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

Author

Listed:
  • 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.
    Georgia Institute of Technology)

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.

Suggested Citation

  • Mengjia Wu & Yi Zhang & Mark Markley & Caitlin Cassidy & Nils Newman & Alan Porter, 2024. "COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7229-7259, November.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-023-04747-w
    DOI: 10.1007/s11192-023-04747-w
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    References listed on IDEAS

    as
    1. Caroline V Fry & Xiaojing Cai & Yi Zhang & Caroline S Wagner, 2020. "Consolidation in a crisis: Patterns of international collaboration in early COVID-19 research," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-15, July.
    2. Ashkan Ebadi & Pengcheng Xi & Stéphane Tremblay & Bruce Spencer & Raman Pall & Alexander Wong, 2021. "Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 725-739, January.
    3. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    4. Qi Yu & Qi Wang & Yafei Zhang & Chongyan Chen & Hyeyoung Ryu & Namu Park & Jae-Eun Baek & Keyuan Li & Yifei Wu & Daifeng Li & Jian Xu & Meijun Liu & Jeremy J. Yang & Chenwei Zhang & Chao Lu & Peng Zha, 2021. "Analyzing knowledge entities about COVID-19 using entitymetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4491-4509, May.
    5. Milad Haghani & Michiel C. J. Bliemer, 2020. "Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2695-2726, December.
    6. Marc A Garcia & Patricia A Homan & Catherine García & Tyson H Brown & Deborah S Carr, 2021. "The Color of COVID-19: Structural Racism and the Disproportionate Impact of the Pandemic on Older Black and Latinx Adults [Racial disparities in mortality in the adult Hispanic population]," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 76(3), pages 75-80.
    7. Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
    8. Bach Xuan Tran & Giang Hai Ha & Long Hoang Nguyen & Giang Thu Vu & Men Thi Hoang & Huong Thi Le & Carl A. Latkin & Cyrus S.H. Ho & Roger C.M. Ho, 2020. "Studies of Novel Coronavirus Disease 19 (COVID-19) Pandemic: A Global Analysis of Literature," IJERPH, MDPI, vol. 17(11), pages 1-16, June.
    9. Huang, Lu & Zhang, Yi & Guo, Ying & Zhu, Donghua & Porter, Alan L., 2014. "Four dimensional Science and Technology planning: A new approach based on bibliometrics and technology roadmapping," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 39-48.
    10. Liu, Meijun & Zhang, Ning & Hu, Xiao & Jaiswal, Ajay & Xu, Jian & Chen, Hong & Ding, Ying & Bu, Yi, 2022. "Further divided gender gaps in research productivity and collaboration during the COVID-19 pandemic: Evidence from coronavirus-related literature," Journal of Informetrics, Elsevier, vol. 16(2).
    11. Xiaocong Yu & Tshidi Tsibane & Patricia A. McGraw & Frances S. House & Christopher J. Keefer & Mark D. Hicar & Terrence M. Tumpey & Claudia Pappas & Lucy A. Perrone & Osvaldo Martinez & James Stevens , 2008. "Neutralizing antibodies derived from the B cells of 1918 influenza pandemic survivors," Nature, Nature, vol. 455(7212), pages 532-536, September.
    12. Meijun Liu & Yi Bu & Chongyan Chen & Jian Xu & Daifeng Li & Yan Leng & Richard B. Freeman & Eric T. Meyer & Wonjin Yoon & Mujeen Sung & Minbyul Jeong & Jinhyuk Lee & Jaewoo Kang & Chao Min & Min Song , 2022. "Pandemics are catalysts of scientific novelty: Evidence from COVID‐19," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1065-1078, August.
    13. Milad Haghani & Pegah Varamini, 2021. "Temporal evolution, most influential studies and sleeping beauties of the coronavirus literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7005-7050, August.
    14. Yi Zhang & Xiaojing Cai & Caroline V. Fry & Mengjia Wu & Caroline S. Wagner, 2021. "Topic evolution, disruption and resilience in early COVID-19 research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4225-4253, May.
    15. Marius Brülhart & Valentin Klotzbücher & Rafael Lalive & Stephanie K. Reich, 2021. "Mental health concerns during the COVID-19 pandemic as revealed by helpline calls," Nature, Nature, vol. 600(7887), pages 121-126, December.
    16. Elisabetta Cameroni & John E. Bowen & Laura E. Rosen & Christian Saliba & Samantha K. Zepeda & Katja Culap & Dora Pinto & Laura A. VanBlargan & Anna Marco & Julia Iulio & Fabrizia Zatta & Hannah Kaise, 2022. "Broadly neutralizing antibodies overcome SARS-CoV-2 Omicron antigenic shift," Nature, Nature, vol. 602(7898), pages 664-670, February.
    17. Bo Xie & Daqing He & Tim Mercer & Youfa Wang & Dan Wu & Kenneth R. Fleischmann & Yan Zhang & Linda H. Yoder & Keri K. Stephens & Michael Mackert & Min Kyung Lee, 2020. "Global health crises are also information crises: A call to action," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(12), pages 1419-1423, December.
    18. Hengchen Dai & Silvia Saccardo & Maria A. Han & Lily Roh & Naveen Raja & Sitaram Vangala & Hardikkumar Modi & Shital Pandya & Michael Sloyan & Daniel M. Croymans, 2021. "Behavioural nudges increase COVID-19 vaccinations," Nature, Nature, vol. 597(7876), pages 404-409, September.
    19. Yi Zhang & Guangquan Zhang & Donghua Zhu & Jie Lu, 2017. "Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(8), pages 1925-1939, August.
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