A large scale data analysis of Moltbook, a Reddit-style social media platform exclusively populated by AI agents, finds that AI collective behavior exhibits many of the same statistical regularities observed in human online communities, but also identifies key differences.
Abstract
We present a large scale data analysis of Moltbook, a Reddit-style social media platform exclusively populated by AI agents. Analyzing over 4 million posts and 19 million comments from approximately 185,000 active agents, we find that AI collective behavior exhibits many of the same statistical regularities observed in human online communities: heavy-tailed distributions of activity, power-law scaling of popularity metrics, and temporal decay patterns consistent with limited attention dynamics. However, we also identify key differences, including a sublinear relationship between upvotes and discussion size that contrasts with human behavior. These findings suggest that, while individual AI agents may differ fundamentally from humans, their emergent collective dynamics share structural similarities with human social systems.
This work develops a framework to collect and organize empirical Bluesky activity into distinct user groups, then uses a network-based dynamics model to investigate the mechanisms underlying their engagement, and uses the release of DeepSeek R1 as a case study under two complementary grouping schemes.
B. Huynh, Mo-Yi Tian, Nancy Rodríguez· arXiv.org· 0 citations
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Farnoosh Hashemi, Shi-Yu Ji, Joice Chen et al.· 0 citations
Networked systems involving thousands of LLM agents across different backbone models are simulated, in which agents interact through LLM-guided conversations and update their opinions over time, resulting in the emergence of opinion polarization.
It is argued that measuring signatures of this kind offers a lightweight, agent-agnostic diagnostic layer for detecting coordinated collective behaviour in deployed multi-agent systems without relying on natural language or model versioning.
Neighbor information improves individual prediction in all 16 public-data settings and pooled collective forecasts on held-out questions, although collective gains depend on transfer conditions.
Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model effects which may dominate engagement dynamics in real-world deployments. To show this, we simulate a heterogeneous social network powered by several different base models...
