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. 2020 Dec 23:5:607286.
doi: 10.3389/frma.2020.607286. eCollection 2020.

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

Chaomei Chen  1
Affiliations

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

Chaomei Chen. Front Res Metr Anal. .

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 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.

Keywords: COVID-19; CiteSpace; Microsoft Academic Services; citation context analysis; epistemic uncertainty; scientometrics; visual analytics.

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Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
An overview of 1,330 top-cited articles in the COVID-19 literature of 77,897 articles. The size of a node represents the number of times the corresponding article has been cited in the dataset. The prominent theme of each cluster of cited articles is algorithmically labeled.
FIGURE 2
FIGURE 2
Making sense of a cluster (#5 pregnant women). The list of citation contexts shown in the left window corresponds to the current mouse-over event on the concept of vertical transmission.
FIGURE 3
FIGURE 3
Making sense of major themes of citations to a specific reference Li Q. (2020).
FIGURE 4
FIGURE 4
Uncertainties of citation contexts of Li et al. (2020).
FIGURE 5
FIGURE 5
Concept tree of a phrase: vaccine.
FIGURE 6
FIGURE 6
An overview of a smaller network to illustrate SVA. The size of a disc in red depicts epistemic uncertainty (E). The largest three discs are labeled with a black background.
FIGURE 7
FIGURE 7
The distribution of citation contexts with uncertainties is uneven. The most uncertainties are in clusters 0, 2, and 5.
FIGURE 8
FIGURE 8
References associated with the strongest sentiment of uncertainty are from Cluster #0 spike protein.
FIGURE 9
FIGURE 9
An article identified by SVA with a high transformative potential according to centrality divergence.
See this image and copyright information in PMC

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