ArticleData and information management2021
Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis.
Article in Data and information management, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Public Attitudes Toward Violence Against Doctors: Sentiment Analysis of Chinese Users.JMIR medical informatics · 2025Article
- Fighting COVID-19 in China's Greater Bay Area: how do different policy styles affect policy effectiveness?Frontiers in public health · 2025Article
- News Coverage of the COVID-19 Pandemic on Social Media and the Public's Negative Emotions: Computational Study.Journal of medical Internet research · 2024Article
- Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media.Journal of medical Internet research · 2024Article
- Article
- The emotions of Chinese netizens toward the opening-up policies for COVID-19: panic, trust, and acceptance.Frontiers in public health · 2024Article
- Topic and Trend Analysis of Weibo Discussions About COVID-19 Medications Before and After China's Exit from the Zero-COVID Policy: Retrospective Infoveillance Study.Journal of medical Internet research · 2023Article
- "Do as I say but not as I do": Influence of political leaders' populist communication styles on public adherence in a crisis using the global case of COVID-19 movement restrictions.Data and information management · 2023Article
- Multilingual deep learning framework for fake news detection using capsule neural network.Journal of intelligent information systems · 2023Article
- Dynamic Characteristics and Evolution Analysis of Information Dissemination Theme of Social Networks under Emergencies.Behavioral sciences (Basel, Switzerland) · 2023Article
- Opinion Leaders and Structural Hole Spanners Influencing Echo Chambers in Discussions About COVID-19 Vaccines on Social Media in China: Network Analysis.Journal of medical Internet research · 2022Article
- The governance of imported 2019-nCov infections: What can be learned from China's experience?Global health research and policy · 2022Article
- China public emotion analysis under normalization of COVID-19 epidemic: Using Sina Weibo.Frontiers in psychology · 2022Article
- Public Social Media Discussions on Agricultural Product Safety Incidents: Chinese African Swine Fever Debate on Weibo.Frontiers in psychology · 2022Article
- Research on the Characteristics and Usefulness of User Reviews of Online Mental Health Consultation Services: A Content Analysis.Healthcare (Basel, Switzerland) · 2021Article
- Are Diverse Media Better than a Single Medium? The Relationship between Mixed Media and Perceived Effect from the Perspective of Online Psychological Counseling.International journal of environmental research and public health · 2021Article
- Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach.IEEE access : practical innovations, open solutions · 2021Article
- Specialists, Scientists, and Sentiments: Word2Vec and Doc2Vec in Analysis of Scientific and Medical Texts.SN computer science · 2021Article
- Frequent and diverse use of electronic health records in the United States: A trend analysis of national surveys.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and "self-media," together contribute to the information spread of positive sentiment.
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Registered trials
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.