COVID-19 disinformation classification dataset

本数据集由谢菲尔德大学计算机科学系创建，包含2192条手动标注的COVID-19虚假信息分类数据，是目前可用的最大规模此类数据集。数据集内容涵盖了来自70多个国家和43种语言的虚假信息，这些信息已被国际事实核查网络（IFCN）的独立事实核查成员揭露。数据集的创建旨在帮助聚焦最具破坏性的COVID-19虚假信息类型，并指导政策制定者有效传递公共卫生信息和反制虚假信息。该数据集的应用领域包括公共卫生危机管理、政策制定支持以及媒体和政府的信息监控与分析。

This dataset was created by the Department of Computer Science at the University of Sheffield. It contains 2,192 manually annotated COVID-19 misinformation classification samples, making it the largest publicly available dataset of its kind to date. The dataset covers misinformation from over 70 countries and 43 languages, all of which have been debunked by independent fact-checking members of the International Fact-Checking Network (IFCN). The dataset was developed to help focus on the most damaging types of COVID-19 misinformation, and to guide policymakers in effectively disseminating public health information and countering misinformation. Its application scenarios include public health crisis management, policy-making support, as well as information monitoring and analysis for media and government institutions.