# Cyberbullying Detection in Hinglish Text Using MURIL and Explainable AI

arXiv: https://arxiv.org/abs/2506.16066 · HTML: https://mlsanity.com/paper/2506.16066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text](https://mlsanity.com/paper/1611.00472/) (2016)
- [Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages](https://mlsanity.com/paper/2112.09301/) (2021)
- [Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models](https://mlsanity.com/paper/1811.05145/) (2018)
- [Shallow Parsing Pipeline for Hindi-English Code-Mixed Social Media Text](https://mlsanity.com/paper/1604.03136/) (2016)
- [Sentiment Identification in Code-Mixed Social Media Text](https://mlsanity.com/paper/1707.01184/) (2017)
- [Ceasing hate withMoH: Hate Speech Detection in Hindi-English Code-Switched Language](https://mlsanity.com/paper/2110.09393/) (2021)
- [Battling Hateful Content in Indic Languages HASOC '21](https://mlsanity.com/paper/2110.12780/) (2021)

## Abstract (arXiv)

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 textitet al., BullyExplain, BullySentemo, Kumar textitet 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.