Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing
Ashkan Ebadi1,2, Pengcheng Xi3, Stéphane Tremblay3
1National Research Council Canada, Montréal, QC H3T 1J4 Canada.
Scientometrics
|November 24, 2020
Summary
This study analyzed COVID-19 research from January to May 2020 using machine learning. Findings reveal distinct research focuses between PubMed and ArXiv, with PubMed covering diverse issues and ArXiv concentrating on intelligent diagnostic tools.
Area of Science:
- Computational epidemiology
- Bibliometrics
- Natural Language Processing
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, significantly impacted global health and society.
- Understanding the research landscape is crucial for guiding future scientific efforts and public health responses.
Purpose of the Study:
- To characterize the landscape of COVID-19 research published between January and May 2020.
- To identify latent topics, analyze temporal evolution, and assess publication similarity and sentiment.
- To compare research trends across different data sources like PubMed and ArXiv.
Main Methods:
- Utilized multiple data sources, including PubMed and ArXiv.
- Developed and applied machine learning models to identify research topics and analyze trends.
- Performed temporal analysis, similarity analysis, and sentiment analysis on research publications.
Main Results:
- Significant differences were observed in research content between PubMed and ArXiv.
- PubMed exhibited greater diversity in COVID-19 related topics.
- ArXiv focused more on intelligent systems and tools for COVID-19 prediction and diagnosis.
- Research highlighted attention to high-risk groups and individuals with complications.
Conclusions:
- Machine learning effectively characterized the evolving COVID-19 research landscape.
- Distinctive research priorities exist between biomedical (PubMed) and computer science (ArXiv) repositories.
- Future research should consider the specialized focus of different scientific communities.
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