Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching
Researchers present a hybrid content moderation system for livestreams that combines supervised classification with multimodal similarity matching, achieving 67-76% recall at 80% precision. The production-deployed framework reduces user views of unwanted content by 6-8%, demonstrating scalable AI-driven moderation for user-generated video platforms.