Mining the Impacts of COVID-19 Pandemic on the Labour Market

被引:0
|
作者
Smallwood, Joshua F. [1 ]
Zhao, Chenru [1 ]
Leung, Carson K. [1 ]
Wen, Yan [1 ,2 ]
Zheng, Hao [1 ]
机构
[1] Univ Manitoba, Dept Comp Sci, Winnipeg, MB, Canada
[2] Yale Univ, Dept Comp Sci, POB 2158, New Haven, CT 06520 USA
基金
加拿大自然科学与工程研究理事会;
关键词
information management; data mining; social computing; social informatics; social analytics; big data; big data application; data science; coronavirus disease; COVID-19;
D O I
10.1109/IMCOM53663.2022.9721772
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Under the influence of the pandemic environment, many people may have lost their jobs or on the verge of being laid off, while there are many new job seekers. Hence, the status of new jobs under the pandemic and how various industries are affected by the pandemic including predicting future work trends have become the focus of attention. In this paper, we present a social informatics solution to mine the impacts of COVID-19 pandemic on the labour market. We make good use of data mining (especially, frequent pattern mining), statistical analysis, and prediction. Evaluation of real-life Canadian labour market data demonstrates the practicality of our tool. Although we illustrate our ideas with the Canadian labour market, our solution can be adaptable to mine labour markets in other geographical locations.
引用
收藏
页数:8
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