摘要:随着互联网和社交媒体的迅猛发展,信息传播和舆情形成机制变得日益复杂。本文系统地回顾了信息传播动力学與情仿真领域的研究进展,重点探讨了不同类型的真实数据如何与传播动力学模型结合,机器学习在参数估计和传播规律预测中的应用,以及传播动力学与大模型的结合在信息传播研究中展现出的广阔前景。本文通过分析大量传播动力学文献,提出了优化模型复杂性与计算成本、提升数据质量、实时数据处理等方面的建议。

With the rapid development of the Internet and social media, the mechanism of intormatlondissemination and public opinion formation has become increasingly complex.This article systematically reviewsthe research progress in the field of information dissemination dynamics and public opinion simulation, focusingon how different types of real data can be combined with communication dynamics models, the application ofmachine learning in parameter estimation and propagation law prediction, and the broad prospects of combiningcommunication dynamics with large models in information dissemination research. This article proposessuggestions for optimizing model complexity and computational costs, improving data quality, and real-timedata processing through an analysis of a large number of communication dynamics literature.