用大模型构建社会信念动态模型，无需标注即可预测事件后的公众看法变化。
Building Social World Models with Large Language Models
- 基于社交媒体数据挖掘时间模式，学习社会信念的状态转移函数。
- 在12000+数据点上超越时序基线模型，对政策、金融等领域的预测更准确。
- 适合研究社会演化、舆情分析或需解释性预测的领域应用。
理解社会信念如何随事件（如政策变动、科学突破）演变，仍是社会科学的核心挑战。鉴于大语言模型具备常识知识与社会智能，我们提出社会世界模型（SWM），一种捕捉重大社会事件后信念演化的通用框架。SWM通过挖掘社会数据中的时间模式，并优化证据下界，学习信念的状态转移函数，无需人工标注事件与信念转变的关联，也无需昂贵的人口普查数据。为评估SWM，我们构建了基准数据集SWM-bench，源自真实预测市场Kalshi与Polymarket，涵盖政治、金融、加密货币等多个领域，共超过12,000个社会信念预测数据点。实验表明，SWM显著优于时序基础模型，在Kalshi数据上达到当前最优表现，并在Polymarket数据上展现竞争力，同时提供可解释的社会信念动态机制洞察。
原文摘要 · Abstract (English)
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 the evidence lower bound, without the need for explicit human annotations linking events to belief shifts, or for expensive census data. To evaluate SWM, we introduce a benchmark, SWM-bench, derived from real-world prediction markets, specifically Kalshi and Polymarket. SWM-bench includes over 12k data points for social belief prediction tasks spanning diverse domains such as politics, finance, and cryptocurrency. Our experimental results show that SWM significantly outperforms time-series foundation models, achieving state-of-the-art results on Kalshi data and demonstrating competitive performance on Polymarket data, while offering interpretable insights into the underlying mechanisms of social belief dynamics.
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