Mapping 3D Digital Heritage at Scale: A ChatGPT-Assisted Analysis of Sketchfab’s “Cultural Heritage & History” Models

This paper evaluates the platform-mediated importance and impact of 3D cultural heritage models stored on Sketchfab by analyzing user engagement and retention metrics (views, likes, and comments), and provides a comparative assessment across other major 3D platforms. Our primary goal is to understand how cultural heritage content performs in terms of reach, engagement, and reuse conditions, and how platform design and taxonomies shape what becomes visible and measurable. We map Sketchfab’s Cultural Heritage & History ecosystem through a reproducible, API-driven workflow built on public metadata for over 1.37 million models (views, likes, comments, tags, and licences). The results depict a domain in rapid expansion between 2018 and 2025, while also revealing a strongly unequal attention economy: most models receive limited interaction, whereas a small minority concentrates visibility and engagement. The category Cultural Heritage & History shows high endorsement relative to reach, consistent with “high-value” engagement once content is discovered. Methodologically, large-scale harvesting required automation to manage cursor pagination, intermittent failures, and rate limits (e.g., HTTP 429). In this context, ChatGPT provided essential support by assisting the design and refinement of the extraction and counting algorithm, replacing what would otherwise have required extensive manual counting and verification at a scale that could plausibly take months.