Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical Perspective

Abstract The popularization of information spreading in online social networks facilitates daily communication among people. Although much work has been done to study the effect of interactions among people on spreading, there is less work that considers the pattern of spreading behaviour when peopl...

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Main Authors: Cangqi Zhou, Qianchuan Zhao, Wenbo Lu
Format: Article
Language:English
Published: Nature Publishing Group 2017-07-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-017-05899-5
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spelling doaj-0f27a13bec2a4409b3d2106f5ede7b1e2020-12-08T01:56:52ZengNature Publishing GroupScientific Reports2045-23222017-07-017111410.1038/s41598-017-05899-5Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical PerspectiveCangqi Zhou0Qianchuan Zhao1Wenbo Lu2Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua UniversityCenter for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua UniversityCenter for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua UniversityAbstract The popularization of information spreading in online social networks facilitates daily communication among people. Although much work has been done to study the effect of interactions among people on spreading, there is less work that considers the pattern of spreading behaviour when people independently make their decisions. By comparing microblogging, an important medium for information spreading, with the disordered spin glass system, we find that there exist interesting corresponding relationships between them. And the effect of aging can be observed in both systems. Based on the analogy with the Trap Model of spin glasses, we derive a model with a unified power-function form for the growth of independent spreading activities. Our model takes several key factors into consideration, including memory effect, the dynamics of human interest, and the fact that older messages are more difficult to discover. We validate our model by a real-world microblogging data set. Our work indicates that, other than various features, some invariable rules should be considered during spreading prediction. This work also contributes a useful methodology for studying human dynamics.https://doi.org/10.1038/s41598-017-05899-5
collection DOAJ
language English
format Article
sources DOAJ
author Cangqi Zhou
Qianchuan Zhao
Wenbo Lu
spellingShingle Cangqi Zhou
Qianchuan Zhao
Wenbo Lu
Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical Perspective
Scientific Reports
author_facet Cangqi Zhou
Qianchuan Zhao
Wenbo Lu
author_sort Cangqi Zhou
title Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical Perspective
title_short Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical Perspective
title_full Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical Perspective
title_fullStr Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical Perspective
title_full_unstemmed Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical Perspective
title_sort cumulative dynamics of independent information spreading behaviour: a physical perspective
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2017-07-01
description Abstract The popularization of information spreading in online social networks facilitates daily communication among people. Although much work has been done to study the effect of interactions among people on spreading, there is less work that considers the pattern of spreading behaviour when people independently make their decisions. By comparing microblogging, an important medium for information spreading, with the disordered spin glass system, we find that there exist interesting corresponding relationships between them. And the effect of aging can be observed in both systems. Based on the analogy with the Trap Model of spin glasses, we derive a model with a unified power-function form for the growth of independent spreading activities. Our model takes several key factors into consideration, including memory effect, the dynamics of human interest, and the fact that older messages are more difficult to discover. We validate our model by a real-world microblogging data set. Our work indicates that, other than various features, some invariable rules should be considered during spreading prediction. This work also contributes a useful methodology for studying human dynamics.
url https://doi.org/10.1038/s41598-017-05899-5
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