Classifying COVID-19 vaccine narratives
This addresses vaccine hesitancy by helping researchers and journalists understand public concerns, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of monitoring and analyzing online COVID-19 vaccine narratives by introducing a classification task with seven categories, achieving 84% accuracy with a neural classifier.
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.