ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection
This addresses misinformation detection during the COVID-19 pandemic for researchers, but is incremental as it focuses on a new dataset for an existing problem.
The authors introduced ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset with 138 verified claims and 9.4K annotated tweets for misinformation detection, and provided benchmarking results for tweet-level verification using state-of-the-art models.
In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020. We collected 138 verified claims, mostly from popular fact-checking websites, and identified 9.4K relevant tweets to those claims. Tweets were manually-annotated by veracity to support research on misinformation detection, which is one of the major problems faced during a pandemic. ArCOV19-Rumors supports two levels of misinformation detection over Twitter: verifying free-text claims (called claim-level verification) and verifying claims expressed in tweets (called tweet-level verification). Our dataset covers, in addition to health, claims related to other topical categories that were influenced by COVID-19, namely, social, politics, sports, entertainment, and religious. Moreover, we present benchmarking results for tweet-level verification on the dataset. We experimented with SOTA models of versatile approaches that either exploit content, user profiles features, temporal features and propagation structure of the conversational threads for tweet verification.