# BIDWESH: A Bangla Regional Based Hate Speech Detection Dataset

arXiv: https://arxiv.org/abs/2507.16183 · HTML: https://mlsanity.com/paper/2507.16183/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language](https://mlsanity.com/paper/2012.14353/) (2020)
- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- [Hate Speech and Offensive Language Detection in Bengali](https://mlsanity.com/paper/2210.03479/) (2022)
- [Multimodal Hate Speech Detection from Bengali Memes and Texts](https://mlsanity.com/paper/2204.10196/) (2022)
- [BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts](https://mlsanity.com/paper/2206.00372/) (2022)
- [HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria](https://mlsanity.com/paper/2211.15262/) (2022)
- [Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language](https://mlsanity.com/paper/2311.11142/) (2023)
- [BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla](https://mlsanity.com/paper/2410.13281/) (2024)
- [Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning](https://mlsanity.com/paper/2304.04087/) (2023)
- [ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition](https://mlsanity.com/paper/2502.11198/) (2025)
- [Evaluation of Hate Speech Detection Using Large Language Models and Geographical Contextualization](https://mlsanity.com/paper/2502.19612/) (2025)
- [Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME](https://mlsanity.com/paper/2506.10154/) (2025)
- [ANUBHUTI: A Comprehensive Corpus For Sentiment Analysis In Bangla Regional Languages](https://mlsanity.com/paper/2506.21686/) (2025)

## Built on by (most cited first, 3 of 3)

- [Gradient Masters at BLP-2025 Task 1: Advancing Low-Resource NLP for Bengali using Ensemble-Based Adversarial Training for Hate Speech Detection](https://mlsanity.com/paper/2511.18324/) (2025)
- [Unified Multi-Dialectal Neural Machine Translation for Bangla Using the Dwadash Benchmark Corpus](https://mlsanity.com/paper/2608.12018/) (2026)
- [Can LLMs Follow the Pulse of a Crisis? Evaluating Crisis Sentiment in Bangladesh's July Uprising](https://mlsanity.com/paper/2609.16997/) (2026)

## Abstract (arXiv)

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.