  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.
