舆论演化的社会影响级联效应模型

舆论建模跳出传统的基于最近邻("person-person")的交互范式,引入次近邻("personperson-person")的影响,刻画网络中邻居的邻居对观点改变的作用,提出舆论演化的社会影响级联模型,分析其在可变聚类系数网络上舆论的演化性质.通过调节网络聚类系数,使用异步更新的方式,观察网络集聚特性对舆论演化的影响.结果表明,1)相比于传统的最近邻影响模型,社会影响级联模型的社会强化作用更大,系统更容易达成共识,初始状态中主流观点的影响将被放大;2)舆论演化结果与网络集聚性和初始状态相关:当系统初始状态p_+≠p_,系统观点演化达到稳态后,网络聚类系数越大,越容易产生主流观点;当初始观点p_+=p_时,即正、负力量势均力敌时,系统共识则难以确定.这种情况和现实社会舆论的演化结果符合.

Opinion evolution of social cascading impact model

Modeling of public opinion is no longer confined to the nereaset neighbourship model (‘person-person’effect), we introduced the influence of next nearest neighbours (‘person-person-person’effect). We proposed an opinion evolution model of social cascading impact, characterized the effect of neighbours' neighbours and investigated the opinion dynamics on tunable clustering network. By adjusting the triad formation parameter which was used to change clustering coefficient of network, we applied asynchronous updating mechanism to observe the clustering coefficient influence on opinion formation. Simulation results show that: 1) compared with traditional nearest neighbour impact model, the social cascading impact model has stronger social reinforcement and as a result, is easier to reach a dominant consensus. 2) when p₊≠ p_- in initial state, a large clustering coefficient favors development of a consensus; when p₊ = p_- in the initial state, consensus results present uncertainty.