Advanced Deep Learning Techniques for Improved Cyberbullying Detection in Arabic Tweets

Cyberbullying has emerged as a pressing issue in the digital era, particularly within Arabic-speaking communities, where research remains limited. This study investigates the detection of Arabic cyberbullying on social media using both traditional machine learning (ML) and deep learning (DL) techniq...

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Bibliographic Details
Published in:Jordanian Journal of Computers and Information Technology
Main Authors: Marah Hawa, Thani Kmail, Ahmad Hasasneh
Format: Article
Language:English
Published: Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT) 2025-04-01
Subjects:
machine learning algorithms
arabic tweets
deep learning techniques
recurrent neural network
cyberbullying
Online Access:http://www.ejmanager.com/fulltextpdf.php?mno=245335
Description
Summary:Cyberbullying has emerged as a pressing issue in the digital era, particularly within Arabic-speaking communities, where research remains limited. This study investigates the detection of Arabic cyberbullying on social media using both traditional machine learning (ML) and deep learning (DL) techniques. A publicly available dataset of Arabic tweets was used to train and evaluate several ML models (SVM, NB, LR, and XGBoost), alongside a recurrent neural network (RNN). The results demonstrate that the RNN significantly outperforms classical ML models, highlighting the efficacy of DL in accurately identifying abusive content in Arabic text. These results emphasize the necessity of incorporating linguistically rich data and advanced neural architectures to improve cyberbullying detection systems in low-resource languages such as Arabic. [JJCIT 2025; 11(3.000): 336-350]
ISSN:2413-9351
2415-1076

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