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

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Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Md Nesarul HoqueBig Data, Information and Knowledge Engineering Lab, Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh.

Rudra Pratap Deb NathBig Data, Information and Knowledge Engineering Lab, Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh.

Abu Nowshed ChyBig Data, Information and Knowledge Engineering Lab, Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh.

Debasish GhoseSchool of Economics, Innovation and Technology, Kristiania University of Applied Sciences, Bergen, Norway.

Md Hanif SeddiquiBig Data, Information and Knowledge Engineering Lab, Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

additional preprocessingBengalicyberbullyinglow-resource languagetransformer integration

Identifiers

PMID41608035

DOI10.3389/frai.2025.1679962

PMCPMC12835245

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