Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches

TL;DR

This paper compares machine learning models and text transformation techniques for hate speech detection on social media, highlighting the superior accuracy of advanced models like BERT and the potential of text transformations to neutralize harmful content.

Contribution

It provides a comprehensive comparison of traditional, neural, and hybrid models for hate speech detection and introduces novel text transformation methods to mitigate harmful language.

Findings

BERT outperforms other models in accuracy.

Hybrid models show improved detection in specific cases.

Text transformation techniques can neutralize negative expressions.

Abstract

The proliferation of hate speech on social media platforms has necessitated the development of effective detection and moderation tools. This study evaluates the efficacy of various machine learning models in identifying hate speech and offensive language and investigates the potential of text transformation techniques to neutralize such content. We compare traditional models like CNNs and LSTMs with advanced neural network models such as BERT and its derivatives, alongside exploring hybrid models that combine different architectural features. Our results indicate that while advanced models like BERT show superior accuracy due to their deep contextual understanding, hybrid models exhibit improved capabilities in certain scenarios. Furthermore, we introduce innovative text transformation approaches that convert negative expressions into neutral ones, thereby potentially mitigating the impact of harmful content. The implications of these findings are discussed, highlighting the strengths and limitations of current technologies and proposing future directions for more robust hate speech detection systems.