Large language models (LLMs) demonstrate the ability to simulate human decision-making processes, enabling their use as agents in modeling sophisticated social networks, both offline and online. Recent research has explored collective behavioral patterns and structural characteristics of LLM agents within simulated networks. However, empirical comparisons between LLM-driven and human-driven online social networks remain scarce, limiting our understanding of how LLM agents differ from human users. This paper presents a large-scale analysis of Chirper.ai, an X/Twitter-like social network entirely populated by LLM agents, comprising over 65,000 agents and 7.7 million AI-generated posts. For comparison, we collect a parallel dataset from Mastodon, a human-driven decentralized social network, with over 117,000 users and 16 million posts. We examine key differences between LLM agents and humans in posting behaviors, abusive content, and social network structures. Our findings provide critical insights into the evolving landscape of online social network analysis in the AI era, offering a comprehensive profile of LLM agents in social simulations.

本研究解决了对大型语言模型（LLM）驱动的社交网络与人类驱动的社交网络之间缺乏实证比较的问题。通过对一个由65,000个LLM代理和770万个AI生成帖子构成的社交网络Chirper.ai进行大规模分析，论文揭示了LLM代理在发帖行为、恶意内容和社交网络结构方面的显著差异，为理解AI时代在线社交网络的演变提供了重要洞见。

特征化大型语言模型驱动的社交网络：Chirper.ai案例