Research On The Micro-blog Information Propagation Law Based On Complex Network

It is an information age, we have daily contacted with mass information, with thecontinuous progress and evolution of online media, Micro-blog has become animportant platform for information exchange and changed the traditional way. Peopleare not only the disseminators of information, but also manufacturers, the content ofinformation is more diverse, the time of information propagation is more fragmented.Mining and discovery about Micro-blog information propagation law can make us aprofound understanding of the nature of people’s interacting with information, andprovide theoretical basis for Micro-blog marketing public opinion monitoring.This paper conducted an overall analysis on the information propagation inMicro-blog combining with the complex network theory, including research on thenetwork structure of information propagation, characteristic and evolution ofMicro-blog information propagation, analysis on the path and time of informationpropagation,as well as a measure of users’capabilities of information propagation.Firstly, this paper randomly selected45,770Micro-blog users and selected theirlatest100Micro-blogs until September30in2013,then extracted resposting recordsfrom these data so that it can characterize the information propagation in Micro-blogwith users’ resposting behavior, built users’ resposting network after cleaning the rawdata, and in order to have a contrast, this article bulit a users’ attention network;analyzed the two networks’ characteristics in terms of topology and topological indices’evolution with sample size’ change in information propagation network. Then, thisarticle conducted further analysis on the path’s distribution of information propagationand its relationship with the Micro-blog users’ fans number, and put an emphasis on thetime pattern of Micro-blog information propagation, including both propagation timeand time delay. Finally, this paper combined three indicators characterizing Micro-blogusers’ self properties with seven indicators characterizing the users’ ability to spreadinformation network, and built a mathematical model to measure users’ capabilities ofinformation propagation on the basis of principal component analysis, and give top-20users.