Optimizing Hate Speech Detection in Malayalam-English Code-Mixed Text: Handling Women's Abuse by Synthetic Data Augmentation

Abstract

The increase of online hate speech, especially against women, has become a big problem in digital communication, especially in low-resource languages like Malayalam and in situations where English is mixed with other languages. This work examines the efficacy of synthetic data augmentation techniques—Machine Translation (MT), Masked Language Modeling (MLM), and Few-Shot Learning (FSL)—in enhancing hate speech identification inside Malayalam-English (Manglish) social media text. We use these three methodologies to improve transformer-based models like mBERT, BERT, and IndicBERT. Our experiments show that classification performance has improved a lot. For example, mBERT got an F1-score of 86.42%, but real data only got 81.24%. LIME's explainability research indicates that contextual clues, not just offending words on their own, are what make detection accurate. Also, synthetic data makes things more fair by cutting down on false positives and false negatives and makes models more broad by exposing them to a larger range of code-mixed expressions. The approach is effective, but it has certain drawbacks. For example, it may be hard to apply to other code-mixed languages or fields, and there are ethical issues with creating synthetic data. The results have practical consequences for implementing fairness-aware, transparent, and resilient hate speech detection algorithms on multilingual social media platforms. This is the first study we know of that looks into synergistic synthetic data augmentation for detecting hate speech that mixes languages, with the goal of reducing online harassment of women.