Linguistic feature fusion for Arabic fake news detection and named entity recognition using reinforcement learning and swarm optimization

Dahou, Abdelghani; Abd Elaziz, Mohamed; Mohamed, Haibaoui; Dahou, Abdelhalim Hafedh; Al-qaness, Mohammed A.A.; Ghetas, Mohamed; Ewess, Ahmed; Zheng, Zhonglong

doi:10.1016/j.neucom.2024.128078

Neurocomputing

2024

DOI: 10.1016/j.neucom.2024.128078

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Linguistic feature fusion for Arabic fake news detection and named entity recognition using reinforcement learning and swarm optimization

Abdelghani Dahou,

Mohamed Abd Elaziz,

Haibaoui Mohamed

et al.

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Cited by 3 publications

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Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles

Alzaidi,

Alshammari,

Hassan

et al. 2025

Ain Shams Engineering Journal

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Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles

Alzaidi,

Alshammari,

Hassan

et al. 2025

Ain Shams Engineering Journal

View full text Add to dashboard Cite

Ensemble based high performance deep learning models for fake news detection

E.Almandouh,

Alrahmawy,

Eisa

et al. 2024

Sci Rep

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Social media has emerged as a dominant platform where individuals freely share opinions and communicate globally. Its role in disseminating news worldwide is significant due to its easy accessibility. However, the increase in the use of these platforms presents severe risks for potentially misleading people. Our research aims to investigate different techniques within machine learning, deep learning, and ensemble learning frameworks in Arabic fake news detection. We integrated FastText word embeddings with various machine learning and deep learning methods. We then leveraged advanced transformer-based models, including BERT, XLNet, and RoBERTa, optimizing their performance through careful hyperparameter tuning. The research methodology involves utilizing two Arabic news article datasets, AFND and ARABICFAKETWEETS datasets, categorized into fake and real subsets and applying comprehensive preprocessing techniques to the text data. Four hybrid deep learning models are presented: CNN-LSTM, RNN-CNN, RNN-LSTM, and Bi-GRU-Bi-LSTM. The Bi-GRU-Bi-LSTM model demonstrated superior performance regarding the F1 score, accuracy, and loss metrics. The precision, recall, F1 score, and accuracy of the hybrid Bi-GRU-Bi-LSTM model on the AFND Dataset are 0.97, 0.97, 0.98, and 0.98, and on the ARABICFAKETWEETS dataset are 0.98, 0.98, 0.99, and 0.99 respectively. The study’s primary conclusion is that when spotting fake news in Arabic, the Bi-GRU-Bi-LSTM model outperforms other models by a significant margin. It significantly aids the global fight against false information by setting the stage for future research to expand fake news detection to multiple languages.

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Multimodal Fake News Detection Based on Contrastive Learning and Similarity Fusion

Li,

Jia,

Wang

2024

IEEE Access

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Linguistic feature fusion for Arabic fake news detection and named entity recognition using reinforcement learning and swarm optimization

Cited by 3 publications

References 29 publications

Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles

Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles

Ensemble based high performance deep learning models for fake news detection

Multimodal Fake News Detection Based on Contrastive Learning and Similarity Fusion

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