The Coronavirus is a Bioweapon: Analysing Coronavirus Fact-Checked Stories
This work addresses the problem of tracking and categorizing misinformation during the COVID-19 pandemic for fact-checkers and researchers, though it is incremental in applying existing NLP techniques.
The study analyzed coronavirus misinformation fact-checked by PolitiFact, Poynter, and Snopes from January to June 2020, clustering stories into six groups and examining trends in validity and agreement, and introduced a BERT-based method to classify story types in fact-checked content and tweets.
The 2020 coronavirus pandemic has heightened the need to flag coronavirus-related misinformation, and fact-checking groups have taken to verifying misinformation on the Internet. We explore stories reported by fact-checking groups PolitiFact, Poynter and Snopes from January to June 2020, characterising them into six story clusters before then analyse time-series and story validity trends and the level of agreement across sites. We further break down the story clusters into more granular story types by proposing a unique automated method with a BERT classifier, which can be used to classify diverse story sources, in both fact-checked stories and tweets.