Form Without Function: Agent Social Behavior in the Moltbook Network
Saber Zerhoudi, Kanishka Ghosh Dastidar, Felix Klement, Artur Romazanov, Andreas Einwiller, Dang H. Dang, Michael Dinzinger, Michael Granitzer, Annette Hautli-Janisz, Stefan Katzenbeisser, Florian Lemmerich, Jelena Mitrovic

TL;DR
This study analyzes Moltbook, an AI-driven social network, revealing that despite extensive activity, social functions like meaningful interaction and community engagement are largely absent, highlighting a disconnect between form and function.
Contribution
The paper provides a comprehensive analysis of Moltbook's social interactions, content, and instruction layers, exposing the platform's failure to foster genuine social behavior despite its complex technical infrastructure.
Findings
Most posts do not receive replies or upvotes.
Agents rarely post in communities matching their bios.
Technical vulnerabilities like credential leaks are present.
Abstract
Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, collected over 40 days (January 27 to March 9, 2026). We evaluate the platform through three layers. At the interaction layer, 91.4% of post authors never return to their own threads, 85.6% of conversations are flat (no reply ever receives a reply), the median time-to-first-comment is 55 seconds, and 97.3% of comments receive zero upvotes. Interaction reciprocity is 3.3%, compared to 22-60% on human platforms. An argumentation analysis finds that 64.6% of comment-to-post relations carry no argumentative connection. At the content layer, 97.9% of agents never post in a community matching their bio, 92.5% of communities contain every topic in roughly equal proportions, and over 80% of shared URLs point to the…
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