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.
BACKGROUND Social network has become one of the main channels for the public to obtain health information. However, it has also become a source for the spread of health-related misinformation. Health-related misinformation seriously threatens the public’s physical and mental health. Topic identification is the premise of health-related misinformation governance. OBJECTIVE This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends. METHODS We used a web crawler tool to capture health rumor-dispelling tweets collected on the rumor-dispelling public account. We collected text information from health-debunking tweets posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model, Latent Dirichlet Assignment, is used to identify and generalize the most common topics. The proportion distribution of themes was calculated, and the negative impact of various health rumors in different periods was subsequently analyzed. Additionally, the prevalence of health rumors was analyzed using the number of health rumors generated at each time point. RESULTS From January 1, 2016 to August 31, 2022, we collected 9,366 rumor-refuting tweets from WeChat official accounts. Through topic modeling, we divided the health rumors into eight topics, including the prevention and treatment of infectious diseases (n = 1,284, 13.71%), disease therapy and its effects (n = 1,037, 11.07%), food safety (n = 1,243, 13.27%), cancer and its causes (n = 946, 10.10%), regimen and disease (n = 1,540, 16.44%), rumors of transmission (n = 914, 9.76%), healthy diet (n = 1,068, 11.40%), and nutrition and health (n = 1,334, 14.24%). Furthermore, we summarized the eight topics into four themes, including public health, disease, diet and health, and rumor spreading. CONCLUSIONS Our study shows that the topic model can provide analysis and insights into health rumor governance. The analysis of rumor development trends shows that public health, disease, and diet and health problems are the most affected areas of rumors. Governments still need to consider national conditions, formulate appropriate policies, and deal with health rumors more comprehensively. While ensuring the health of the Internet, we should also improve the level of national quality education. We recommend that additional sentiment analysis-related studies be conducted to verify the impact of health rumor-related topics.
BACKGROUND Social network has become one of the main channels for the public to obtain health information. However, it has also become a source for the spread of health-related misinformation. Health-related misinformation seriously threatens the public’s physical and mental health. Topic identification is the premise of health-related misinformation governance. OBJECTIVE This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends. METHODS We used a web crawler tool to capture health rumor-dispelling tweets collected on the rumor-dispelling public account. We collected text information from health-debunking tweets posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model, Latent Dirichlet Assignment, is used to identify and generalize the most common topics. The proportion distribution of themes was calculated, and the negative impact of various health rumors in different periods was subsequently analyzed. Additionally, the prevalence of health rumors was analyzed using the number of health rumors generated at each time point. RESULTS From January 1, 2016 to August 31, 2022, we collected 9,366 rumor-refuting tweets from WeChat official accounts. Through topic modeling, we divided the health rumors into eight topics, including the prevention and treatment of infectious diseases (n = 1,284, 13.71%), disease therapy and its effects (n = 1,037, 11.07%), food safety (n = 1,243, 13.27%), cancer and its causes (n = 946, 10.10%), regimen and disease (n = 1,540, 16.44%), rumors of transmission (n = 914, 9.76%), healthy diet (n = 1,068, 11.40%), and nutrition and health (n = 1,334, 14.24%). Furthermore, we summarized the eight topics into four themes, including public health, disease, diet and health, and rumor spreading. CONCLUSIONS Our study shows that the topic model can provide analysis and insights into health rumor governance. The analysis of rumor development trends shows that public health, disease, and diet and health problems are the most affected areas of rumors. Governments still need to consider national conditions, formulate appropriate policies, and deal with health rumors more comprehensively. While ensuring the health of the Internet, we should also improve the level of national quality education. We recommend that additional sentiment analysis-related studies be conducted to verify the impact of health rumor-related topics.
Background The COVID-19 vaccine is an effective tool in the fight against the COVID-19 outbreak. As the main channel of information dissemination in the context of the epidemic, social media influences public trust and acceptance of the vaccine. The rational application of health behavior theory is a guarantee of effective public health information dissemination. However, little is known about the application of health behavior theory in web-based COVID-19 vaccine messages, especially from Chinese social media posts. Objective This study aimed to understand the main topics and communication characteristics of hot papers related to COVID-19 vaccine on the WeChat platform and assess the health behavior theory application with the aid of health belief model (HBM). Methods A systematic search was conducted on the Chinese social media platform WeChat to identify COVID-19 vaccine–related papers. A coding scheme was established based on the HBM, and the sample was managed and coded using NVivo 12 (QSR International) to assess the application of health behavior theory. The main topics of the papers were extracted through the Latent Dirichlet Allocation algorithm. Finally, temporal analysis was used to explore trends in the evolution of themes and health belief structures in the papers. Results A total of 757 papers were analyzed. Almost all (671/757, 89%) of the papers did not have an original logo. By topic modeling, 5 topics were identified, which were vaccine development and effectiveness (267/757, 35%), disease infection and protection (197/757, 26%), vaccine safety and adverse reactions (52/757, 7%), vaccine access (136/757, 18%), and vaccination science popularization (105/757, 14%). All papers identified at least one structure in the extended HBM, but only 29 papers included all of the structures. Descriptions of solutions to obstacles (585/757, 77%) and benefit (468/757, 62%) were the most emphasized components in all samples. Relatively few elements of susceptibility (208/757, 27%) and the least were descriptions of severity (135/757, 18%). Heat map visualization revealed the change in health belief structure before and after vaccine entry into the market. Conclusions To the best of our knowledge, this is the first study to assess the structural expression of health beliefs in information related to the COVID-19 vaccine on the WeChat public platform based on an HBM. The study also identified topics and communication characteristics before and after the market entry of vaccines. Our findings can inform customized education and communication strategies to promote vaccination not only in this pandemic but also in future pandemics.
BACKGROUND The COVID-19 vaccine is an effective tool in the fight against the COVID 19 outbreak and is the best way to restore social functioning. As the main channel of information dissemination in the context of the epidemic, social media influences public trust and acceptance of the vaccine. The rational application of health behavior theory is a guarantee of effective public health information dissemination. However, little is known about the application of health behavior theory in online COVID-19 vaccine messages, especially from Chinese social media posts. OBJECTIVE This study aimed to understand the main topics and communication characteristics of hot articles related to COVID-19 vaccine on the WeChat platform and to assess the health behavior theory application with the aid of health belief model. METHODS A systematic search was conducted on the Chinese social media platform WeChat to identify COVID-19 vaccine-related articles. A coding scheme was established based on the health belief model, and the sample was managed and coded using NVivo12 (QSR International) to assess the application of health behavior theory. And the main topics of the articles were extracted through the Latent Dirichlet Allocation (LDA) algorithm. Finally, temporal analysis was used to explore trends in the evolution of themes and health belief structures in the articles. RESULTS A total of 757 articles were analyzed. Almost all (671/757, 89%) of the articles were not have an original logo. By topic modeling, five topics were identified which were vaccine development and effectiveness (267/757, 35%), disease infection and protection (197/757, 26%), vaccine safety and adverse reactions (52/757, 7%), vaccine access (136/757, 18%), and vaccination science popularization (105/757, 14%). All articles identified at least one structure in the extended health belief model, but only 29 articles included all of the structures. Descriptions of solutions to obstacles (585/757, 77%) and benefit (468/757, 62%) were the most emphasized components in all samples. Relatively few elements of susceptibility (208/757, 27%) and the least were descriptions of severity (135/757, 18%). The heat map visualization shows the change in health belief structure before and after vaccine entry into the market. CONCLUSIONS To the best of our knowledge, this is the first study to assess the structural expression of health beliefs in information related to the COVID-19 vaccine on the WeChat public platform based on a health belief model. The study also identified topics and communication characteristics before and after the market entry of vaccines. Our findings can inform customized education and communication strategies to promote vaccination not only in this pandemic but also in future pandemics.
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