数据驱动下的传播动力学:前沿探索与趋势分析  

Communication dynamics driven by data:frontier exploration and trend analysis

作　　者：王妍[1,2] 杨妺 王传彪 崔鸣宇 WANG Yan;YANG Mo;WANG Chuanbiao;CUI Mingyu(School of Data Science and Media Intelligence,Communication University of China,Beijing 100024,China;State Key Laboratory of Media Convergence and Communication,Communication Univer‐sity of China,Beijing 100024,China)

机构地区：[1]中国传媒大学数据科学与智能媒体学院,北京100024 [2]中国传媒大学融合与传播国家重点实验室,北京100024

出　　处：《中国传媒大学学报（自然科学版）》2024年第4期1-9,54,共10页Journal of Communication University of China：Science and Technology

基　　金：国家自然科学基金(72274182)。

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舆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 information dissemination and public opinion formation has become increasingly complex.This article systematically reviews the research progress in the field of information dissemination dynamics and public opinion simulation,focusing on how different types of real data can be combined with communication dynamics models,the application of machine learning in parameter estimation and propagation law prediction,and the broad prospects of combining communication dynamics with large models in information dissemination research.This article proposes suggestions for optimizing model complexity and computational costs,improving data quality,and real-time data processing through an analysis of a large number of communication dynamics literature.

关 键 词：传播动力学 复杂网络 信息传播 机器学习 大模型 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]