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Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Hala J. Alshahrani1, Abdulkhaleq Q. A. Hassan2, Khaled Tarmissi3, Amal S. Mehanna4, Abdelwahed Motwakel5,*, Ishfaq Yaseen5, Amgad Atta Abdelmageed5, Mohamed I. Eldesouki6

1 Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of English, College of Science and Arts at Mahayil, King Khalid University, Muhayil, 63763, Saudi Arabia
3 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Makkah, 24211, Saudi Arabia
4 Department of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11845, Egypt
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
6 Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Abdelwahed Motwakel. Email: email

Computers, Materials & Continua 2023, 75(2), 4255-4272. https://doi.org/10.32604/cmc.2023.034821

Abstract

Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format. Besides, the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network (LSTM-RNN) model for fake news detection and classification. Finally, hunter prey optimization (HPO) algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model. The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets. The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57% and 93.53% on Covid19Fakes and satirical datasets, respectively.

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APA Style
Alshahrani, H.J., Hassan, A.Q.A., Tarmissi, K., Mehanna, A.S., Motwakel, A. et al. (2023). Hunter prey optimization with hybrid deep learning for fake news detection on arabic corpus. Computers, Materials & Continua, 75(2), 4255-4272. https://doi.org/10.32604/cmc.2023.034821
Vancouver Style
Alshahrani HJ, Hassan AQA, Tarmissi K, Mehanna AS, Motwakel A, Yaseen I, et al. Hunter prey optimization with hybrid deep learning for fake news detection on arabic corpus. Comput Mater Contin. 2023;75(2):4255-4272 https://doi.org/10.32604/cmc.2023.034821
IEEE Style
H.J. Alshahrani et al., "Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus," Comput. Mater. Contin., vol. 75, no. 2, pp. 4255-4272. 2023. https://doi.org/10.32604/cmc.2023.034821



cc 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.
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