Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology
It addresses the impact of AIGC on online content ecosystems for platform operators and policymakers, though it is incremental in analyzing existing data.
This study examined how AI-generated content (AIGC) affects online platforms, finding that AIGC achieves engagement similar to human-generated content through high-volume production, despite consumers preferring human content, with algorithms moderating these dynamics.
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.