COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution

Mengjia Wu (), Yi Zhang (), Mark Markley (), Caitlin Cassidy (), Nils Newman () and Alan Porter ()
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Mengjia Wu: University of Technology Sydney
Yi Zhang: University of Technology Sydney
Mark Markley: Search Technology, Inc.
Caitlin Cassidy: Search Technology, Inc.
Nils Newman: Search Technology, Inc.
Alan Porter: Search Technology, Inc.

Scientometrics, 2024, vol. 129, issue 11, No 33, 7229-7259

Abstract: Abstract COVID-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization.

Keywords: COVID-19; Topic analysis; Knowledge retrieval; Intelligent bibliometrics (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-023-04747-w

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