摘要
【目的/意义】研究泊松网络的拓扑结构与舆情演化的内生性关系,将连接的关系强度作为控制因素考察对二者的影响,有助于更好理解舆情演化的内在机制。【方法/过程】在观点动力学Deffuant模型的基础上进行改进,通过引入关系强度并构建微观层面的数学模型,探讨在演化过程中舆情与网络结构的相互影响。采用ABM研究范式,在基于Java的Repast平台构建加权泊松网络,跟踪不同参数组合下的仿真实验结果并分析演化过程。【结果/结论】有界信任范围对观点簇数量的形成起到决定性作用,由关系强度和观点值触发的网络结构之动态变化不会对舆情演化的最终结果造成颠覆性影响,仅仅改变其演化的速度和节奏。【创新/局限】从关系强度视角构建了动态泊松网络,并在此基础上观察舆情演化的结果。对个体和舆情网络刻画的不够充分,可以融入个体异质性(心理、记忆等方面)、意见领袖、社团结构等因素,提升与现实网络的拟合度。
【Purpose/significance】To study the endogenous relationship between the topological structure of Poisson network and the evolution of public opinion,and use the strength of the connection as a controlling factor to investigate the influence on the two,which is helpful to better understand the internal mechanism of the evolution of public opinion.【Method/process】Based on the opinion dy⁃namics Deffuant model,it is improved.By introducing the relationship strength and constructing a mathematical model at the micro level,the interaction between the evolution of public opinion and the network structure in the evolution process is discussed.Using the ABM research paradigm,a weighted Poisson network is constructed on the Repast platform based on Java to track the simulation re⁃sults under different parameter combinations and analyze the evolution process.【Result/conclusion】The bounded trust range plays a decisive role in the formation of the number of opinion clusters.The dynamic changes of the network structure triggered by the relation⁃ship strength and opinion value will not have a subversive impact on the final result of the evolution of public opinion,but only change speed and rhythm of the evolution.【Innovation/limitation】A dynamic Poisson network is constructed from the perspective of relation⁃ship strength,and the results of public opinion evolution are observed on this basis.The description of individuals and public opinion networks is not sufficient,and individual heterogeneity(psychology,memory,etc.),opinion leaders,community structure and other fac⁃tors can be integrated to improve the fitting degree with the real network.
作者
肖毅
薛晓斐
易明
肖明
XIAO Yi;XUE Xiaofei;YI Ming;XIAO Ming(School of Information Management,Central China Normal University,Wuhan 430079,China;Information Office,Central China Normal University,Wuhan 430079,China;Language and Language Education Research Center,Central China Normal University,Wuhan 430079,China)
出处
《情报科学》
北大核心
2023年第6期70-78,共9页
Information Science
基金
教育部人文社会科学研究规划基金项目“基于群智能优化策略的大数据混合预测方法及其应用研究”(21YJA630098)
教育部人文社会科学研究规划基金项目“基于语义依存图的汉语有标复句研究”(20YJA740047)。
