ArticleJournal of medical Internet research2021
COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers.
What it found
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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
134 citing papers in PubMed, 352 citations in OpenAlex.
- Interplay between vaccine-related informational milestones, social media discourse, and vaccine administration rates for human papillomavirus and measles/mumps/rubella vaccines in the United States.Human vaccines & immunotherapeutics · 2026Article
- Comparison of Artificial Intelligence Tools With Human Coding for Sentiment, Topic, and Thematic Analysis Tasks of Public Health Datasets During the COVID-19 Pandemic in Australia: Case Study.Online journal of public health informatics · 2026Article
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- Digital epidemiology and public health surveillance: scientometric mapping of emerging technologies and challenges (2000-2025).Frontiers in digital health · 2026Review
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- What do cancer patients discuss online regarding CINV management? A social media-based topic modeling study.Frontiers in oncology · 2026Article
- Social Media Metrics and Popular Legitimacy: Content Analysis of Pre- and Post-COVID-19 Public Engagement With the World Health Organization on X.Journal of medical Internet research · 2025Article
- Themes, Policies, and Attention Shifts Regarding COVID-19 Vaccinations in German-Speaking Regions: Infoveillance Study Using Tweets.Journal of medical Internet research · 2025Article
- Information Pathways and Voids in Critical German Online Communities During the COVID-19 Vaccination Discourse: Cross-Platform and Mixed Methods Analysis.Journal of medical Internet research · 2025Article
- Public Attention to Mpox in China During the Pandemic: Qualitative Analysis of TikTok Data Using Latent Dirichlet Allocation Topic Modeling.Journal of medical Internet research · 2025Article
- Analysis of Social Media Perceptions During the COVID-19 Pandemic in the United Kingdom: Social Listening Study (2019-2022).JMIR formative research · 2025Article
- Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study.JMIR formative research · 2025Article
- Exploring Topics, Emotions, and Sentiments in Health Organization Posts and Public Responses on Instagram: Content Analysis.JMIR infodemiology · 2025Article
- Insight into public sentiment and demand in China's public health emergency response: a weibo data analysis.BMC public health · 2025Article
- Public Attitudes Toward Violence Against Doctors: Sentiment Analysis of Chinese Users.JMIR medical informatics · 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
- Harnessing Social Media Data to Understand Information Needs About Kidney Diseases and Emotional Experiences With Disease Management: Topic and Sentiment Analysis.Journal of medical Internet research · 2025Article
- A thematic analysis of UK COVID-19 vaccine hesitancy discussions on Twitter.BMC public health · 2025Article
74 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundVaccination is a cornerstone of the prevention of communicable infectious diseases; however, vaccines have traditionally met with public fear and hesitancy, and COVID-19 vaccines are no exception. Social media use has been demonstrated to play a role in the low acceptance of vaccines.
objectiveThe aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine-related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals.
methodsTweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users. We used R to perform latent Dirichlet allocation for topic modeling as well as sentiment and emotion analysis using the National Research Council of Canada Emotion Lexicon.
resultsTopic modeling of tweets related to COVID-19 vaccines yielded 16 topics, which were grouped into 5 overarching themes. Opinions about vaccination (227,840/1,499,421 tweets, 15.2%) was the most tweeted topic and remained a highly discussed topic during the majority of the period of our examination. Vaccine progress around the world became the most discussed topic around August 11, 2020, when Russia approved the world's first COVID-19 vaccine. With the advancement of vaccine administration, the topic of instruction on getting vaccines gradually became more salient and became the most discussed topic after the first week of January 2021. Weekly mean sentiment scores showed that despite fluctuations, the sentiment was increasingly positive in general. Emotion analysis further showed that trust was the most predominant emotion, followed by anticipation, fear, sadness, etc. The trust emotion reached its peak on November 9, 2020, when Pfizer announced that its vaccine is 90% effective.
conclusionsPublic COVID-19 vaccine-related discussion on Twitter was largely driven by major events about COVID-19 vaccines and mirrored the active news topics in mainstream media. The discussion also demonstrated a global perspective. The increasingly positive sentiment around COVID-19 vaccines and the dominant emotion of trust shown in the social media discussion may imply higher acceptance of COVID-19 vaccines compared with previous vaccines.
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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.