A Large Language Model Approach to Detect Hate Speech in Political Discourse Using Multiple Language Corpora

In this era of unprecedented digital connectivity and interactions, the issue of hate speech has become a focal point in societal discussions. The rise of digital communication platforms has fundamentally transformed how hate speech spreads. Online social media and messaging apps have rapidly disseminated hate speech, exacerbated by the internet's anonymity. Computational technology has emerged as a valuable tool for identifying and mitigating hate speech on social media. In this work, we employed five distinct corpora representing the English, Italian, Filipino, German, and Turkish languages. We propose employing a Large Language Model (GPT-3) enhanced with Cross-Lingual Learning to improve hate speech detection in English and Italian. Our investigation employs a strategy, namely JL/CL+, which combines two strategies: Joint Learning (JL) and Cascade Learning (CL). Even using data with lexical disparities, our findings demonstrate substantial success, yielding an F1-score of 96.58% for English and 92.05% for Italian languages.