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.

Content moderation at scale represents one of the most persistent technical and operational challenges facing social platforms. This research addresses a genuine gap in existing approaches by acknowledging that no single detection method handles both explicit violations and novel, adversarial edge cases equally well. The hybrid architecture leverages supervised learning for known offense patterns while deploying similarity-based matching for emerging or subtle violations that traditional classifiers miss. This dual-pathway approach reflects practical engineering wisdom: strict rule-based systems fail against novel attacks, while pure similarity matching generates excessive false positives.

The integration of multimodal large language models (MLLMs) to distill knowledge into both pipelines addresses a critical production constraint—inference efficiency. By using MLLMs as a knowledge source rather than the primary inference engine, the authors maintain lightweight runtime performance suitable for real-time livestream environments where latency directly impacts user experience. The production metrics (67% and 76% recall respectively at consistent 80% precision) suggest complementary rather than overlapping detection capabilities.

The 6-8% reduction in views of unwanted content translates directly to improved platform safety and user experience, which carries real business value. For platform operators, this framework provides a replicable template for balancing detection coverage against false positive rates. For the broader AI community, the work demonstrates how hybrid classical-and-neural approaches can outperform monolithic solutions when constrained by production requirements. Future iterations will likely focus on automated retraining pipelines as adversarial content evolves, and on extending the approach to other moderation domains beyond livestreaming.

• →Hybrid approach combining supervised classification and similarity matching detects both known violations and novel edge cases more effectively than single-method systems.
• →Production deployment achieves 67-76% recall at 80% precision across multimodal inputs, resulting in measurable 6-8% reduction in unwanted content exposure.
• →MLLM distillation into both detection pipelines maintains lightweight inference suitable for real-time livestream processing requirements.
• →Complementary detection pathways suggest different violation types require different algorithmic approaches rather than unified solutions.
• →Framework demonstrates scalable template for production AI moderation systems balancing safety coverage against computational and false positive constraints.