Abstract

The growth of social media has led to an increase in the scale of cyber harassment, from texts to images, videos, memes, deepfakes, and identity-based assaults. Traditional machine learning approaches are limited to identify such multimodal and context-dependent misuse. This study gives a complete assessment of AI based cyber harassment detection including datasets, preprocessing, deep learning, transformer models, multimodal fusion, big language models, explainable AI, federated learning, and privacy-preserving frameworks. Transformer based and multi-modal techniques offer better contextual and semantic comprehension compared to conventional approaches. The poll also highlights important research problems, such as multilingual detection, bias reduction, explainability, real-time scalability, and the identification of developing AI-generated offensive material.

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