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

2026-03-24

AI Summary

This study addresses the challenges of automated semantic annotation in broadcast television content, which arise from complex audiovisual structures, distinctive editing patterns, and stringent operational constraints. While general-purpose multimodal large language models have shown promise in various domains, their effectiveness in this specific context remains underexplored. To bridge this gap, the authors develop a multimodal annotation framework tailored to Italian television news, integrating visual features, automatic speech recognition, speaker diarization, and metadata. They systematically evaluate state-of-the-art models—including Gemini 3.0 Pro, LLaMA 4 Maverick, Qwen-VL, and Gemma 3—across four semantic dimensions. The work establishes the first fine-grained multimodal annotation benchmark for broadcast media, reveals trade-offs between model scale and input length, achieves minute-level annotations across 14 full episodes, and demonstrates the feasibility of content-driven audience analysis by linking topical semantics to viewership behavior.

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.

Problem

• broadcast television
• multimodal annotation
• semantic understanding
• audience analytics
• video content analysis

Innovation

• multimodal annotation
• broadcast television analytics
• audience measurement integration
• pipeline architecture evaluation
• domain-specific benchmark