Advancing cyberbullying detection in low-resource languages: a transformer- stacking framework for Bengali.

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 strate

SUBMITTER: Hoque MN 

PROVIDER: S-EPMC12835245 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature