ArticleComputer methods and programs in biomedicine2021
Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic.
Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 31 citations in OpenAlex.
- Understanding Public Emotions: Spatiotemporal Dynamics in the Post-Pandemic Era Through Weibo Data.Behavioral sciences (Basel, Switzerland) · 2025Article
- Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response.Open forum infectious diseases · 2024Article
- The emotions of Chinese netizens toward the opening-up policies for COVID-19: panic, trust, and acceptance.Frontiers in public health · 2024Article
- Human behavior in the time of COVID-19: Learning from big data.Frontiers in big data · 2023Review
- The Emotional Anatomy of the Wuhan Lockdown: Sentiment Analysis Using Weibo Data.JMIR formative research · 2022Article
- SMD-YOLO: An efficient and lightweight detection method for mask wearing status during the COVID-19 pandemic.Computer methods and programs in biomedicine · 2022Article
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith outbreaks of COVID-19 around the world, lockdown restrictions are routinely imposed to limit the spread of the virus. During periods of lockdown, social media has become the main channel for citizens to exchange information with others. Public emotions are being generated and shared rapidly online with citizens using internet platforms to reduce anxiety and stress, and stay connected while isolated.
objectivesThis study aims to explore the regularity of emotional evolution by examining public emotions expressed in online discussions about the Wuhan lockdown event in January 2020.
methodsData related to the Wuhan lockdown was collected from Sina Weibo by web crawler. In this study, the Ortony, Clore, and Collins (OCC) model, Word2Vec, and Bi-directional Long Short-Term Memory model were employed to determine emotional types, train vectorization of words, and identify each text emotion for the training set. Latent Dirichlet Allocation models were also employed to mine the various topic categories, while topic emotional evolution was visualized.
resultsSeven types of emotions and four phases were categorized to describe emotional evolution on the Wuhan lockdown event. The study found that negative emotions such as blame and fear dominated in the early days, and public attitudes towards the lockdown gradually alleviated and reached a balance as the situation improved. Emotional expression about Wuhan lockdown event were significantly related to users' gender, location, and whether or not their account was verified. There were statistically significant correlations between different emotions within the subtle emotional categories. In addition, the evolution of emotions presented a different path due to different topics.
conclusionsMultiple emotional categories were determined in our study, providing a detailed and explainable emotion analysis to explored emotional appeal of citizen. The public emotions were gradually easing related to the Wuhan lockdown event, there yet exists regional discrimination and post-traumatic stress disorder in this process, which would lead us to pay continuous attention to citizens lives and psychological status post-pandemic. In addition, this study provided an appropriate method and reference case for the government's public opinion control and emotional appeasement.
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