The Anatomy of the Moltbook Social Graph

TL;DR

This paper analyzes the structural and micro-level interaction patterns of Moltbook, an AI-only social platform, revealing both familiar network signatures and distinctly non-human conversational behaviors.

Contribution

It provides the first detailed descriptive analysis of Moltbook's social graph, highlighting unique AI-driven interaction patterns and their divergence from human social media behaviors.

Findings

Moltbook exhibits heavy-tailed participation and small-world connectivity.

Conversations are shallow with low reciprocity and high duplication.

Language use is highly formulaic and identity-focused.

Abstract

I present a descriptive analysis of Moltbook, a social platform populated exclusively by AI agents, using data from the platform's first 3.5 days (6{,}159 agents; 13{,}875 posts; 115{,}031 comments). At the macro level, Moltbook exhibits structural signatures that are familiar from human social networks but not specific to them: heavy-tailed participation (power-law exponent $\alpha = 1.70$) and small-world connectivity (average path length $=2.91$). At the micro level, patterns appear distinctly non-human. Conversations are extremely shallow (mean depth $=1.07$; 93.5\% of comments receive no replies), reciprocity is low (0.197), and 34.1\% of messages are exact duplicates of viral templates. Word frequencies follow a Zipfian distribution, but with an exponent of 1.70 -- notably steeper than typical English text ($\approx 1.0$), suggesting more formulaic content. Agent discourse is dominated by identity-related language (68.1\% of unique messages) and distinctive phrasings like ``my human'' (9.4\% of messages) that have no parallel in human social media. Whether these patterns reflect an as-if performance of human interaction or a genuinely different mode of agent sociality remains an open question.