Measuring the impacts of knowledge information from different media platforms on COVID-19 vaccination behavior among rural residents in central China

In response to the severe outbreak of COVID-19, the world is making every effort to promote vaccination. The Chinese government reports on the progress of the epidemic and related information about vaccines through the media, especially the official media, which has some influence on residents’ attitude and vaccination behavior. But few studies have discussed the potential public health impact of vaccines on social media and analyzed them alongside the impact of official media. In addition, from the perspective of SEIR models and the diffusion of knowledge, it is proposed that information about the epidemic situation and knowledge of vaccines before infection will prompt rural residents in central China to correctly understand vaccines and take the initiative to vaccinate. A survey of 1453 rural residents in Hubei, Henan, and Anhui provinces in central China showed that residents’ belief in official media coverage affecting vaccination behavior was established, while entertainment network platforms hindered the dissemination of health knowledge due to excessive entertainment information. In addition, perceived effectiveness, perceived security, perceived hazard, and knowledge related to vaccines can affect vaccination behavior. In this study, the influence factors of media information on vaccination behavior were discussed in theory and practice.

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Publication Details

Journal
Journal of Computational Methods in Sciences and Engineering
Published
2025-08-11
DOI
https://doi.org/10.1177/14727978251369166
Primary Topic
Misinformation and Its Impacts
Type
article
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article

Measuring the impacts of knowledge information from different media platforms on COVID-19 vaccination behavior among rural residents in central China

Qijie Ruan, Ning Li, Mengyu Li, Abdul Rahim Ahmed Soliman Darweesh
Journal of Computational Methods in Sciences and Engineering
Misinformation and Its Impacts
article

Measuring the impacts of knowledge information from different media platforms on COVID-19 vaccination behavior among rural residents in central China

Qijie Ruan, Ning Li, Mengyu Li, Abdul Rahim Ahmed Soliman Darweesh
article en

Abstract

In response to the severe outbreak of COVID-19, the world is making every effort to promote vaccination. The Chinese government reports on the progress of the epidemic and related information about vaccines through the media, especially the official media, which has some influence on residents’ attitude and vaccination behavior. But few studies have discussed the potential public health impact of vaccines on social media and analyzed them alongside the impact of official media. In addition, from the perspective of SEIR models and the diffusion of knowledge, it is proposed that information about the epidemic situation and knowledge of vaccines before infection will prompt rural residents in central China to correctly understand vaccines and take the initiative to vaccinate. A survey of 1453 rural residents in Hubei, Henan, and Anhui provinces in central China showed that residents’ belief in official media coverage affecting vaccination behavior was established, while entertainment network platforms hindered the dissemination of health knowledge due to excessive entertainment information. In addition, perceived effectiveness, perceived security, perceived hazard, and knowledge related to vaccines can affect vaccination behavior. In this study, the influence factors of media information on vaccination behavior were discussed in theory and practice.

Journal of Computational Methods in Sciences and Engineering
Beni-Suef University (EG), Sanjiang University (CN), Henan University of Technology (CN), Huanghe Science and Technology College (CN)
Openalex Percentile: Top 73%
Misinformation and Its Impacts
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Measuring the impacts of knowledge information from different media platforms on COVID-19 vaccination behavior among rural residents in central China — Qijie Ruan, Ning Li, et al. · Journal of Computational Methods in Sciences and Engineering (2025) | TGRS Research Map | TGRS