Li, Y., Scarton, C. orcid.org/0000-0002-0103-4072, Song, X. orcid.org/0000-0002-4188-6974 et al. (1 more author) (2023) Classifying COVID-19 vaccine narratives. In: Mitkov, R. and Angelova, G., (eds.) Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing. 14th International Conference on Recent Advances in Natural Language Processing (RANLP 2023), 04-06 Sep 2023, Varna, Bulgaria. INCOMA Ltd., Shoumen , Bulgaria , pp. 648-657. ISBN 978-954-452-092-2
Abstract
Vaccine hesitancy is widespread, despite the government's information campaigns and the efforts of the World Health Organisation (WHO). Categorising the topics within vaccinerelated narratives is crucial to understand the concerns expressed in discussions and identify the specific issues that contribute to vaccine hesitancy. This paper addresses the need for monitoring and analysing vaccine narratives online by introducing a novel vaccine narrative classification task, which categorises COVID-19 vaccine claims into one of seven categories. Following a data augmentation approach, we first construct a novel dataset for this new classification task, focusing on the minority classes. We also make use of fact-checker annotated data. The paper also presents a neural vaccine narrative classifier that achieves an accuracy of 84% under cross-validation. The classifier is publicly available for researchers and journalists.
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 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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 EP/W011212/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Feb 2025 10:05 |
Last Modified: | 14 Feb 2025 10:06 |
Status: | Published |
Publisher: | INCOMA Ltd., Shoumen |
Refereed: | Yes |
Identification Number: | 10.26615/978-954-452-092-2_070 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223249 |