Social opinions prediction utilizes fusing dynamics equation with LLM-based agents
Abstract In the context where social media emerges as a pivotal platform for social movements and shaping public opinion, accurately simulating and predicting the dynamics of user opinions is of significant importance. Such insights are vital for understanding social phenomena, informing policy deci...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-99704-3 |
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| _version_ | 1850206230830645248 |
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| author | Junchi Yao Hongjie Zhang Jie Ou Dingyi Zuo Zheng Yang Zhicheng Dong |
| author_facet | Junchi Yao Hongjie Zhang Jie Ou Dingyi Zuo Zheng Yang Zhicheng Dong |
| author_sort | Junchi Yao |
| collection | DOAJ |
| description | Abstract In the context where social media emerges as a pivotal platform for social movements and shaping public opinion, accurately simulating and predicting the dynamics of user opinions is of significant importance. Such insights are vital for understanding social phenomena, informing policy decisions, and guiding public opinion. Unfortunately, traditional algorithms based on idealized models and disregarding social data often fail to capture the complexity and nuance of real-world social interactions. This study proposes the Fusing Dynamics Equation-Large Language Model (FDE-LLM) algorithm. This innovative approach aligns the actions and evolution of opinions in Large Language Models (LLMs) with the real-world data on social networks. The FDE-LLM divides users into two roles: opinion leaders and followers. Opinion leaders use LLM for role-playing and employ Cellular Automata(CA) to constrain opinion changes. In contrast, opinion followers are integrated into a dynamic system that combines the CA model with the Susceptible-Infectious-Recovered (SIR) model. This innovative design significantly improves the accuracy of the simulation. Our experiments utilized four real-world datasets from Weibo. The result demonstrates that the FDE-LLM significantly outperforms traditional Agent-Based Modeling (ABM) algorithms and LLM-based algorithms. Additionally, our algorithm accurately simulates the decay and recovery of opinions over time, underscoring LLMs potential to revolutionize the understanding of social media dynamics. |
| format | Article |
| id | doaj-art-ade9e048f4a8449c8f9950f8c051d27c |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ade9e048f4a8449c8f9950f8c051d27c2025-08-20T02:10:53ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-99704-3Social opinions prediction utilizes fusing dynamics equation with LLM-based agentsJunchi Yao0Hongjie Zhang1Jie Ou2Dingyi Zuo3Zheng Yang4Zhicheng Dong5College of Computer Science, Sichuan Normal UniversityCollege of Computer Science, Sichuan Normal UniversitySchool of Information and Software Engineering, University of Electronic Science and Technology of ChinaCollege of Computer Science, Sichuan Normal UniversityFujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal UniversityCollege of Information Science Technology, Tibet UniversityAbstract In the context where social media emerges as a pivotal platform for social movements and shaping public opinion, accurately simulating and predicting the dynamics of user opinions is of significant importance. Such insights are vital for understanding social phenomena, informing policy decisions, and guiding public opinion. Unfortunately, traditional algorithms based on idealized models and disregarding social data often fail to capture the complexity and nuance of real-world social interactions. This study proposes the Fusing Dynamics Equation-Large Language Model (FDE-LLM) algorithm. This innovative approach aligns the actions and evolution of opinions in Large Language Models (LLMs) with the real-world data on social networks. The FDE-LLM divides users into two roles: opinion leaders and followers. Opinion leaders use LLM for role-playing and employ Cellular Automata(CA) to constrain opinion changes. In contrast, opinion followers are integrated into a dynamic system that combines the CA model with the Susceptible-Infectious-Recovered (SIR) model. This innovative design significantly improves the accuracy of the simulation. Our experiments utilized four real-world datasets from Weibo. The result demonstrates that the FDE-LLM significantly outperforms traditional Agent-Based Modeling (ABM) algorithms and LLM-based algorithms. Additionally, our algorithm accurately simulates the decay and recovery of opinions over time, underscoring LLMs potential to revolutionize the understanding of social media dynamics.https://doi.org/10.1038/s41598-025-99704-3 |
| spellingShingle | Junchi Yao Hongjie Zhang Jie Ou Dingyi Zuo Zheng Yang Zhicheng Dong Social opinions prediction utilizes fusing dynamics equation with LLM-based agents Scientific Reports |
| title | Social opinions prediction utilizes fusing dynamics equation with LLM-based agents |
| title_full | Social opinions prediction utilizes fusing dynamics equation with LLM-based agents |
| title_fullStr | Social opinions prediction utilizes fusing dynamics equation with LLM-based agents |
| title_full_unstemmed | Social opinions prediction utilizes fusing dynamics equation with LLM-based agents |
| title_short | Social opinions prediction utilizes fusing dynamics equation with LLM-based agents |
| title_sort | social opinions prediction utilizes fusing dynamics equation with llm based agents |
| url | https://doi.org/10.1038/s41598-025-99704-3 |
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