Multilingual Hate Speech Detection Through Sentiment and Semantic Representation Fusion
Keywords:
multilingual hate speech detection, sentiment analysis, semantic representation, fusion architecture, fairness, platform governance, deployment sustainabilityAbstract
Multilingual hate speech detection remains a persistent challenge that sits at the intersection of language technology, platform governance, and public safety. This paper develops a systems-oriented analysis of detection architectures that combine sentiment and semantic representation fusion for multilingual environments. Rather than treating hate speech detection as a monolingual classification problem or as a simple extension of sentiment analysis, the argument positions affective signals and cross-lingual semantic encoders as complementary sources of evidence that must be integrated through carefully designed fusion mechanisms. The analysis emphasizes structural trade-offs between fine-grained affect modeling and large-scale multilingual semantic representation, including issues of representation alignment, data scarcity, operational latency, and annotation reliability. It also examines the implications of fusion design for robustness, fairness, auditability, and long-term deployment sustainability. The paper draws on cross-domain comparisons with social media content moderation, cross-lingual natural language processing, and human-in-the-loop review systems. It argues that effective multilingual hate speech detection requires not only advances in model architecture but also governance frameworks that account for dataset bias, evaluation validity, and the institutional contexts in which automated systems operate. The discussion contributes a system-level perspective for researchers and practitioners seeking to build detection infrastructures that are technically sound and socially accountable.
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