复杂网络上的网络舆情演化模型研究

发布时间：2018-05-23 22:47

本文选题：网络舆情 + 元胞自动机　；参考：《河南科技大学》2017年硕士论文

【摘要】：随着互联网的飞速发展,网络舆情广受社会关注。网络舆情研究内容涵盖社会科学和自然科学,是当前的研究热点。研究网络信息传播规律,有助于理解信息传播机制,分析影响信息传播的因素,了解网络用户拓扑特征,揭示复杂现象背后的本质规律,进而为网络话题发展趋势预测的研究和应用提供理论指导。本文以真实网络信息交互现象为出发点,结合交叉学科领域的研究和建模方法,分析网络用户行为特征,针对观点交流和传播过程建模,研究群体观点聚合、分化、演进,并探讨了网络舆论的导控策略。本文主要工作包括:1.在传统元胞自动机模型的基础上结合Bass理论加入外部影响力,并引入邻域影响度和外部影响度两个变量,建立考虑外部影响的元胞自动机网络舆情传播模型。提出了改进的倾向性转换计算公式,并使用粗、细粒度统计方法对模型进行了仿真分析。新模型揭示了网络舆情传播的一些规律,有助于网络舆情管理者分析、预测、管理和控制舆情传播趋势。2.研究舆论主体的主观能动性对观点演化的影响,改进经典有限信任模型,引入了坚定度、信任度和记忆长度的概念,提出新的度量个体间交互影响力的方法,建立基于加速增长HK网络的含有记忆机制的网络舆情演化模型。新模型综合考虑用户间影响力、信任阈值和个体记忆策略对信任关系的影响,更加符合实际网络环境中群体观点交流的特点。
[Abstract]:With the rapid development of the Internet, network public opinion is widely concerned by the society. The research content of network public opinion covers social science and natural science, and it is a hot research topic at present. Studying the law of network information dissemination is helpful to understand the mechanism of information dissemination, analyze the factors that influence information dissemination, understand the topological characteristics of network users, and reveal the essential laws behind complex phenomena. Then it provides theoretical guidance for the research and application of network topic trend prediction. Based on the phenomenon of real network information interaction, this paper analyzes the behavior characteristics of network users by combining the cross-disciplinary research and modeling methods, models the process of view exchange and dissemination, studies the aggregation, differentiation and evolution of group views. And discusses the network public opinion guidance and control strategy. The main work of this paper includes: 1. Based on the traditional cellular automata model, the external influence is added to the Bass theory, and two variables, the neighborhood influence degree and the external influence degree, are introduced to establish the cellular automata network public opinion propagation model considering the external influence. An improved formula for calculating tendency conversion is proposed, and the model is simulated and analyzed by using coarse and fine grained statistical methods. The new model reveals some laws of the spread of network public opinion and helps network public opinion managers to analyze, predict, manage and control the trend of public opinion communication. This paper studies the influence of subjective initiative of public opinion on the evolution of opinions, improves the classical finite trust model, introduces the concepts of firmness, trust and memory length, and puts forward a new method to measure the interaction influence between individuals. A network public opinion evolution model with memory mechanism based on accelerating growth HK network is established. The new model considers the influence of user influence, trust threshold and individual memory strategy on trust relationship, which is more consistent with the characteristics of group view exchange in real network environment.
【学位授予单位】：河南科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：O157.5

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