A Sarcasm-aware Cyberbully Detection Framework for Code-Mixed Hinglish Text Using Deep Learning

Simple item page
dc.contributor.authorRai, Sweety
dc.date.accessioned2026-09-23T11:26:57Z
dc.date.available2026-09-23T11:26:57Z
dc.date.issued2026
dc.description.abstractCyberbullying 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.
dc.identifier.urihttp://nits.ndl.gov.in/handle/123456789/128
dc.language.isoen
dc.publisherNational Institute of Technology, Silchar
dc.titleA Sarcasm-aware Cyberbully Detection Framework for Code-Mixed Hinglish Text Using Deep Learning
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Sweety_M.Tech_THESIS_BINDING - Sweety Rai.pdf
Size:
1.12 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Department of Computer Science and Engineering