From Content to Audience: A Multimodal Annotation Framework for Broadcast Television Analytics
arXiv:2603.26772v2 Announce Type: replace-cross Abstract: Automated semantic annotation of broadcast television content presents distinctive challenges, combining structured audiovisual composition, domain-specific editorial patterns, and strict operational constraints. While multimodal large language models (MLLMs) have demonstrated strong general-purpose video understanding capabilities, their comparative effectiveness across pipeline architectures and input configurations in broadcast-specific settings remains empirically undercharacterized. This paper presents a systematic evaluation of mu
The proliferation of multimodal large language models and increasing demand for automated content analysis in broadcast media are driving this research now.
This development can significantly enhance the efficiency and depth of broadcast content analysis, offering new insights for media organizations, advertisers, and national security agencies monitoring foreign media.
The empirical validation of MLLMs in specialized broadcast environments advances their application beyond general-purpose video understanding, enabling more sophisticated and automated media intelligence.
- · Media analytics companies
- · Broadcast media organizations
- · Advertising platforms
- · AI model developers
- · Manual content annotation services
- · Legacy video analysis software
Automated systems can more rapidly identify trends, sentiment, and compliance issues within broadcast content.
This could lead to highly personalized advertising and content recommendations, alongside faster identification of disinformation campaigns.
The advanced annotation capabilities might enable real-time, global monitoring of public sentiment and narrative propagation through broadcast media, with potential geopolitical implications.
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Read at arXiv cs.AI