Tracking and Mining the COVID-19 Research Literature

• Main Authors: Alan L. Porter, Yi Zhang, Ying Huang, Mengjia Wu
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-11-01
• Series: Frontiers in Research Metrics and Analytics
• Subjects: text analysis; tech mining; bibliometrics; COVID-19; coronavirus; pandemic
• Online Access: https://www.frontiersin.org/articles/10.3389/frma.2020.594060/full

The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering means for researchers to probe more deeply to address specific questions. We further probe and reassemble COVID-19 topical content to address research issues concerning topical evolution and emphases on tactical vs. strategic approaches to mitigate this pandemic and reduce future viral threats. Data suggest that heightened attention to strategic, immunological factors is warranted. Connecting with and transferring in research knowledge from outside the COVID-19 domain demand a viable COVID-19 knowledge model. This study provides complementary topical categorizations to facilitate such modeling to inform future Literature-Based Discovery endeavors.