A novel opinion dynamics model based on expanded observation ranges and individuals' social influences in social networks

In this paper, we propose an opinion dynamics model in order to investigate opinion evolution and interactions and the behavior of individuals. By introducing social influence and its feedback mechanism, the proposed model can highlight the heterogeneity of individuals and reproduce realistic online opinion interactions. It can also expand the observation range of affected individuals. Combining psychological studies on the social impact of majorities and minorities, affected individuals update their opinions by balancing social impact from both supporters and opponents. It can be seen that complete consensus is not always obtained. When the initial density of either side is greater than 0.8, the enormous imbalance leads to complete consensus. Otherwise, opinion clusters consisting of a set of tightly connected individuals who hold similar opinions appear. Moreover, a tradeoff is discovered between high interaction intensity and low stability with regard to observation ranges. The intensity of each interaction is negatively correlated with observation range, while the stability of each individual's opinion positively affects the correlation. Furthermore, the proposed model presents the power-law properties in the distribution of individuals' social influences, which is in agreement with people's daily cognition. Additionally, it is proven that the initial distribution of individuals' social influences has little effect on the evolution.