The rapid growth of social media content during the current pandemic provides useful tools for disseminating information which has also become a root for misinformation. Therefore, there is an urgent need for fact-checking and effective techniques for detecting misinformation in social media. In this work, we study the misinformation in the Arabic content of Twitter. We construct a large Arabic dataset related to COVID-19 misinformation and gold-annotate the tweets into two categories: misinformation or not. Then, we apply eight different traditional and deep machine learning models, with different features including word embeddings and word frequency. The word embedding models (\textsc{FastText} and word2vec) exploit more than two million Arabic tweets related to COVID-19. Experiments show that optimizing the area under the curve (AUC) improves the models' performance and the Extreme Gradient Boosting (XGBoost) presents the highest accuracy in detecting COVID-19 misinformation online.

中文翻译：

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吃大蒜可预防COVID-19感染：在Twitter的阿拉伯内容上检测错误信息

在当前大流行期间，社交媒体内容的快速增长提供了有用的工具来传播信息，这也已成为错误信息的根源。因此，迫切需要用于检查社交媒体中的错误信息的事实检查和有效技术。在这项工作中，我们研究了Twitter阿拉伯语内容中的错误信息。我们构建了一个与COVID-19错误信息和金色注释推文相关的大型阿拉伯数据集，分为两类：信息错误与否。然后，我们应用八个不同的传统和深度机器学习模型，它们具有包括单词嵌入和单词频率在内的不同功能。单词嵌入模型（\ textsc {FastText}和word2vec）利用了超过200万条与COVID-19相关的阿拉伯推文。