动态影响力下网络舆论传播的计算实验研究

基于以往研究较少考虑影响力的即时动态更新,对于网络舆论传播的某些现象,可以融入这一因素进行分析.借鉴Krause有界信任的思想,引入Festinger的认知不一致理论对观点交互条件进行改进,并对信任阈值的作用进行了重新认识.本文认为,信任阈值并非判定个体是否交互的分界线,而是决定影响力增大或减小的分割线.进一步,对影响力的动态性进行了深入研究,引入短记忆过程和惩罚机制,从而构建动态影响力下的网络舆论传播模型,进行了计算实验分析.结果表明:随着群体信任闽值的提高,观点分化程度降低.群体观点演化也受初始影响力分布影响,初始影响力较高,网络群体更容易形成稳定的观点,且收敛速度更快.而投入干预个体的影响作用与群体信任阈值负相关,与干预个体所占的比例正相关.

Computational experiments study of network opinion propagation under dynamic influences

Taking account of the fact that the dynamics of influences has been ignored in the most research of opinion propagation with network, this paper introduces the factor of dynamic influences to the research. With the idea of Krause bounded confidence model, this paper introduces Festinger's cognitive dissonance theory to improve interaction conditions of opinions and has a different understanding of the confidence threshold, this threshold isn't the critical value that whether individuals have interactions or not, it is the critical value that whether the influence between individuals are increased or decreased. And then, it introduces the short-term memory and punitive mechanism to give a further study for the dynamic influence. Accordingly, this paper builds an opinion propagation model under dynamic influence, then conducts a series of analysis based on computational experiments. The results show that, when group's confidence threshold increased, individuals' differentiation degree will reduce. And the initial influence distribution affect the opinion differentiation, a higher influence would lead to a more stable opinion and the convergence speed is faster. The effect of the influencer has a negative relationship with the confidence threshold, but has a positive relationship with the influencers' proportion.