基于对冲作用的社交网络中口碑传播模型及控制策略研究
|
摘要
[目的/意义]文章旨在探究社交网络中的口碑传播规律,为现代企业应对口碑传播提供决策参考,对完善网络传播理论具有重要意义。[方法 /过程]本文在经典传播模型的基础上,考虑到口碑的时效性及正、负面口碑的对冲作用,构建一种社交网络口碑传播离散模型,分析口碑的传播规律;随后,通过仿真实验分别研究了网络拓扑结构、初始传播节点的度与比例对传播过程的影响;最后,在企业宣传成本的约束条件下,提出了社交网络中负面口碑传播的最优控制策略。[结果 /结论]结果表明,在匀质网络中,企业应优先提高传播正面口碑的人群数量;而在异质网络中,选择具有强影响力的媒体或公众人物传播正面口碑是企业的最优选择。
[Purpose/Significance] This paper aimed to explore the rule of word-of-mouth( WOM) propagation in social networks,and to provide a decision reference for modern enterprises. It is of great significance to perfect the theory of network propagation. [Method/Process] Based on the classic propagation model,this paper considered the timeless of WOM and the hedging effect of positive and negative WOM,constructed a discrete model of social network WOM dissemination,and analyzed the rules of WOM propagation. Then,through the simulation experiments,the influences of the network topology and the degree and proportion of the initial propagation nodes on the spreading process were discussed respectively. Finally,under the constraints of enterprise publicity costs,the control strategies of negative WOM diffusion in social networks were proposed. [Result/Conclusion] Results showed that increasing the proportion of individual who propagated positive WOM was preferred in homogeneous networks; whereas,choosing a medium with high influence power to disseminates positive WOM was the priority selection in heterogeneous networks.
[Purpose/Significance] This paper aimed to explore the rule of word-of-mouth( WOM) propagation in social networks,and to provide a decision reference for modern enterprises. It is of great significance to perfect the theory of network propagation. [Method/Process] Based on the classic propagation model,this paper considered the timeless of WOM and the hedging effect of positive and negative WOM,constructed a discrete model of social network WOM dissemination,and analyzed the rules of WOM propagation. Then,through the simulation experiments,the influences of the network topology and the degree and proportion of the initial propagation nodes on the spreading process were discussed respectively. Finally,under the constraints of enterprise publicity costs,the control strategies of negative WOM diffusion in social networks were proposed. [Result/Conclusion] Results showed that increasing the proportion of individual who propagated positive WOM was preferred in homogeneous networks; whereas,choosing a medium with high influence power to disseminates positive WOM was the priority selection in heterogeneous networks.
引文
[1]金永生,田慧.网站特征对消费者口碑传播意愿的影响机制研究[J].现代情报,2016,36(10):107-112.
[2]朱茂然,林星凯,陆颋,等.基于情感分析的社交网络意见领袖的识别——以汽车论坛为例[J].情报理论与实践,2017,40(6):76-81.
[3] Chen Y,Wang Q,Xie J. Online Social Interactions:A Natural Experiment on Word of Mouth Versus Observational Learning[J].Social Science Electronic Publishing,2011,48(2):138-254.
[4] Rosario AB,Sotgiu F,Valck KD,Bijmolt THA. The Effect of Electronic Word of Mouth on Sales:A Meta-analysis Review of Platform,Product and Metric Factors[J]. Journal of Marketing Research,2016,53(3):297-318.
[5] Zanette. DH. Dynamics of Rumor Propagation on Small-world Networks[J]. Physical Review E,2002,65(4):1-9.
[6] Zhou J,Liu Z,Li B. Influence of Network Structure on Rumor Propagation[J]. Physics Letters A,2007,38(6):458-463.
[7]李丹丹,马静.双层社交网络上的舆情传播动力学分析[J].系统工程理论与实践,2017,37(10):2672-2679.
[8] Isham V,Harden S,Nekovee M. Stochastic Epidemics and Rumors on Finite Random Networks[J]. Physica A:Statistical Mechanics and its Applications,2010,389(3):561-576.
[9]郭韧,李红,陈福集.基于可拓聚类的网络舆情演化预测研究[J].情报理论与实践,2017,40(1):83-88.
[10] Jiang P,Yan X. Stability Analysis and Control Models for Rumor Spreading in Online Social Networks[J]. International Journal of Modern Physics C,2017,28(5):1-22.
[11] Zhang N,Huang H,Su B,Zhao J,Zhang B. Dynamic 8-state ICSAR Rumor Propagation Model Considering Official Rumor Refutation[J]. Physica A:Statistical Mechanics and its Applications,2014,415(C):333-346.
[12] Sun L,Liu Y,Zeng Q,Xiong F. A Novel Rumor Diffusion Model Considering the Effect of Truth in Online Social Media[J].International Journal of Modern Physics C,2015,26(7):646-651.
[13] Zhao L,Qiu X,Wang X,Wang J. Rumor Spreading Model Considering Forgetting and Remembering Mechanisms in Inhomogeneous Networks[J]. Physica A:Statistical Mechanics and its Applications,2013,392(4):987-994.
[14]蒋知义,马王荣,邹凯,等.基于情感倾向性分析的网络舆情情感演化特征研究[J].现代情报,2018,38(4):50-57.
[15]李春发,刘凯,王晟锴.基于Multi-Agent的政府干预下虚假舆情传播规律与控制决策[J].现代情报,2018,38(5):53-59.
[16]蔡淑琴,袁乾,周鹏.企业响应下负面口碑线性阈值传播模型研究[J].系统工程学报,2017,32(2):145-155.
[17]洪巍,王虎.基于SIRT的网络谣言传播演化模型研究[J].现代情报,2017,37(6):36-42.
[18]覃志华,刘咏梅.基于三层微信网络的谣言传播模型仿真研究[J].情报科学,2017,35(5):119-124.
[19] Pastor-Satorras R,Vespignani A. Immunization of Complex Networks[J]. Physical Review E,2002,65(3 Pt 2A):1-8.
[20] Cohen R, Havlin S, Benavraham D. Efficient Immunization Strategies for Computer Networks Populations[J]. Physical Review Letters,2003,91(24):1-4.
[21] Kim Y,Tran V. Assessing the Ripple Effects of Online Opinion Leaders with Trust and Distrust Metrics[J]. Expert Systems with Applications,2013,40(9):3500-3511.
[22] Haenlein M,Libai B. Targeting Revenue Leaders for a New Product[J]. Journal of Marketing,2013,77(3):65-80.
[23]兰月新,夏一雪,刘冰月,等.网络舆情传播阶段精细化建模与仿真研究[J].现代情报,2018,38(1):76-86.
[24] Ababou M,Vandewalle N,Moussa N,Bouziani ME,Ludewing F. Spreading of Periodic Diseases and Synchronization Phenomena on Networks[J]. Physica A:Statistical Mechanics and its Applications,2013,392(10):2526-2531.
[25] Xu D,Xu X,Yang C,Gui W. Spreading Dynamics and Synchronization Behavior of Periodic Diseases on Complex Networks[J]. Physica A:Statistical Mechanics and its Applications,2017,466:544-551.
[26] Granovetter M. Threshold Models of Collective Behavior[J]. American Journal of Sociology,1978,83(6):1420-1443.
[27] Yagan O,Gligor V. Analysis of Complex Contagions in Random Multiplex Networks[J]. Physical Review E,2012,86(3):1-11.
[2]朱茂然,林星凯,陆颋,等.基于情感分析的社交网络意见领袖的识别——以汽车论坛为例[J].情报理论与实践,2017,40(6):76-81.
[3] Chen Y,Wang Q,Xie J. Online Social Interactions:A Natural Experiment on Word of Mouth Versus Observational Learning[J].Social Science Electronic Publishing,2011,48(2):138-254.
[4] Rosario AB,Sotgiu F,Valck KD,Bijmolt THA. The Effect of Electronic Word of Mouth on Sales:A Meta-analysis Review of Platform,Product and Metric Factors[J]. Journal of Marketing Research,2016,53(3):297-318.
[5] Zanette. DH. Dynamics of Rumor Propagation on Small-world Networks[J]. Physical Review E,2002,65(4):1-9.
[6] Zhou J,Liu Z,Li B. Influence of Network Structure on Rumor Propagation[J]. Physics Letters A,2007,38(6):458-463.
[7]李丹丹,马静.双层社交网络上的舆情传播动力学分析[J].系统工程理论与实践,2017,37(10):2672-2679.
[8] Isham V,Harden S,Nekovee M. Stochastic Epidemics and Rumors on Finite Random Networks[J]. Physica A:Statistical Mechanics and its Applications,2010,389(3):561-576.
[9]郭韧,李红,陈福集.基于可拓聚类的网络舆情演化预测研究[J].情报理论与实践,2017,40(1):83-88.
[10] Jiang P,Yan X. Stability Analysis and Control Models for Rumor Spreading in Online Social Networks[J]. International Journal of Modern Physics C,2017,28(5):1-22.
[11] Zhang N,Huang H,Su B,Zhao J,Zhang B. Dynamic 8-state ICSAR Rumor Propagation Model Considering Official Rumor Refutation[J]. Physica A:Statistical Mechanics and its Applications,2014,415(C):333-346.
[12] Sun L,Liu Y,Zeng Q,Xiong F. A Novel Rumor Diffusion Model Considering the Effect of Truth in Online Social Media[J].International Journal of Modern Physics C,2015,26(7):646-651.
[13] Zhao L,Qiu X,Wang X,Wang J. Rumor Spreading Model Considering Forgetting and Remembering Mechanisms in Inhomogeneous Networks[J]. Physica A:Statistical Mechanics and its Applications,2013,392(4):987-994.
[14]蒋知义,马王荣,邹凯,等.基于情感倾向性分析的网络舆情情感演化特征研究[J].现代情报,2018,38(4):50-57.
[15]李春发,刘凯,王晟锴.基于Multi-Agent的政府干预下虚假舆情传播规律与控制决策[J].现代情报,2018,38(5):53-59.
[16]蔡淑琴,袁乾,周鹏.企业响应下负面口碑线性阈值传播模型研究[J].系统工程学报,2017,32(2):145-155.
[17]洪巍,王虎.基于SIRT的网络谣言传播演化模型研究[J].现代情报,2017,37(6):36-42.
[18]覃志华,刘咏梅.基于三层微信网络的谣言传播模型仿真研究[J].情报科学,2017,35(5):119-124.
[19] Pastor-Satorras R,Vespignani A. Immunization of Complex Networks[J]. Physical Review E,2002,65(3 Pt 2A):1-8.
[20] Cohen R, Havlin S, Benavraham D. Efficient Immunization Strategies for Computer Networks Populations[J]. Physical Review Letters,2003,91(24):1-4.
[21] Kim Y,Tran V. Assessing the Ripple Effects of Online Opinion Leaders with Trust and Distrust Metrics[J]. Expert Systems with Applications,2013,40(9):3500-3511.
[22] Haenlein M,Libai B. Targeting Revenue Leaders for a New Product[J]. Journal of Marketing,2013,77(3):65-80.
[23]兰月新,夏一雪,刘冰月,等.网络舆情传播阶段精细化建模与仿真研究[J].现代情报,2018,38(1):76-86.
[24] Ababou M,Vandewalle N,Moussa N,Bouziani ME,Ludewing F. Spreading of Periodic Diseases and Synchronization Phenomena on Networks[J]. Physica A:Statistical Mechanics and its Applications,2013,392(10):2526-2531.
[25] Xu D,Xu X,Yang C,Gui W. Spreading Dynamics and Synchronization Behavior of Periodic Diseases on Complex Networks[J]. Physica A:Statistical Mechanics and its Applications,2017,466:544-551.
[26] Granovetter M. Threshold Models of Collective Behavior[J]. American Journal of Sociology,1978,83(6):1420-1443.
[27] Yagan O,Gligor V. Analysis of Complex Contagions in Random Multiplex Networks[J]. Physical Review E,2012,86(3):1-11.