Hate Speech Detection Method

Hate speech detection aims to automatically identify hateful content online, mitigating its harmful effects. Current research focuses on improving model robustness against adversarial attacks and biases, enhancing interpretability through techniques like rationale extraction using large language models, and addressing data scarcity, particularly in low-resource languages, often employing deep learning architectures such as transformers and LSTMs. These advancements are crucial for creating more accurate and fair hate speech detection systems, impacting online safety and fostering more constructive online discourse.