Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks

TL;DR

This paper presents a deep learning-based method, specifically an LSTM model, for detecting adversarial cyberbullying and hate speech in social network text data, achieving high accuracy and outperforming previous approaches.

Contribution

It introduces a novel LSTM-based detection model with a correction algorithm tailored for adversarial attack content in social media texts.

Findings

LSTM model achieved 87.57% accuracy.

Model outperformed previous studies.

High precision, recall, and AUC-ROC scores.

Abstract

Cyberbullying is a significant concern intricately linked to technology that can find resolution through technological means. Despite its prevalence, technology also provides solutions to mitigate cyberbullying. To address growing concerns regarding the adverse impact of cyberbullying on individuals' online experiences, various online platforms and researchers are actively adopting measures to enhance the safety of digital environments. While researchers persist in crafting detection models to counteract or minimize cyberbullying, malicious actors are deploying adversarial techniques to circumvent these detection methods. This paper focuses on detecting cyberbullying in adversarial attack content within social networking site text data, specifically emphasizing hate speech. Utilizing a deep learning-based approach with a correction algorithm, this paper yielded significant results. An LSTM model with a fixed epoch of 100 demonstrated remarkable performance, achieving high accuracy, precision, recall, F1-score, and AUC-ROC scores of 87.57%, 88.73%, 87.57%, 88.15%, and 91% respectively. Additionally, the LSTM model's performance surpassed that of previous studies.