Hate Speech Detection

Hate speech detection research aims to automatically identify and classify hateful content online, mitigating its harmful effects. Current research focuses on improving detection accuracy using advanced deep learning models, such as transformer-based architectures (e.g., BERT, T5) and contrastive learning methods, often incorporating multimodal data (text and images) to enhance performance. This field is crucial for creating safer online environments and is driving advancements in natural language processing, particularly in addressing biases within models and datasets, and developing more robust and explainable systems.