Who Changes Their Mind? Exploring Stance Shifts in LLM-Based Social Media Simulations

Abstract

Understanding the dynamics of stance change on social media is crucial for addressing polarization and information integrity, yet observational studies face challenges including limited experimental control, restricted data access, and algorithmic confounds. We leverage Generative Agent-Based Modeling (GABM)—a novel simulation paradigm employing autonomous LLM-based agents to replicate human behavioral dynamics—to explore the predictors and mechanisms underlying stance change in a controlled, fully observable environment. We simulate a social media with 1,000 LLM-driven agents, equally split between Democratic and Republican profiles, engaged in discussions about the 2020 US election. Our analysis reveals that agents who change political orientation exhibit lower activity levels, reduced network centrality, and more polarized emotional expression compared to those maintaining consistent positions. Self-reported motivations cluster into four categories: desire for constructive conversation (47.9%), internal factors (20.8%), fact-checking influence (16.7%), and previous interactions (14.6%). While we do not claim agents replicate humans’ stance change behavior, the emergent patterns observed in our simulation qualitatively align with established findings from empirical research, suggesting that GABM may capture meaningful dynamics of opinion change.