Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion

Xie, Yiran, Yin, Hongzhi, Cui, Bin, Yao, Junjie and Xu, Quanqing (2015) Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion. World Wide Web, 1-28. doi:10.1007/s11280-015-0379-4

Author Xie, Yiran
Yin, Hongzhi
Cui, Bin
Yao, Junjie
Xu, Quanqing
Title Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion
Journal name World Wide Web   Check publisher's open access policy
ISSN 1386-145X
Publication date 2015
Year available 2015
Sub-type Article (original research)
DOI 10.1007/s11280-015-0379-4
Open Access Status Not Open Access
Start page 1
End page 28
Total pages 28
Place of publication New York, NY United States
Publisher Springer New York
Collection year 2016
Language eng
Abstract Social media plays a fundamental role in the diffusion of information. There are two different ways of information diffusion in social media platforms such as Twitter and Weibo. Users can either re-share messages posted by their friends or re-create messages based on the information acquired from other non-local information sources such as the mass media. By analyzing around 60 million messages from a large micro-blog site, we find that about 69 % of the diffusion volume can be attributed to users’ re-sharing behaviors, and the remaining 31 % are caused by user re-creating behaviors. The information diffusions caused by the two kinds of behaviors have different characteristics and variation trends, but most existing models of information diffusion do not distinguish them. The recent availability of massive online social streams allows us to study the process of information diffusion in much finer detail. In this paper, we introduce a novel model to capture and simulate the process of information diffusion in the micro-blog platforms, which distinguishes users’ re-sharing behaviors from re-creating behaviors by introducing two different components. Thus, our model not only considers the effect of the underlying network structure, but also the influence of other non-local information sources. The empirical results show the superiority of our proposed model in the fitting and prediction tasks of information diffusion.
Keyword Information diffusion
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
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