From Who They Are to How They Act: Behavioral Traits in Generative Agent-Based Models of Social Media

2026-01-21

AI Summary

This work addresses the limitation of existing generative agents in social media simulation, which often fail to model users’ behavioral tendencies, leading to homogeneous interaction patterns. To overcome this, the study introduces behavioral traits as an explicit, independent representational layer within the generative agent framework. A parameterized mechanism modulates each agent’s propensity toward specific actions—such as posting, sharing, commenting, liking, or remaining inactive—emphasizing that “how to act” is as critical as “who one is.” Large-scale multi-agent simulations involving 980 agents powered by large language models demonstrate that this approach effectively preserves consistent yet heterogeneous participation patterns across individuals. The method successfully replicates key dynamics of content propagation observed on real-world social platforms, advancing social simulation from static user profiles toward dynamic behavioral evolution.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Generative Agent-Based Modeling
• behavioral traits
• social media simulation
• user engagement patterns
• heterogeneous participation

Innovation

• Behavioral Traits
• Generative Agent-Based Modeling
• Social Media Simulation
• Heterogeneous Participation
• Content Propagation Dynamics