Simulating Aggregate Electoral Opinion Trajectories with LLM Personas and Media Exposure

LLM-based election simulation has largely been evaluated against a single final outcome, in many cases neglecting how much it can recover the trajectory shaped by media exposure and shifting candidate support. We propose evaluating election simulations in terms of trajectory fidelity: whether persona-conditioned LLM agents can update their beliefs in response to media to reproduce the temporal movement of public opinion, both in aggregate and across demographic subgroups. We design a framework in which each LLM agent carries its belief across time and updates it as new campaign information arrives. We further investigate how performance can vary across simulation design choices such as media exposure model, election context, and survey questionnaire. In a study on the 2026 Seoul mayoral and local elections, we validate simulated trajectories against weekly public-opinion polls and find that simulations that carry prior beliefs across time—rather than simulating each poll date independently—substantially improves alignment with observed polling movement, while the way media exposure is personalized leaves aggregate movement largely intact but reshapes subgroup-level dynamics. We propose a framework for validating simulated opinion trajectories against real-world traces, and discuss the simulation design choices that can influence the performance.