From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework
In the rapidly evolving landscape of social video platforms, the need for effective controversy detection has become paramount. Recent research has proposed a novel approach to this challenge through a training-free multi-agent framework, named AuDisAgent, which redefines Multimodal Controversy Detection (MCD) as a dynamic process of audience dissemination. This innovative framework aims to enhance risk management and improve the understanding of controversial content across various audience segments.
Understanding Multimodal Controversy Detection
Traditionally, MCD has been approached as a static representation learning task, wherein features are extracted from videos and their related user comments. However, these conventional methods often fall short in acknowledging the diverse perspectives that different audience groups bring to the table. The AuDisAgent framework addresses this critical gap by modeling the propagation of content through a structured multi-agent system.
The AuDisAgent Framework
AuDisAgent comprises several specialized agents that work collaboratively to assess controversial content from multiple viewpoints. The framework includes:
- Video Agent: Evaluates the visual content of the video to identify potential controversial elements.
- Comment Agent: Analyzes user comments for sentiment and thematic relevance to detect underlying controversies.
- Interaction Agent: Examines the interplay between videos and comments, assessing how they influence audience perception.
In instances where consensus among the three agents is not reached, a Viewing Panel Agent is activated. This agent simulates discussions among a diverse audience, allowing for a richer interpretation of the content. Such interactions can reveal latent controversies that may not be apparent through isolated analyses.
Final Judgment and Cold-Start Scenario
To conclude the assessment process, an Arbitration Agent synthesizes the insights from the previous agents and renders a final judgment. This comprehensive reasoning chain ensures that the evaluation of controversial content is well-informed and reflective of various audience perspectives.
Additionally, the framework addresses the “cold-start” scenario, which occurs when newly released videos have few or no comments. To combat this challenge, AuDisAgent employs a Comment Bootstrapping Strategy. This strategy utilizes historical public comments from semantically similar videos to provide initial context for comment analysis, thereby enhancing the framework’s ability to evaluate new content effectively.
Experimental Validation
Extensive experiments conducted on a public dataset have demonstrated that AuDisAgent significantly outperforms existing state-of-the-art (SOTA) methods in both rich-comment and limited-comment scenarios. The results indicate that the dynamic and audience-centric approach of AuDisAgent not only improves controversy detection but also fosters a deeper understanding of how diverse audience interpretations can shape the perception of controversial content.
In conclusion, the AuDisAgent framework represents a significant advancement in the field of multimodal controversy detection. By shifting the focus from static analysis to dynamic audience dissemination, this innovative system offers a more nuanced understanding of controversial content, ultimately contributing to better risk management strategies for social video platforms.
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