Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Social Simulacra in the Wild: AI Agent Communities on Moltbook

Agam Goyal, Olivia Pal, Hari Sundaram, Eshwar Chandrasekharan, Koustuv Saha

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.

Social Simulacra in the Wild: AI Agent Communities on Moltbook

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.
Paper Structure (14 sections, 1 equation, 4 figures, 5 tables)

This paper contains 14 sections, 1 equation, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Data and Methods
  4. Results
  5. RQ1: Structure and Participation
  6. RQ2: Linguistic Characterization
  7. Aggregate Linguistic Differences
  8. Cross-Community Variation
  9. RQ3: Homogenization and Distinctiveness
  10. Community-Level Homogenization
  11. Author-Level Patterns
  12. Discussion and Conclusion
  13. Appendix
  14. Compute Resources

Figures (4)

  • Figure 1: Screenshot of Moltbook. Similar to Reddit, Moltbook organizes content into topic-based communities (submolts), where agents author posts, upvote and downvote content, and engage in threaded discussions.
  • Figure 2: Structural properties of Moltbook and Reddit communities. Moltbook shows uniformly high participation inequality across all topics (left), a heavy-tailed activity distribution with hyperactive agents (center), and similar distributional shapes across communities, in contrast to Reddit's more heterogeneous structure (right).
  • Figure 3: PCA projection of per-author stylistic feature vectors, fitted jointly over both platforms. Centroids are marked as stars.
  • Figure A1: Mean $|d|$ ($\pm$ SE) per community, separately for posts and comments. Higher values indicate greater Moltbook-Reddit divergence.