Problem
Research questions and friction points this paper is trying to address.
AIGC
content ecology
user preference
algorithmic distribution
online platforms
Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology
🤖 AI Summary
This study investigates the impact of artificial intelligence–generated content (AIGC) on online content ecosystems, focusing on the tension between user preference and content production scale. Leveraging longitudinal data from a video platform with tens of millions of users, combined with user behavior modeling and algorithmic distribution analysis, the research provides the first empirical evidence that, despite users’ stronger preference for human-generated content (HGC), AIGC achieves comparable aggregate engagement due to its significantly higher output volume—demonstrating a “scale-over-preference” dynamic in content dissemination. The study further reveals that algorithmic recommendation mechanisms play a critical moderating role in reconciling this tension, offering both theoretical grounding and practical guidance for platform governance strategies tailored to the unique characteristics of AIGC.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AIGC
scale-over-preference
algorithmic distribution
content ecology
user preference
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