Hate Speech Detection Using Social Media Discourse: A Multilingual Approach With Large Language Model

Abstract

Online social networks (OSN) and microblogging websites are attracting Internet users and have revolutionized how we communicate with individuals, share their feelings, and exchange ideas across the world with ease. In the extensive age of social media, there is increasing online hate speech, which can provoke violence and contribute to societal division. Hate speech based on race, gender, or religion puts those affected at risk of mental health problems and exacerbates social problems. While current protocols have reduced overt hate speech, subtler forms known as implicit hate speech have emerged, making detection more challenging. This study focuses on hate speech detection using social media discourse, by creating a comprehensive multilingual dataset [25] in Urdu and English and applied multiple machine learning, deep learning, transfer learning, and Large Language model models, such as GPT-3.5 Turbo. By comparing GPT-3.5 Turbo, we identified the effectiveness of large language models in detecting both explicit and implicit forms of hate speech. Our analysis underscores the potential of automated classification systems to reduce reliance on human intervention and to promote constructive online discourse. Our proposed methodology achieved the highest accuracy of 0.91, and achieved the highest performance improvement of 5.81% over transformer models such as BERT. This research adds to the growing body of work on multilingual natural language processing (NLP) and offers insights for reducing hate speech and fostering respectful communication across diverse communities.