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.Comment: Accepted at ICWSM 202