Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis.

COVID-19 China Weibo attitude behavior content analysis data mining infodemic infodemiology infoveillance knowledge social media

Journal

JMIR public health and surveillance
ISSN: 2369-2960
Titre abrégé: JMIR Public Health Surveill
Pays: Canada
ID NLM: 101669345

Informations de publication

Date de publication:
07 12 2020
Historique:
received: 04 09 2020
accepted: 06 11 2020
revised: 29 10 2020
pubmed: 12 11 2020
medline: 22 12 2020
entrez: 11 11 2020
Statut: epublish

Résumé

The COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people's knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences. This study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese. We used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19-related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures. We identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak's seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior. Between the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.

Sections du résumé

BACKGROUND
The COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people's knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences.
OBJECTIVE
This study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese.
METHODS
We used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19-related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures.
RESULTS
We identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak's seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior.
CONCLUSIONS
Between the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.

Identifiants

pubmed: 33175693
pii: v6i4e24125
doi: 10.2196/24125
pmc: PMC7722484
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e24125

Informations de copyright

©Qing Xu, Ziyi Shen, Neal Shah, Raphael Cuomo, Mingxiang Cai, Matthew Brown, Jiawei Li, Tim Mackey. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 07.12.2020.

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Auteurs

Qing Xu (Q)

Department of Healthcare Research and Policy, University of California, San Diego - Extension, La Jolla, CA, United States.
Global Health Policy and Data Institute, San Diego, CA, United States.
S-3 Research LLC, San Diego, CA, United States.

Ziyi Shen (Z)

Masters Program in Computer Science, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States.

Neal Shah (N)

Department of Healthcare Research and Policy, University of California, San Diego - Extension, La Jolla, CA, United States.
Global Health Policy and Data Institute, San Diego, CA, United States.

Raphael Cuomo (R)

Global Health Policy and Data Institute, San Diego, CA, United States.
Department of Anesthesiology, School of Medicine, University of California, San Diego, La Jolla, CA, United States.

Mingxiang Cai (M)

Global Health Policy and Data Institute, San Diego, CA, United States.
S-3 Research LLC, San Diego, CA, United States.
Masters Program in Computer Science, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States.

Matthew Brown (M)

US Embassy, National Cancer Institute, National Institutes of Health, Beijing, China.

Jiawei Li (J)

Department of Healthcare Research and Policy, University of California, San Diego - Extension, La Jolla, CA, United States.
Global Health Policy and Data Institute, San Diego, CA, United States.
S-3 Research LLC, San Diego, CA, United States.
Department of Anesthesiology and Division of Infectious Diseases and Global Public Health, School of Medicine, University of California, San Diego, La Jolla, CA, United States.

Tim Mackey (T)

Department of Healthcare Research and Policy, University of California, San Diego - Extension, La Jolla, CA, United States.
Global Health Policy and Data Institute, San Diego, CA, United States.
S-3 Research LLC, San Diego, CA, United States.
Department of Anesthesiology and Division of Infectious Diseases and Global Public Health, School of Medicine, University of California, San Diego, La Jolla, CA, United States.

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