Can Persona-Conditioned Language Agents Simulate Counterfactual Online Discourse Dynamics?

Language agents are increasingly used as social simulators, but most are evaluated by local utterance plausibility rather than their ability to reproduce longitudinal discourse dynamics. We introduce PRISM, a calibrated language-agent framework for forecasting and counterfactual simulation of social media threads. PRISM separates three components that are often conflated in prompt-only agents: latent affective evolution, participation control, and language generation. It models affect with a profile-conditioned stochastic differential equation, selects participation through a partially observed activation policy, and uses a language model as a controlled generator conditioned on the resulting state. Agent profiles are instantiated with MBTI-like anchors, but treated only as behavioral priors rather than psychological ground truth. On 47,207 post-rooted discussion threads spanning 159 trending topics and 356,593 hierarchical comments, PRISM outperforms homogeneous, random, continuous-trait, prompt-only, and memory-only baselines in trajectory alignment, held-out thread forecasting, and blind human evaluation. Reliability analysis further validates LLM-derived weak labels against human and independent-model annotations. These results suggest that calibrated state-space agents offer a more reliable foundation for online discourse simulation than persona prompting alone.