Yanchao Zhang

An opinion dynamics model for Online Mass Incident

Online Mass Incident appears with increasing frequency. Its features, e.g. lack of regulation, having a real trigger, make selective attention governed by individual relevance play a significant role in interaction between internet users. Drawing on the research findings of social psychology, we proposed a multi-agent opinion dynamics model for Online Mass Incident, in which selecting likely-minded neighbors or opponents is decided by individual relevance. Numerical simulations show that, high individual relevance favors appearance of large clusters and lessening of extremism, which is qualitatively agree with suggestions of sociologists and actual practice of governments.

THE RESEARCH ON STABILITY OF DIFFUSION AND COMPETITION BETWEEN ONLINE TOPICS

Online topics are becoming an important form of network information communication, and are playing a significant role in consensus formation. When several online topics are published at the same time, the spread of these topics will affect each other. We propose a model to study the development tendency of relationship between two different online topics. We make use of stability theory of differential equations to analyze the model, utilize the phase trajectory to analyze the stability of the equilibrium point, and finally simulate the model with actual data and varied parameters. Simulation results indicate that the proposed model is useful in understanding and explaining the phenomena presented in the diffusion of online topics, especially the interaction and competition among different topics.

COMPARING EFFECTS OF CLUSTER-COUPLED PATTERNS ON OPINION DYNAMICS

Community structure is another important feature besides small-world and scale-free property of complex networks. Communities can be coupled through specific fixed links between nodes, or occasional encounter behavior. We introduce a model for opinion evolution with multiple cluster-coupled patterns, in which the interconnectivity denotes the coupled degree of communities by fixed links, and encounter frequency controls the coupled degree of communities by encounter behaviors. Considering the complicated cognitive system of people, the CODA (continuous opinions and discrete actions) update rules are used to mimic how people update their decisions after interacting with someone. It is shown that, large interconnectivity and encounter frequency both can promote consensus, reduce competition between communities and propagate some opinion successfully across the whole population. Encounter frequency is better than interconnectivity at facilitating the consensus of decisions. When the degree of social cohesion is same, small interconnectivity has better effects on lessening the competence between communities than small encounter frequency does, while large encounter frequency can make the greater degree of agreement across the whole populations than large interconnectivity can.