Open Access


Arabic Fake News Detection Using Deep Learning

Khaled M. Fouad1,3, Sahar F. Sabbeh1,2,*, Walaa Medhat1,3
1 Faculty of Computers & Artificial Intelligence, Benha University, Egypt
2 University of Jeddah, College of Computer Science and Engineering, Jeddah, 21493, Saudi Arabia
3 Information Technology and Computer Science, Nile University, Egypt
* Corresponding Author: Sahar F. Sabbeh. Email:

Computers, Materials & Continua 2022, 71(2), 3647-3665.

Received 03 July 2021; Accepted 14 October 2021; Issue published 07 December 2021


Nowadays, an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication. However, because user-generated content is unregulated, it may contain offensive content such as fake news, insults, and harassment phrases. The identification of fake news and rumors and their dissemination on social media has become a critical requirement. They have adverse effects on users, businesses, enterprises, and even political regimes and governments. State of the art has tackled the English language for news and used feature-based algorithms. This paper proposes a model architecture to detect fake news in the Arabic language by using only textual features. Machine learning and deep learning algorithms were used. The deep learning models are used depending on conventional neural nets (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), CNN+LSTM, and CNN + BiLSTM. Three datasets were used in the experiments, each containing the textual content of Arabic news articles; one of them is real-life data. The results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.


Fake news detection; deep learning; machine learning; natural language processing

Cite This Article

K. M. Fouad, S. F. Sabbeh and W. Medhat, "Arabic fake news detection using deep learning," Computers, Materials & Continua, vol. 71, no.2, pp. 3647–3665, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 817


  • 534


  • 0


Share Link

WeChat scan