AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking
This addresses misinformation in Arabic by providing a multi-domain dataset, but it is incremental as it applies existing methods to new data.
The authors tackled the problem of misinformation by creating AraStance, a new dataset of 4,063 Arabic claim-article pairs for stance detection, and their best model achieved 85% accuracy and 78% macro F1 score.
With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages. One task of interest is claim veracity prediction, which can be addressed using stance detection with respect to relevant documents retrieved online. To this end, we present our new Arabic Stance Detection dataset (AraStance) of 4,063 claim--article pairs from a diverse set of sources comprising three fact-checking websites and one news website. AraStance covers false and true claims from multiple domains (e.g., politics, sports, health) and several Arab countries, and it is well-balanced between related and unrelated documents with respect to the claims. We benchmark AraStance, along with two other stance detection datasets, using a number of BERT-based models. Our best model achieves an accuracy of 85\% and a macro F1 score of 78\%, which leaves room for improvement and reflects the challenging nature of AraStance and the task of stance detection in general.