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

TL;DR

This paper presents an attention-based recurrent neural network model for detecting hate speech in Bengali social media comments, achieving improved accuracy and interpretability over previous methods.

Contribution

It introduces an encoder-decoder NLP model with attention mechanism for Bengali hate speech detection, addressing language-specific challenges and improving accuracy.

Findings

Attention-based decoder achieved 77% accuracy.

Model outperforms previous approaches in Bengali hate speech detection.

The approach enhances interpretability of hate speech classification.

Abstract

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%).