文献类型：期刊文献

英文题名：Characterizing Information Propagation in Social Media with Branching Processes

作者：Luo, Xiaofang[1];Hu, Haibo[1];Sun, Qingsong[2]

机构：[1]East China Univ Sci & Technol, Dept Management Sci & Engn, Shanghai 200237, Peoples R China;[2]Anhui Finance & Trade Vocat Coll, Sch Future Technol, Hefei 230601, Peoples R China

年份：2026

卷号：28

期号：5

外文期刊名：ENTROPY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001774444800001)】；

基金：This work was partially supported by the National Natural Science Foundation of China (Grant No. 61973121) and the Natural Science Research Project of the Anhui Higher Education Institution (Grant No. 2024AH050021).

语种：英文

外文关键词：information propagation; branching process; social media; data-driven modeling

摘要：Information propagation in social media has attracted the wide attention of scholars, with great progress made in empirical and modeling studies. Branching processes, extensively utilized in theoretical biology, are increasingly applied to model information diffusion dynamics. However, detailed and data-driven studies that implement this methodology remain rare. This study, utilizing empirical data, characterizes and models information diffusion in social media with branching processes. The reliability of the branching model is verified through the comparison of theoretical predictions, numerical simulations, and empirical results, and the model can replicate the key statistical characteristics observed in realistic cascades. The research results validate the applicability of branching processes in information diffusion, and contribute to the development of more elaborate, data-driven models of information spreading in complex real-world scenarios.