AI生成合成人物中数字内容使用情境的潜在类别分析
Latent Class Analysis of Digital Content Use Contexts in AI-Generated Synthetic Personas
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中文总结 AI 辅助
本研究通过潜在类别分析NVIDIA合成人物数据，识别出四种数字内容使用情境类别，发现年龄是主要影响因素，为评估合成人物多样性提供基础。
中文摘要 AI 辅助
AI生成的合成人物日益用于内容规划和虚拟用户模拟，但其叙事中嵌入的数字内容使用情境仍未得到充分研究。本研究利用NVIDIA的Nemotron-Personas-Korea全部1,000,000条记录，使用公开发布的韩语编码词典对五种参与模式（观看、发现与分享、互动、阅读和收听）的提及进行了编码，并采用考虑分类误差的三步法进行潜在类别分析。结果浮现出四类：观看中心型（20.8%）、阅读中心型（3.8%）、收听中心型（2.1%）和低提及型（73.3%）。年龄显示出最强关联：与低提及型相比，观看中心型的几率每增加10岁乘以0.15，而任何非观看模式的提及从30岁以下人群的49.4%降至70岁及以上人群的2.3%。预测类别概率中的性别差异低于1个百分点，省级差异最多达到5.0个百分点。这些类别描述的是叙事配置，而非消费者细分，并为评估合成人物使用情境的多样性提供了基础。
英文摘要
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.
发表机构
- Department of Education, Korea University（韩国大学教育学院）
机构由 AI 辅助整理，请以论文原文为准。
