Safeguarding the Integrity of Online Social Networks (OSN): Leveraging the Efficacy of Conv-LSTM-Based Siamese Network to Predict Hate Speech in Low Resource Hindi-English Code-Mixed Text

Document Type

Article

Publication Title

IEEE Access

Abstract

In the era of free speech and rapid internet expansion, curbing the dissemination of offensive content on social media has become a pressing concern for linguists and regulatory bodies. Hate speech not only targets individuals or groups but also impacts their mental well-being, often leading to feelings of anxiety, isolation, and helplessness. Therefore, hate speech detection should be viewed not just as a linguistic task but also as a public health imperative. In multilingual and culturally diverse countries like India, the challenge is heightened by the presence of code-mixed language. While most existing studies focus on monolingual data, our work addresses hate speech detection in Hindi-English code-mixed text. We propose a Convolution-LSTM network that incorporates spatial and temporal features. Furthermore, the model’s performance is enhanced by constructing an ensemble network using an early-fusion-based Siamese architecture. Experimental results demonstrate that our approach outperforms existing baselines in identifying hate speech in low-resource, code-mixed scenarios.

First Page

141598

Last Page

141608

DOI

10.1109/ACCESS.2025.3597144

Publication Date

1-1-2025

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