2026/02/03 by David Holtz · 1 voice · 1 citation
Computer Science · Physics and Astronomy · Social Sciences · #Authorship Attribution and Profiling #Complex Network Analysis Techniques #Exponent #Graph #Human language #Language and cultural evolution #Reciprocity (cultural anthropology) #Social relation #Sociality #Word (group theory) #Zipf's law #cs.AI #cs.CY #cs.SI
paper · pdf · doi:10.48550/arxiv.2602.10131
openalex publication_date 2026/02/03 · arxiv published 2026/02/03 · arxiv updated 2026/02/03 · openalex created_date 2026/02/13 · openalex updated_date 2026/07/28
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 α= 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 (≈ 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.