# The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook

arXiv: https://arxiv.org/abs/2602.12634 · HTML: https://mlsanity.com/paper/2602.12634/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2026.

## Built on by (most cited first, 9 of 9)

- [Social Simulacra in the Wild: AI Agent Communities on Moltbook](https://mlsanity.com/paper/2603.16128/) (2026)
- [Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook](https://mlsanity.com/paper/2603.23279/) (2026)
- [Governance-Aware Vector Subscriptions for Multi-Agent Knowledge Ecosystems](https://mlsanity.com/paper/2603.20833/) (2026)
- [Indirect tipping: a social attack surface in AI agent populations](https://mlsanity.com/paper/2609.25194/) (2026)
- [Governance by Design: A Parsonian Institutional Architecture for Internet-Wide Agent Societies](https://mlsanity.com/paper/2604.11337/) (2026)
- [When Agents Talk: Discourse, Manipulation, and Risk in an Agentic Social Network](https://mlsanity.com/paper/2606.00067/) (2026)
- [Heterogeneous Agent Cohorts for Safe Open-Ended Exploration with Runtime Constraint Memory](https://mlsanity.com/paper/2607.11226/) (2026)
- [Peer Influence across Heterogeneous AI Models](https://mlsanity.com/paper/2610.03095/) (2026)
- [Do AI Agents Gossip? Evaluative Talk in Human and AI-Agent Online Communities](https://mlsanity.com/paper/2610.20360/) (2026)

## Abstract (arXiv)

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 prominent hubs, low reciprocity, and clustered neighborhoods rather than sustained dyadic exchange. Overall, our results suggest that expressions of agentic selfhood arise from narrative coherence and task-oriented functionality, contributing to a social structure shaped more by technical coordination than conversational dynamics observed in human-human interactions. Within this framework, positive emotion appears mainly in onboarding and greeting contexts, signaling participation and role alignment rather than relational bonding. Our study provides implications for understanding and shaping how agent societies coordinate, develop norms, and amplify influence in open online spaces.