Dynamic evolution of government's public trust in online collective behaviour: a social computing approach

作者：Xiao, Renbin^*; Hou, Jundong^*; Li, Jin

来源：International Journal of Bio-Inspired Computation, 2017, 9(1): 1-18.

DOI：10.1504/IJBIC.2017.10002823

• online collective behaviour
• government's public trust
• dynamic evolution
• network driving effect
• group convergence effect
• social computing

There is a dearth of research on why public trust in government rises and falls over time following online collective behaviours. For the problem of dynamic process and micro-macro evolution mechanism of this change in trust whenever it occurs over any specific campaign, in our research, a proposed social computing approach is employed to simulate the change of public trust in government on the basis of a heterogeneous network under three ideal network topologies including random network, scale-free network, and small world network. The results show the dynamics of a change in public trust of the government exhibited in online collective behaviour can be dependent on the interplay between the participants and event, where the former mainly occurs on a social network layer, and the latter on an information layer. This leads to a significantly integrated role between macro network driving effect and micro group convergence effect. Furthermore, several parameters have phase change phenomena in this process, while phase critical value and degree of impact vary from different network structures. The trigger contextual intensity is an important evolutionary power, and plays an integrated role in the evolution process of a shift in the public's trust of government.

• 出版日期2017
• 单位华中科技大学; 中国地质大学（武汉）