Opinion Guidance Research Articles

The Creation of Real Me through Deliberation and Production of Genuine Knowledge through Discernment --A study on the Path for Journalists to Enhance News and Opinion Guidance in the Era of All-media

The Creation of Real Me through Deliberation and Production of Genuine Knowledge through Discernment --A study on the Path for Journalists to Enhance News and Opinion Guidance in the Era of All-media

Consensus achievement strategy of opinion dynamics based on deep reinforcement learning with time constraint

Group opinion often has an important influence on the development and decision-making of major events. However, there are existing two problems with group opinion: (1) As opinions evolve, group opinion may diverge sharply, which is not conducive to obtaining final decision opinion. (2) The evolution of opinions can also cause serious systemic biases in group, which can lead to a final decision that is far from the truth. Hence, this paper deeply investigates two important strategies of consensus boost and opinion guidance in opinion management. Meantime, considering the urgency of some decision-making problems, such as major public crisis events, opinion management process is also subject to time constraint. In this paper, we firstly formalize the minimum adjustment cost consensus boost and opinion guidance with time constraint as Markov decision process because of the intersection and evolution rule of opinions described by opinion dynamics holds Markov property. In this case, the minimum adjustment cost can improve the efficiency of opinion management. We further propose consensus boost algorithm and opinion guidance algorithm based on deep reinforcement learning, which availably mirrors human learning by exploring and receiving feedback from opinion dynamics. Then, by combining the above-mentioned algorithms, we design a new opinion management framework with deep reinforcement learning (OMFDRL). Finally, through comparison experiments, we verify the advantages of our proposed OMFDRL, which can provide managers with more flexible usage conditions.

Simulation study on opinion formation models of heterogeneous agents based on game theory and complex networks

This study investigates a new opinion formation model of heterogeneous agents, a network stubborn individuals and orators (NSO) model based on game theory and complex social networks. Game theory solves economists’ rational choice-making problems, and complex social networks reflect the social impact on opinion evolution. The NSO model involves both social and individual heterogeneous characteristics. In a society, the more unequal the members, and the closer the social distances, the faster opinions spread. In the real world, the power-law degree distribution and the short paths in social networks can generate the rapid spread of an opinion. This study also investigates opinion control under the NSO model. The results show that opinion guidance is most likely to separate the public into different groups rather than converge to the guide’s opinion.