Classifying COVID-19 vaccine narratives

• URL: http://arxiv.org/abs/2207.08522v2
• Date: Fri, 17 Nov 2023 09:13:27 GMT
• Title: Classifying COVID-19 vaccine narratives
• Authors: Yue Li, Carolina Scarton, Xingyi Song, Kalina Bontcheva (University of Sheffield)
• Abstract summary: Vaccine hesitancy is widespread, despite the government's information campaigns and the efforts of the World Health Organisation (WHO) This paper introduces a novel vaccine narrative classification task, which categorises COVID-19 vaccine claims into one of seven categories. The paper also presents a neural vaccine narrative classifier that achieves an accuracy of 84% under cross-validation.
• Score: 7.784326429148358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vaccine hesitancy is widespread, despite the government's information campaigns and the efforts of the World Health Organisation (WHO). Categorising the topics within vaccine-related 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.

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