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Graph-based hostile content detection in Hindi language
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DOI：10.1007/s10791-025-09790-0.png)
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Abstract
En 中文
Organizations and governments are struggling to handle the hostile content on social media sites (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Facebook<^>{TM}$$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Twitter<^>{TM}$$\end{document}, etc.). While extensive research exists for English-language content, regional languages like Hindi lack robust tools and datasets for effective moderation. This study proposes a scalable AI-based framework for detecting hostile posts in Hindi, the most widely spoken language in the Indian subcontinent and the third most spoken globally. We employ both binary (coarse-grained) and multi-class, multi-label (fine-grained) classification using contextual and semantic features. Our approach integrates various BERT-based embeddings with Relational Graph Convolutional Networks (R-GCN), forming a hybrid BRGCN architecture trained on the Constraint 2021 Hindi dataset. To enhance performance, we implement a hard voting-based ensemble classifier. The proposed model achieves superior F1-scores compared to existing baselines: 0.98 for coarse-grained classification and 0.84, 0.61, 0.49, and 0.64 for the fine-grained categories of Fake, Hate, Defamation, and Offensive, respectively. Code and data will be made publicly available in https://github.com/mani-design/B-RGCN.
Keywords:
Natural language processing
Hindi
Hostility detection
Social networking
BERT
R-GCN
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