Abstract
Since the first confirmed case of COVID-19, in-formation 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 important 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 construct a network from users' behaviors to identify information superspreaders during the month of March, 2020. black We employ several techniques including Centrality Metrics, HITS, PageRank, VoteRank algorithms, and the weighted correlated influence measure (WCI) to analyze the influence of information spreading, and compare the ranking of the users. The results show that the most of superspreaders were news and governments accounts