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Recent Trends on Artificial Intelligence in Automated Hate Speech Detection
Abstract
This study investigates the performance of AI in detecting HS in diverse cultural and contextual settings. Existing AI models, trained primarily on English datasets, struggle with regional dialects, idiomatic phrases, and cultural nuances. A systematic review of NLP techniques, including traditional methods (n-grams, Bag of Words) and advanced architectures (BERT, GPT, RoBERTa, CNNs, LSTMs), evaluates their effectiveness. Multilingual models like mBERT and XLM-R are assessed for low-resource scenarios while emerging trends like multimodal learning (CLIP) and adversarial training (GANs) are explored for robustness. Challenges such as data bias, false positives, and cultural insensitivity are addressed through contextual embeddings, data augmentation, and Pragmatics-oriented NLP. Metrics like precision, recall, and F1-score reveal significant accuracy drops in non-English contexts. The study emphasizes culturally aware datasets, Explainable AI (LIME, SHAP), and hybrid AI-human moderation to ensure ethical, inclusive online spaces.
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