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Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model

Mohamed A. Mahdi1, Suliman Mohamed Fati2,*, Mohamed A.G. Hazber1, Shahanawaj Ahamad3, Sawsan A. Saad4

1 Information and Computer Science Department, College of Computer Science and Engineering, University of Ha’il, Ha’il, 55476, Saudi Arabia
2 Information Systems Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
3 Software Engineering Department, College of Computer Science and Engineering, University of Ha’il, Ha’il, 55476, Saudi Arabia
4 Computer Engineering Department, College of Computer Science and Engineering, University of Ha’il, Ha’il, 55476, Saudi Arabia

* Corresponding Authors: Suliman Mohamed Fati. Email: email,email

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)

Computer Modeling in Engineering & Sciences 2024, 141(2), 1651-1671. https://doi.org/10.32604/cmes.2024.052291

Abstract

Cyberbullying, a critical concern for digital safety, necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces. To tackle this challenge, our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers (BERT) base model (cased), originally pretrained in English. This model is uniquely adapted to recognize the intricate nuances of Arabic online communication, a key aspect often overlooked in conventional cyberbullying detection methods. Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media (SM) tweets showing a notable increase in detection accuracy and sensitivity compared to existing methods. Experimental results on a diverse Arabic dataset collected from the ‘X platform’ demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods. E-BERT shows a substantial improvement in performance, evidenced by an accuracy of 98.45%, precision of 99.17%, recall of 99.10%, and an F1 score of 99.14%. The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications, offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.

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Cite This Article

APA Style
Mahdi, M.A., Fati, S.M., Hazber, M.A., Ahamad, S., Saad, S.A. (2024). Enhancing arabic cyberbullying detection with end-to-end transformer model. Computer Modeling in Engineering & Sciences, 141(2), 1651-1671. https://doi.org/10.32604/cmes.2024.052291
Vancouver Style
Mahdi MA, Fati SM, Hazber MA, Ahamad S, Saad SA. Enhancing arabic cyberbullying detection with end-to-end transformer model. Comput Model Eng Sci. 2024;141(2):1651-1671 https://doi.org/10.32604/cmes.2024.052291
IEEE Style
M.A. Mahdi, S.M. Fati, M.A. Hazber, S. Ahamad, and S.A. Saad "Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model," Comput. Model. Eng. Sci., vol. 141, no. 2, pp. 1651-1671. 2024. https://doi.org/10.32604/cmes.2024.052291



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
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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