ArticleJMIR public health and surveillance2020
Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis.
Article in JMIR public health and surveillance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 48 citations in OpenAlex.
- Dynamic monitoring of public opinion on fertility intentions: based on the intersection of empirical and social media perspectives.Frontiers in public health · 2026Article
- Exploring emotions and perspectives of families with autism spectrum disorder individuals on sports participation: a case study on Weibo using machine learning method.BMC sports science, medicine & rehabilitation · 2025Article
- Social media crisis communication and public engagement during COVID-19 analyzing public health and news media organizations' tweeting strategies.Scientific reports · 2025Article
- Promoting Public Engagement in Palliative and End-of-Life Care Discussions on Chinese Social Media: Model Development and Analysis.Journal of medical Internet research · 2025Article
- Fighting COVID-19 in China's Greater Bay Area: how do different policy styles affect policy effectiveness?Frontiers in public health · 2025Article
- COVID-19 and Masking Disparities: Qualitative Analysis of Trust on the CDC's Facebook Page.International journal of environmental research and public health · 2023Article
- "Help Us!": a content analysis of COVID-19 help-seeking posts on Weibo during the first lockdown.BMC public health · 2023Article
- Article
- Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses.Journal of medical Internet research · 2022Article
- Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review.JMIR mental health · 2022Article
- Panic Buying: Modeling What Drives it and How it Deteriorates Emotional Well-being.Family and consumer sciences research journal · 2021Article
- Obesity-Related Communication in Digital Chinese News From Mainland China, Hong Kong, and Taiwan: Automated Content Analysis.JMIR public health and surveillance · 2021Article
- The Impact of Public Health Events on COVID-19 Vaccine Hesitancy on Chinese Social Media: National Infoveillance Study.JMIR public health and surveillance · 2021Article
- Designing a conceptual framework for misinformation on social media: a qualitative study on COVID-19.BMC research notes · 2021Article
- A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States.BMC public health · 2021Article
- Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study.JMIR public health and surveillance · 2021Article
- COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records.IEEE access : practical innovations, open solutions · 2021Article
- Capturing the Trajectory of Psychological Status and Analyzing Online Public Reactions During the Coronavirus Disease 2019 Pandemic Through Weibo Posts in China.Frontiers in psychology · 2021Article
- Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19.Frontiers in public health · 2021Article
- Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThis 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.
methodsWe 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.
resultsWe 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.
conclusionsBetween 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.