School of Information Management, Wuhan University, Wuhan, China.
Sorbonne Université, UMR_S 1142, LIMICS, Paris, France.
Stud Health Technol Inform. 2023 Jun 29;305:123-126. doi: 10.3233/SHTI230439.
The proliferation of health misinformation in recent years has prompted the development of various methods for detecting and combatting this issue. This review aims to provide an overview of the implementation strategies and characteristics of publicly available datasets that can be used for health misinformation detection. Since 2020, a large number of such datasets have emerged, half of which are focused on COVID-19. Most of the datasets are based on fact-checkable websites, while only a few are annotated by experts. Furthermore, some datasets provide additional information such as social engagement and explanations, which can be utilized to study the spread of misinformation. Overall, these datasets offer a valuable resource for researchers working to combat the spread and consequences of health misinformation.
近年来，健康类错误信息的大量传播促使人们开发了各种检测和应对此类问题的方法。本综述旨在概述可用于健康类错误信息检测的公开数据集的实施策略和特点。自 2020 年以来，出现了大量此类数据集，其中一半专注于 COVID-19。大多数数据集基于可事实核查的网站，只有少数由专家标注。此外，一些数据集还提供了社交参与度和解释等附加信息，可用于研究错误信息的传播。总体而言，这些数据集为研究人员抗击健康类错误信息的传播和影响提供了宝贵的资源。
