Paper Title
MULTILINGUAL HATE SPEECH DETECTION USING TRANSFORMER MODELS
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Registration ID: IJNRD_324179
Published ID: IJNRD2604755
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Keywords
Hate Speech Detection, Multilingual NLP, XLM RoBERTa, Transformer Models, Explainable AI, Content Moderation.
Abstract
The fast rise of social media and online communication platforms has accelerated the distribution of undesirable content, including hate speech and abusive language. Detecting such stuff automatically is critical for ensuring secure online communities. Traditional moderation strategies that rely on keyword filtering or simple machine learning models frequently fail to recognize context, multilingual content, and mixed-language phrases. This work offers a multilingual hate speech detection system built on the XLM-RoBERTa transformer model and a scalable full-stack architecture. The system uses a React-based frontend, a FastAPI backend, and PyTorch-based model inference to categorize text as hate speech, offensive language, or neutral material. To promote transparency, the system also uses explainable AI approaches to identify key phrases that influence the forecast. A browser extension allows for real-time identification of hazardous information on online pages. Experiments reveal that the transformer-based technique outperforms classic models like SVM and BiLSTM in terms of accuracy and F1-score. The suggested architecture offers an effective approach for detecting hate speech in many languages and in real time.
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How To Cite (APA)
BANKA SRIMANNARAYANA REDDY, AZMEERU RAVIKANTH, BANOTHU SAI PRANEETH, AMBATI SANDEEP REDDY, & DR. M. PADMA PRIYA (April-2026). MULTILINGUAL HATE SPEECH DETECTION USING TRANSFORMER MODELS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), h421-h427. https://ijnrd.org/papers/IJNRD2604755.pdf
Issue
Volume 11 Issue 4, April-2026
Pages : h421-h427
Other Publication Details
Paper Reg. ID: IJNRD_324179
Published Paper Id: IJNRD2604755
Research Area: Other area not in list
Author Type: Indian Author
Country: Chennai, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2604755.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604755
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