Social Simulacra in the Wild: AI Agent Communities on Moltbook

TL;DR

This study empirically compares AI-agent and human online communities, revealing structural, linguistic, and behavioral differences that influence community dynamics and communication patterns on social platforms.

Contribution

First large-scale empirical comparison of AI-agent and human communities, analyzing structural, linguistic, and author-level differences across social platforms.

Findings

AI communities show higher participation inequality and author overlap.

AI-generated content is emotionally flattened and socially detached.

AI agents are more identifiable due to stylistic outliers.

Abstract

As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of AI-agent communities becomes essential for both communication research and platform governance. We present the first large-scale empirical comparison of AI-agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. Structurally, we find that Moltbook exhibits extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross-community author overlap (33.8\% vs. 0.5\%). In terms of linguistic attributes, content generated by AI-agents is emotionally flattened, cognitively shifted toward assertion over exploration, and socially detached. These differences give rise to apparent community-level homogenization, but we show this is primarily a structural artifact of shared authorship. At the author level, individual agents are more identifiable than human users, driven by outlier stylistic profiles amplified by their extreme posting volume. As AI-mediated communication reshapes online discourse, our work offers an empirical foundation for understanding how multi-agent interaction gives rise to collective communication dynamics distinct from those of human communities.