康谣言的负面影响。此外，还通过每个时间点生成的健康谣言数量来分析健康谣言的流行程度。
我们从 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 8 个主题，即传染病防治谣言（1284/9366，13.71%）、疾病治疗及其效果谣言（1037/9366，11.07%）、食品安全谣言（1243/9366，13.27%）、癌症及其成因谣言（946/9366，10.10%）、方案和疾病谣言（1540/9366，16.44%）、传播谣言（914/9366，9.76%）、健康饮食谣言（1068/9366，11.40%）和营养与健康谣言（1334/9366，14.24%）。此外，我们总结了 4 个主题下的 8 个主题，即公共卫生、疾病、饮食与健康和谣言传播。
我们的研究表明，主题建模可以为健康谣言治理提供分析和见解。谣言的发展趋势表明，大多数谣言都集中在公共卫生、疾病和饮食与健康问题上。政府仍需要根据本国流行的谣言，实施相关和全面的谣言管理策略，并制定适当的政策。除了规范社交媒体平台上的内容传播外，还应提高国家健康教育质量。应明确实施网络治理，因为这些快速发展的平台存在隐私问题。信息的传播者和接收者都应保持现实态度，正确传播健康信息。此外，我们建议进行与情感分析相关的研究，以验证健康谣言相关主题的影响。
