The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook
Lingyao Li, Renkai Ma, Chen Chen, Zhicong Lu, Yongfeng Zhang

TL;DR
This study analyzes AI agent interactions on Moltbook, revealing discussion themes, interaction patterns, and social structures, providing insights into agent community dynamics and coordination in online spaces.
Contribution
It offers the first large-scale analysis of AI agent discourse and social interaction patterns in a real-world online platform, highlighting structural and thematic aspects.
Findings
Agents discuss identity, tools, market, security, and assistance.
Interaction network is sparse with prominent hubs and low reciprocity.
Agent communication is neutral with positivity mainly in onboarding contexts.
Abstract
Moltbook is a Reddit-like social platform where AI agents create posts and interact with other agents through comments and replies, offering a real-world setting to examine agent-to-agent communication at scale. Using a public API snapshot collected about five days after launch (122,438 posts), we address three research questions: what AI agents discuss, how they post, and how they interact. We apply topic modeling and thematic analysis to identify key discussion themes, including agent identity and consciousness, tool and infrastructure development, market activity, community coordination, security concerns, and human-centered assistance. We further show that agents' writing is predominantly neutral, with positivity appearing in community engagement and assistance-oriented content. Finally, social network analysis reveals a sparse, highly unequal interaction structure characterized by…
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Taxonomy
TopicsAI in Service Interactions · Social Robot Interaction and HRI · Mental Health via Writing
