A human-machine collaborative dynamic group consensus mechanism for mitigating manipulative tendencies - Yuzhou Hou; Xuanhua Xu; Zongrun Wang; Weiwei Zhang (Humanities & Social Sciences Communications)
Abstract With the increasing frequency of emergencies, reliable public opinion fusion has become an important research topic in public opinion analysis and management. However, the public is unorganized and susceptible to manipulation, which poses a challenge. Therefore, from the perspective of coevolution, a human-machine collaborative decision-making mechanism considering manipulative behavior is constructed to ensure the timeliness, democracy, and reliability of public opinion fusion in emergencies. First, an opinion-trust coevolution process is proposed to simulate the human group decision-making environment. Next, a function of the degree of manipulation tendency is constructed based on the extreme opinion expression and influence behaviors of individuals. Then, a machine moderator is trained to manage manipulative behaviors via the feedback adjustment parameters of the human group’s social network, and a human-machine collaborative decision-making mechanism is constructed. Finally, the proposed method is applied to public opinion fusion by considering a torrential rainstorm in Fujian Province, China, as a case study. The results of simulation analyses verify the reliability and effectiveness of the proposed mechanism.