Evaluation of Machine Learning and Deep Learning Models for Fake News Detection in Arabic Headlines

The headline plays a pivotal role in news content, designed to capture attention and provoke curiosity, thereby encouraging readers to engage with the full article or share it on social media. However, social media platforms have increasingly facilitated the rapid spread of misinformation, with misleading headlines often serving as vectors for fake news. Traditional methods, such as manual fact-checking, are inadequate in addressing the scale and speed of misinformation dissemination. This study investigates computational approaches for fake news detection in Arabic headlines using machine learning, deep learning, and transformer-based models specifically pre-trained for the Arabic language. We utilize two benchmark datasets: the AFND dataset—a large-scale collection of Arabic news headlines—and the Arabic News Stance (ANS) dataset, a smaller, unbalanced corpus of annotated claims. Transformer-based models demonstrated superior performance across both datasets. AraBERTv02 achieved the highest accuracy on the large-scale AFND dataset at 70.41%, while AraELECTRA reached 77% accuracy on the smaller ANS dataset. These results highlight the robustness and effectiveness of transformer architectures in capturing nuanced linguistic features relevant to fake news detection in Arabic. Additionally, our experiments reveal that preprocessing choices—such as the application of stemming—impact performance differently across model types, emphasizing the need for tailored preprocessing strategies. This study contributes to the growing body of research in Arabic natural language processing and offers practical insights to improve automated detection of fake news in Arabic media.