- Title
- Tracing the Pace of COVID-19 research : topic modeling and evolution
- Creator
- Liu, Jiaying; Nie, Hansong; Li, Shihao; Ren, Jing; Xia, Feng
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176860
- Identifier
- vital:15195
- Identifier
-
https://doi.org/10.1016/j.bdr.2021.100236
- Identifier
- ISBN:2214-5796 (ISSN)
- Abstract
- 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. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren and Feng Xia" is provided in this record**
- Publisher
- Elsevier Inc.
- Relation
- Big Data Research Vol. 25, no. (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright ©2021 Elsevier Inc
- Rights
- Open Access
- Subject
- 0104 Statistics; 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; Bibliometric analysis; COVID-19; Deep learning; Science of Science; Topic modeling
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