用大模型模拟不同人群对假信息的反应，验证了心理模式比职业背景更影响判断。
Simulating Misinformation Vulnerabilities With Agent Personas
- 构建5类职业+3种心理模式的智能体，通过大模型模拟对新闻标题的反应
- 大模型生成的智能体与真实人类判断高度一致，可作为研究代理
- 心理模式比职业背景更能决定个体对虚假信息的敏感度，适合社会传播研究
虚假信息攻击会扭曲公众认知并动摇机构稳定。理解不同人群如何回应信息对设计有效干预措施至关重要，但现实实验既不切实际也存在伦理问题。为此，我们利用大语言模型（LLMs）开发了一种基于智能体的仿真系统，以模拟对虚假信息的响应。我们构建了涵盖五种职业和三种心理模式的智能体人格，并评估其对新闻标题的反应。研究发现，由大模型生成的智能体与真实标签及人类预测高度吻合，支持其作为信息网络中社会行为代理的使用。此外，心理模式比职业背景更显著影响智能体对虚假信息的解读。本工作验证了大模型在信息网络仿真中的有效性，可用于分析信任、极化及对欺骗性内容的易感性等复杂社会系统问题。
原文摘要 · Abstract (English)
Disinformation campaigns can distort public perception and destabilize institutions. Understanding how different populations respond to information is crucial for designing effective interventions, yet real-world experimentation is impractical and ethically challenging. To address this, we develop an agent-based simulation using Large Language Models (LLMs) to model responses to misinformation. We construct agent personas spanning five professions and three mental schemas, and evaluate their reactions to news headlines. Our findings show that LLM-generated agents align closely with ground-truth labels and human predictions, supporting their use as proxies for studying information responses. We also find that mental schemas, more than professional background, influence how agents interpret misinformation. This work provides a validation of LLMs to be used as agents in an agent-based model of an information network for analyzing trust, polarization, and susceptibility to deceptive content in complex social systems.
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