Benson Shu Yan Lam, Amanda Man Ying Chu, Jacky Ngai Lam Chan, Mike Ka Pui So
{"title":"学者在讨论 COVID-19 问题时的反应速度是否快于谷歌趋势?文本大数据的一种方法。","authors":"Benson Shu Yan Lam, Amanda Man Ying Chu, Jacky Ngai Lam Chan, Mike Ka Pui So","doi":"10.34133/hds.0116","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> Scholars generally have a faster response in discussing COVID-19 issues than Google Trends.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"4 ","pages":"0116"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10895931/pdf/","citationCount":"0","resultStr":"{\"title\":\"Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? 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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. <b>Results:</b> 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. <b>Conclusion:</b> Scholars generally have a faster response in discussing COVID-19 issues than Google Trends.</p>\",\"PeriodicalId\":73207,\"journal\":{\"name\":\"Health data science\",\"volume\":\"4 \",\"pages\":\"0116\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10895931/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health data science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.34133/hds.0116\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health data science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34133/hds.0116","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data.
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.