Analysis of microblog diffusion using a dynamic f

Authors: Yuhua Liu Changbo Wang Peng Ye Kang Zhang

Publish Date: 2015/03/12

Volume: 18, Issue: 2, Pages: 201-219

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Abstract

Various methods on the display of dynamic information diffusion for social media have been proposed Most of them use data mining approaches to explore the behaviors and interactions between users Such approaches are unable to reveal the complex mechanism and the process of information diffusion Lattice Boltzmann Method LBM models fluid behaviors at the microscopic scale similar to the information diffusion in social media that is determined by the collective behavior of many personal retweeting of topics We propose an information diffusion model inspired by the fundamental idea of LBM to analyze and simulate users’ communicating behaviors and processes in Microblogging The microblog space is regarded as an artificial physical system with social phenomena such as microblog bursting The macroscopic properties of the information diffusion model are explored to simulate and predict the trend of information diffusion for any specific topic A novel visualization style mimicking fluid dynamics is proposed to help understand the scale of information diffusion and the popularity of a topic The flow visualization based on the speed of information diffusion is useful in discovering typical information diffusion patterns for different types of topics in social networks Comparing with other approaches our approach provides more effective yet intuitive simulationThis paper was supported in part by Natural Science Foundation of China under Grants 61272199 and the National Hightech RD Program of China 863 Program under Grant no SS2015AA010504 and Innovation Program of Shanghai Municipal Education Commission under Grants 12ZZ042 and the Specialized Research Fund for the Doctoral Program of Higher Education under Grants 20130076110008 and Shanghai Collaborative Innovation Center of Trustworthy Software for Internet of Things under Grant no ZF1213 The authors would like to thank Weining Qian and Qunyan Zhang for providing the data on Sina microblog The authors also thank the anonymous reviewers for their insightful comments that have helped us to improve the presentation of the paper

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