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Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution

COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial i...

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Detalles Bibliográficos
Autores principales: Liu, Jiaying, Nie, Hansong, Li, Shihao, Chen, Xiangtai, Cao, Huazhu, Ren, Jing, Lee, Ivan, Xia, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064901/
http://dx.doi.org/10.1016/j.bdr.2021.100236
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author Liu, Jiaying
Nie, Hansong
Li, Shihao
Chen, Xiangtai
Cao, Huazhu
Ren, Jing
Lee, Ivan
Xia, Feng
author_facet Liu, Jiaying
Nie, Hansong
Li, Shihao
Chen, Xiangtai
Cao, Huazhu
Ren, Jing
Lee, Ivan
Xia, Feng
author_sort Liu, Jiaying
collection PubMed
description COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic.
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spelling pubmed-80649012021-04-26 Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution Liu, Jiaying Nie, Hansong Li, Shihao Chen, Xiangtai Cao, Huazhu Ren, Jing Lee, Ivan Xia, Feng Big Data Research Article COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. Elsevier Inc. 2021-07-15 2021-04-24 /pmc/articles/PMC8064901/ http://dx.doi.org/10.1016/j.bdr.2021.100236 Text en © 2021 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Liu, Jiaying
Nie, Hansong
Li, Shihao
Chen, Xiangtai
Cao, Huazhu
Ren, Jing
Lee, Ivan
Xia, Feng
Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution
title Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution
title_full Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution
title_fullStr Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution
title_full_unstemmed Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution
title_short Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution
title_sort tracing the pace of covid-19 research: topic modeling and evolution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064901/
http://dx.doi.org/10.1016/j.bdr.2021.100236
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