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.

翻译：疫苗犹豫现象十分普遍，尽管政府开展了信息宣传活动且世界卫生组织也做出了努力。对疫苗相关叙述中的主题进行分类，对于理解讨论中表达的关切并识别导致疫苗犹豫的具体问题至关重要。本文通过引入一项新颖的疫苗叙述分类任务来满足对在线疫苗叙述进行监测和分析的需求，该任务将COVID-19疫苗言论归类为七个类别之一。遵循数据增强方法，我们首先针对这一新的分类任务构建了一个新颖的数据集，重点关注少数类别。我们还利用了事实核查员注释的数据。本文还提出了一种神经疫苗叙述分类器，在交叉验证下准确率达到84%。该分类器向研究人员和记者公开提供。