Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying

TL;DR

This paper presents a deep learning approach using BERT and BiLSTM architectures to detect cyberbullying in social media, achieving high accuracy and contributing to safer online environments.

Contribution

It introduces hateBERT, an extension of BERT for hate speech detection, demonstrating its effectiveness over other models in cyberbullying identification.

Findings

hateBERT achieved 89.16% accuracy

Deep learning models outperform traditional methods

Enhanced detection accuracy improves online safety

Abstract

In today's digital world, cyberbullying is a serious problem that can harm the mental and physical health of people who use social media. This paper explains just how serious cyberbullying is and how it really affects indi-viduals exposed to it. It also stresses how important it is to find better ways to detect cyberbullying so that online spaces can be safer. Plus, it talks about how making more accurate tools to spot cyberbullying will be really helpful in the future. Our paper introduces a deep learning-based ap-proach, primarily employing BERT and BiLSTM architectures, to effective-ly address cyberbullying. This approach is designed to analyse large vol-umes of posts and predict potential instances of cyberbullying in online spaces. Our results demonstrate the superiority of the hateBERT model, an extension of BERT focused on hate speech detection, among the five mod-els, achieving an accuracy rate of 89.16%. This research is a significant con-tribution to "Computational Intelligence for Social Transformation," prom-ising a safer and more inclusive digital landscape.