{"ID":2844667,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06051","arxiv_id":"2511.06051","title":"Efficient Hate Speech Detection: A Three-Layer LoRA-Tuned BERTweet Framework","abstract":"This paper addresses the critical challenge of developing computationally efficient hate speech detection systems that maintain competitive performance while being practical for real-time deployment. We propose a novel three-layer framework that combines rule-based pre-filtering with a parameter-efficient LoRA-tuned BERTweet model and continuous learning capabilities. Our approach achieves 0.85 macro F1 score - representing 94% of the performance of state-of-the-art large language models like SafePhi (Phi-4 based) while using a base model that is 100x smaller (134M vs 14B parameters). Compared to traditional BERT-based approaches with similar computational requirements, our method demonstrates superior performance through strategic dataset unification and optimized fine-tuning. The system requires only 1.87M trainable parameters (1.37% of full fine-tuning) and trains in approximately 2 hours on a single T4 GPU, making robust hate speech detection accessible in resource-constrained environments while maintaining competitive accuracy for real-world deployment.","short_abstract":"This paper addresses the critical challenge of developing computationally efficient hate speech detection systems that maintain competitive performance while being practical for real-time deployment. We propose a novel three-layer framework that combines rule-based pre-filtering with a parameter-efficient LoRA-tuned BE...","url_abs":"https://arxiv.org/abs/2511.06051","url_pdf":"https://arxiv.org/pdf/2511.06051v1","authors":"[\"Mahmoud El-Bahnasawi\"]","published":"2025-11-08T15:47:18Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Language Model\",\"LoRA\"]","has_code":false}