Hate Speech Detection Model

Hate speech detection models aim to automatically identify hateful content online, a crucial task given the proliferation of such material. Current research focuses on improving model performance across diverse languages and platforms, often employing transformer-based architectures like BERT and T5, and exploring techniques like data augmentation and cross-lingual transfer learning to address data scarcity and linguistic variations. These advancements are vital for mitigating the harms of online hate speech, informing content moderation policies, and furthering our understanding of online hate's dynamics and impact.