A Glimpse of the First Eight Months of the COVID-19 Literature on Microsoft Academic Graph: Themes, Citation Contexts, and Uncertainties

TL;DR

This paper presents a generic method for analyzing rapidly growing COVID-19 literature by examining structural, temporal, thematic, and uncertainty patterns to aid researchers and society in navigating complex scientific information.

Contribution

The paper introduces a novel, comprehensive method for analyzing large-scale COVID-19 research data, integrating structural, temporal, thematic, and uncertainty analyses.

Findings

Identified key thematic concentrations in COVID-19 literature

Mapped structural and temporal publication patterns

Highlighted types of uncertainties in scientific research

Abstract

As scientists worldwide search for answers to the overwhelmingly unknown behind the deadly pandemic, the literature concerning COVID-19 has been growing exponentially. Keeping abreast of the body of literature at such a rapidly advancing pace poses significant challenges not only to active researchers but also to the society as a whole. Although numerous data resources have been made openly available, the analytic and synthetic process that is essential in effectively navigating through the vast amount of information with heightened levels of uncertainty remains a significant bottleneck. We introduce a generic method that facilitates the data collection and sense-making process when dealing with a rapidly growing landscape of a research domain such as COVID-19 at multiple levels of granularity. The method integrates the analysis of structural and temporal patterns in scholarly publications with the delineation of thematic concentrations and the types of uncertainties that may offer additional insights into the complexity of the unknown. We demonstrate the application of the method in a study of the COVID-19 literature.