Abstract

People are efficient on websites and social media platforms for news and updates as their popularity has grown. Even official media outlets to publish news use social media networks. However, due to the massive volume of user-generated material, verifying the veracity of the presented information is necessary. To handle the large volume of posts being made, this procedure should be implemented automatically and effectively. Fake news detection (FND) estimates the chance that a certain news story (news report, editorial, expose, and the like) is purposefully misleading. Over the past ten years, there has been an increase in interest in Arabic FND, and several detection techniques have shown some promise in identifying fake news across various datasets. This paper provides an overview of the fake news definition, consequences, detection strategies, and datasets that are utilized for detecting Arabic fake news. The design of Arabic FND systems is mainly based on two methods. The first one uses machine learning (ML) methods that rely on manually produced statistical data extracted from the text and used as a feature to distinguish between real and fake news. In the second strategy, “end-to-end” systems for detection are created using deep learning (DL) approaches. The investigation conducted in this paper may help researchers understand the advantages and uses of Arabic FND systems to develop more efficient algorithms in this field.