Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling

作者全名："Li, Ziyu; Wu, Xiaoqian; Xu, Lin; Liu, Ming; Huang, Cheng"

作者地址："[Li, Ziyu; Wu, Xiaoqian; Xu, Lin; Liu, Ming; Huang, Cheng] Chongqing Med Univ, Coll Med Informat, 1 Med Coll Rd, Chongqing 400016, Peoples R China; [Wu, Xiaoqian] Army Med Univ, Mil Med Univ 3, Daping Hosp, Dept Qual Management, Chongqing, Peoples R China; [Xu, Lin] Army Med Univ, Mil Med Univ 2, Xinqiao Hosp, Dept Qual Management, Chongqing, Peoples R China"

通信作者："Huang, C (通讯作者)，Chongqing Med Univ, Coll Med Informat, 1 Med Coll Rd, Chongqing 400016, Peoples R China."

来源：JOURNAL OF MEDICAL INTERNET RESEARCH

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001096983300003

JCR分区：Q1

影响因子：5.8

年份：2023

卷号：25

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：topic model; health rumors; social media; WeChat official account; content analysis; public health; machine learning; Twitter; social network; misinformation; users; disease; diet

摘要："Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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."

基金机构："Intelligent Medicine Research Project of Chongqing Medical University [YJSZHYX202223, ZHYX202105]"

基金资助正文：Acknowledgments This research was supported by the Intelligent Medicine Research Project of Chongqing Medical University (YJSZHYX202223 and ZHYX202105) .