Cyberbullying Detection in Hinglish Text Using MURIL and Explainable AI

TL;DR

This paper introduces a MURIL-based framework for detecting cyberbullying in Hinglish text, outperforming existing models and incorporating explainability, with insights into challenges like sarcasm and cultural nuances.

Contribution

The study develops a novel multilingual detection framework using MURIL architecture tailored for Hinglish, with explainability features and comprehensive evaluation across multiple datasets.

Findings

MURIL-based approach outperforms RoBERTa and IndicBERT in accuracy.

Achieves up to 94.63% accuracy on Mendeley dataset.

Identifies key challenges like sarcasm and cultural context in detection.

Abstract

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 \textit{et al.}, BullyExplain, BullySentemo, Kumar \textit{et 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.