[2603.04698] Hate Speech Detection using Large Language Models with Data Augmentation and Feature Enhancement

Computer Science > Computation and Language arXiv:2603.04698 (cs) [Submitted on 5 Mar 2026] Title:Hate Speech Detection using Large Language Models with Data Augmentation and Feature Enhancement Authors:Brian Jing Hong Nge, Stefan Su, Thanh Thi Nguyen, Campbell Wilson, Alexandra Phelan, Naomi Pfitzner View a PDF of the paper titled Hate Speech Detection using Large Language Models with Data Augmentation and Feature Enhancement, by Brian Jing Hong Nge and 5 other authors View PDF HTML (experimental) Abstract:This paper evaluates data augmentation and feature enhancement techniques for hate speech detection, comparing traditional classifiers, e.g., Delta Term Frequency-Inverse Document Frequency (Delta TF-IDF), with transformer-based models (DistilBERT, RoBERTa, DeBERTa, Gemma-7B, gpt-oss-20b) across diverse datasets. It examines the impact of Synthetic Minority Over-sampling Technique (SMOTE), weighted loss determined by inverse class proportions, Part-of-Speech (POS) tagging, and text data augmentation on model performance. The open-source gpt-oss-20b consistently achieves the highest results. On the other hand, Delta TF-IDF responds strongly to data augmentation, reaching 98.2% accuracy on the Stormfront dataset. The study confirms that implicit hate speech is more difficult to detect than explicit hateful content and that enhancement effectiveness depends on dataset, model, and technique interaction. Our research informs the development of hate speech detection by highli...