Cyberbullying Detection in Hinglish Text Using MURIL and Explainable AI

The growth of digital communication platforms has led to increased cyberbullying incidents worldwide, creating a need for automated detection systems to protect users. The rise of code-mixed Hindi-English (Hinglish) communication on digital platforms poses challenges for existing cyberbullying detection systems, which were designed primarily for monolingual text. This paper presents a framework for cyberbullying detection in Hinglish text using the Multilingual Representations for Indian Languages (MURIL) architecture to address limitations in current approaches. Evaluation across six benchmark datasets – Bohra $\mathit{\text{et}}\mathit{\text{al.}}$, BullyExplain, BullySentemo, Kumar $\mathit{\text{et}}\mathit{\text{al.}}$, HASOC 2021, and Mendeley Indo-HateSpeech – shows that the MURIL-based approach outperforms existing multilingual models including RoBERTa and IndicBERT, with improvements of 1.36 to 13.07 percentage points and accuracies of 86.97% on Bohra, 84.62% on BullyExplain, 86.03% on BullySentemo, 75.41% on Kumar datasets, 83.92% on HASOC 2021, and 94.63% on Mendeley dataset. The framework includes explainability features through attribution analysis and cross-linguistic pattern recognition. Ablation studies show that selective layer freezing, appropriate classification head design, and specialized preprocessing for code-mixed content improve detection performance, while failure analysis identifies challenges including context-dependent interpretation, cultural understanding, and cross-linguistic sarcasm detection, providing directions for future research in multilingual cyberbullying detection.