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arXiv · 2609.33525

Latent Class Analysis of Digital Content Use Contexts in AI-Generated Synthetic Personas

Eunjeong Song·Sehee Hong
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Abstract

AI-generated synthetic personas are increasingly used for content planning and virtual-user simulation, yet the digital content use contexts embedded in their narratives remain underexamined. Using all 1,000,000 records of NVIDIA's Nemotron-Personas-Korea, this study coded mentions of five engagement modes-viewing, discovering and sharing, interacting, reading, and listening-with an openly released Korean coding dictionary and applied latent class analysis with a three-step approach accounting for classification error. Four classes emerged: viewing-centered (20.8%), reading-centered (3.8%), listening-centered (2.1%), and low-mention (73.3%). Age showed the strongest association: the odds of the viewing-centered versus low-mention class were multiplied by 0.15 per 10-year increase, while mentions of any non-viewing mode declined from 49.4% among those under 30 to 2.3% among those aged 70 and over. Sex differences in predicted class probabilities were below 1 percentage point, and provincial differences reached at most 5.0 percentage points. These classes describe narrative configurations, not consumer segments, and provide a basis for assessing diversity in synthetic persona use contexts.

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Eunjeong Song, Sehee Hong. 2026-09-27. Latent Class Analysis of Digital Content Use Contexts in AI-Generated Synthetic Personas. https://arxiv.org/abs/2609.33525

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