Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data  

作　　者：Benson Shu Yan Lam Amanda Man Ying Chu Jacky Ngai Lam Chan Mike Ka Pui So 

机构地区：[1]Department of Mathematics,Statistics and Insurance,The Hang Seng University of Hong Kong,New Territories,Hong Kong [2]Department of Social Sciences,The Education University of Hong Kong,New Territories,Hong Kong [3]Department of Information Systems,Business Statistics and Operations Management,The Hong Kong University of Science and Technology,New Territories,Hong Kong

出　　处：《Health Data Science》2024年第1期364-377,共14页健康数据科学(英文)

基　　金：supported by the grant from Research Grants Council of the Hong Kong Special Administrative Region,China(Project UGC/FDS14/P04/23);the Research Matching Grant from the Research Grants Council of the Hong Kong Special Administrative Region(project:700006 Applications of SAS Viya in Big Data Analytics);the Big Data Intelligence Centre of The Hang Seng University of Hong Kong.This work was also partially supported by TTe Hong Kong University of Science and Technology research grant“Risk Analytics and Applications”(grant number SBMDF21BM07).

摘　　要：Background:The COVID-19 pandemic has posed various difficulties for policymakers,such as the identification of health issues,establishment of policy priorities,formulation of regulations,and promotion of economic competitiveness.Evidence-based practices and data-driven decision-making have been recognized as valuable tools for improving the policymaking process.Nevertheless,due to the abundance of data,there is a need to develop sophisticated analytical techniques and tools to efficiently extract and analyze the data.Methods:Using Oxford COVID-19 Government Response Tracker,we categorize the policy responses into 6 different categories:(a)containment and closure,(b)health systems,(c)vaccines,(d)economic,(e)country,and(f)others.We proposed a novel research framework to compare the response times of the scholars and the general public.To achieve this,we analyzed more than 400,000 research abstracts published over the past 2.5 years,along with text information from Google Trends as a proxy for topics of public concern.We introduced an innovative text-mining method:coherent topic clustering to analyze the huge number of abstracts.Results:Our results show that the research abstracts not only discussed almost all of the COVID-19 issues earlier than Google Trends did,but they also provided more in-depth coverage.This should help policymakers identify core COVID-19 issues and act earlier.Besides,our clustering method can better reflect the main messages of the abstracts than a recent advanced deep learning-based topic modeling tool.Conclusion:Scholars generally have a faster response in discussing COVID-19 issues than Google Trends.

关 键 词：GOOGLE policy FASTER 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]