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.

该研究针对在线上的虚假信息和误导信息的不断传播，通过使用检索到的相关文档的立场检测来解决争论真实性预测的问题，并推出了一个有多个语言支持的自动化系统来进行大规模的对抗，其中最佳的 BERT-based model 可达到 85％的准确率和 78％的宏 F1 分数。