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Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models

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arxiv 1811.05145 v1 pith:CSAAHAAH submitted 2018-11-13 cs.CL

Satyajit Kamble , Aditya Joshi This is my paper · ORCID
classification cs.CL
keywords code-mixedtweetsdeepdetectiondomain-specificembeddingsenglish-hindihate
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper reports an increment to the state-of-the-art in hate speech detection for English-Hindi code-mixed tweets. We compare three typical deep learning models using domain-specific embeddings. On experimenting with a benchmark dataset of English-Hindi code-mixed tweets, we observe that using domain-specific embeddings results in an improved representation of target groups, and an improved F-score.

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  1. Cyberbullying Detection in Hinglish Text Using MURIL and Explainable AI

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A MURIL-based classifier outperforms RoBERTa, IndicBERT, and several published baselines on six Hinglish cyberbullying datasets, with reported gains of 1.36 to 13.07 percentage points.

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