Abstract
Hot events on Internet always attract many people who usually form one or several opinion camps through discussion. For the problem of polarization in Internet group opinions, we propose a new model based on Cellular Automata by considering neighbors, opinion leaders, and external influences. Simulation results show the following: (1) It is easy to form the polarization for both continuous opinions and discrete opinions when we only consider neighbors influence, and continuous opinions are more effective in speeding the polarization of group. (2) Coevolution mechanism takes more time to make the system stable, and the global coupling mechanism leads the system to consensus. (3) Opinion leaders play an important role in the development of consensus in Internet group opinions. However, both taking the opinion leaders as zealots and taking some randomly selected individuals as zealots are not conductive to the consensus. (4) Double opinion leaders with consistent opinions will accelerate the formation of group consensus, but the opposite opinions will lead to group polarization. (5) Only small external influences can change the evolutionary direction of Internet group opinions.
Highlights
IntroductionA paradigm in computer simulation studies of social sciences problems is the emergence of consensus [1,2,3,4]
During the last years, a paradigm in computer simulation studies of social sciences problems is the emergence of consensus [1,2,3,4]
Based on the opinions spread networks model and the Cellular Automata model, we numerically investigate how the three factors of inner influence, opinion leader’s influence, and external influence affect the dynamics of group polarization in Internet
Summary
A paradigm in computer simulation studies of social sciences problems is the emergence of consensus [1,2,3,4]. Each individual affects his neighbors and is affected by his neighbors, and individual’s options evolve dynamically by learning, imitation and conformity, which will result in the emergence of consensus. The dynamical models of consensus can be divided into two main categories: discrete dynamical model (the value of opinions is integer numbers of +1 or −1) and continuous dynamical model (the value of opinions is real numbers between 0 and 1). The discrete dynamical model of consensus includes Ising model [6], Vote model [7], Sznajd model, and other classical models [2], and continuous model includes Deffuant model [4] and Krause-Hegselmann model [8]. A standard review on consensus models can be found in [9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
