Content Detection

Content detection research focuses on automatically identifying harmful, offensive, or otherwise undesirable content in various digital formats, aiming to create safer online environments. Current efforts concentrate on improving the robustness and generalizability of detection models, addressing issues like bias against marginalized groups and the limitations of existing datasets and evaluation methods. This involves exploring diverse approaches, including multimodal models, graph-based methods leveraging social context, and continual learning frameworks to adapt to evolving language and content types. The field's impact is significant, with applications ranging from content moderation on social media platforms to safeguarding large language models from misuse.