COVID-19 Vaccine-Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis

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

作者地址："[Wu, Xiaoqian; Li, Ziyu; Liu, Ming; Huang, Cheng] Chongqing Med Univ, Coll Med Informat, Chongqing, Peoples R China; [Xu, Lin] Army Med Univ, Third Mil Med Univ, Xiaoqiao Hosp, Dept Informat, Chongqing, Peoples R China; [Li, Pengfei] Weifang Med Univ, Sch Publ Hlth, Weifang, Peoples R China; [Huang, Cheng] Chongqing Med Univ, Coll Med Informat, 1 Yixueyuan Rd, Chongqing 400016, Peoples R China"

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

来源：JOURNAL OF MEDICAL INTERNET RESEARCH

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001006098100001

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：health belief model; COVID-19 vaccines; WeChat; content analysis; topic modeling; public health; COVID-19

摘要："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."

基金机构："Intelligent Medicine Research Project of Chongqing Medical University [YJSZHYX202124, ZHYX202105]"

基金资助正文：Acknowledgments This research was supported by the Intelligent Medicine Research Project of Chongqing Medical University (YJSZHYX202124 and ZHYX202105) .