Building Social World Model with Large Language Models

Understanding and predicting how social beliefs evolve in response to events—from policy changes to scientific breakthroughs—remains a fundamental challenge in social science. Given LLMs’ commonsense knowledge and social intelligence, we ask: Can LLMs model the dynamics of social beliefs following social events? In this work, we introduce the concept of the Social World Model (SWM), a general framework designed to capture how social beliefs evolve in response to major events. SWM learns state-transition functions for social beliefs by mining temporal patterns in social data and optimizing evidence lower bound, without the need for explicit human annotations that link events to belief shifts or expensive census data. To evaluate SWM, we introduce a benchmark, SWM-Bench, derived from real-world prediction market data from both Kalshi and Polymarket. SWM-Bench includes over 10k datapoints for social belief prediction tasks spanning diverse domains such as politics, sports, cryptocurrency, and elections. Our experimental results show that SWM significantly outperforms time-series foundation models, achieving RMSE reductions of 8.4% and 11.2% on Polymarket and Kalshi respectively, while offering interpretable insights into the underlying mechanisms of social belief dynamics.