ArticleVaccines2022
Examining the Prevailing Negative Sentiments Related to COVID-19 Vaccination: Unsupervised Deep Learning of Twitter Posts over a 16 Month Period.
Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Mapping Vaccine Sentiment by Analyzing Spanish-Language Social Media Posts and Survey-Based Public Opinion: Dual Methods Study.JMIR infodemiology · 2025Observational
- Article
- Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance.Social science & medicine (1982) · 2024Article
- Public perception on active aging after COVID-19: an unsupervised machine learning analysis of 44,343 posts.Frontiers in public health · 2024Article
- Piecing together the narrative of #longcovid: an unsupervised deep learning of 1,354,889 X (formerly Twitter) posts from 2020 to 2023.Frontiers in public health · 2024Article
- Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022.PloS one · 2024Article
- Analyzing information sharing behaviors during stance formation on COVID-19 vaccination among Japanese Twitter users.PloS one · 2024Article
- Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter.Frontiers in public health · 2024Article
- Fight Against the Mandatory COVID-19 Immunity Passport on Twitter: Natural Language Processing Study.Journal of medical Internet research · 2023Article
- Article
- Exploring Community Perceptions of COVID-19 and Vaccine Hesitancy in Selected Cities of Ethiopia: A Qualitative Study.Vaccines · 2023Article
- Knowledge and training of Italian students in Healthcare Settings on COVID-19 vaccines and vaccination strategies, one year after the immunization campaign.Journal of preventive medicine and hygiene · 2023Article
- Article
- Examining the Public Messaging on 'Loneliness' over Social Media: An Unsupervised Machine Learning Analysis of Twitter Posts over the Past Decade.Healthcare (Basel, Switzerland) · 2023Article
- Real-World Effectiveness of SARS-CoV-2 Vaccine Booster in Hemodialysis Patients with COVID-19 Receiving Molnupiravir.Viruses · 2023Article
- Article
- Effect of COVID-19 Vaccination on the In-Hospital Prognosis of Patients Admitted during Delta and Omicron Waves in Italy.Vaccines · 2023Article
- Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts.Heliyon · 2023Article
- Review
- Social interactions and COVID-19 vaccine hesitancy: Evidence from a full population study in Sweden.PloS one · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the demonstrated efficacy, safety, and availability of COVID-19 vaccines, efforts in global mass vaccination have been met with widespread scepticism and vaccine hesitancy or refusal. Understanding the reasons for the public's negative opinions towards COVID-19 vaccination using Twitter may help make new headways in improving vaccine uptake. This study, therefore, examined the prevailing negative sentiments towards COVID-19 vaccination via the analysis of public twitter posts over a 16 month period. Original tweets (in English) from 1 April 2021 to 1 August 2022 were extracted. A bidirectional encoder representations from transformers (BERT)-based model was applied, and only negative sentiments tweets were selected. Topic modelling was used, followed by manual thematic analysis performed iteratively by the study investigators, with independent reviews of the topic labels and themes. A total of 4,448,314 tweets were analysed. The analysis generated six topics and three themes related to the prevailing negative sentiments towards COVID-19 vaccination. The themes could be broadly understood as either emotional reactions to perceived invidious policies or safety and effectiveness concerns related to the COVID-19 vaccines. The themes uncovered in the present infodemiology study fit well into the increasing vaccination model, and they highlight important public conversations to be had and potential avenues for future policy intervention and campaign efforts.
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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.