Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical Perspective

Cangqi Zhou,Qianchuan Zhao,Wenbo Lu

doi:10.1038/s41598-017-05899-5

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.

Highlights

Through empirical data, we trace the reposted messages of popular initialized/original messages by direct retwitters in Sina Weibo, China’s most popular microblogging service
We try to model the growth of retweets by the analogy with spin glass models
The first one is the inspiration from the works of Johansen and co-workers[14, 17, 18], who reported several experiments on the Internet which could be explained by the models similar to the Trap Model of spin glasses

Summary

Introduction

Through empirical data, we trace the reposted messages (retweets) of popular initialized/original messages (tweets) by direct retwitters in Sina Weibo, China’s most popular microblogging service. We aim to come up with a modeling methodology that is able to derive a mathematical model for phenomenal results based on some intuitive microscopic conjectures To achieve this goal, we try to model the growth of retweets by the analogy with spin glass models. Based on some intuitive conjectures, we derive a power-function model to describe the change of cumulative number of retweets over time These conjectures, such as memory effect, the dynamics of human interest, and the fact that it gets more difficult to discover older messages, are the key elements in our modeling process. Our work indicates that, other than various features adopted in well-tuned machine learning models, some invariable rules, such as the power-law growth of independent retweeting activities, the memory effect in human behaviour, should be taken into consideration during the prediction of information spreading

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Methods

Conclusion