There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems. As a result, several LLM-based ABMs have been proposed in the past year. While they hold promise, each simulator is specifically designed to study a particular scenario, making it time-consuming and resource-intensive to explore other phenomena using the same ABM. Additionally, these models simulate only a limited number of agents, whereas real-world social media platforms involve millions of users. To this end, we propose OASIS, a generalizable and scalable social media simulator. OASIS is designed based on real-world social media platforms, incorporating dynamically updated environments (i.e., dynamic social networks and post information), diverse action spaces (i.e., following, commenting), and recommendation systems (i.e., interest-based and hot-score-based). Additionally, OASIS supports large-scale user simulations, capable of modeling up to one million users. With these features, OASIS can be easily extended to different social media platforms to study large-scale group phenomena and behaviors. We replicate various social phenomena, including information spreading, group polarization, and herd effects across X and Reddit platforms. Moreover, we provide observations of social phenomena at different agent group scales. We observe that the larger agent group scale leads to more enhanced group dynamics and more diverse and helpful agents' opinions. These findings demonstrate OASIS's potential as a powerful tool for studying complex systems in digital environments.

翻译：近年来，随着基于规则的智能体模型（ABM）在社交媒体平台（如X、Reddit）研究中的应用日益广泛，研究者们开始探索如何通过引入更真实的大型语言模型（LLM）智能体来增强这些模型，从而更细致地研究复杂系统。因此，过去一年中已出现了多个基于LLM的ABM。尽管这些模型展现出潜力，但每个模拟器通常针对特定场景设计，使得利用同一ABM探索其他现象既耗时又耗费资源。此外，现有模型仅能模拟有限数量的智能体，而现实世界中的社交媒体平台往往涉及数百万用户。为此，我们提出了OASIS，一个可泛化且可扩展的社交媒体模拟器。OASIS的设计基于真实社交媒体平台，包含动态更新的环境（即动态社交网络与帖子信息）、多样化的动作空间（如关注、评论）以及推荐系统（如基于兴趣和热度的推荐）。此外，OASIS支持大规模用户模拟，能够建模多达一百万个用户。凭借这些特性，OASIS可以轻松扩展到不同的社交媒体平台，用于研究大规模群体现象与行为。我们在X和Reddit平台上复现了多种社会现象，包括信息传播、群体极化和从众效应。同时，我们还提供了不同智能体群体规模下的社会现象观察。我们发现，更大的智能体群体规模会增强群体动态，并产生更多样化、更有益的智能体观点。这些发现证明了OASIS作为研究数字环境中复杂系统的强大工具的潜力。