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

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...

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Autores principales: Wu, Xiaoqian, Li, Ziyu, Xu, Lin, Li, Pengfei, Liu, Ming, Huang, Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132036/
https://www.ncbi.nlm.nih.gov/pubmed/37058349
http://dx.doi.org/10.2196/45051
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author Wu, Xiaoqian
Li, Ziyu
Xu, Lin
Li, Pengfei
Liu, Ming
Huang, Cheng
author_facet Wu, Xiaoqian
Li, Ziyu
Xu, Lin
Li, Pengfei
Liu, Ming
Huang, Cheng
author_sort Wu, Xiaoqian
collection PubMed
description 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.
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spelling pubmed-101320362023-04-27 COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis Wu, Xiaoqian Li, Ziyu Xu, Lin Li, Pengfei Liu, Ming Huang, Cheng J Med Internet Res Original Paper 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. JMIR Publications 2023-04-14 /pmc/articles/PMC10132036/ /pubmed/37058349 http://dx.doi.org/10.2196/45051 Text en ©Xiaoqian Wu, Ziyu Li, Lin Xu, Pengfei Li, Ming Liu, Cheng Huang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.04.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Wu, Xiaoqian
Li, Ziyu
Xu, Lin
Li, Pengfei
Liu, Ming
Huang, Cheng
COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis
title COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis
title_full COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis
title_fullStr COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis
title_full_unstemmed COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis
title_short COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis
title_sort covid-19 vaccine–related information on the wechat public platform: topic modeling and content analysis
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132036/
https://www.ncbi.nlm.nih.gov/pubmed/37058349
http://dx.doi.org/10.2196/45051
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