ArticleJMIR public health and surveillance2020
Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study.
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 161 papers, 1 of them a synthesis that pooled it.
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Who cites it
161 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Uncovering the hierarchical determinants of continuous usage in online health communities: integrating meta-analysis with FUZZY-DEMATEL-AISM.Scientific reports · 2026Pooled it
- Analysis of Water Fluoridation Debates on Meta Platforms Using Advanced Machine Learning Approaches.JDR clinical and translational research · 2026Article
- Partisan and Geographic Variation in Emotional Responses to COVID-19 Vaccination on Social Media.JAMA network open · 2026Article
- Comparison of Mask-Wearing Behavior on Social Media and Its Relationship With Demographic Characteristics During the COVID-19 Pandemic: Social Media Data Analysis Between the United States and Japan.JMIR human factors · 2026Article
- Examining contraception-related discourse on social media after the Dobbs v. Jackson Women's Health Organization Supreme Court decision: a textual analysis of user-generated content on X (formerly Twitter).Reproductive health · 2026Article
- Digital epidemiology and public health surveillance: scientometric mapping of emerging technologies and challenges (2000-2025).Frontiers in digital health · 2026Review
- Group cognitive space of online rumors in public health emergencies: a theoretical and empirical study.Frontiers in public health · 2026Article
- A bimodal longitudinal investigation on changes in sentiments over social media interactions owing to COVID-19 pandemic.Scientific reports · 2025Article
- Article
- A call for government-driven health promotion solutions surveillance in public health: Nests and networks.Canadian journal of public health = Revue canadienne de sante publique · 2025Article
- Mapping the "X" Debate: Water Fluoridation Sentiment Analysis With Advanced Machine Learning.Journal of public health dentistry · 2025Article
- Public Discourse Toward Older Drivers in Japan Using Social Media Data From 2010 to 2022: Longitudinal Analysis.JMIR infodemiology · 2025Article
- Impact of COVID-19 Pandemic on Social Determinants of Health Issues of Marginalized Black and Asian Communities: A Social Media Analysis Empowered by Natural Language Processing.Journal of racial and ethnic health disparities · 2025Article
- Social media crisis communication and public engagement during COVID-19 analyzing public health and news media organizations' tweeting strategies.Scientific reports · 2025Article
- Impact of the COVID-19 Pandemic and the 2021 National Institute for Health and Care Excellence Guidelines on Public Perspectives Toward Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Thematic and Sentiment Analysis on Twitter (Rebranded as X).Journal of medical Internet research · 2025Article
- Food Access in New York City During the COVID-19 Pandemic: Social Media Monitoring Study.JMIR formative research · 2025Article
- Mapping Infodemic Responses: A Geospatial Analysis of COVID-19 Discourse on Twitter in Italy.International journal of environmental research and public health · 2025Article
- COVID-19 Public Health Communication on X (Formerly Twitter): Cross-Sectional Study of Message Type, Sentiment, and Source.JMIR formative research · 2025Article
- Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.Journal of medical Internet research · 2025Article
- Public Health Advocacy in Times of Pandemic: An Analysis of the Medicare-For-All Debate on Twitter During COVID-19.Behavioral sciences (Basel, Switzerland) · 2025Article
101 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data.
objectiveThe aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic.
methodsData mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis.
resultsThe results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19.
conclusionsSentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.
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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.