High Performance Hate

High-performance hate detection research focuses on accurately identifying and mitigating hateful online content across diverse languages and platforms. Current efforts utilize various machine learning models, including transformer-based architectures and XGBoost, to analyze textual and multimodal data (e.g., memes), often incorporating explainability techniques to improve transparency and user trust. This research is crucial for creating safer online environments and informing the development of effective content moderation strategies, with implications for both social science and the technological design of online platforms.