Public Opinion Evolution Based on the Flocking Algorithm

Surrounded by the explosive development of information, people always present different views and attitudes towards a social event, and how to create a topic to have an impact on the public opinion evolution imperceptibly and effectively has become a promising topic. In order to have a public opinion evolution model close to reality, this paper proposes a dynamic topological network with a high degree of verisimilitude and improves the public opinion evolution algorithm based on the flocking algorithm. Moreover, we propose an innovate attempt of combining the movement-mode with the opinion-exchange algorithm. Experimental results demonstrate that the proposed network public opinion evolution model can impact the public opinion evolution with autonomous, efficient and complete convergency, while simultaneously show the powerful capability of our model in handling the problem of public opinion guidance in social networks.