A method for the dynamic collaboration of the public and experts in large-scale group emergency decision-making: Using social media data to evaluate the decision-making quality. (February 2023)

• Record Type: Journal Article
• Authors: Zhu, Yucheng; Xu, Xuanhua; Pan, Bin
• Is Part Of: Computers & industrial engineering. Volume 176(2023)
• Publication Date: 2023-02
• Subjects: Large-scale group emergency decision-making -- Collaboration of the public and experts -- Social media data -- Decision-making quality; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108943
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Propose an innovative dynamic large-scale group emergency decision-making method. Evaluate emergency decision-making quality based on social media data. Update attribute weights and expert weights according to decision-making quality. Apply the method in the prevention plan selection during the COVID-19 in China. Abstract: Aiming at the complex and changeable environment and the low public participation in emergency decision-making, this article proposes a method for the dynamic collaboration of the public and experts in large-scale group emergency decision-making (LSGEDM) based on social media data. First, sentiment analysis is carried out on text data from social media platforms to evaluate the quality of LSGEDM at both the attribute and comprehensive levels. Then, according to the decision-making quality at the attribute level, a method for the dynamic updating of attribute weights is proposed. Next, in the social network environment, the trust relationship between experts is dynamically updated based on the comprehensive quality of decision-making and the distance between the expert and group preferences, and expert weights are calculated by the improved PageRank algorithm. Finally, the effectiveness and superiority of the proposed method are verified via its application to the COVID-19 epidemic in China and a comparative analysis.