CEU Electronic Theses and Dissertations, 2025
| Author | Miller, Seneca Fox |
|---|---|
| Title | Formula 1 Media Dynamics: Uncovering What Drives Broadcast Coverage Client-Facing Analytics to Decode Regional, Temporal, and Sponsor-Level Patterns in Global F1 Exposure |
| Summary | This thesis develops a scalable analytics pipeline for measuring sponsor visibility across broadcast and social media channels, using Formula 1 as a high-visibility test case. The project integrates unstructured content from TikTok, Instagram, and Twitter (via the Vetric API), race metadata (via the Jolpica API), and visual detection outputs from a custom-trained YOLOv8 model applied to broadcast footage. A timestamp-based matching script aligns posts and video content to official race events, enabling structured comparisons by geography, platform, and event timing. Through exploratory analysis of over 10,000 social media posts, the study reveals that prime- time races and newer circuits in the Americas and Gulf regions generate more timely and engaging content. Text-based sponsor mentions were sparse, underscoring the importance of future visual analytics via logo detection. The broadcast component, while not central to the final dataset, informed interface improvements in eMM’s Alpha platform and validated the visual recognition pipeline for future deployment. The result is a modular, reusable system for multi-source media analysis. It demonstrates how eMM can extend its monitoring infrastructure to support insight-driven reporting — offering scalable solutions for brand tracking, campaign evaluation, and regional media strategy. |
| Supervisor | de la Rubia, Eduardo Arino; Netousek, Thomas |
| Department | Economics MSc |
| Full text | https://www.etd.ceu.edu/2025/miller_seneca.pdf |
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