Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology
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Abstract (arXiv)
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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References · 9
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2017Neural Collaborative Filteringcited by 1,108
- 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
- 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
- 2024Macro Graph Neural Networks for Online Billion-Scale Recommender Systemscited by 40
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- 2017In reply to Faes et al. and Barnett et al. regarding "A study of problems encountered in Granger causality analysis from a neuroscience perspective"cited by 14
- 2025The Impact of Generative AI on Social Media: An Experimental Studycited by 6
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