mlsanityData to 14 Oct 2026

An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines

arXiv 2306.13797 ↗First posted 23 Jun 2023cs.SI cs.CLcited by 5

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Abstract (arXiv)

Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic had fear and uncertainty about vaccines which has been well expressed on social media platforms such as Twitter. We analyse Twitter sentiments from the beginning of the COVID-19 pandemic and study the public behaviour during the planning, development and deployment of vaccines expressed in tweets worldwide using a sentiment analysis framework via deep learning models. In this way, we provide visualisation and analysis of anti-vaccine sentiments over the course of the COVID-19 pandemic. Our results show a link between the number of tweets, the number of cases, and the change in sentiment polarity scores during major waves of COVID-19 cases. We also found that the first half of the pandemic had drastic changes in the sentiment polarity scores that later stabilised which implies that the vaccine rollout had an impact on the nature of discussions on social media.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 7 of its 87 references that are in the map. First posted on arXiv 23 Jun 2023.

Builds on

7 of 87 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2018A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translationcited by 23
  3. 2020SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemiccited by 16
  4. 2020Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemiccited by 11
  5. 2020COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversationscited by 5
  6. 2020Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from A State in Australiacited by 5
  7. 2023Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicroncited by 4

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Citing papers per year: 2024: 3; 2025: 2.

5 papers.