Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data

Huosong Xia, Wuyue An, Jiaze Li and Zhang, Zuopeng (Justin)

Socio-Economic Planning Sciences, 2022, vol. 80, issue C

Abstract: Based on complex adaptive system theory and information theory for investigating heterogeneous situations, this paper develops an outlier knowledge management framework based on three aspects—dimension, object, and situation—for dealing with extreme public health events. In the context of the COVID-19 pandemic, we apply advanced natural language processing (NLP) technology to conduct data mining and feature extraction on the microblog data from the Wuhan area and the imported case province (Henan Province) during the high and median operating periods of the epidemic. Our experiment indicates that the semantic and sentiment vocabulary of words, the sentiment curve, and the portrait of patients seeking help were all heterogeneous in the context of COVID-19. We extract and acquire the outlier knowledge of COVID-19 and incorporate it into the outlier knowledge base of extreme public health events for knowledge sharing and transformation. The knowledge base serves as a think tank for public opinion guidance and platform suggestions for dealing with extreme public health events. This paper provides novel ideas and methods for outlier knowledge management in healthcare contexts.

Keywords: COVID-19; Analysis of public opinion; Natural language processing; Outlier knowledge management; Governance suggestion (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:80:y:2022:i:c:s0038012120306066

DOI: 10.1016/j.seps.2020.100941

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