The Coronavirus is a Bioweapon: Analysing Coronavirus Fact-Checked Stories

TL;DR

This paper analyzes coronavirus-related misinformation stories from fact-checking organizations, categorizing them, examining trends, and developing an automated BERT-based classifier for story validation across sources.

Contribution

It introduces a novel automated method using BERT to classify diverse coronavirus misinformation stories from fact-checks and social media.

Findings

Identified six main story clusters of misinformation.

Analyzed temporal trends and cross-site agreement.

Developed a BERT classifier for story validation.

Abstract

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.