Virality, only the tip of the iceberg: ways of spread and interaction around COVID-19 misinformation in Twitter

Abstract

Misinformation has long been a weapon that helps the political, social, and economic interests of different sectors. This became more evident with the transmission of false information in the COVID-19 pandemic, compromising citizens’ health by anti-vaccine recommendations, the denial of the coronavirus and false remedies. Online social networks are the breeding ground for falsehoods and conspiracy theories. Users can share viral misinformation or publish it on their own. This encourages a double analysis of this issue: the need to capture the deluge of false information as opposed to the real one and the study of users’ patterns to interact with that infodemic. As a response to this, our work combines the use of artificial intelligence and journalism through fact-checked false claims to provide an in-depth study of the number of retweets, likes, replies, quotes and repeated texts in posts stating or contradicting misinformation in Twitter. The large sample of tweets was collected and automatically analysed through Natural Language Processing (NLP) techniques, not to give all the attention only to the posts with a big impact but to all the messages contributing to the expansion of false information or its rejection regardless of their virality. This analysis revealed that the diffusion of tweets surrounding coronavirus-related misinformation is not only a domain of viral tweets, but also from posts without interactions, which represent most of the sample, and that there are no big differences between misinformation and its contradiction in general, except for the use of replies.