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A sequential game-theoretic study of the retweeting behavior in Sina Weibo

Authors:

Wandong CaiAuthors Info & Claims

The Journal of Supercomputing, Volume 71, Issue 9

Pages 3301 - 3319

https://doi.org/10.1007/s11227-015-1456-2

Published: 01 September 2015 Publication History

Abstract

Retweeting behavior is a key mechanism for information diffusion in microblogging services such as Sina Weibo. It is the mechanism of retweeting that leads to the fast and wide diffusion of information in Weibo. Game theory has been applied to various fields as an important tool for studying user's behavior. In this paper, a sequential game model has been applied on studying Weibo user's retweeting behavior. We propose a retweeting game model of Weibo users. By Markov perfect equilibrium strategy we estimate the retweeting probability of the message, of which the continuity and existence have been proved. Comparing the retweeting game model with the Weibo data, we find that the retweeting game model can be used to describe the user's retweeting behaviors. Experimental results show that in the retweeting game model the trust factor $$\delta $$ and the amount of information k have a great influence on the retweeting rate.

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Cited By

Wang RCai WShen B(2018)The study of the dynamic model on KAD network information spreadingTelecommunications Systems10.1007/s11235-015-0127-163:3(371-379)Online publication date: 30-Dec-2018
https://dl.acm.org/doi/10.1007/s11235-015-0127-1
Wang RRho SChen BCai W(2017)Modeling of large-scale social network services based on mechanisms of information diffusionFuture Generation Computer Systems10.1016/j.future.2016.03.01874:C(291-301)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.future.2016.03.018
Wang RRho SCai W(2017)High-performance social networkingCluster Computing10.1007/s10586-017-0782-y20:2(1209-1221)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10586-017-0782-y
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Published In

cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 71, Issue 9

September 2015

422 pages

ISSN:0920-8542

Issue’s Table of Contents

Copyright © Copyright © 2015 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2015

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Cited By

Wang RCai WShen B(2018)The study of the dynamic model on KAD network information spreadingTelecommunications Systems10.1007/s11235-015-0127-163:3(371-379)Online publication date: 30-Dec-2018
https://dl.acm.org/doi/10.1007/s11235-015-0127-1
Wang RRho SChen BCai W(2017)Modeling of large-scale social network services based on mechanisms of information diffusionFuture Generation Computer Systems10.1016/j.future.2016.03.01874:C(291-301)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.future.2016.03.018
Wang RRho SCai W(2017)High-performance social networkingCluster Computing10.1007/s10586-017-0782-y20:2(1209-1221)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10586-017-0782-y
Shen BZhou XKim M(2016)Mixed scheduling with heterogeneous delay constraints in cyber-physical systemsFuture Generation Computer Systems10.1016/j.future.2015.10.02161:C(108-117)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.future.2015.10.021

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