基于SIS模型的网络舆情无监督预警机制研究
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摘要
【目的/意义】随着网络信息传递的快速增长,对网络舆情的实时监控日益重要,由此带来了不断攀升的管理成本。因此利用无监督的舆情预警来替代人工监控具有普遍的实践意义。【方法/过程】基于SIS的传染病模型,具体分析了舆情传播的时间演化特征,定义了舆情爆发的临界时刻。通过对舆情扩散系数以及遗忘率的分析,实现了以无监督预警时间为目标的优化策略。【结果/结论】优化结果显示:预设的扩散系数并非越小越好,在无监督预警时间达到最大时,存在一个最优的扩散系数。同时,最优扩散系数可以随着舆情传播环境的变化实现动态的自适应调整,始终保持无监督预警效用的最大化。
【Purpose/significance】With the rapid growth of message transfer on Internet, the real-time monitoring of message propagation is getting more and more important. However, it results in increased cost of management. Therefore, it is of general practical meaning to replace the manual monitoring by unsupervised propagation alert.【Method/process】Based on the SIS epidemic model, the characteristics of time evolution of message propagation on Internet are analyzed, as well as the definition of the critical time of burst. By using the coefficient of message diffusion and the rate of forgetting, we build up an optimized strategy for extending the span of unsupervised alert.【Result/conclusion】The optimized results show that the span of unsupervised alert is not always increasing as the diffusion coefficient decreased. There exists an optimal diffusion coefficient maximizing the span. Moreover, the optimized diffusion coefficient is self-adapted when the message propagation circumstance changed. Therefore, the effect of the unsupervised alert strategy can be maximized all the time.
【Purpose/significance】With the rapid growth of message transfer on Internet, the real-time monitoring of message propagation is getting more and more important. However, it results in increased cost of management. Therefore, it is of general practical meaning to replace the manual monitoring by unsupervised propagation alert.【Method/process】Based on the SIS epidemic model, the characteristics of time evolution of message propagation on Internet are analyzed, as well as the definition of the critical time of burst. By using the coefficient of message diffusion and the rate of forgetting, we build up an optimized strategy for extending the span of unsupervised alert.【Result/conclusion】The optimized results show that the span of unsupervised alert is not always increasing as the diffusion coefficient decreased. There exists an optimal diffusion coefficient maximizing the span. Moreover, the optimized diffusion coefficient is self-adapted when the message propagation circumstance changed. Therefore, the effect of the unsupervised alert strategy can be maximized all the time.
引文
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4陈福集,游丹丹.基于系统动力学的网络舆情事件传播研究[J].情报杂志,2015,34(9):118-122.
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8赵卫东,赵旭东,戴伟辉,等.突发事件的网络情绪传播机制及仿真研究[J].系统工程理论与实践,2015,35(10)2573-2581.
9丁学君.基于SCIR的微博舆情话题传播模型研究[J].计算机工程与应用,2015,51(8):20-26.
10林芹,郭东强.优化SIS模型的社交网络舆情传播研究--基于用户心理特征[J].情报科学,2017,35(3):53-56.
11王治莹,李勇建.政府干预下突发事件舆情传播规律与控制决策[J].管理科学学报,2017,20(2):43-52.
12陈波,于泠,刘君亭,等.泛在媒体环境下的网络舆情传播控制模型[J].系统工程理论与实践,2011,31(11)2140-2150.
13马颖,张园园,宋文广.食品行业突发事件风险感知的传染病模型研究[J].科研管理,2013,34(9):123-130.
2 Gruhl D,Guha R,Kumar R,et al.The predictive power of online chatter[C]//Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining,New York,ACM Press,2005:78-87.
3 Leskovec J,Mcglohon M,Faloutsos C,et al.Patterns of cascading behavior in large blog graphs[C]//Proceedings of the SIAM International Conference on Data Mining.New York:ACM Press,2007,7:551-556.
4陈福集,游丹丹.基于系统动力学的网络舆情事件传播研究[J].情报杂志,2015,34(9):118-122.
5赵剑华,万克文.基于信息传播模型-SIR传染病模型的社交网络舆情传播动力学模型研究[J].情报科学,2017,35(12):34-38.
6丁鑫,刘其成,张伟.一种改进的微博网络信息传播与预测模型[J].中国科学技术大学学报,2014,44(7)582-589.
7刘丹,殷亚文,宋明.基于SIR模型的微博信息扩散规律仿真分析[J].北京邮电大学学报(社会科学版),2014,16(3):28-33.
8赵卫东,赵旭东,戴伟辉,等.突发事件的网络情绪传播机制及仿真研究[J].系统工程理论与实践,2015,35(10)2573-2581.
9丁学君.基于SCIR的微博舆情话题传播模型研究[J].计算机工程与应用,2015,51(8):20-26.
10林芹,郭东强.优化SIS模型的社交网络舆情传播研究--基于用户心理特征[J].情报科学,2017,35(3):53-56.
11王治莹,李勇建.政府干预下突发事件舆情传播规律与控制决策[J].管理科学学报,2017,20(2):43-52.
12陈波,于泠,刘君亭,等.泛在媒体环境下的网络舆情传播控制模型[J].系统工程理论与实践,2011,31(11)2140-2150.
13马颖,张园园,宋文广.食品行业突发事件风险感知的传染病模型研究[J].科研管理,2013,34(9):123-130.