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

Saurabh Mishra, Shivani Thakur, Radhika Mamidi

TL;DR

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, suggesting that while advanced models like BERT show superior accuracy, hybrid models exhibit improved capabilities in certain scenarios.

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.

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

TL;DR

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, suggesting that while advanced models like BERT show superior accuracy, hybrid models exhibit improved capabilities in certain scenarios.

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.
Paper Structure (79 sections, 24 figures, 17 tables)

This paper contains 79 sections, 24 figures, 17 tables.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Research Objectives
  4. Paper Structure
  5. Background and Related Work
  6. Literature Review
  7. Gaps in Previous Research
  8. Review of Existing Models
  9. General Tabular and Graphical Analysis of Models
  10. Exploratory Data Analysis (EDA)
  11. Dataset Description
  12. Attributes
  13. Data Insights
  14. Statistical Summary
  15. Data Preparation
  16. ...and 64 more sections

Figures (24)

  • Figure 1: Percent that agree “People should be able to make statements that are offensive to minority groups publicly” (2015). Source: Pew Research Center.
  • Figure 2: Performance of various models in hate speech detection. The graph shows Precision, Recall, and F1-Score for each model.
  • Figure 3: Distribution of sentiment classes in the dataset.
  • Figure 4: Distribution of tweet text lengths.
  • Figure 5: Word cloud of most frequent words in the dataset, highlighting prevalent themes and language.
  • ...and 19 more figures