论文标题
分类意识到的神经主题模型及其在新的Covid-19中的应用中的应用
Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
论文作者
论文摘要
Covid-19的大流行伴随着虚假信息的爆炸使事实检查者和媒体在全球范围内超负荷，并为全球政府的回应带来了新的重大挑战。虚假信息不仅在于公民对医学科学的混乱，而且还在增强对政策制定者和政府的不信任。为了帮助解决这个问题，我们开发了计算方法来对COVID-19的虚假信息进行分类。 COVID-19的虚假信息类别可用于a）将事实核对工作集中在最具破坏性的COVID-19-19虚假信息上； b）指导决策者试图传达有效的公共卫生信息并有效地证明199号的虚假信息。本文介绍：1）包含当前最大的手动注释Covid-19个虚假信息类别的语料库； 2）专为COVID-19的分类神经主题模型（CANTM），用于COVID-19的虚假信息类别分类和主题发现； 3）关于时间，数量，错误类型，媒体类型和原始源的Covid-19模糊类别类别的广泛分析。
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.
