# Bangla hate speech detection on social media using attention-based recurrent neural network

arXiv: https://arxiv.org/abs/2203.16775 · HTML: https://mlsanity.com/paper/2203.16775/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [Detecting Hate Speech in Social Media](https://mlsanity.com/paper/1712.06427/) (2017)

## Built on by (most cited first, 9 of 9)

- [Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning](https://mlsanity.com/paper/2304.04087/) (2023)
- [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024)
- [Deep Learning Based Cyberbullying Detection in Bangla Language](https://mlsanity.com/paper/2401.06787/) (2024)
- [HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text](https://mlsanity.com/paper/2403.11108/) (2024)
- [BanglaNirTox: A Large-scale Parallel Corpus for Explainable AI in Bengali Text Detoxification](https://mlsanity.com/paper/2511.01512/) (2025)
- [HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments](https://mlsanity.com/paper/2312.13193/) (2023)
- [A Real-time Face Mask Detection and Social Distancing System for COVID-19 using Attention-InceptionV3 Model](https://mlsanity.com/paper/2411.05312/) (2024)
- [How Effectively Can BERT Models Interpret Context and Detect Bengali Communal Violent Text?](https://mlsanity.com/paper/2506.19831/) (2025)
- [Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection](https://mlsanity.com/paper/2607.11597/) (2026)

## Abstract (arXiv)

Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements in both accuracy and interpretability. This article proposed encoder decoder based machine learning model, a popular tool in NLP, to classify user's Bengali comments on Facebook pages. A dataset of 7,425 Bengali comments, consisting of seven distinct categories of hate speeches, was used to train and evaluate our model. For extracting and encoding local features from the comments, 1D convolutional layers were used. Finally, the attention mechanism, LSTM, and GRU based decoders have been used for predicting hate speech categories. Among the three encoder decoder algorithms, the attention-based decoder obtained the best accuracy (77%).