Calibrated Co-Evolving Social Simulation for Multi-Step Sentiment Forecasting

Sentiment forecasting predicts the evolution of population sentiment distributions as events unfold. Most existing methods treat this as single-step prediction. These methods prompt LLMs to role-play users from historical posts, assume fixed social graphs, and score the prediction at a single target time point. These three assumptions do not hold as the event unfolds. We formulate the task as a calibrated, closed-loop social simulation that couples post generation, network evolution and population-level feedback. The population generates its own future inputs. Each LLM agent represents one user. At each step, active agents respond to their neighbors' posts from the previous step, and the expressed stances drive edge formation and dissolution. History-based participation rates capture heterogeneous participation, while stance-decomposed camp summaries provide population context and preserve within-camp differences through agent-specific injection strengths. To calibrate the resulting dynamics, we combine individual-level and population-level objectives over observed multi-step trajectories, together with homophily-constrained opinion updates. Calibration uses only the observation windows and then freezes the parameters. The simulator runs with no access to future real posts or sentiment distributions. On a 2020 US election Twitter benchmark and a self-built event dataset, our method lowers the macro JSD in the free-forecast window by 85% and 75% over the single-step baseline and raises per-agent accuracy there from 43.0% to 55.5%. The simulated networks are consistent with the real graph on polarization, echo chamber, and opinion assortativity.