Toxicity Detection Datasets

Toxicity detection datasets are crucial for training and evaluating models that identify harmful online content, aiming to mitigate the spread of hate speech, misinformation, and other forms of toxic language across various modalities (text, audio). Current research focuses on creating more comprehensive and nuanced datasets, addressing limitations like language diversity, contextual understanding, and the influence of annotator bias, often employing transformer-based models and graph neural networks for improved performance. These advancements are vital for enhancing the safety and reliability of online platforms and AI systems, impacting content moderation, brand safety, and the development of more responsible AI technologies.