# Cyberbullying Detection Using Deep Neural Network from Social Media Comments in Bangla Language

arXiv: https://arxiv.org/abs/2106.04506 · HTML: https://mlsanity.com/paper/2106.04506/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)

## Built on by (most cited first, 6 of 6)

- [Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning](https://mlsanity.com/paper/2304.04087/) (2023)
- [Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data](https://mlsanity.com/paper/2303.07484/) (2023)
- [Deep Learning Based Cyberbullying Detection in Bangla Language](https://mlsanity.com/paper/2401.06787/) (2024)
- [A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data](https://mlsanity.com/paper/2308.09722/) (2023)
- [Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying](https://mlsanity.com/paper/2404.03686/) (2024)
- [BengaliSent140: A Large-Scale Bengali Binary Sentiment Dataset for Hate and Non-Hate Speech Classification](https://mlsanity.com/paper/2601.20129/) (2026)

## Abstract (arXiv)

Cyberbullying or Online harassment detection on social media for various major languages is currently being given a good amount of focus by researchers worldwide. Being the seventh most speaking language in the world and increasing usage of online platform among the Bengali speaking people urge to find effective detection technique to handle the online harassment. In this paper, we have proposed binary and multiclass classification model using hybrid neural network for bully expression detection in Bengali language. We have used 44,001 users comments from popular public Facebook pages, which fall into five classes - Non-bully, Sexual, Threat, Troll and Religious. We have examined the performance of our proposed models from different perspective. Our binary classification model gives 87.91% accuracy, whereas introducing ensemble technique after neural network for multiclass classification, we got 85% accuracy.