AdaGraph-HATE for real-time gendered hate speech detection in multilingual social media

Social networking sites process millions of messages daily, yet detecting implicit misogyny remains challenging due to sarcasm, cultural variation, and language-dependent expressions. Existing approaches demonstrate promising results but suffer from limited language adaptation and impracticality for real-time deployment. This paper introduces AdaGraph-HATE, a hybrid ensemble framework combining seven diverse classifiers SVM, KNN, logistic regression, ANN, CNN, BERT, and SBERT with a novel graph-aware dynamic weighting mechanism that adapts model weights based on confidence scores, inter-model diversity, and correlation structure captured through graph representations. Comprehensive evaluation across five datasets spanning eight languages with 124,456 labeled tweets reveals that AdaGraph-HATE achieves 90.02% accuracy and 0.7354 Macro F1, showing a small statistically significant improvement over SBERT (88.92% accuracy, 0.6753 Macro F1) with p < 0.001 but overlapping confidence intervals. However, critical analysis exposes substantial trade-offs: hate class detection F1 drops from 0.2867 (SBERT) to 0.1102 (AdaGraph) a 61.6% relative decrease while severe misclassifications increase by 82.6%. Cross-dataset evaluation further reveals limited generalization, with AdaGraph-HATE underperforming SBERT on four of five datasets. From a systems perspective, the framework demonstrates production-ready scalability, processing 18,234 tweets per second with 68.4 ms P95 latency and costing $0.19 per million tweets. These findings indicate that while adaptive multi-model integration offers throughput and cost benefits, it introduces significant minority-class detection trade-offs. SBERT remains superior for safety–critical moderation, while AdaGraph-HATE suits high-volume environments where training and deployment distributions match. This study provides both theoretical insights into ensemble design and practical guidance for deploying reliable hate speech detection systems in real-world scenarios.