OBJECTIVES

: Social networks are recognized as a source of real-world data. The COVID-19 pandemic crisis has been an important topic covered on Twitter. Some users have created social bonding, while others have spread, knowingly or not, many fake news. Some media organizations (i.e. les Décodeurs du Monde) are known to list misinformations and fact checking. The goal of this study was to identify and characterize fake news shared on Twitter related to this pandemia.

METHODS

: French tweets associated to COVID-19 and lockdown were retrieved by Twitter API between March and June 2020. Tweets containing fake news, listed by fact checking medias were identified by searching words sequences (N-gram) based on lexical fields of these news and misinformation. Posts associated to each fake news are clustered to be subsequently categorized and to identify and classify users spreading them and modelling their propagation network. The latter is based on retweeting fake news.

RESULTS

: Among 2.5 million of extracted tweets, 20 fake news were identified by words sequences (intox/Buzyn/chloroquine, etc) and assembled into 5 groups. The biggest group (39%) refers to the potential involvement of the Buzyn/Lévy couple in the non-prescription of chloroquine. Focusing on this one, the propagation network shows that Agnès Buzyn and Didier Raoult hold the 2 most retweeted accounts. Several clusters of heterogeneous users, in terms of influence (number of followers, etc), have been identified. The majority of these users gravitated towards medias or reporters’ accounts. Over half of the tweets debunked this misinformation by sharing Les Décodeurs Du Monde’s report.

CONCLUSIONS

: The propagation network highlighted the different kinds of users spreading fake news and their existence on Twitter. An algorithm that can automatically detect health crisis misinformations, could help health authorities fight against them.