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Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis
Background: COVID-19 has greatly attacked China, spreading in the whole world. Articles were posted on many official WeChat accounts to transmit health information about this pandemic. The public also sought related information via social media more frequently. However, little is known about what ki...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7399940/ https://www.ncbi.nlm.nih.gov/pubmed/32674510 http://dx.doi.org/10.3390/ijerph17145081 |
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author | Ma, Rongyang Deng, Zhaohua Wu, Manli |
author_facet | Ma, Rongyang Deng, Zhaohua Wu, Manli |
author_sort | Ma, Rongyang |
collection | PubMed |
description | Background: COVID-19 has greatly attacked China, spreading in the whole world. Articles were posted on many official WeChat accounts to transmit health information about this pandemic. The public also sought related information via social media more frequently. However, little is known about what kinds of information satisfy them better. This study aimed to explore the characteristics of health information dissemination that affected users’ information behavior on WeChat. Methods: Two-wave data were collected from the top 200 WeChat official accounts on the Xigua website. The data included the change in the number of followers and the total number of likes on each account in a 7-day period, as well as the number of each type of article and headlines about coronavirus. It was used to developed regression models and conduct content analysis to figure out information characteristics in quantity and content. Results: For nonmedical institution accounts in the model, report and story types of articles had positive effects on users’ following behaviors. The number of headlines on coronavirus positively impacts liking behaviors. For medical institution accounts, report and science types had a positive effect, too. In the content analysis, several common characteristics were identified. Conclusions: Characteristics in terms of the quantity and content in health information dissemination contribute to users’ information behavior. In terms of the content in the headlines, via coding and word frequency analysis, organizational structure, multimedia applications, and instructions—the common dimension in different articles—composed the common features in information that impacted users’ liking behaviors. |
format | Online Article Text |
id | pubmed-7399940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73999402020-08-17 Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis Ma, Rongyang Deng, Zhaohua Wu, Manli Int J Environ Res Public Health Article Background: COVID-19 has greatly attacked China, spreading in the whole world. Articles were posted on many official WeChat accounts to transmit health information about this pandemic. The public also sought related information via social media more frequently. However, little is known about what kinds of information satisfy them better. This study aimed to explore the characteristics of health information dissemination that affected users’ information behavior on WeChat. Methods: Two-wave data were collected from the top 200 WeChat official accounts on the Xigua website. The data included the change in the number of followers and the total number of likes on each account in a 7-day period, as well as the number of each type of article and headlines about coronavirus. It was used to developed regression models and conduct content analysis to figure out information characteristics in quantity and content. Results: For nonmedical institution accounts in the model, report and story types of articles had positive effects on users’ following behaviors. The number of headlines on coronavirus positively impacts liking behaviors. For medical institution accounts, report and science types had a positive effect, too. In the content analysis, several common characteristics were identified. Conclusions: Characteristics in terms of the quantity and content in health information dissemination contribute to users’ information behavior. In terms of the content in the headlines, via coding and word frequency analysis, organizational structure, multimedia applications, and instructions—the common dimension in different articles—composed the common features in information that impacted users’ liking behaviors. MDPI 2020-07-14 2020-07 /pmc/articles/PMC7399940/ /pubmed/32674510 http://dx.doi.org/10.3390/ijerph17145081 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ma, Rongyang Deng, Zhaohua Wu, Manli Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis |
title | Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis |
title_full | Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis |
title_fullStr | Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis |
title_full_unstemmed | Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis |
title_short | Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis |
title_sort | effects of health information dissemination on user follows and likes during covid-19 outbreak in china: data and content analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7399940/ https://www.ncbi.nlm.nih.gov/pubmed/32674510 http://dx.doi.org/10.3390/ijerph17145081 |
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