AudCraft: Understanding the Audience-Oriented Creation Gap Between Human Creators and LLMs with Real-World Platform Data

As low-cost LLM-generated content enters online platforms at scale, understanding how LLMs create for different audiences is increasingly important for assessing their potential impact on content ecosystems. Yet existing evaluations largely treat LLM-generated content as isolated textual artifacts, focusing on properties such as creativity and detectability while overlooking a key aspect of real-world content creation: creators target specific audiences and adapt content to their needs. To address this gap, we introduce AudCraft, an observational corpus of real-world human-created content spanning diverse audiences, and evaluate LLMs from two complementary dimensions of audience-oriented creation: audience orientation, which characterizes whom LLMs create for, and audience design, which characterizes how they adapt their expression to different audiences. Our analysis reveals systematic human-LLM gaps in both dimensions. For audience orientation, LLMs disproportionately concentrate their generations on a narrow subset of audience segments, a pattern that persists across models and prompting strategies. A Tullock contest-based simulation further shows that such concentrated orientation can be amplified through competition for limited audience attention, resulting in increasingly uneven content supply and intensified competition with human creators. For audience design, LLMs exhibit rich and audience-responsive expressions, but this flexibility does not necessarily translate into human-like adaptation: their changes are selective, often misaligned with human patterns, and less audience-specific in vocabulary. Code and data are available at https://anonymous.4open.science/r/AudCraft-54E4.