Detecting COVID-19 Fake News on Twitter: Followers, Emotions, Relationships, and Uncertainty

Abstract

Fake news about coronavirus disease 2019 (COVID-19) can discourage people from taking preventive measures (masks, social distancing), thereby increasing infections and deaths; thus, this study tests whether attributes of users or COVID-19 tweets can distinguish tweets of true news versus fake news. We analyzed 4,165 spell-checked English tweets with a link to 1 of 20 matched COVID-19 news stories (10 true and 10 fake), across the world during 1 year, via computational linguistics and advanced statistics. Tweets with common words, negative emotional valence, higher arousal, greater dominance, first person singular pronouns, third person pronouns or by users with more followers were more likely to be true news tweets. By contrast, tweets with second person pronouns, bald starts, or hedges were more likely to be fake news tweets. Accuracy (F1 score) was 95%. While some tweet attributes for detecting fake news might be universal (pronouns, politeness, followers), others might be topic specific (common words, emotions, hedges).