信息变异下的谣言传播及其漂移机制研究
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摘要
[目的/意义]在谣言传播过程中,个体的信息加工行为常引发谣言漂移现象,为了研究这种漂移现象及其机制,基于经典SIR模型及无标度网络模型,构建信息变异下的谣言传播模型。[方法/过程]将谣言信息表示为10纬向量,定义个体信息交互规则,从微观角度刻画谣言传播过程。引入谣言漂移熵及谣言漂移图谱衡量谣言传播的特征。[结果/结论]网民心理状态对谣言传播有显著影响,网民越是偏激,谣言传播就越快,持续时间越长,谣言混乱度越大,且存在一个网民心理状态的阈值,当冷静个体比例超过该阈值时,谣言漂移熵值显著减小;关键节点对谣言传播有显著影响,在1000个结点的无标度网络中,控制少数关键节点就可以显著抑制谣言且效用最高;初始传播者数量对谣言变异程度无显著影响,但初始传播者数量的增加会加快谣言传播,缩短谣言持续时间。
[Purpose/Significance]In the process of rumor spreading,the information processing behaviors of netizens can cause rumor to drift. In order to study this drifting phenomenon and its mechanism,based on the SIR model and the BA scale-free network model,the rumor propagation model under the information variation is constructed.[Method/Process]The rumor information is represented as a 10 latitude vector. The rules of information interaction are defined,describing the process of rumor spreading from a microscopic perspective. The entropy and map of rumor drifting are used to measure the characteristics of rumor spreading process.[Result/Conclusion]The psychological state of netizens has a significant influence on the process of rumor spreading. The more extreme they are,the faster rumor spreads,the longer rumor lasts,and the more disordered the system is. And there is a threshold of psychological state of netizens,when the proportion of calm individuals exceeds the threshold,the entropy of rumor drifting decreases significantly; key nodes have a significant influence on the process of rumor propagation. In the scale-free network of 1000 nodes,controlling a fewnodes with the highest degree can significantly inhibit the rumor and has the highest utility. The number of initial spreaders has no significant impact on variation of the rumor,but the increase of the initial spreaders can accelerate the spreading of rumors,thus shortening the duration.
[Purpose/Significance]In the process of rumor spreading,the information processing behaviors of netizens can cause rumor to drift. In order to study this drifting phenomenon and its mechanism,based on the SIR model and the BA scale-free network model,the rumor propagation model under the information variation is constructed.[Method/Process]The rumor information is represented as a 10 latitude vector. The rules of information interaction are defined,describing the process of rumor spreading from a microscopic perspective. The entropy and map of rumor drifting are used to measure the characteristics of rumor spreading process.[Result/Conclusion]The psychological state of netizens has a significant influence on the process of rumor spreading. The more extreme they are,the faster rumor spreads,the longer rumor lasts,and the more disordered the system is. And there is a threshold of psychological state of netizens,when the proportion of calm individuals exceeds the threshold,the entropy of rumor drifting decreases significantly; key nodes have a significant influence on the process of rumor propagation. In the scale-free network of 1000 nodes,controlling a fewnodes with the highest degree can significantly inhibit the rumor and has the highest utility. The number of initial spreaders has no significant impact on variation of the rumor,but the increase of the initial spreaders can accelerate the spreading of rumors,thus shortening the duration.
引文
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[2]任群.国内网络谣言的传播规律研究---基于2016年以来的100条热点网络谣言的实证研究[D].济南:山东大学,2017.
[3]王晗啸,蔡培.微博谣言传播网络研究[J].图书情报研究,2018,11(1):37-42,49.
[4]苏宏元,黄晓曦.突发事件中网络谣言的传播机制---基于清晰集定性比较分析[J].当代传播(汉文版),2018(1):64-67.
[5]许晓东,肖银涛,朱士瑞.微博社区的谣言传播仿真研究[J].计算机工程,2011,37(10):272-274.
[6]薛一波,鲍媛媛,易成岐.SPNR:社交网络中的新型谣言传播模型[J].信息网络安全,2014(1):5-9.
[7]Nekovee M,Moreno Y,Bianconi G,et al.Theory of rumour spreading in complex social networks[J].Physica A Statistical Mechanics&Its Applications,2008,374(1):457-470.
[8]王亚奇,杨晓元,韩益亮,等.Rumor spreading model with trust mechanism in complex social networks[J].Communications in Theoretical Physics,2013,59(4):510-516.
[9]熊茵,张艺馨.论突发事件中的信息变异及其类型划分[J].新闻研究导刊,2013(4):25-28.
[10]熊茵.突发事件信息变异与应对策略研究[D].武汉:华中科技大学,2012.
[11]何绍华,孙琛.知识服务中信息传递的信息变异研究[J].情报学报,2006(S1):211-213.
[12]Bailey N T J.The mathemtaical theory of infectious diseases and Its applications[M].NewYork:Hafner Press,1975.
[13]Anderson R M,May R M.Infectious diseases of humans[M].Oxford:Oxford University Press,1992.
[14]Hethcote H W.The mathematics of infectious diseases[J].SIMAReview,2000,42(4):599-653
[15]张芳,司光亚,罗批.谣言传播模型研究综述[J].复杂系统与复杂性科学,2009,6(4):1-11.
[16]何西培,何坤振.信息熵辨析与熵的泛化[J].情报杂志,2006,25(12):109-112.
[17]于同洋.网络环境下信息扩散的多智能体仿真研究[D].武汉:华中科技大学,2010.
[18]Northwestern University.NetLogo user manual[EB/OL].http://ccl.northwestern.edu/netlogo/.
[19]Barabsi A L,Bonabeau E.Scale-free networks[J].Scientific A-merican,2003,288(5):60.
[20]Barabsi A-L,Albert R.Emergence of scaling in random networks[J].Science,1999,286:509-512.
[21]Barabsi,A-L,Albert R,Jeong H.Mean-field theory for scalefree random networks[J].Phys A,1999,272:173-187.
[22]吴江,贺超城,朱侯.集成复杂网络与多智能体仿真的人肉搜索效率研究[J].情报学报,2018,37(1):68-75.
[23]Zhu H,Hu B.Agent based simulation on the process of human flesh search-from perspective of knowledge and emotion[J].Physica A Statistical Mechanics&Its Applications,2017,469:71-80.
[24]朱侯,胡斌.信息与情绪驱动的舆论演化的QSIM-ABS模拟[J].情报学报,2016,35(3):310-316.
[25]兰月新.突发事件网络谣言传播规律模型研究[J].图书情报工作,2012,56(14):57-61.
[26]潘灶烽,汪小帆,李翔.可变聚类系数无标度网络上的谣言传播仿真研究[J].系统仿真学报,2006,18(8):2346-2348.
[27]黄天常.信息熵的内涵与外延[J].陇东学院学报(自然科学版),2006(1):15-18.
[28]王曰芬,杭伟梁,丁洁.微博舆情社会网络关键节点识别与应用研究[J].情报资料工作,2016(3):6-11.
[29]Lazarsfeld Paul F,Katz Elihu.Personal influence[M].NewYork:Free Press,1957.