Advancing cyberbullying detection in low-resource languages: a transformer- stacking framework for Bengali
Md. Nesarul Hoque, Rudra Pratap Deb Nath, Abu Nowshed Chy, Debasish Ghose, Md Hanif Seddiqui
TL;DR
This paper introduces a new framework for detecting cyberbullying in Bengali, a low-resource language, using stacked transformer models and achieves high accuracy.
Contribution
The novel contribution is the Transformer-stacking framework with Bengali-specific preprocessing for improved cyberbullying detection.
Findings
Transformer-stacking achieved 93.61% F1-score and 93.62% accuracy for binary cyberbullying classification.
The framework outperformed eight baseline models and recent state-of-the-art methods.
It demonstrated scalability and adaptability on external Bengali datasets for hate speech and abusive language.
Abstract
Cyberbullying on social networks has emerged as a pressing global issue, yet research in low-resource languages such as Bengali remains underdeveloped due to the scarcity of high-quality datasets, linguistic resources, and targeted methodologies. Many existing approaches overlook essential language-specific preprocessing, neglect the integration of advanced transformer-based models, and do not adequately address model validation, scalability, and adaptability. To address these limitations, this study introduces three Bengali-specific preprocessing strategies to enhance feature representation. It then proposes Transformer-stacking, an effective hybrid detection framework that combines three transformer models, XLM-R-base, multilingual BERT, and Bangla-Bert-Base, via a stacking strategy with a multi-layer perceptron classifier. The framework is evaluated on a publicly available Bengali…
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Bullying, Victimization, and Aggression · Topic Modeling