DualMind: 通过多智能体模拟理解公共意见传播中的认知-情感 cascades
DualMind: Towards Understanding Cognitive-Affective Cascades in Public Opinion Dissemination via Multi-Agent Simulation
AI总结：
DualMind通过多智能体模拟，建模公共意见传播中认知与情感的相互作用，提升危机管理的预测能力。
摘要：
在公关危机期间预测公众意见具有挑战性，因为现有框架往往忽视了短暂的情感反应与持久的认知信念之间的相互作用。为此，我们提出了DualMind，一种由大型语言模型驱动的多智能体平台，旨在建模这种双成分的相互作用。我们在2024年8月之后发生的15个现实危机中使用社交媒体数据作为真实数据进行评估。实证结果表明，DualMind能够忠实重建意见轨迹，并显著优于最先进的基线方法。这项工作提供了一个高保真度的工具，用于主动危机管理。代码可在https://github.com/EonHao/DualMind上获取。
英文摘要：
Forecasting public opinion during PR crises is challenging, as existing frameworks often overlook the interaction between transient affective responses and persistent cognitive beliefs. To address this, we propose DualMind, an LLM-driven multi-agent platform designed to model this dual-component interplay. We evaluate the system on 15 real-world crises occurring post-August 2024 using social media data as ground truth. Empirical results demonstrate that DualMind faithfully reconstructs opinion trajectories, significantly outperforming state-of-the-art baselines. This work offers a high-fidelity tool for proactive crisis management. Code is available at https://github.com/EonHao/DualMind.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
