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基于正面信息源影响力最大化的舆情共演模型

李川 王雅琼 严瑛 陈敬良

李川, 王雅琼, 严瑛, 陈敬良. 基于正面信息源影响力最大化的舆情共演模型[J]. 上海工程技术大学学报, 2023, 37(1): 88-95. doi: 10.12299/jsues.22-0210
引用本文: 李川, 王雅琼, 严瑛, 陈敬良. 基于正面信息源影响力最大化的舆情共演模型[J]. 上海工程技术大学学报, 2023, 37(1): 88-95. doi: 10.12299/jsues.22-0210
LI Chuan, WANG Yaqiong, YAN Ying, CHEN Jingliang. Public opinion co-performance model based on maximizing influence of positive information sources[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 88-95. doi: 10.12299/jsues.22-0210
Citation: LI Chuan, WANG Yaqiong, YAN Ying, CHEN Jingliang. Public opinion co-performance model based on maximizing influence of positive information sources[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 88-95. doi: 10.12299/jsues.22-0210

基于正面信息源影响力最大化的舆情共演模型

doi: 10.12299/jsues.22-0210
基金项目: 教育部人文社会科学研究青年基金项目资助(19YJCZH130,16YJCZH165)
详细信息
    作者简介:

    李川:李 川(1977−),男,在读博士,研究方向为传媒管理. E-mail:lichuan@usst.edu.cn

    通讯作者:

    王雅琼(1989−),女,讲师,博士,研究方向为复杂系统. E-mail:wyq113114@126.com

  • 中图分类号: C939

Public opinion co-performance model based on maximizing influence of positive information sources

  • 摘要: 随着移动社交网络平台的普及,个体通过移动设备迅速地接收、传播和交流信息,也使得真假不一的信息在这些平台上广泛传播,加剧了危机传播的频度和广度. 基于正面信息源的影响力最大化,构建一个强关系与弱关系社交平台共演传播模型,以探索利用社交网络中的影响力个体来提升正面信息的传播效率. 系统仿真试验发现,信息对个体影响度及弱关系社交层的信息传播效率在网络舆情共演过程中起着关键作用,增加影响力个体的比例可以减缓或抑制舆情进一步发酵,从而提升公共危机管理水平.
  • 图  1  基于影响力的网络舆情共演模型图例

    Figure  1.  Network public opinion co-performance model based on influence

    图  2  基于正面信息影响力控制的网络舆情共演模型示意图

    Figure  2.  Schematic diagram of network public opinion co-performance model based on positive information influence control

    图  3  状态改变概率树形图

    Figure  3.  Probability tree of state change

    图  4  信息源传播差异和影响力对网络舆情共演的影响

    Figure  4.  Effect of information source communication and influence on network public opinion co-performance

    图  5  信息影响度和影响力共同对网络舆情共演的影响

    Figure  5.  Effect of information acceptance and influence on network public opinion co-performance

    图  6  信息传播率和影响力比例不同对网络舆情共演的影响

    Figure  6.  Effect of information transmission and influence on network public opinion co-performance

  • [1] 董靖巍. 基于复杂网络的网络舆情动态演进影响机制研究[D]. 哈尔滨: 哈尔滨工业大学, 2016.
    [2] LIU J G, REN Z M, GUO Q. Ranking the spreading influence in complex networks[J] . Physica A: Statistical Mechanies and its Applications,2013,392:4154 − 4159. doi: 10.1016/j.physa.2013.04.037
    [3] GAO S, MA J, CHEN Z M, et al. Ranking the spreading ability of nodes in complex networks based on local structure[J] . Physica A: Statistical Mechanics and its Applications,2014,403:130 − 147. doi: 10.1016/j.physa.2014.02.032
    [4] BAE J, KIM S. Identifying and ranking influential spreaders in complex networks by neighborhood coreness[J] . Physica A: Statistical Mechanics and its Applications,2014,395:549 − 559. doi: 10.1016/j.physa.2013.10.047
    [5] CHEN D B, XIAO R, ZENG A, et al. Path diversity improves the identification of influential spreaders[J] . EPL,2013,104(6):68006.
    [6] DODDS P S, PAYNE J L. Analysis of a threshold model of social contagion on degree-correlated networks[J] . Physical Review E,2009,79(6):066115. doi: 10.1103/PhysRevE.79.066115
    [7] TANG S T, TENG X, PEI S, et al. Identification of highly susceptible individuals in complex networks[J] . Physica A: Statistical Mechanics and its Applications,2015,432:363 − 372. doi: 10.1016/j.physa.2015.03.046
    [8] WEI D J, DENG X Y, ZHANG X G, et al. Identifying influential nodes in weighted networks based on evidence theory[J] . Physica A: Statistical Mechanics and its Applications,2013,392(10):2564 − 2575. doi: 10.1016/j.physa.2013.01.054
    [9] BOTTCHER L, WOOLLEY-MEZA O, GOLES E, et al. Connectivity disruption sparks explosive epidemic spreading[J] . Physical Review E,2016,93(4):042315.
    [10] CURATO G, LILLO F. Optimal information diffusion in stochastic block models[J] . Physical Review E,2016,94(3):032310.
    [11] LIU Q H, LU F M, ZHANG Q, et al. Impacts of opinion leaders on social contagions[J] . Chaos:An Interdisciplinary Journal of Nonlinear Science,2018,28(5):053103. doi: 10.1063/1.5017515
    [12] SRIVASTAVA A, CHELMIS C, PRASANNA V K. Computing competing cascades on signed networks[J] . Social Network Analysis and Mining,2016,6(1):82. doi: 10.1007/s13278-016-0392-3
    [13] GALSTYAN A, MUSOYAN V, COHEN P. Maximizing influence propagation in networks with community structure[J] . Physical Review E,2009,79(5):056102. doi: 10.1103/PhysRevE.79.056102
    [14] GALSTYAN A, COHEN P. Cascading dynamics in modular networks[J] . Physical Review E,2007,75(3):036109. doi: 10.1103/PhysRevE.75.036109
    [15] WANG S, LI B, LIU X J, et al. Division of community-based influence maximization algorithm[J] . Computer Engineering and Applications,2016,52(19):42 - 47.
    [16] KITSAK M, GALLOS L K, HAVLIN S, et al. Identification of influential spreaders in complex networks[J] . Nature physics,2010,6(11):888 - 893. doi: 10.1038/nphys1746
    [17] 胡庆成. 基于复杂网络的信息传播模型研究[D]. 北京: 清华大学, 2015.
    [18] DOMINGOS P, RICHARDSON M. Mining the network value of customers [C] //Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. SanFrancisco: ACM, 2001: 57−66.
    [19] KEMPE D, KLEINBERG J, TARDOS É. Maximizing the spread of influence through a social network[C]//Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington: ACM, 2003: 137 − 146.
    [20] LESKOVEC J, KRAUSE A, GUESTRIN C, et al. Cost-effective outbreak detection in networks[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge D iscovery and data mining. San Jose: ACM, 2007: 420−429.
    [21] LIU C, ZHOU L X, FAN C J, et al. Activity of nodes reshapes the critical threshold of spreading dynamics in complex networks[J] . Physica A: Statistical Mechanics and its Applications,2015,432:269 − 278. doi: 10.1016/j.physa.2015.03.054
    [22] PASTOR-SATORRAS R, VESPIGNANI A. Epidemic spreading in scale-free networks.[J] . Physical Review Letters,2001,86(14):3200 − 3203. doi: 10.1103/PhysRevLett.86.3200
    [23] FAN C H, JIN Y, HUO L A, et al. Effect of individual behavior on the interplay between awareness and disease spreading in multiplex networks[J] . Physica A:Statistical Mechanics and its Applications,2016,461:523 − 530. doi: 10.1016/j.physa.2016.06.050
    [24] PASTOR-SATORRAS R, VESPIGNANI A. Evolution and structure of the Internet: A statistical physics approach[M]. Cambridge: Cambridge University Press, 2007.
    [25] PASTOR-SATORRAS R, VESPIGNANI A. Epidemic dynamics and endemic states in complex networks[J] . Physical Review E,2001,63(6):066117. doi: 10.1103/PhysRevE.63.066117
    [26] BARABASI A L, ALBERT R, JEONG H. Mean-field theory for scalefree random networks[J] . Physica A: Statistical Mechanics and its Applications,1999,272(1):173 − 187.
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出版历程
  • 收稿日期:  2022-07-09
  • 刊出日期:  2023-03-31

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