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

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.