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With the continuing spread of misinformation and disinformation online, it isof increasing importance to develop combating mechanisms at scale in the formof automated systems that support multiple languages. One task of interest isclaim veracity prediction, which can be addressed using stance detection withrespect to relevant documents retrieved online. To this end, we present our newArabic Stance Detection dataset (AraStance) of 4,063 claim--article pairs froma diverse set of sources comprising three fact-checking websites and one newswebsite. AraStance covers false and true claims from multiple domains (e.g.,politics, sports, health) and several Arab countries, and it is well-balancedbetween related and unrelated documents with respect to the claims. Webenchmark AraStance, along with two other stance detection datasets, using anumber of BERT-based models. Our best model achieves an accuracy of 85\% and amacro F1 score of 78\%, which leaves room for improvement and reflects thechallenging nature of AraStance and the task of stance detection in general.

AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking