Information Propagation and Intervention on Online Social Networks Using Cellular Automata

Abstract

Abstract:

Online crisis happens so frequently that many researchers and experts have developed various models to explore information propagation mechanism. Existing information propagation models were mainly based on epidemiology or some features affected information propagation. However, few models considered the influence from neighbors or user's own resistance, which had influence on information propagation. These factors are discussed in this paper and they are(1) influence from neighbors, (2) user's own resistance, and (3) outside environment. The information propagation cellular automata model is developed in this paper to study information propagation and intervention mechanism based on these factors. Their impacts on information propagation and intervention are analyzed. The study is classified into two stages:information diffusion stage and information intervention stage. More attention is paid to the impact of intervention intensity and intervention time on the information propagation. Some inspirations are drawn based on analysis on the impacts of these factors on information controlling to support government online crisis response.

Key words: cellular automata, online crisis, information propagation, social relationship

Deng Qing, Liu Yi, Ma Yaping, Zhang Hui. Information Propagation and Intervention on Online Social Networks Using Cellular Automata[J]. , 2016, 28(8): 106-114.

References

[1] 邓青,马晔风,刘艺,等.基于BP神经网络的微博转发量的预测[J].清华大学学报(自然科学版),2015,55(12):1342-1347
[2] Wen S.,Haghighi M.,Chen C.,et al.A Sword with Two Edges:Propagation Studies on Both Positive and Negative Information in Online Social Networks[J].IEEE Transactions on Computers,2015,64(3):640-653
[3] Trajanovski S.,Guo D.,Mieghem,P.From Epidemics to Information Propagation:Striking Differences in Structurally Similar Adaptive Network Models[J].Physical Review E,2015,92(3):1-5
[4] Guan W.,Gao H.,Yang M.Analyzing User Behavior of the Micro-blogging Website Sina Weibo during Hot Social Events[J].Physica A:Statistical Mechanics and its Applications,2014,395:340-351
[5] Rudat A.,Buder J.,Hesse F.Audience Design in Twitter:Retweeting Behavior Between Informational Value and Followers' Interests[J].Computers in Human Behavior,2014,35:132-139
[6] Morchid M.,Dufour R.,Bousquet P.,et al.Torres-Moreno J.Feature Selection using Principal Component Analysis for Massive Retweet Detection[J].Pattern Recognition Letters,2014,49:33-39
[7] Song G.,Li Z.,Tu H.Forward or Ignore:User Behavior Analysis and Prediction on Microblogging[C].Advanced Research in Applied Artificial Intelligence,Springer Berlin Heidelberg,2012
[8] Liu H.,Li Y.Weibo Information Propagation Dissemination Based on User Behavior Using ELM[J].Mathematical Problems in Engineering,2015,501:1-11
[9] Neumann J.,Burks A.W.Theory of self-reproducing Automata[M].USA:University of Illinois Press Champaign,IL,1966
[10] Deutsch A.Cellular Automaton Models for Collective Cell Behaviour[C].Cellular Automata and Discrete Complex Systems,Springer Berlin Heidelberg,2015
[11] Lizier J.,Prokopenko M.,Zomaya A.Information Transfer by Particles in Cellular Automata[C].Progress in Artificial Life,Springer Berlin Heidelberg,2007
[12] 钱静,刘奕,刘呈,等.案例分析的多维情景空间方法及其在情景推演中的应用[J].系统工程理论与实践,2015,35(10):2588-2595
[13] Plikynas D.,Raudys A.,Raudys S.Agent-based Modelling of Excitation Propagation in Social Media Groups[J].Journal of Experimental&Theoretical Artificial Intelligence,2015,27(4):373-388
[14] 杨馥泽.基于元胞自动机的政务微博中危机信息扩散模型研究[J].商,2015,(3):185-186
[15] 于春,何明星.基于元胞自动机的微博舆情传播研究[J].西华大学学报(自然科学版),2014,33(5):16-19
[16] 宋姜,吴鹏,甘利人.网民沉默因素的元胞自动机舆情演化建模及仿真[J].情报理论与实践,2015,38(8):124-129
[17] Cha M.,Mislove A.,Gummadi K.A Measurement-driven Analysis of Information Propagation in the flickr Social Network[C].Proceedings of the 18th International Conference on World Wide Web,ACM,2009
[18] 朱毅华,张超群.基于影响模型的网络舆情演化与传播仿真研究[J].情报杂志,2015,34(2):28-36
[19] 张艳辉,李宗伟,陈滇.社会网络与企业技术创新绩效的关系研究-以苏州电子信息产业为例[J].管理评论,2012,24(6):42-49
[20] Narayanam R.,Narahari Y.A Shapley Value-based Approach to Discover Influential Nodes in Social Networks[J].IEEE Transactions on Automation Science and Engineering,2011,8(1):130-147
[21] 刘艺,邓青,李从东,等.任务驱动的应急资源集成方式与组织过程建模[J].系统工程理论与实践,2015,35(10):2613-2620