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

TL;DR

This study examines how AI-generated content influences online ecosystems, revealing a scale-over-preference dynamic and emphasizing the need for AIGC-aware algorithms and governance for platform health.

Contribution

It provides the first large-scale longitudinal analysis of AIGC's creation and consumption behaviors, highlighting the impact of distribution mechanisms on content ecology.

Findings

AIGC creators produce high-volume content achieving engagement similar to HGC creators.

Consumers prefer HGC over AIGC, but AIGC's scale compensates for this preference.

Content distribution algorithms influence the balance between AIGC and HGC engagement.

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.