A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape

Aristovnik, Aleksander; Ravšelj, Dejan; Umek, Lan

doi:10.20944/preprints202006.0299.v1

Cited by 16 publications

(17 citation statements)

References 9 publications

(12 reference statements)

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“…It is unclear whether this fact reflects a high quality of the research work or a high interest in This study has a number of strengths. To our knowledge, it is the most recent and comprehensive study of the COVID-19 literature using multiple bibliographic tools (30)(31)(32).…”

Section: Discussionmentioning

confidence: 99%

The Pandemic of the COVID-19 Literature: A Bibliometric Analysis Running Title: Bibliometric Analysis of the COVID-19 Literature

Akl

Meho

Farran

et al. 2020

Preprint

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BackgroundThe research interest in COVID-19, one of the most serious pandemics in recent human history, is unprecedented. This study aims to determine the volume of COVID-19 research and to assess the characteristics of its production and publication.MethodsWe searched Scopus, Embase, PubMed, and the Web of Science databases for publications, up to August 20, 2020. We included all types of documents except corrections, interviews, personal narratives, and retracted publications. We analyzed publication count, type, status, research themes, publication venues, authorship trends, language, institutions, countries, collaboration, and funding.ResultsOf 40,519 eligible documents, 49% were original articles. Forty-nine percent of the original articles and reviews were published in top quartile journals, and 19% were single-authored. More than half of the documents were produced in the United States, China, the United Kingdom, and Italy. Twenty-two percent of the documents involved international collaboration and 17% reported financial support by at least one agency, with the National Natural Science Foundation of China being the most frequently reported funding source (n=982). There are already more documents published on COVID19 than documents ever published on the Ebola, MERS, HIN1, and SARS combined.ConclusionsThe first few months’ research output on COVID-19 is relatively large and originated mostly from four countries. Single-authored publications, international collaboration, and governmental funding activities were relatively common.

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Section: Discussionmentioning

confidence: 99%

The Pandemic of the COVID-19 Literature: A Bibliometric Analysis Running Title: Bibliometric Analysis of the COVID-19 Literature

Akl

Meho

Farran

et al. 2020

Preprint

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“…Several studies have used AI methods to conduct a bibliometric analysis of research on the COVID-19 pandemic [6][7][8][10][11][12]. However, we identified the following research gaps in these studies.…”

Section: Research Problem and Aimmentioning

confidence: 99%

“…The scientific literature in this domain area has exponentially increased [6,7]. By the end of May 2020, Aristovnik et al [6] and Doanvo et al [8] retrieved 10,344 and 18,412 COVID-19-related publications written in the English language, respectively, from the Scopus database and the COVID-19 Open Research Dataset (CORD-19). In addition, as of July 13, 2020, more than 1711 clinical trials were registered in different clinical trial registries (eg, NCT, EUCTR, and ISRCTN) [9].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, as of July 13, 2020, more than 1711 clinical trials were registered in different clinical trial registries (eg, NCT, EUCTR, and ISRCTN) [9]. It is very important to have a comprehensive overview of the current state of the literature on COVID-19 for several reasons, namely: (1) to organize and coordinate the literature; (2) to explore research topics addressed; (3) to prioritize research needs or gaps; (4) to understand the evolution of the literature; (5) to recognize the leading researchers, institutes, and countries in this area; and (6) to explore connections between research topics and areas.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis (Preprint)

Abd‐Alrazaq¹,

Schneider²,

Mifsud³

et al. 2020

Preprint

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BACKGROUND Shortly after the emergence of the novel coronavirus disease (COVID-19), researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccination. This led to a rapid increase in the number of COVID-19-related publications. Identifying trends and areas of interest using traditional review methods (e.g, scoping review and systematic reviews) for such a large domain area is challenging. OBJECTIVE We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. METHODS We used the COVID-19 Open Research Dataset (CORD-19) that consists of enormous number of articles related to all coronaviruses. We used machine learning method to analyse most relevant COVID-19 related articles and extracted most prominent topics. Specifically, we used clustering algorithm to group articles based on similarity of their abstracts to identify the research hotspots and current research directions. RESULTS Of the 196,630 publications retrieved from the database, we included 28,904 in the analysis. The mean number of weekly publications was 990 (SD=789.3). The country that published the highest number of articles was China (n=2,950). The largest number of documents was published in BioRxiv. Lei Liu affiliated in the Southern University of Science and Technology in China published the highest number of documents (n=46). Based on titles and abstracts alone, we were able to identify 1,515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, 362 randomized control trials. We identified 19 different topics addressed by the included studies. The most dominant topic was public health response followed by clinical care practices during COVID-19, its clinical characteristics and risk factors, and epidemic models for its spread. CONCLUSIONS We provided an overview of the COVID-19 literature and identified current hotspots and research directions. Our findings can be helpful to the research community by helping prioritize research needs, and recognize leading COVID-19 researchers, institutes, countries, and publishers. This study showed that an AI-based bibliometric analysis has the potential to rapidly explore large corpora of academic publications during a public health crisis. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.

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“…The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has provoked a pandemic of coronavirus disease 19 (Covid-19)-related publications. Within 1 year after its emergence, more than 85,000 research papers about Covid-19 have been published in the areas of health, life, physical and social sciences [1] . Research interest has focused on the evaluation of novel anti-viral treatments, with more than 4,300 intervention studies being registered in the Clinicaltrials.gov database.…”

Section: Introductionmentioning

confidence: 99%

A metaresearch study revealed susceptibility of Covid-19 treatment research to white hat bias: first, do no harm

Bellos

2021

Journal of Clinical Epidemiology

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Objective : To investigate the presence of white hat bias in Covid-19 treatment research by evaluating the effects of citation and reporting bias. Study design and setting : Citation bias was investigated by assessing the degree of agreement between evidence provided by a remdesivir randomized controlled trial and its citing articles. The dissimilarity of outcomes derived from non-randomized and randomized studies was tested by a meta-analysis of hydroxychloroquine effects on mortality. The differential influence of studies with beneficial over those with neutral results was evaluated by a bibliometric analysis. Results : The articles citing the ACTT-1 remdesivir trial preferentially presented its positive outcomes in 55.83% and its negative outcomes in 6.43% of cases. The hydroxychloroquine indicated no significant effect by randomized studies, but a significant survival benefit by non-randomized ones. Citation mapping revealed that the study reporting survival benefit from the hydroxychloroquine-azithromycin combination was the most influential, despite subsequent studies reporting potential harmful effects. Conclusion : The present study raises concerns about citation bias and a predilection of reporting beneficial over harmful effects in the Covid-19 treatment research, potentially in the context of white hat bias. Pre-registration, data sharing and avoidance of selective reporting are crucial to ensure the credibility of future research.

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A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape

Cited by 16 publications

References 9 publications

The Pandemic of the COVID-19 Literature: A Bibliometric Analysis Running Title: Bibliometric Analysis of the COVID-19 Literature

The Pandemic of the COVID-19 Literature: A Bibliometric Analysis Running Title: Bibliometric Analysis of the COVID-19 Literature

A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis (Preprint)

A metaresearch study revealed susceptibility of Covid-19 treatment research to white hat bias: first, do no harm

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