Articlehybrid2020

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

Ashkan Ebadi · Pengcheng Xi · Stéphane Tremblay · Bruce Spencer · Raman Pall · Alexander M. Wong

81 citations0/0 files storedScientometrics

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scientometrics and bibliometrics research Academic Publishing and Open Access Diverse Approaches in Healthcare and Education Studies

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.

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References

2020Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey 2020Mental health care for medical staff in China during the COVID-19 outbreak 2020The Psychological Causes of Panic Buying Following a Health Crisis 2015Computer-Assisted Text Analysis for Comparative Politics 2014stm: R Package for Structural Topic Models 2017A Web Application Software for Causal-based Machine Learning Discrimination Estimation 2020Identifying Research Trends and Gaps in the Context of COVID-19

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2021Evolution and structure of research fields driven by crises and environmental threats: the COVID-19 research 2022Spotlight on Early COVID-19 Research Productivity: A 1-Year Bibliometric Analysis 2022Deep forecasting of translational impact in medical research 2022Discovering temporal scientometric knowledge in COVID-19 scholarly production 2022A topic trend analysis on COVID-19 literature 2023An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains

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