When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platforms

2026-01-14

AI Summary

This study addresses the rapidly evolving content risks on short-video platforms, where traditional manual detection mechanisms suffer from low efficiency and delayed policy updates, thereby limiting governance effectiveness. The authors propose the first automated problem discovery framework based on a multimodal large language model (LLM) agent. The approach identifies potentially violating videos, applies a two-stage clustering method to generate emerging issue clusters, and automatically produces executable annotation policies. This work pioneers the use of multimodal LLM agents for defining and detecting novel content risks, enabling rapid iteration of annotation strategies. Experimental results demonstrate that, compared to manual processes, the proposed method improves the F1 score for emerging issue detection by over 20%, reduces associated video views by approximately 15%, and significantly shortens the policy update cycle.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Trends on short-video platforms evolve at a rapid pace, with new content issues emerging every day that fall outside the coverage of existing annotation policies. However, traditional human-driven discovery of emerging issues is too slow, which leads to delayed updates of annotation policies and poses a major challenge for effective content governance. In this work, we propose an automatic issue discovery method based on multimodal LLM agents. Our approach automatically recalls short videos containing potential new issues and applies a two-stage clustering strategy to group them, with each cluster corresponding to a newly discovered issue. The agent then generates updated annotation policies from these clusters, thereby extending coverage to these emerging issues. Our agent has been deployed in the real system. Both offline and online experiments demonstrate that this agent-based method significantly improves the effectiveness of emerging-issue discovery (with an F1 score improvement of over 20%) and enhances the performance of subsequent issue governance (reducing the view count of problematic videos by approximately 15%). More importantly, compared to manual issue discovery, it greatly reduces time costs and substantially accelerates the iteration of annotation policies.

Problem

• emerging content issues
• short video platforms
• annotation policies
• content governance
• issue discovery

Innovation

• multimodal LLM agents
• emerging issue discovery
• two-stage clustering
• annotation policy generation
• short video content governance