Generative Agent-Based Modeling (GABM) leverages Large Language Models to create autonomous agents that simulate human behavior in social media environments, demonstrating potential for modeling information propagation, influence processes, and network phenomena. While existing frameworks characterize agents through demographic attributes, personality traits, and interests, they lack mechanisms to encode behavioral dispositions toward platform actions, causing agents to exhibit homogeneous engagement patterns rather than the differentiated participation styles observed on real platforms. In this paper, we investigate the role of behavioral traits as an explicit characterization layer to regulate agents' propensities across posting, re-sharing, commenting, reacting, and inactivity. Through large-scale simulations involving 980 agents and validation against real-world social media data, we demonstrate that behavioral traits are essential to sustain heterogeneous, profile-consistent participation patterns and enable realistic content propagation dynamics through the interplay of amplification- and interaction-oriented profiles. Our findings establish that modeling how agents act-not only who they are-is necessary for advancing GABM as a tool for studying social media phenomena.

翻译：生成式多智能体建模通过大型语言模型构建自主智能体，模拟社交媒体环境中的人类行为，在信息传播、影响过程及网络现象建模方面展现出潜力。现有框架主要通过人口属性、人格特质和兴趣特征来刻画智能体，但缺乏对平台行为倾向的编码机制，导致智能体表现出同质化的参与模式，而非真实平台中观察到的差异化参与风格。本文研究行为特质作为显式表征层的作用，用以调节智能体在发帖、转发、评论、互动及静默等行为中的倾向性。通过涉及980个智能体的大规模仿真实验，并结合真实社交媒体数据进行验证，我们证明行为特质对于维持异质性、符合用户画像的参与模式至关重要，并能通过放大导向型与互动导向型画像的相互作用实现真实的内容传播动态。研究结果表明，对智能体行为方式（而不仅是其身份特征）的建模，是推动生成式多智能体建模成为社交媒体现象研究工具的必要条件。