Fusing Dynamics Equation: A Social Opinions Prediction Algorithm with LLM-based Agents

• URL: http://arxiv.org/abs/2409.08717v3
• Date: Wed, 13 Nov 2024 02:35:50 GMT
• Title: Fusing Dynamics Equation: A Social Opinions Prediction Algorithm with LLM-based Agents
• Authors: Junchi Yao, Hongjie Zhang, Jie Ou, Dingyi Zuo, Zheng Yang, Zhicheng Dong,
• Abstract summary: This paper proposes an innovative simulation method for the dynamics of social media user opinions. The FDE-LLM algorithm incorporates opinion dynamics and epidemic model. It categorizes users into opinion leaders and followers.
• Score: 6.1923703280119105
• License:
• Abstract: In the context where social media is increasingly becoming a significant platform for social movements and the formation of public opinion, accurately simulating and predicting the dynamics of user opinions is of great importance for understanding social phenomena, policy making, and guiding public opinion. However, existing simulation methods face challenges in capturing the complexity and dynamics of user behavior. Addressing this issue, this paper proposes an innovative simulation method for the dynamics of social media user opinions, the FDE-LLM algorithm, which incorporates opinion dynamics and epidemic model. This effectively constrains the actions and opinion evolution process of large language models (LLM), making them more aligned with the real cyber world. In particular, the FDE-LLM categorizes users into opinion leaders and followers. Opinion leaders are based on LLM role-playing and are constrained by the CA model, while opinion followers are integrated into a dynamic system that combines the CA model with the SIR model. This innovative design significantly improves the accuracy and efficiency of the simulation. Experiments were conducted on four real Weibo datasets and validated using the open-source model ChatGLM. The results show that, compared to traditional agent-based modeling (ABM) opinion dynamics algorithms and LLM-based opinion diffusion algorithms, our FDE-LLM algorithm demonstrates higher accuracy and interpretability.

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