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From Content to Audience: A Multimodal Annotation Framework for Broadcast Television Analytics

Paolo Cupini, Francesco Pierri

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 multimodal annotation pipelines applied to broadcast television news in the Italian setting. We construct a domain-specific benchmark of clips labeled across four semantic dimensions: visual environment classification, topic classification, sensitive content detection, and named entity recognition. Two different pipeline architectures are evaluated across nine frontier models, including Gemini 3.0 Pro, LLaMA 4 Maverick, Qwen-VL variants, and Gemma 3, under progressively enriched input strategies combining visual signals, automatic speech recognition, speaker diarization, and metadata. Experimental results demonstrate that gains from video input are strongly model-dependent: larger models effectively leverage temporal continuity, while smaller models show performance degradation under extended multimodal context, likely due to token overload. Beyond benchmarking, the selected pipeline is deployed on 14 full broadcast episodes, with minute-level annotations integrated with normalized audience measurement data provided by an Italian media company. This integration enables correlational analysis of topic-level audience sensitivity and generational engagement divergence, demonstrating the operational viability of the proposed framework for content-based audience analytics.

From Content to Audience: A Multimodal Annotation Framework for Broadcast Television Analytics

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 multimodal annotation pipelines applied to broadcast television news in the Italian setting. We construct a domain-specific benchmark of clips labeled across four semantic dimensions: visual environment classification, topic classification, sensitive content detection, and named entity recognition. Two different pipeline architectures are evaluated across nine frontier models, including Gemini 3.0 Pro, LLaMA 4 Maverick, Qwen-VL variants, and Gemma 3, under progressively enriched input strategies combining visual signals, automatic speech recognition, speaker diarization, and metadata. Experimental results demonstrate that gains from video input are strongly model-dependent: larger models effectively leverage temporal continuity, while smaller models show performance degradation under extended multimodal context, likely due to token overload. Beyond benchmarking, the selected pipeline is deployed on 14 full broadcast episodes, with minute-level annotations integrated with normalized audience measurement data provided by an Italian media company. This integration enables correlational analysis of topic-level audience sensitivity and generational engagement divergence, demonstrating the operational viability of the proposed framework for content-based audience analytics.

Paper Structure

This paper contains 30 sections, 10 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology: Dataset Construction and Pipeline Design
  4. Source Material and Clip Extraction
  5. Annotation Taxonomy and Tasks
  6. Annotation Protocol and Dataset Characterization
  7. Pipeline Design
  8. Evaluation Protocol
  9. Experimental Results
  10. Cross-Task Summary and Error Patterns
  11. Operational Deployment and Audience Analytics
  12. Content Characterization
  13. Audience Integration and Demographic Analysis
  14. Lessons Learned
  15. Conclusion
  16. ...and 15 more sections

Figures (10)

  • Figure 1: Representative annotation: studio interview on international politics with sensitive content signal.
  • Figure 2: Topic label distribution. The long-tail structure introduces per-class sparsity as a non-trivial classification challenge.
  • Figure 3: Unified pipeline architecture. Dashed components are optional; the visual branch is instantiated as static frames (image-frames pipeline) or full video clip (video-based pipeline). The audio-textual branch is identical across both configurations.
  • Figure 4: Environment classification accuracy vs. frame count. Performance plateaus beyond 12 frames across all models.
  • Figure 5: Downstream performance vs. transcription latency (ASR + diarization + metadata). Left: topic accuracy. Right: person recognition precision.
  • ...and 5 more figures