Fake news detection has become a major issue in the digital age, with social media playing a major role in its spread. This paper outlines the dataset and methodology used to model Arabic fake news. This paper is about our participation on CERIST Natural Language Processing Challenge. We used the dataset provided for the Task1.c. Arabic sentiment analysis and fake news detection within covid-19. The model used for this task is a simple transformer fake news model based on the Arabic pre-trained language model CAMeL-BERT. This model was utilized in two variants: a fine-tuned model and a Bidirectional long short-term model. The experiment results of this modeling CAMeL-BERT provides the best result by achieving 0.959 F1, thus outperforming all other models variants in detecting fake news.
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