An Unsupervised Subspace Weighting Co-Clustering Framework for Hate Speech Detection Patterns in Social Media

The exponential growth of social media has revolutionized global communication, enabling instant idea exchange and transforming information sharing into a worldwide phenomenon while simultaneously accelerating the spread of abusive and hateful content that threatens online harmony and poses a serious risk to online community integrity and public trust. Although supervised deep learning approaches achieve impressive accuracy for hate speech detection, they remain fundamentally reliant on extensive annotated corpora, and their lack of interpretability makes them insufficient for transparent and scalable real-world hate speech detection. This study presents a category-oriented unsupervised architecture for English hate-speech detection and classification that substantially reduces reliance on large labeled datasets by requiring only minimal supervision (10% of labels for post hoc cluster interpretation), ensuring transparency and a high degree of semantic interpretability. We introduce an unsupervised Subspace Weighting Co-Clustering framework that uses HateBERT-driven contextual embeddings, enabling simultaneous interpretable feature weighting and semantic understanding for robust hate-speech detection. The obtained embeddings are further structured using the Subspace Weighting Co-Clustering approach, which enables the unsupervised discovery of latent subspaces and the organization of tweets into semantically coherent hate categories. The comprehensive evaluation shows that the framework achieves superior accuracy over existing methods, providing a more robust and effective mechanism for digital platforms to identify and mitigate hate speech and promote safer online interactions.