对COVID-19疫苗叙事的分类
Classifying COVID-19 vaccine narratives
AI总结：
针对疫苗犹豫问题，本文提出一种疫苗叙事分类任务，将COVID-19疫苗主张分为七类，并构建数据集及神经分类器，交叉验证准确率达84%，可供研究者和记者使用。
摘要：
疫苗犹豫现象普遍存在，尽管政府开展了信息宣传活动，世界卫生组织（WHO）也做出了努力。对疫苗相关叙事中的主题进行分类，对于理解讨论中表达的关切以及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事分类任务，解决了在线监测和分析疫苗叙事的需求，该任务将COVID-19疫苗主张分为七个类别之一。遵循数据增强方法，我们首先为这一新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标注的数据。本文还提出了一种神经疫苗叙事分类器，在交叉验证下达到了84%的准确率。该分类器可公开提供给研究人员和记者使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
