Detecting Hate Speech against People with Disabilities in Social Media Comments using RAG-Enhanced LLMs, Fine-Tuning, and Prompt Engineering

Social media is now deeply integrated into people’s daily life, enabling rapid information exchange and global connectivity. Unfortunately, harmful content can be easily disseminated among all communities, including hate speech and biases against vulnerable groups such as people with disabilities. While social media platforms employ a mix of automated systems and skillful experts for content moderation, significant challenges remain in detecting nuanced hate speech, particularly when expressed through indirect or coded language. This paper proposes a novel approach to address these challenges through HEROL (Hate-speech Evaluation via RAG and Optimized LLM), a unified model that combines RAG-Enhanced Large Language Models with Prompt Engineering and Fine-Tuning. Experimental results, obtained through a structured evaluation methodology using annotated social media datasets, demonstrated that HEROL achieved an accuracy improvement by up to 10% compared to baseline models. This highlights its effectiveness in identifying subtle and indirect forms of hate speech and its potential to contribute to safer, more inclusive online environments.