COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis
BackgroundThe 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 hea...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-04-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2023/1/e45051 |
| _version_ | 1797734166217883648 |
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| author | Xiaoqian Wu Ziyu Li Lin Xu Pengfei Li Ming Liu Cheng Huang |
| author_facet | Xiaoqian Wu Ziyu Li Lin Xu Pengfei Li Ming Liu Cheng Huang |
| author_sort | Xiaoqian Wu |
| collection | DOAJ |
| description |
BackgroundThe 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.
ObjectiveThis 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).
MethodsA 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.
ResultsA 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.
ConclusionsTo 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. |
| first_indexed | 2024-03-12T12:40:20Z |
| format | Article |
| id | doaj.art-649d2330499445208675ec45fb85a335 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:40:20Z |
| publishDate | 2023-04-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-649d2330499445208675ec45fb85a3352023-08-28T23:53:35ZengJMIR PublicationsJournal of Medical Internet Research1438-88712023-04-0125e4505110.2196/45051COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content AnalysisXiaoqian Wuhttps://orcid.org/0000-0001-6416-2445Ziyu Lihttps://orcid.org/0000-0003-0334-9075Lin Xuhttps://orcid.org/0000-0002-2678-0493Pengfei Lihttps://orcid.org/0000-0001-9935-616XMing Liuhttps://orcid.org/0000-0002-3937-0557Cheng Huanghttps://orcid.org/0000-0001-7937-7166 BackgroundThe 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. ObjectiveThis 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). MethodsA 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. ResultsA 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. ConclusionsTo 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.https://www.jmir.org/2023/1/e45051 |
| spellingShingle | Xiaoqian Wu Ziyu Li Lin Xu Pengfei Li Ming Liu Cheng Huang COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis Journal of Medical Internet Research |
| 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 |
| url | https://www.jmir.org/2023/1/e45051 |
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