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In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter datasetfor misinformation detection composed of tweets containing claims from 27thJanuary till the end of April 2020. We collected 138 verified claims, mostlyfrom popular fact-checking websites, and identified 9.4K relevant tweets tothose claims. Tweets were manually-annotated by veracity to support research onmisinformation detection, which is one of the major problems faced during apandemic. ArCOV19-Rumors supports two levels of misinformation detection overTwitter: verifying free-text claims (called claim-level verification) andverifying claims expressed in tweets (called tweet-level verification). Ourdataset covers, in addition to health, claims related to other topicalcategories that were influenced by COVID-19, namely, social, politics, sports,entertainment, and religious. Moreover, we present benchmarking results fortweet-level verification on the dataset. We experimented with SOTA models ofversatile approaches that either exploit content, user profiles features,temporal features and propagation structure of the conversational threads fortweet verification.

ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection