VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter


  • Yida Mu The University of Sheffield
  • Mali Jin The University of Sheffield
  • Charlie Grimshaw The University of Sheffield
  • Carolina Scarton The University of Sheffield
  • Kalina Bontcheva The University of Sheffield
  • Xingyi Song The University of Sheffield



, Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, Web and Social Media, Social network analysis; communities identification; expertise and authority discovery


Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vaccines online alongside those posting pro- and anti-vaccine content. Predictably, since the first mentions of a COVID-19 vaccine, social media users posted about their fears and concerns or about their support and belief into the effectiveness of these rapidly developing vaccines. Identifying and understanding the reasons behind public hesitancy towards COVID-19 vaccines is important for policy markers that need to develop actions to better inform the population with the aim of increasing vaccine take-up. In the case of COVID-19, where the fast development of the vaccines was mirrored closely by growth in anti-vaxx disinformation, automatic means of detecting citizen attitudes towards vaccination became necessary. This is an important computational social sciences task that requires data analysis in order to gain in-depth understanding of the phenomena at hand. Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination. To this end, we created a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance). Besides, we also develop a domain-specific language model (VaxxBERT) that achieves the best predictive performance (73.0 accuracy and 69.3 F1-score) as compared to a robust set of baselines. To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance.




How to Cite

Mu, Y., Jin, M., Grimshaw, C., Scarton, C., Bontcheva, K., & Song, X. (2023). VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 1052-1062.