论文标题
分类Covid-19疫苗叙事
Classifying COVID-19 vaccine narratives
论文作者
论文摘要
尽管政府的信息运动以及世界卫生组织（WHO）的努力，疫苗犹豫却很普遍。对与疫苗相关的叙述中的主题进行分类对于了解讨论中表达的问题并确定导致疫苗犹豫的特定问题至关重要。本文通过引入一项新型的疫苗叙事分类任务来解决在线监视和分析疫苗叙事的需求，该任务将Covid-19疫苗索赔分类为七个类别之一。遵循数据增强方法，我们首先为这项新的分类任务构建了一个新颖的数据集，重点是少数群体。我们还利用事实检查器注释的数据。该论文还提出了一种神经疫苗叙事分类器，该分类器的精度在交叉验证下的准确性为84％。分类器可公开用于研究人员和记者。
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.
