Managing public opinion in consensus-reaching processes for large-scale group decision-making problems

In a public-related decision-making event, some people whose interests are not consistent with the result of the event may post some false information on social media to guide others against the event. In order to reduce the impact of false information on the evolution of people's opinions, this paper develops two opinion dynamics models to investigate the evolution process of opinions of decision makers and other related people on social media. Then, in order to manage the public opinion formed by all related people's opinions, a public opinion management-based decision-making process is proposed. Meanwhile, to improve the efficiency of management measures, people are divided into two categories based on their confidence levels. Eventually, simulations and a comparison experiment are provided to demonstrate the feasibility and effectiveness of the proposed model for managing public opinion.