突发事件下基于SIRS模型的网络情感传播研究

发布时间：2019-01-06 15:03

【摘要】：近年来,随着互联网高速发展,网民的数量也随之越来越多,导致大量的舆情信息充斥在网络空间之中,这些舆情信息深刻地影响着网民的心理与行为,特别是网络突发事件频发,导致民众在获知网络舆情时,受其影响而产生的或积极,或消极的情感,这些情感在网络群体的影响下,很容易产生极化,从而影响网民个体的行为,如果是消极性的极化情绪,不加以疏导,便会造成严重的社会后果。因此,通过把握网民的情感走向,有针对性采取相应的引导策略,对企事业单位以及政府具有重要意义。本文基于SIRS模型,对情感传播的关键影响因素进行分析,以2016年的魏则西网络事件为例,收集相关数据,运用Gephi软件首先生成传播网络模型,然后采用Netlogo软件、python语言对模型进行实验仿真,通过采取结合情感和中心性的挖掘指标,找出关键节点,然后便可以采用选择的目标免疫策略进行免疫。具体研究内容包括以下三个方面:1、通过阐述信息传播的机制,分析信息与情绪相互之间的关系,引出情感的产生机理以及情感的传播机制,将两者进行融合。采取图论建模的方法,基于社交网络,收集某条微博的数据,建立情感的传播网络。2、基于SIRS模型,再根据社交网络的特点,加入出生率和恢复损失率两个参量,并且运用李雅普诺夫稳定性法则证明了模型的稳定型。通过改变各个参量的值,观察对感染者比例的影响,最后确定出免疫率和恢复损失率是影响感染者数量的关键因素。3、根据第四章的出的结论,知道免疫率和恢复损失率是影响情感传播效果的关键因素,第五章采用相应的免疫策略对网络中的节点进行免疫,通过对比三种免疫策略和一种改进免疫策略,得出了目标免疫策略为最佳。最后采用结合情感指标与中心性指标的方法,挖掘出关键节点,得出免疫效果的对比图。
[Abstract]:In recent years, with the rapid development of the Internet, the number of Internet users has become more and more, resulting in a large number of public opinion information flooded in the cyberspace, these public opinion information profoundly affect the psychology and behavior of Internet users. In particular, the frequent occurrence of network emergencies results in people being influenced by the public opinion on the Internet, either positive or negative, which are easily polarized under the influence of network groups. Therefore, if the behavior of individual Internet users is influenced by negative polarization, it will result in serious social consequences. Therefore, it is of great significance for enterprises and governments to grasp the emotional trend of Internet users and adopt corresponding guiding strategies. Based on the SIRS model, this paper analyzes the key influencing factors of emotional communication, taking Wei Zexi's network event in 2016 as an example, collecting relevant data, using Gephi software to generate the propagation network model first, and then using Netlogo software. Python language is used to simulate the model. By combining emotional and central mining indexes, key nodes can be found, and then the selected target immune strategy can be used to immune the model. The specific research contents include the following three aspects: 1. Through expounding the mechanism of information dissemination, analyzing the relationship between information and emotion, eliciting the generation mechanism of emotion and the communication mechanism of emotion, combining the two. This paper adopts the method of graph theory modeling, based on social network, collects some Weibo's data, establishes the transmission network of emotion. 2. Based on the SIRS model, and according to the characteristics of social network, two parameters, birth rate and recovery loss rate, are added. And the stability type of the model is proved by using Lyapunov stability law. By changing the values of each parameter, observing the effect on the proportion of infected people, and finally determining that the immune rate and recovery loss rate are the key factors affecting the number of infected people. 3. According to the conclusion of Chapter 4, We know that immune rate and recovery loss rate are the key factors to affect the effect of emotional transmission. In Chapter 5, we use the corresponding immune strategy to immune nodes in the network, and compare the three immune strategies and one improved immune strategy. The target immunization strategy is the best. Finally, the key nodes are excavated by combining the emotional index with the central index, and the immune effects are compared.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：G206

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