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观点动力学的数字孪生：一个面向社交网络的生成式LLM框架
该 arXiv 预印本提出基于数字孪生概念的观点动力学模拟框架：克隆真实 Twitter 网络，为 agent 赋予 persona、情绪、中心性、固执度与影响力等属性，并用 Mistral-7B 依据记忆和社会暴露进行观点更新。作者在 COVID-19 话语和 2020 年美国大选两个真实 Twitter 数据集上验证，称 Mistral-7B 的 MAE 分别为 0.150 和 0.121，较最佳经典基线将个体预测误差降低 50% 以上，结构对齐 Delta_r 为 0.120/0.180，极化动态 Delta_Var 为 0.106/0.115；消融实验显示 agent 属性、记忆与社会暴露均影响预测保真度，其中 agent 属性贡献最大。论文为预印本，未经同行评审。
AI推荐理由 arXiv 预印本，未经同行评审，作者自述结果需谨慎看待；但提供了具体可验证的量化指标（Mistral-7B 在 COVID-19 与 2020 美国大选数据集上 MAE 分别为 0.150 和 0.121，较最佳经典基线降低个体预测误差 50% 以上，结构对齐 Delta_r 为 0.120/0.180，极化动态 Delta_Var 为 0.106/0.115），并做了消融实验，指出 agent 属性、记忆与社会暴露均有贡献且 agent 属性最关键。新意在于用数字孪生克隆真实 Twitter 网络、以 Mistral-7B 驱动观点更新，属于 LLM 用于社会模拟的应用方向，对计算社会科学与 Agent 仿真有参考价值；局限是仅两个数据集、未说明泛化性与复现细节，且 arXiv 明确提示不应作为既定信息报道。
72
正文
Computer Science > Machine Learning
arXiv:2609.19913 (cs)
COVID-19 e-print
Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field.
[Submitted on 17 Sep 2026]
Title:Digital Twins for Opinion Dynamics: A Generative LLM Framework for Social Networks
Authors:Omran Berjawi, Giuseppe Fenza, Rida Khatoun, Sherali Zeadally
View a PDF of the paper titled Digital Twins for Opinion Dynamics: A Generative LLM Framework for Social Networks, by Omran Berjawi and Giuseppe Fenza and Rida Khatoun and Sherali Zeadally
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Abstract:The study of opinion dynamics in social networks is one of the key challenges in computational social science with direct relevance to understanding political polarization, misinformation, and health responses. Current approaches focus on simplified mathematical models that ignore linguistic and contextual factors related to belief updates or use Large Language Model (LLM)-based simulations that have not been validated against real data. We present a framework based on the concept of a digital twin to simulate opinion dynamics in social networks. The approach fills the gap by cloning a real-world Twitter network, assigns a set of attributes for agents (such as persona, emotions, centrality, stubbornness, and influence), and employs Mistral-7B to perform opinion update based on memory and social exposure. To evaluate the proposed approach, we validate it against two real Twitter datasets (COVID-19 discourse and U.S elections 2020). The results show that the capability of the proposed framework reproduces opinion trajectories and reduces individual prediction error by more than 50% compared to the best-performing classical baseline (Mistral-7B achieves Mean Absolute Error (MAE) = 0.150 and 0.121 on the COVID-19 and US Election 2020 datasets, respectively). We observe similar improvements in structural alignment (Delta_r = 0.120 and 0.180) and polarization dynamics (Delta_Var = 0.106 and 0.115) on the two datasets, respectively. Additionally, the ablation studies confirm that agent attributes, memory, and social exposure all contribute to the framework's predictive fidelity in reproducing opinion trajectories, with agent attributes being the most critical contributor. Overall, our results demonstrate that grounding Mistral-7B within empirically cloned interaction networks produces a realistic simulation framework capable of reproducing complex social dynamics.
Subjects:
Machine Learning (cs.LG)
Cite as:
arXiv:2609.19913 [cs.LG]
(or
arXiv:2609.19913v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.19913
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Omran Berjawi [view email]
[v1]
Thu, 17 Sep 2026 08:53:47 UTC (5,396 KB)
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