Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching

TL;DR

This paper introduces a hybrid content moderation system for livestreams that combines supervised classification with MLLM-boosted similarity matching, effectively detecting both known violations and novel unwanted content.

Contribution

It presents a scalable, multimodal moderation framework that integrates supervised learning and reference-based matching, enhanced by large language models for improved accuracy.

Findings

Achieves 67% recall at 80% precision for classification

Achieves 76% recall at 80% precision for similarity matching

Reduces unwanted livestream views by 6-8% in production

Abstract

Content moderation remains a critical yet challenging task for large-scale user-generated video platforms, especially in livestreaming environments where moderation must be timely, multimodal, and robust to evolving forms of unwanted content. We present a hybrid moderation framework deployed at production scale that combines supervised classification for known violations with reference-based similarity matching for novel or subtle cases. This hybrid design enables robust detection of both explicit violations and novel edge cases that evade traditional classifiers. Multimodal inputs (text, audio, visual) are processed through both pipelines, with a multimodal large language model (MLLM) distilling knowledge into each to boost accuracy while keeping inference lightweight. In production, the classification pipeline achieves 67% recall at 80% precision, and the similarity pipeline achieves 76% recall at 80% precision. Large-scale A/B tests show a 6-8% reduction in user views of unwanted livestreams}. These results demonstrate a scalable and adaptable approach to multimodal content governance, capable of addressing both explicit violations and emerging adversarial behaviors.