Detecting the Major Trends of Information Systems in the COVID-19 Research Landscape
As the volume and diversity of COVID-19 manuscripts grow, trend topic detection has become a more crucial issue to utilize information from pandemic-specific literature. Latent Dirichlet Allocation (LDA) and bibliometric analysis are common ways of detecting trend topics. In this study, a hybrid app...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Regional Information Center for Science and Technology (RICeST)
2023-07-01
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| Series: | International Journal of Information Science and Management |
| Online Access: | https://ijism.ricest.ac.ir/article_705745_58864058bc6f5c6d08db64dbda738abd.pdf |
| _version_ | 1797787930713915392 |
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| author | Keziban Seckin Codal Eda Sönmez |
| author_facet | Keziban Seckin Codal Eda Sönmez |
| author_sort | Keziban Seckin Codal |
| collection | DOAJ |
| description | As the volume and diversity of COVID-19 manuscripts grow, trend topic detection has become a more crucial issue to utilize information from pandemic-specific literature. Latent Dirichlet Allocation (LDA) and bibliometric analysis are common ways of detecting trend topics. In this study, a hybrid approach is suggested by combining both techniques as a novelty perspective to attain comprehensive information. The topics studied in the COVID-19 literature were outlined with the LDA analysis, and then the COVID-19 studies were examined specifically in the field of information systems (IS) with bibliometric analysis. As an outcome of LDA analysis, it has been determined that the topics studied on COVID-19 are concentrated under the categories of clinical studies, epidemiology and transmission of COVID-19, national and global policy responses to the COVID-19 pandemic, and the impacts of the COVID-19. Infodemiology in social media, computer-aided detection methods for diagnosis, information systems for contact tracing and health systems, distance learning solutions, data analytics for modeling and forecasting COVID-19, epidemiology, molecular docking of COVID-19 are primary topics of IS literature in COVID-19 era. This paper assists researchers in providing a comprehensive view of the compatibility of COVID-19 literature at a macro level and in the scope of IS and also offers suggestions for future work by IS researchers. |
| first_indexed | 2024-03-13T01:29:23Z |
| format | Article |
| id | doaj.art-b5118a70c2344f13a86fba9ea481a6f6 |
| institution | Directory Open Access Journal |
| issn | 2008-8302 2008-8310 |
| language | English |
| last_indexed | 2024-03-13T01:29:23Z |
| publishDate | 2023-07-01 |
| publisher | Regional Information Center for Science and Technology (RICeST) |
| record_format | Article |
| series | International Journal of Information Science and Management |
| spelling | doaj.art-b5118a70c2344f13a86fba9ea481a6f62023-07-04T09:51:02ZengRegional Information Center for Science and Technology (RICeST)International Journal of Information Science and Management2008-83022008-83102023-07-0121327328810.22034/ijism.2023.1977689.0705745Detecting the Major Trends of Information Systems in the COVID-19 Research LandscapeKeziban Seckin Codal0Eda Sönmez1Ankara Yıldırım Beyazıt UniversityAnkara Yıldırım Beyazıt UniversityAs the volume and diversity of COVID-19 manuscripts grow, trend topic detection has become a more crucial issue to utilize information from pandemic-specific literature. Latent Dirichlet Allocation (LDA) and bibliometric analysis are common ways of detecting trend topics. In this study, a hybrid approach is suggested by combining both techniques as a novelty perspective to attain comprehensive information. The topics studied in the COVID-19 literature were outlined with the LDA analysis, and then the COVID-19 studies were examined specifically in the field of information systems (IS) with bibliometric analysis. As an outcome of LDA analysis, it has been determined that the topics studied on COVID-19 are concentrated under the categories of clinical studies, epidemiology and transmission of COVID-19, national and global policy responses to the COVID-19 pandemic, and the impacts of the COVID-19. Infodemiology in social media, computer-aided detection methods for diagnosis, information systems for contact tracing and health systems, distance learning solutions, data analytics for modeling and forecasting COVID-19, epidemiology, molecular docking of COVID-19 are primary topics of IS literature in COVID-19 era. This paper assists researchers in providing a comprehensive view of the compatibility of COVID-19 literature at a macro level and in the scope of IS and also offers suggestions for future work by IS researchers.https://ijism.ricest.ac.ir/article_705745_58864058bc6f5c6d08db64dbda738abd.pdf |
| spellingShingle | Keziban Seckin Codal Eda Sönmez Detecting the Major Trends of Information Systems in the COVID-19 Research Landscape International Journal of Information Science and Management |
| title | Detecting the Major Trends of Information Systems in the COVID-19 Research Landscape |
| title_full | Detecting the Major Trends of Information Systems in the COVID-19 Research Landscape |
| title_fullStr | Detecting the Major Trends of Information Systems in the COVID-19 Research Landscape |
| title_full_unstemmed | Detecting the Major Trends of Information Systems in the COVID-19 Research Landscape |
| title_short | Detecting the Major Trends of Information Systems in the COVID-19 Research Landscape |
| title_sort | detecting the major trends of information systems in the covid 19 research landscape |
| url | https://ijism.ricest.ac.ir/article_705745_58864058bc6f5c6d08db64dbda738abd.pdf |
| work_keys_str_mv | AT kezibanseckincodal detectingthemajortrendsofinformationsystemsinthecovid19researchlandscape AT edasonmez detectingthemajortrendsofinformationsystemsinthecovid19researchlandscape |