Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching
arXiv:2512.03553v3 Announce Type: replace-cross 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 ev
The proliferation of user-generated content and live streaming necessitates more dynamic and robust moderation solutions, pushing the boundaries of AI application in real-time environments.
This development addresses a critical challenge for large-scale digital platforms by improving content safety and compliance while reducing operational overhead and brand risk for platforms and advertisers.
Content moderation shifts from purely reactive or rule-based systems to hybrid, AI-driven approaches capable of identifying novel and subtle violations in real-time, improving the safety and quality of online interactions.
- · Large-scale user-generated video platforms
- · Advertisers
- · Online communities
- · AI content moderation solution providers
- · Content bad actors
- · Manual moderation services lacking AI integration
More effective and efficient real-time content moderation is achieved on live streaming platforms.
Public trust and user engagement in moderated online environments increase due to reduced exposure to harmful content.
This hybrid AI moderation approach could set a new industry standard, leading to broader adoption across different forms of digital content.
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Read at arXiv cs.AI