Identifying Information Superspreaders of COVID-19 from Arabic Tweets

Since the first confirmed case of COVID-19, information was spreading in large amounts over social media platforms. Information spreading about the COVID-19 pandemic can strongly influence people’s behavior. Therefore, identifying information superspreaders (or influencers) during the COVID-19 pandemic is an im- portant step towards understanding public reactions and information dissemination. In this work, we present an analysis over a large Arabic tweets collected during the COVID-19 pandemic. The presented study con- struct a network from users’ behaviors to identify information superspreaders during the month of March, 2020. We employed both HITS and PageRank algorithms to analyze the influence of information spreading, and compared the ranking of the users. The results show that both HITS and PageRank discovered a similar subset of superspreaders with 40% were found to be verified Twitter accounts.