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

Source: [Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches](<https://www.sota2.com/research/paper/699e81e86017c4460ffacd42>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:00:04.574Z

Authors: Saurabh Mishra, Shivani Thakur, Radhika Mamidi

Published: 2026-02-24T07:26:17.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Hate Speech Detection on Hate Speech Detection Dataset](<https://www.sota2.com/research/sota/hate-speech-detection-on-hate-speech-detection-dataset.md>): Hate Speech Detection; Hate Speech Detection Dataset; Precision: 79.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.20634>)

[PDF](<https://arxiv.org/pdf/2602.20634.pdf>)