基于平台推荐机制和有界信任影响的网络群体观点演化研究

Evolution of Network Group Views Based on Platform Recommendation Mechanisms and Bounded Trust Influences

[目的/意义]基于现实背景扩展了Deffuant-Weisbuch(DW)模型,并对网络媒体平台中群体观点演化规律进行了系统研究,这对开发舆情预警系统以及完善舆情导控政策都具有一定的理论价值和社会实践意义。[方法/过程]在是否存在智能推荐机制的信息推送机制的基础上,考虑平台影响力、话题类型等因素,运用仿真实验分析了不同参数对群体观点演化过程的影响。[结果/结论]平台影响力和智能推荐机制都会影响网络群体观点的收敛速率;在无智能推荐机制情景下,对于敏感程度不同的话题,其网络群体观点分裂程度依旧具有显著差异。

[Purpose/Significance]The Deffuant-Weisbuch(DW)model is extended based on the factual background,and the evolution of group views in online media platforms are systematically studied,which has absolute theoretical value and social practice significance for the development of public opinion early warning systems and the improvement of public opinion guidance and control policies.[Method/Process]Based on whether there is an intelligent recommendation mechanism for message pushing,the authors analyzed the influence of different parameters on the evolution of group views using simulation experiments,taking into account factors such as platform influence and topic type.[Result/Conclusion]Both platform influence and intelligent recommendation mechanism affect the convergence rate of group viewpoints;in the absence of an intelligent recommendation mechanism,the degree of splitting group viewpoints is still significantly different for topics with different sensitivity levels.