A Sarcasm-aware Cyberbully Detection Framework for Code-Mixed Hinglish Text Using Deep Learning
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Date
2026
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National Institute of Technology, Silchar
Abstract
Cyberbullying detection in Hinglish social media text is challenging due to code-mixing, informal language, and sarcasm that often masks harmful intent. Traditional text-based approaches struggle to capture such implicit expressions. This research pro-poses a two-phase sarcasm-aware framework for cyberbullying detection. In Phase 1, a MuRIL-based model is trained to learn sarcasm-aware representations from Hinglish cyberbullying-related data. In Phase 2, these representations are integrated with tex-tual and emoji signals for classification. An experimental setup involving model variants indicates the consistent improvement, with the proposed model achieving F1-score of 85.70%, in comparison to the text-only baseline which achieves 83.69%. Stratified analysis further shows improved performance on sarcastic samples, reducing the sarcasm gap from 27.19% to 18.83%. Cross-dataset evaluation on Mendeley and TRAC datasets shows improvements in AUC and overall F1-score. However, the observed F1 score im-provement is mainly due to better detection of non-aggressive samples, highlighting the effect of domain mismatch on aggressive classes. These findings highlight the im-portance of sarcasm-aware modelling while also reveals the limitations in cross-domain generalization.