Department of Computer Science, University of Sheffield, Sheffield, United Kingdom.
Austrian Research Institute for Artificial Intelligence, Vienna, Austria.
PLoS One. 2021 Feb 18;16(2):e0247086. doi: 10.1371/journal.pone.0247086. eCollection 2021.
The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation. The COVID-19 disinformation categories could be used for a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation; b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation. This paper presents: 1) a corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories; 2) a classification-aware neural topic model (CANTM) designed for COVID-19 disinformation category classification and topic discovery; 3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source.
伴随 COVID-19 大流行的假消息爆炸式传播，使全世界的事实核查人员和媒体不堪重负，也给各国政府的应对措施带来了新的重大挑战。假消息不仅在公民中对医学科学造成混淆，还加剧了公众对政策制定者和政府的不信任。为了帮助解决这个问题，我们开发了用于对 COVID-19 假消息进行分类的计算方法。COVID-19 假消息类别可用于：a）将事实核查工作集中在最具破坏性的 COVID-19 假消息上；b）为试图传达有效公共卫生信息和有效应对 COVID-19 假消息的政策制定者提供指导。本文提出了：1）一个包含当前最大手动标注 COVID-19 假消息类别集合的语料库；2）一个针对 COVID-19 假消息类别分类和主题发现而设计的分类感知神经主题模型（CANTM）；3）对 COVID-19 假消息类别进行了关于时间、数量、错误类型、媒体类型和来源的广泛分析。
