| Summary: | During the covid-19 pandemic, a considerable amount of data travels fast worldwide on the net, mainly on the social media platform where people all over the world have constant and easy access to submit materials and posts. A considerable amount of shared news embeds misleading information which affects negatively the cognitive and psychological health of its readers. The present case study focuses on fake news being tweeted during the coronavirus pandemic for the purpose to mislead the targeted population. In this context, this paper exhibits a new approach to detect fake news on Twitter during the Covid-19 period. The proposed method consists of a classification approach that uses new tweets’ features and it is based on natural language processing, machine learning, and deep learning. The method is implemented in parallel with apache spark. Experimental results show that our approach yields very valuable results once it is used with the random forest algorithm with an accuracy equal to 79%. We also demonstrate that the sentiment of tweets plays an important role in the detection of fake news. Indeed, the model we present outperforms those models lacking consideration of new tweets’ features.
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