Cargando…

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

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 conve...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Qing, Shen, Ziyi, Shah, Neal, Cuomo, Raphael, Cai, Mingxiang, Brown, Matthew, Li, Jiawei, Mackey, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722484/
https://www.ncbi.nlm.nih.gov/pubmed/33175693
http://dx.doi.org/10.2196/24125
_version_ 1783620162688122880
author Xu, Qing
Shen, Ziyi
Shah, Neal
Cuomo, Raphael
Cai, Mingxiang
Brown, Matthew
Li, Jiawei
Mackey, Tim
author_facet Xu, Qing
Shen, Ziyi
Shah, Neal
Cuomo, Raphael
Cai, Mingxiang
Brown, Matthew
Li, Jiawei
Mackey, Tim
author_sort Xu, Qing
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7722484
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-77224842020-12-11 Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis Xu, Qing Shen, Ziyi Shah, Neal Cuomo, Raphael Cai, Mingxiang Brown, Matthew Li, Jiawei Mackey, Tim JMIR Public Health Surveill Original Paper 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. JMIR Publications 2020-12-07 /pmc/articles/PMC7722484/ /pubmed/33175693 http://dx.doi.org/10.2196/24125 Text en ©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. 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 JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Xu, Qing
Shen, Ziyi
Shah, Neal
Cuomo, Raphael
Cai, Mingxiang
Brown, Matthew
Li, Jiawei
Mackey, Tim
Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis
title Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis
title_full Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis
title_fullStr Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis
title_full_unstemmed Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis
title_short Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis
title_sort characterizing weibo social media posts from wuhan, china during the early stages of the covid-19 pandemic: qualitative content analysis
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722484/
https://www.ncbi.nlm.nih.gov/pubmed/33175693
http://dx.doi.org/10.2196/24125
work_keys_str_mv AT xuqing characterizingweibosocialmediapostsfromwuhanchinaduringtheearlystagesofthecovid19pandemicqualitativecontentanalysis
AT shenziyi characterizingweibosocialmediapostsfromwuhanchinaduringtheearlystagesofthecovid19pandemicqualitativecontentanalysis
AT shahneal characterizingweibosocialmediapostsfromwuhanchinaduringtheearlystagesofthecovid19pandemicqualitativecontentanalysis
AT cuomoraphael characterizingweibosocialmediapostsfromwuhanchinaduringtheearlystagesofthecovid19pandemicqualitativecontentanalysis
AT caimingxiang characterizingweibosocialmediapostsfromwuhanchinaduringtheearlystagesofthecovid19pandemicqualitativecontentanalysis
AT brownmatthew characterizingweibosocialmediapostsfromwuhanchinaduringtheearlystagesofthecovid19pandemicqualitativecontentanalysis
AT lijiawei characterizingweibosocialmediapostsfromwuhanchinaduringtheearlystagesofthecovid19pandemicqualitativecontentanalysis
AT mackeytim characterizingweibosocialmediapostsfromwuhanchinaduringtheearlystagesofthecovid19pandemicqualitativecontentanalysis