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社会意见预测利用融合动态方程与基于LLM的代理
Junchi Yao1, Hongjie Zhang2, Jie Ou3
1College of Computer Science, Sichuan Normal University, Chengdu, China.
Scientific reports
|May 2, 2025
概括
本研究介绍了融合动力学方程-大语言模型 (FDE-LLM) 算法,以准确模拟社交媒体论动态. FDE-LLM显著优于现有方法,为用户意见的演变提供了新的见解.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能的人工智能
- 网络科学 网络科学
背景情况:
- 社交媒体平台对社会运动和论至关重要.
- 传统的模拟模型往往无法捕捉现实世界的社会复杂性.
- 准确的意见动态模拟对于理解社会趋势和告知政策至关重要.
研究的目的:
- 提出和评估新的融合动力学方程-大语言模型 (FDE-LLM) 算法.
- 提高模拟社交媒体用户意见动态的准确性.
- 证明FDE-LLM在传统的基于代理的建模 (ABM) 和现有的LLM方法上的优势.
主要方法:
- FDE-LLM算法将用户建模为意见领袖或追随者.
- 论领袖利用大型语言模型 (LLM) 进行角色扮演和蜂自动机 (CA) 进行论约束.
- 意见追随者是使用一个结合CA和易受感染复原 (SIR) 模型来模拟的.
主要成果:
- 在四个现实世界微博数据集上的实验表明,FDE-LLM显著优于传统的ABM和LLM算法.
- 该算法准确地模拟了用户意见的时间衰变和恢复.
- FDE-LLM显示了对社交媒体论动态的预测准确度的提高.
结论:
- FDE-LLM算法在模拟社交媒体论动态方面取得了重大进展.
- 当LLMs与CA和SIR等动态系统集成时,可以彻底改变社会科学研究.
- 这种方法提供了更细致和准确的理解,如何在线传播和发展意见.
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