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

• Published in: Journal of medical Internet research 2023-09, Vol.25 (6), p.e45019-e45019
• Main Authors: Li, Ziyu, Wu, Xiaoqian, Xu, Lin, Liu, Ming, Huang, Cheng
• Format: Article
• Language: English
• Subjects: Cancer; Communicable diseases; Communication; COVID-19 vaccines; Data mining; Diet; Dissemination; Epidemics; Exercise; False information; Food; Food safety; Gossip; Governance; Health aspects; Health education; Health information; Health problems; Health status; Healthy food; Infectious diseases; Keywords; Mental health; Misinformation; Nutrition; Original Paper; Political aspects; Privacy; Public health; Safety and security measures; Segmentation; Sentiment analysis; Social media; Social networks; Strategic planning (Business); Topics
• ISSN: 1438-8871; 1439-4456; 1438-8871
• 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 formula