This is the latest version of this eprint.
Mu, Y., Jin, M., Grimshaw, C. et al. (3 more authors) (2023) VaxxHesitancy: A dataset for studying hesitancy towards COVID-19 vaccination on Twitter. In: Lin, Y.-R., Cha, M. and Quercia, D., (eds.) Proceedings of the International AAAI Conference on Web and Social Media. Seventeenth International AAAI Conference on Web and Social Media, 05-08 Jun 2023, Limassol, Cyprus. Association for the Advancement of Artificial Intelligence (AAAI) , pp. 1052-1062. ISBN 9781577358794
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2023, Association for the Advancement of Artificial Intelligence. |
Keywords: | 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 |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number UK RESEARCH AND INNOVATION 101070093 10039055 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Feb 2025 10:39 |
Last Modified: | 14 Feb 2025 10:48 |
Status: | Published |
Publisher: | Association for the Advancement of Artificial Intelligence (AAAI) |
Refereed: | Yes |
Identification Number: | 10.1609/icwsm.v17i1.22213 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223243 |
Available Versions of this Item
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VaxxHesitancy: a dataset for studying hesitancy towards COVID-19 vaccination on Twitter. (deposited 14 Feb 2025 11:08)
- VaxxHesitancy: A dataset for studying hesitancy towards COVID-19 vaccination on Twitter. (deposited 14 Feb 2025 10:39) [Currently Displayed]