# Hate Speech detection in the Bengali language: A dataset and its baseline evaluation

arXiv: https://arxiv.org/abs/2012.09686 · HTML: https://mlsanity.com/paper/2012.09686/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 30 of 37)

- [CultureLLM: Incorporating Cultural Differences into Large Language Models](https://mlsanity.com/paper/2402.10946/) (2024)
- [A systematic review of Hate Speech automatic detection using Natural Language Processing](https://mlsanity.com/paper/2106.00742/) (2021)
- [CulturePark: Boosting Cross-cultural Understanding in Large Language Models](https://mlsanity.com/paper/2405.15145/) (2024)
- [Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://mlsanity.com/paper/2204.12543/) (2022)
- [L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models](https://mlsanity.com/paper/2203.13778/) (2022)
- [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)
- [Language and Multimodal Models in Sports: A Survey of Datasets and Applications](https://mlsanity.com/paper/2406.12252/) (2024)
- [Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art](https://mlsanity.com/paper/2308.15745/) (2023)
- [Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment](https://mlsanity.com/paper/2306.01105/) (2023)
- [LLM-Based Multi-Task Bangla Hate Speech Detection: Type, Severity, and Target](https://mlsanity.com/paper/2510.01995/) (2025)
- [HS-BAN: A Benchmark Dataset of Social Media Comments for Hate Speech Detection in Bangla](https://mlsanity.com/paper/2112.01902/) (2021)
- [A Review of Challenges in Machine Learning based Automated Hate Speech Detection](https://mlsanity.com/paper/2209.05294/) (2022)
- [Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation](https://mlsanity.com/paper/2310.02876/) (2023)
- [The Uli Dataset: An Exercise in Experience Led Annotation of oGBV](https://mlsanity.com/paper/2311.09086/) (2023)
- [OutSafe-Bench: A Benchmark for Multimodal Offensive Content Detection in Large Language Models](https://mlsanity.com/paper/2511.10287/) (2025)
- [SOLD: Sinhala Offensive Language Dataset](https://mlsanity.com/paper/2212.00851/) (2022)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024)
- [Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions](https://mlsanity.com/paper/2401.07847/) (2024)
- [HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text](https://mlsanity.com/paper/2403.11108/) (2024)
- [HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation](https://mlsanity.com/paper/2507.04350/) (2025)
- [An exploratory experiment on Hindi, Bengali hate-speech detection and transfer learning using neural networks](https://mlsanity.com/paper/2201.01997/) (2022)
- [Textual Toxicity in Social Media: Understanding the Bangla Toxic Language Expressed in Facebook Comment](https://mlsanity.com/paper/2312.05467/) (2023)
- [Cross-Linguistic Offensive Language Detection: BERT-Based Analysis of Bengali, Assamese, & Bodo Conversational Hateful Content from Social Media](https://mlsanity.com/paper/2312.10528/) (2023)
- [Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in Indic Languages](https://mlsanity.com/paper/2401.03677/) (2024)
- [Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management](https://mlsanity.com/paper/2406.19543/) (2024)
- [Mitigating Extrinsic Gender Bias for Bangla Classification Tasks](https://mlsanity.com/paper/2411.10636/) (2024)
- [SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text Generation](https://mlsanity.com/paper/2502.07101/) (2025)

## Abstract (arXiv)

Social media sites such as YouTube and Facebook have become an integral part of everyone's life and in the last few years, hate speech in the social media comment section has increased rapidly. Detection of hate speech on social media websites faces a variety of challenges including small imbalanced data sets, the findings of an appropriate model and also the choice of feature analysis method. further more, this problem is more severe for the Bengali speaking community due to the lack of gold standard labelled datasets. This paper presents a new dataset of 30,000 user comments tagged by crowd sourcing and varified by experts. All the comments are collected from YouTube and Facebook comment section and classified into seven categories: sports, entertainment, religion, politics, crime, celebrity and TikTok & meme. A total of 50 annotators annotated each comment three times and the majority vote was taken as the final annotation. Nevertheless, we have conducted base line experiments and several deep learning models along with extensive pre-trained Bengali word embedding such as Word2Vec, FastText and BengFastText on this dataset to facilitate future research opportunities. The experiment illustrated that although all deep learning models performed well, SVM achieved the best result with 87.5% accuracy. Our core contribution is to make this benchmark dataset available and accessible to facilitate further research in the field of in the field of Bengali hate speech detection.