  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.
