An opinion formation model with dissipative structure

Double transition in kinetic exchange opinion models with activation dynamics

“…But they can become active due to peers influence. As discussed in [11,13], active agents may gradually lose their interests in the discussion and drop out of it, which is related to our above Rule 1. In addition, our above Rule 2 is based on the fact that only active agents participate in the opinion dynamics.…”

“…In addition, our above Rule 2 is based on the fact that only active agents participate in the opinion dynamics. Finally, as also discussed in [11,13], active agents can motivate their inactive like-minded peers. If an inert agent holds the same opinion of an active neighbor and they have a positive interaction (with takes place with probability 1 − p) then, the inert agent is driven to be active with probability (1 − p)g. This last sentence justifies our above Rule 3.…”

“…However, many social environments especially online communities are not static in a way that agents can become inactive and withdraw from the discussion. In other words, social users don't concentrate on the discussion all the time, and they may lose interest and drop out of it [11]. However, these dormant agents can become active again following peers [12].…”

“…This kind of activation dynamics leads to interesting results. The authors in [11] verified that if dissipation stays below a threshold value, the system evolves to a balance (paramagnetic) state where the average concentration of one opinion is equal to that of the other. On the other hand, it was verified that, in activation-like opinion dynamics that evolves on the top of complex networks, the topology of network also evolves in time [12].…”

Network Opinion Evolution Model Incorporating the Influence of Media Heterogeneity

“…In the process of simulation, on the one hand, we need to explore the influence of media opinions and parameters of the improved model on the evolution of regular users' opinions, and on the other hand, we need to consider the influence of social network topology on the model. Therefore, in the simulation of this section, four different complex network structures [14], namely, regular network [15,16], random network [17], scalefree network [18,19] and small-world network [20,21], are used to evaluate the model. There are 1000 network users of different types of network structures, and each experiment runs 100 times independently, and then the arithmetic mean value is taken to draw simulation results.…”