BIDWESH: A Bangla Regional Based Hate Speech Detection Dataset

TL;DR

BIDWESH is a pioneering multi-dialect Bangla hate speech dataset that enhances detection of harmful content across regional dialects, addressing a critical gap in low-resource language NLP tools.

Contribution

This study introduces the first multi-dialectal Bangla hate speech dataset, translating and annotating over 9,000 instances across major regional dialects for improved detection.

Findings

Dataset covers three major Bangla dialects with 9,183 annotated instances.

Manual verification ensures linguistic and contextual accuracy.

Provides a resource for developing dialect-sensitive hate speech detection models.

Abstract

Hate speech on digital platforms has become a growing concern globally, especially in linguistically diverse countries like Bangladesh, where regional dialects play a major role in everyday communication. Despite progress in hate speech detection for standard Bangla, Existing datasets and systems fail to address the informal and culturally rich expressions found in dialects such as Barishal, Noakhali, and Chittagong. This oversight results in limited detection capability and biased moderation, leaving large sections of harmful content unaccounted for. To address this gap, this study introduces BIDWESH, the first multi-dialectal Bangla hate speech dataset, constructed by translating and annotating 9,183 instances from the BD-SHS corpus into three major regional dialects. Each entry was manually verified and labeled for hate presence, type (slander, gender, religion, call to violence), and target group (individual, male, female, group), ensuring linguistic and contextual accuracy. The resulting dataset provides a linguistically rich, balanced, and inclusive resource for advancing hate speech detection in Bangla. BIDWESH lays the groundwork for the development of dialect-sensitive NLP tools and contributes significantly to equitable and context-aware content moderation in low-resource language settings.