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

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.

Socio-economic and environmental impacts of COVID-19 pandemic in Pakistan—an integrated analysis

“…Countries are actively supporting their financial markets through reduction of debt burden on SMEs such as loan deferment, extensions and moratoriums are being allowed. The volatility of stock market is being eased through the temporary prohibition of short-selling, and reduction or exemption of fees for securities services (UNESCAP 2020; Xia et al 2020).…”

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

“…It shows that at this stage, a concentrated and contiguous outbreak of epidemics is extremely prone to occur. This phenomenon reminds managers that after the outbreak of infectious diseases, it is recommended to manage and control regions of different scales as units and cut off the connection between regions if necessary, which can strictly limit the epidemic to a certain space and prevent large-scale spread (Hsiang et al 2020;Xia et al 2020;Yin et al 2021).…”

Trend Prediction of Event Popularity from Microblogs

“…In order to reveal the propagation mechanism and the evolution law of microblog events, the pioneering work [7] carried out feature extraction on Weibo data and developed an outlier knowledge management framework for dealing with public events. Compared with traditional methods, the graph theory-based approach performed well in modeling the interaction between users [8].…”