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Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing

Author

Listed:
  • Ashkan Ebadi

    (National Research Council Canada
    Concordia University)

  • Pengcheng Xi

    (National Research Council Canada)

  • Stéphane Tremblay

    (National Research Council Canada)

  • Bruce Spencer

    (National Research Council Canada
    University of New Brunswick)

  • Raman Pall

    (National Research Council Canada)

  • Alexander Wong

    (University of Waterloo
    Waterloo Artificial Intelligence Institute)

Abstract

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January–May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:1:d:10.1007_s11192-020-03744-7
    DOI: 10.1007/s11192-020-03744-7
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    References listed on IDEAS

    as
    1. Margaret Roberts & Brandon Stewart & Tingley, Dustin, 2014. "stm: R Package for Structural Topic Models," Working Paper 176291, Harvard University OpenScholar.
    2. Lucas, Christopher & Nielsen, Richard A. & Roberts, Margaret E. & Stewart, Brandon M. & Storer, Alex & Tingley, Dustin, 2015. "Computer-Assisted Text Analysis for Comparative Politics," Political Analysis, Cambridge University Press, vol. 23(2), pages 254-277, April.
    3. Ebadi, Ashkan & Tremblay, Stéphane & Goutte, Cyril & Schiffauerova, Andrea, 2020. "Application of machine learning techniques to assess the trends and alignment of the funded research output," Journal of Informetrics, Elsevier, vol. 14(2).
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    Cited by:

    1. Seyyed Reza Taher Harikandeh & Sadegh Aliakbary & Soroush Taheri, 2023. "An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1567-1582, March.
    2. Thavavel Vaiyapuri & Sharath Kumar Jagannathan & Mohammed Altaf Ahmed & K. C. Ramya & Gyanendra Prasad Joshi & Soojeong Lee & Gangseong Lee, 2023. "Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    3. W. Benedikt Schmal, 2024. "Academic Knowledge: Does it Reflect the Combinatorial Growth of Technology?," Papers 2409.20282, arXiv.org.
    4. Wadim Strielkowski & Svetlana Zenchenko & Anna Tarasova & Yana Radyukova, 2022. "Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    5. Breno Santana Santos & Ivanovitch Silva & Luciana Lima & Patricia Takako Endo & Gisliany Alves & Marcel da Câmara Ribeiro-Dantas, 2022. "Discovering temporal scientometric knowledge in COVID-19 scholarly production," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1609-1642, March.

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