Sentiment Analysis-Driven Approach for Enhancing Arabic Fake News Detection Using Machine and Deep Learning

Abstract

The rapid spread of misleading Arabic news stories threatens societal harmony and facts-based choices, yet determining what is truthful remains complicated because of the language's distinctive linguistic qualities. Current techniques generally focus solely on surface textual patterns, neglecting the combined potential of merging advanced sentiment metrics with hybrid machine learning and deep learning frameworks. We introduce an innovative system for identifying deceptive Arabic news that combines sentiment analysis and several approaches, including both machine learning (RF, Naive Bayes, SVM, LR) as well as deep learning (LSTM, RNN). Experiments on a comprehensive corpus of over 6100 Arabic news show that our sentiment-analysis enhanced hybrid model achieves notably superior performance, with SVM attaining 96 % accuracy, surpassing all baseline methods. These insights provide both a practical solution for detecting Arabic misinformation and a framework that can be adapted to other under-resourced languages.

Keywords

• Fake news detection
• Arabic NLP
• sentiment analysis

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