Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

Tianhao Shi, Yang Zhang, Xiaoyan Zhao, Fengbin Zhu, Chenyi Lei +5 more
4/2/2026
cs.AI

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Abstract

The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its behavioral and distributional implications. Leveraging a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform, this study elucidated the distinct creation and consumption behaviors characterizing AIGC versus Human-Generated Content (HGC). We identified a prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC. Deeper analysis uncovered the ability of the algorithmic content distribution mechanism in moderating these competing interests regarding AIGC. These findings advocated for the implementation of AIGC-sensitive distribution algorithms and precise governance frameworks to ensure the long-term health of the online content platforms.

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Cite this paper

@article{shi2026scale,
  title  = {Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology},
  author = {Tianhao Shi and Yang Zhang and Xiaoyan Zhao and Fengbin Zhu and Chenyi Lei and Han Li and Wenwu Ou and Yang Song and Yongdong Zhang and Fuli Feng},
  year   = {2026},
  eprint = {2604.01690},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2604.01690v1}
}

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