社会网络环境下基于公众行为大数据属性挖掘的大群体应急决策方法及应用

针对社会网络环境下复杂大群体应急决策中决策属性信息难以获得问题,提出社会网络环境下公众行为大数据驱动的大群体应急决策方法.首先,通过挖掘社交平台上的公众行为大数据,利用TF-IDF、Word2vec技术进行关键词提取、聚类及其影响力分析,从大量行为数据中挖掘大群体决策属性信息以辅助专家决策,使决策结果具有更高的科学性和有效性;其次,构建决策者间基于信任关系和观点相似度的社会网络,采用同时考虑信任和相似度的聚类方法对决策者进行聚类,并基于社会网络分析获得决策者权重;然后,提出基于决策者间信任关系的共识调整方法进行共识调整以获得最终群体决策矩阵和方案排序,通过引入决策者客观自信度避免个别决策者过分自信行为的影响;最后,通过一个新冠疫情案例分析说明方法的可行性和有效性.

A large group emergency decision making method and application based on attribute mining of public behaviour big data in social network environment

To solve the problem that it''s difficult to obtain the information of decision attributes in the complex large group emergency decision under the social network environment, a large group emergency decision-making method driven by big data of public behavior under the social network environment is proposed. Firstly, by mining the big data of public behavior on social platforms, the TF-IDF and Word2vec technology are used for the extraction, clustering and influence analysis of Keywords. Large group decision attributes and their weights are mined from a large amount of behavioral data to assist expert decision making, so as to make the decision results more scientific and effective. Secondly, a social network based on trust relationship and similarity of views among decision makers is constructed. The decision makers are clustered by a clustering method considering both trust and similarity, and the weights of decision makers are obtained based on social network analysis. Then, a consensus adjustment method considering the relationship of trust among decision makers is proposed to obtain the final group decision matrix and alternatives ranking. The objective confidence is introduced to avoid the influence of overconfidence behavior of individual decision makers. Finally, a case study about coronavirus is given to illustrate the feasibility and effectiveness of the proposed method.