Abstract
Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.
Highlights
Online social networks have become an indispensable part of our modern society for obtaining and spreading information
There have been efforts in issues such as network and opinion coevolution [2], users participation comparison for topics of current interest [3], information diffusion patterns in different domains [4,5], the dynamics of users’ activity across topics and time [6,7], users behavior modeling on networks [8,9], popular topic-style analysis in the Twitter-like social media [10,11,12], users influence in social networks [13], and language geography studies of Twitter data set [14]
Online social network systems are becoming increasingly ubiquitous in a modern society
Summary
Online social networks have become an indispensable part of our modern society for obtaining and spreading information. A piece of breaking news can activate a corresponding online social network, through which the news topic can spread rapidly to many individuals. By its very nature an online network is necessarily time dependent, growing rapidly in size initially as the news spreads out and saturating after certain amount of time. Since online social networks concerning certain topics can be active for only a transient period of time, they are extremely dynamic, which is quite distinct from, e.g., the typical networks studied in the literature where they can be regarded as stationary with respect to the time scale of typical dynamical processes supported. A question of interest is whether there are general rules underlying the evolution of online social networks. There have been efforts in issues such as network and opinion coevolution [2], users participation comparison for topics of current interest [3], information diffusion patterns in different domains [4,5], the dynamics of users’ activity across topics and time [6,7], users behavior modeling on networks [8,9], popular topic-style analysis in the Twitter-like social media [10,11,12], users influence in social networks [13], and language geography studies of Twitter data set [14]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
