Abstract
In March 2020, the World Health Organization declared the COVID-19 outbreak to be a pandemic. Soon afterwards, people began sharing millions of posts on social media without considering their reliability and truthfulness. While there has been extensive research on COVID-19 in the English language, there is a lack of research on the subject in Arabic. In this paper, we address the problem of detecting fake news surrounding COVID-19 in Arabic tweets. We collected more than seven million Arabic tweets related to the corona virus pandemic from January 2020 to August 2020 using the trending hashtags during the time of pandemic. We relied on two fact-checkers: the France-Press Agency and the Saudi Anti-Rumors Authority to extract a list of keywords related to the misinformation and fake news topics. A small corpus was extracted from the collected tweets and manually annotated into fake or genuine classes. We used a set of features extracted from tweet contents to train a set of machine learning classifiers. The manually annotated corpus was used as a baseline to build a system for automatically detecting fake news from Arabic text. Classification of the manually annotated dataset achieved an F1-score of 87.8% using Logistic Regression (LR) as a classifier with the n-gram-level Term Frequency-Inverse Document Frequency (TF-IDF) as a feature, and a 93.3% F1score on the automatically annotated dataset using the same classifier with count vector feature. The introduced system and datasets could help governments, decision-makers, and the public judge the credibility of information published on social media during the COVID-19 pandemic.