Large group two-stage risk emergency decision-making method based on big data analysis of social media

摘要

In decision-making for major emergencies, the risk of a decision is caused by both the uncertainty of the decision maker and his or her deviation from group consistency. In this paper, a two-stage risk emergency decision-making method for large groups based on social media big data is hence proposed. In this method, the user-generated content related to major emergencies is first collected from social media. Then, an emergency decision attribute system is constructed based on the public opinion information about events, which is obtained by natural language processing. Term frequency-inverse document frequency and expert evaluation are used to determine the weights of the attributes. Second, an open two-stage decision-making process is designed to quantify decision risk according to the reliability and accuracy of decision makers' opinions. Here, clustering is used to calculate the value of group members. Then, the technique for order preference by similarity to an ideal solution (TOPSIS) method is employed to rank decision alternatives. Finally, a case analysis and comparison of the major explosions in Tianjin Port on August 12, 2015, demonstrates the feasibility and effectiveness of the proposed method.