HateGPT: Unleashing GPT-3.5 Turbo to Combat Hate Speech on X

TL;DR

This paper explores using GPT-3.5 Turbo with prompting techniques to classify tweets as hate, offensive, or non-offensive, demonstrating high and consistent performance in hate speech detection.

Contribution

The study applies GPT-3.5 Turbo for hate speech classification on Twitter data, showcasing its effectiveness and robustness across multiple runs.

Findings

Macro-F1 scores around 0.75 indicate high classification performance

Model demonstrates consistent results with minimal variance across runs

GPT-3.5 Turbo effectively detects hate and offensive content in tweets

Abstract

The widespread use of social media platforms like Twitter and Facebook has enabled people of all ages to share their thoughts and experiences, leading to an immense accumulation of user-generated content. However, alongside the benefits, these platforms also face the challenge of managing hate speech and offensive content, which can undermine rational discourse and threaten democratic values. As a result, there is a growing need for automated methods to detect and mitigate such content, especially given the complexity of conversations that may require contextual analysis across multiple languages, including code-mixed languages like Hinglish, German-English, and Bangla. We participated in the English task where we have to classify English tweets into two categories namely Hate and Offensive and Non Hate-Offensive. In this work, we experiment with state-of-the-art large language models like GPT-3.5 Turbo via prompting to classify tweets into Hate and Offensive or Non Hate-Offensive. In this study, we evaluate the performance of a classification model using Macro-F1 scores across three distinct runs. The Macro-F1 score, which balances precision and recall across all classes, is used as the primary metric for model evaluation. The scores obtained are 0.756 for run 1, 0.751 for run 2, and 0.754 for run 3, indicating a high level of performance with minimal variance among the runs. The results suggest that the model consistently performs well in terms of precision and recall, with run 1 showing the highest performance. These findings highlight the robustness and reliability of the model across different runs.