A scientometric overview of CORD-19

LDA2Net Digging under the surface of COVID-19 scientific literature topics via a network-based approach

During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically. These literary items encompass a varied set of topics, ranging from vaccination to protective equipment efficacy as well as lockdown policy evaluations. As a result, the development of automatic methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in on its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis, to investigate topics under their surface. More specifically, LDA2Net exploits the frequencies of consecutive words pairs (i.e. bigram) to build those network structures underlying the hidden topics extracted from large volumes of text by Latent Dirichlet Allocation (LDA). Results are promising and suggest that the topic model efficacy is magnified by the network-based representation. In particular, such enrichment is noticeable when it comes to displaying and exploring the topics at different levels of granularity.

A brief review and scientometric analysis on ensemble learning methods for handling COVID-19

Numerous efforts and research have been conducted worldwide to combat the coronavirus disease 2019 (COVID-19) pandemic. In this regard, some researchers have focused on deep and machine-learning approaches to discover more about this disease. There have been many articles on using ensemble learning methods for COVID-19 detection. Still, there seems to be no scientometric analysis or a brief review of these researches. Hence, a combined method of scientometric analysis and brief review was used to study the published articles that employed an ensemble learning approach to detect COVID-19. This research used both methods to overcome their limitations, leading to enhanced and reliable outcomes. The related articles were retrieved from the Scopus database. Then a two-step procedure was employed. A concise review of the collected articles was conducted. Then they underwent scientometric and bibliometric analyses. The findings revealed that convolutional neural network (CNN) is the mostly employed algorithm, while support vector machine (SVM), random forest, Resnet, DenseNet, and visual geometry group (VGG) were also frequently used. Additionally, China has had a significant presence in the numerous top-ranking categories of this field of research. Both study phases yielded valuable results and rankings.

COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution

COVID-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization.

Citation advantage of COVID-19-related publications

With the global spread of the COVID-19 pandemic, scientists from various disciplines responded quickly to this historical public health emergency. The sudden boom of COVID-19-related papers in a short period of time may bring unexpected influence to some commonly used bibliometric indicators. By a large-scale investigation using Science Citation Index Expanded and Social Sciences Citation Index, this brief communication confirms the citation advantage of COVID-19-related papers empirically through the lens of Essential Science Indicators’ highly cited paper. More than 8% of COVID-19-related papers published during 2020 and 2021 were selected as Essential Science Indicators highly cited papers, which was much higher than the set global benchmark value of 1%. The citation advantage of COVID-19-related papers for different Web of Science categories/countries/journal impact factor quartiles was also demonstrated. The distortions of COVID-19-related papers’ citation advantage to some bibliometric indicators such as journal impact factor were discussed at the end of this brief communication.

Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data

With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.

A scientometric approach to psychological research during the COVID-19 pandemic

During the COVID-19 pandemic, modern science demonstrated its ability to respond well to the health crisis by publishing useful and reliable information. This disease has also led to an increase in psychological publications in this field. However, most scientometric studies have focused on medical aspects, and social science research has been neglected. Therefore, to fill this research gap, we analyzed the research on COVID-19 in the field of psychology to provide an insight into the perspective, research fields, and international collaborations. Data were collected from the Web of Science database and analyzed using Citespace and Bibliometrix (Biblioshiny). The overall performance of the documents was described, and then keyword co-occurrence and co-authorship networks were visualized. Fifteen main clusters were formed by drawing document co-citation network. The result indicates that Anxiety, mental health, delirium, loneliness, and suicide were important topics for researchers. Considering the special conditions that COVID-19 created for human societies, perhaps one of the most important subjects in the field of health is psychological studies. Using the results of this study, psychology researchers can identify their potential colleagues and research gaps in the subject of Covid-19.

COVID-19 and the scientific publishing system: growth, open access and scientific fields

We model the growth of scientific literature related to COVID-19 and forecast the expected growth from 1 June 2021. Considering the significant scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing models using the Dimensions database. This source has the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by domain-specific repository (SSRN and MedRxiv) and by several research fields. We conclude by discussing our findings.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11192-022-04536-x.

The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges

The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.

A topic trend analysis on COVID-19 literature

Objective

In the past 2 years, the number of scientific publications has grown exponentially. The COVID-19 outbreak hugely contributed to this dramatic increase in the volume of published research. Currently, text mining of the volume of SARS-CoV-2 and COVID-19 publications is limited to the first months of the outbreak. We aim to identify the major topics in COVID-19 literature collected from several citational sources and analyze the temporal trend from November 2019 to December 2021.

Methods

We performed an extensive literature search on SARS-Cov-2 and COVID-19 publications on PubMed, Scopus, and Web of Science (WoS) and a structural topic modelling on the retrieved abstracts. The temporal trend of the recognized topics was analyzed. Furthermore, a comparison between our corpus and the COVID-19 Open Research Dataset (CORD-19) repository was performed.

Results

We collected 269,186 publications and identified 10 topics. The most popular topic was related to the clinical pictures of the COVID-19 outbreak, which has a constant trend, and the least popular includes studies on COVID-19 literature and databases. "Telemedicine", "Vaccine development", and "Epidemiology" were popular topics in the early phase of the pandemic; increasing topics in the last period are "COVID-19 impact on mental health", "Forecasting", and "Molecular Biology". "Education" was the second most popular topic, which emerged in September 2020.

Conclusions

We identified 10 topics for classifying COVID-19 research publications and estimated a nonlinear temporal trend that gives an overview of their unfolding over time. Several citational databases must be searched to retrieve a complete set of studies despite the efforts to build repositories for COVID-19 literature. Our collected data can help build a more focused literature search between November 2019 and December 2021 when carrying out systematic and rapid reviews and our findings can give a complete picture on the topic.

Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing

Over the past two years, organizations and businesses have been forced to constantly adapt and develop effective responses to the challenges of the COVID-19 pandemic. The acuteness, global scale and intense dynamism of the situation make online news and information even more important for making informed management and policy decisions. This paper focuses on the economic impact of the COVID-19 pandemic, using natural language processing (NLP) techniques to examine the news media as the main source of information and agenda-setters of public discourse over an eight-month period. The aim of this study is to understand which economic topics news media focused on alongside the dominant health coverage, which topics did not surface, and how these topics influenced each other and evolved over time and space. To this end, we used an extensive open-source dataset of over 350,000 media articles on non-medical aspects of COVID-19 retrieved from over 60 top-tier business blogs and news sites. We referred to the World Economic Forum’s Strategic Intelligence taxonomy to categorize the articles into a variety of topics. In doing so, we found that in the early days of COVID-19, the news media focused predominantly on reporting new cases, which tended to overshadow other topics, such as the economic impact of the virus. Different independent news sources reported on the same topics, showing a herd behavior of the news media during this global health crisis. However, a temporal analysis of news distribution in relation to its geographic focus showed that the rise in COVID-19 cases was associated with an increase in media coverage of relevant socio-economic topics. This research helps prepare for the prevention of social and economic crises when decision-makers closely monitor news coverage of viruses and related topics in other parts of the world. Thus, monitoring the news landscape on a global scale can support decision-making in social and economic crises. Our analyses point to ways in which this monitoring and issues management can be improved to remain alert to social dynamics and market changes.

The role of blogs and news sites in science communication during the COVID-19 pandemic

We present a brief review of literature related to blogs and news sites; our focus is on publications related to COVID-19. We primarily focus on the role of blogs and news sites in disseminating research on COVID-19 to the wider public, that is knowledge transfer channels. The review is for researchers and practitioners in scholarly communication and social media studies of science who would like to find out more about the role of blogs and news sites during the COVID-19 pandemic. From our review, we see that blogs and news sites are widely used as scholarly communication channels and are closely related to each other. That is, the same research might be reported in blogs and news sites at the same time. They both play a particular role in higher education and research systems, due to the increasing blogging and science communication activity of researchers and higher education institutions (HEIs). We conclude that these two media types have been playing an important role for a long time in disseminating research, which even increased during the COVID-19 pandemic. This can be verified, for example, through knowledge graphs on COVID-19 publications that contain a significant amount of scientific publications mentioned in blogs and news sites.

Health Information Technology During the COVID-19 Epidemic: A Review via Text Mining

Background

Due to the prevalence of the COVID-19 epidemic in all countries of the world, the need to apply health information technology is of great importance. hence, the study has identified the role of health information technology during the period of the COVID-19 epidemic.

Methods

The present research is a review study by employing text mining techniques. Therefore, 941 published documents related to health information technology's role during the COVID-19 epidemic were extracted by keyword searching in the Web of Science database. In order to analyze the data and implement the text mining and topic modeling algorithms, Python programming language was applied.

Results

The results indicated that the highest number of publications related to the role of health information technology in the period of the COVID-19 epidemic was respectively on the following topics: "Models and smart systems," "Telemedicine," "Health care," "Health information technology," "Evidence-based medicine," "Big data and Statistic analysis."

Conclusion

Health information technology has been extensively used during the COVID-19 epidemic. Therefore, different communities can apply these technologies, considering the conditions and facilities to manage the COVID-19 epidemic better.

Spotlight on Early COVID-19 Research Productivity: A 1-Year Bibliometric Analysis

Coronavirus disease 2019 (COVID-19), one of the most serious public health crises in over a century, has led to an unprecedented surge of publications across all areas of knowledge. This study assessed the early research productivity on COVID-19 in terms of vaccination, diagnosis, treatment, symptoms, risk factors, nutrition, and economy. The Scopus database was searched between January 1, 2020 and December 31, 2020 to initially examine the research productivity on COVID-19, as measured by total publications by the 20 highest-ranked countries according to gross domestic product. The literature search was then refined, and research productivity was assessed across seven major research domains related to COVID-19: vaccination, diagnosis, treatment, symptoms, risk factors, nutrition, and economy. The initial literature search yielded 53,348 publications. Among these, 27,801 publications involved authorship from a single country and 22,119 publications involved authorship from multiple countries. Overall, the United States was the most productive country (n = 13,491), with one and a half times or more publications than any other country, on COVID-19 and the selected domains related to it. However, following adjustment for population size, gross domestic product, and expenditure for research and development, countries of emerging economies such as India along countries of lower population density such as Switzerland, Indonesia, and Turkey exhibited higher research productivity. The surge of COVID-19 publications in such a short period of time underlines the capacity of the scientific community to respond against a global health emergency; however where future research priorities and resource distribution should be placed on the respective thematic fields at an international level, warrants further investigation.

Construction of A Linked Dataset of COVID-19 Knowledge Graphs: Development and Applications

Background

With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data.

Objective

To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character.

Methods

In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain–specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19.

Results

OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described.

Conclusions

A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.

Exploring machine learning: a scientometrics approach using bibliometrix and VOSviewer

Machine Learning has found application in solving complex problems in different fields of human endeavors such as intelligent gaming, automated transportation, cyborg technology, environmental protection, enhanced health care, innovation in banking and home security, and smart homes. This research is motivated by the need to explore the global structure of machine learning to ascertain the level of bibliographic coupling, collaboration among research institutions, co-authorship network of countries, and sources coupling in publications on machine learning techniques. The Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was applied to clustering prediction of authors dominance ranking in this paper. Publications related to machine learning were retrieved and extracted from the Dimensions database with no language restrictions. Bibliometrix was employed in computation and visualization to extract bibliographic information and perform a descriptive analysis. VOSviewer (version 1.6.16) tool was used to construct and visualize structure map of source coupling networks of researchers and co-authorship. About 10,814 research papers on machine learning published from 2010 to 2020 were retrieved for the research. Experimental results showed that the highest degree of betweenness centrality was obtained from cluster 3 with 153.86 from the University of California and Harvard University with 24.70. In cluster 1, the national university of Singapore has the highest degree betweenness of 91.72. Also, in cluster 5, the University of Cambridge (52.24) and imperial college London (4.52) having the highest betweenness centrality manifesting that he could control the collaborative relationship and that they possessed and controlled a large number of research resources. Findings revealed that this work has the potential to provide valuable guidance for new perspectives and future research work in the rapidly developing field of machine learning.

Validation of scientific topic models using graph analysis and corpus metadata

Probabilistic topic modeling algorithms like Latent Dirichlet Allocation (LDA) have become powerful tools for the analysis of large collections of documents (such as papers, projects, or funding applications) in science, technology an innovation (STI) policy design and monitoring. However, selecting an appropriate and stable topic model for a specific application (by adjusting the hyperparameters of the algorithm) is not a trivial problem. Common validation metrics like coherence or perplexity, which are focused on the quality of topics, are not a good fit in applications where the quality of the document similarity relations inferred from the topic model is especially relevant. Relying on graph analysis techniques, the aim of our work is to state a new methodology for the selection of hyperparameters which is specifically oriented to optimize the similarity metrics emanating from the topic model. In order to do this, we propose two graph metrics: the first measures the variability of the similarity graphs that result from different runs of the algorithm for a fixed value of the hyperparameters, while the second metric measures the alignment between the graph derived from the LDA model and another obtained using metadata available for the corresponding corpus. Through experiments on various corpora related to STI, it is shown that the proposed metrics provide relevant indicators to select the number of topics and build persistent topic models that are consistent with the metadata. Their use, which can be extended to other topic models beyond LDA, could facilitate the systematic adoption of this kind of techniques in STI policy analysis and design.

How has academia responded to the urgent needs created by COVID-19? A multi-level global, regional and national analysis

In the context of the COVID-19 pandemic, gaining insights into how academia has responded to this urgent challenge is of great significance. This article presents academic response patterns at a global, regional and national level from an analysis of publication volume versus reported cases of COVID-19, scientific collaboration and research focus. We also compare academic activity associated with this newly emerging infection to that related to long-standing infections. Our results show that the research community has responded quickly to COVID-19. The highly developed countries, which have the highest number of confirmed cases, are also the major academic contributors. National-level analysis reveals diverse response patterns from different countries. Specifically, academic research in the United Kingdom remained at a relatively constant level throughout the whole year (2020), while the global share of China’s research output was prone to shift as its domestic pandemic status changed. Strong alliances have formed among countries with academic capabilities in response to the COVID-19 pandemic. The distribution of disciplines is relatively decentralised, indicating that a diverse and broad knowledge base contributes to the COVID-19 literature. Most of the analysed countries show dynamic patterns of research focus that vary over time as the pandemic evolves, except India. As one of the world’s biggest suppliers of vaccines, India makes consistent efforts on vaccine research, especially those related to pharmaceutical preparations. Our findings may serve as resources for fostering strategies to respond to future threats of pandemics.

Discovering temporal scientometric knowledge in COVID-19 scholarly production

The mapping and analysis of scientific knowledge makes it possible to identify the dynamics and/or growth of a particular field of research or to support strategic decisions related to different research entities, based on bibliometric and/or scientometric indicators. However, with the exponential growth of scientific production, a systematic and data-oriented approach to the analysis of this large set of productions becomes increasingly essential. Thus, in this work, a data-oriented methodology was proposed, combining Data Analysis, Machine Learning and Complex Network Analysis techniques, and Data Version Control (DVC) tool, for the extraction of implicit knowledge in scientific production bases. In addition, the approach was validated through a case study in a COVID-19 manuscripts dataset, which had 199,895 articles published on arXiv, bioRxiv, medRxiv, PubMed and Scopus databases. The results suggest the feasibility of the proposed methodology, indicating the most active countries and the most explored themes in each period of the pandemic. Therefore, this study has the potential to instrument and expand strategic decisions by the scientific community, aiming at extracting knowledge that supports the fight against the COVID-19 pandemic.

PROSPERO's systematic review protocols of traditional Chinese medicine for COVID-19: An overview

BACKGROUND

A large number of protocols for Systematic Reviews (SR) of Traditional Chinese Medicine (TCM) for coronavirus disease 2019 (COVID-19) have been registered in the International Prospective Register of Systematic Reviews (PROSPERO). This study aimed to analyze the innovativeness and rigorousness of the SR protocols and make recommendations for the design and implementation of future SRs on TCM for COVID-19. This effort is likely to enhance the value of the produced information and prevent the futility of the research.

METHODS

PROSPERO was searched comprehensively for identifying SRs of TCM for COVID-19 from the inception of the database to August 2020. Two researchers independently screened the literature, extracted the data, and cross-checked the retrieved information for consistency. The following details were recorded: database, registration time, organizations, types of research included, participants, interventions, and outcome measures. All extracted data were analyzed by an overview. The "P - participants, I - interventions, C - controls, and O - outcomes (PICO)" included in the protocols were compared for similarity. The outcomes of the included SR protocols were compared with the newly published Core Outcome Sets (COSs).

RESULTS

A total of 80 protocols of SR related to TCM for COVID-19 were obtained after a primary search, and finally 71 protocols were included. The majority of the protocols were from China. Thirty-two organizations participated in the protocol registrations, including 11 hospitals and 21 universities/colleges. However, some protocols were not innovative or rigorous enough, as the PICO of some protocols were similar and non-specific, and the searched literature was incomprehensive. In addition, COS is not commonly adopted.

CONCLUSIONS

Registering a protocol of SR is an effective way to ensure the usefulness of the produced information, and to avoid the duplication of research and the wastage of resources. In future SR protocols, it is important to focus on and solve the methodological problems such as non-specific PICO, incomprehensive literature retrieval, and improper outcome measures.

How Has Covid-19 Affected Published Academic Research? A Content Analysis of Journal Articles Mentioning the Virus

Purpose

Methods to tackle Covid-19 have been developed by a wave of biomedical research but the pandemic has also influenced many aspects of society, generating a need for research into its consequences, and potentially changing the way existing topics are investigated. This article investigates the nature of this influence on the wider academic research mission.

Design/methodology/approach

This article reports an inductive content analysis of 500 randomly selected journal articles mentioning Covid-19, as recorded by the Dimensions scholarly database on 19 March 2021. Covid-19 mentions were coded for the influence of the disease on the research.

Findings

Whilst two thirds of these articles were about biomedicine (e.g. treatments, vaccines, virology), or health services in response to Covid-19, others covered the pandemic economy, society, safety, or education. In addition, some articles were not about the pandemic but stated that Covid-19 had increased or decreased the value of the reported research or changed the context in which it was conducted.

Research limitations

The findings relate only to Covid-19 influences declared in published journal articles.

Practical implications

Research managers and funders should consider whether their current procedures are effective in supporting researchers to address the evolving demands of pandemic societies, particularly in terms of timeliness.

Originality/value

The results show that although health research dominates the academic response to Covid-19, it is more widely disrupting academic research with new demands and challenges.

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

Adjusting the use of preprints to accommodate the 'quality' factor in response to COVID-19

Preprints are typically crude precursors of peer-reviewed papers that are placed almost immediately, save for some superficial screening, on an open-access repository to allow the information to reach readers quickly, circumventing the long-drawn process typically associated with processing in peer-reviewed journals. For early-career researchers who might be enthusiastic about obtaining some recognition for their efforts, or wanting open and public input about their work, preprints are certainly a useful publication choice. However, if health-related data and information have not been carefully scrutinised, they may pose a risk and may even serve as a source of public health misinformation. Surging growth and competition among preprint servers, coupled with a massive volume of COVID-19-related preprints, mainly on bioRxiv and medRxiv, as well as select indexing now being tested on PubMed, suggests that preprints are being increasingly used in the biomedical sciences. Stronger and more robust ethical policies are needed to screen preprints before they are released to the public, and even if this implies a slight delay in publication, it may increase academics' trust in this form of scientific information and communication. Clear and stringent ethical policies need to be urgently introduced by ethics groups such as COPE and the ICMJE, whose many member journals allow preprints to be posted before traditional peer review. Stringent ethical guidelines that treat misconduct equally in preprints and peer-reviewed papers will boost the integrity of academic publishing.

Intricate scientometric analysis and citation trend of COVID-19-related publications in Indian Journal of Ophthalmology during COVID-19 pandemic

Purpose:

To analyze the trend of COVID-19-related publications in the Indian Journal of Ophthalmology (IJO) and assess the specialty wise correlation, distribution, and citation trend during the COVID-19 pandemic.

Methods:

A retrospective analysis of all COVID-19-related articles was performed from April 2020 to May 2021. The bibliographic records were obtained from the website of IJO, Editor IJO email, and PubMed. The data was then exported as XML into Microsoft access for scientometric analysis. The articles were segregated as Original, Review, Case Report/Series, Letter to the Editor/Commentary, Guest Editorial, PointCounterpoint, Consensus Criteria, Ophthalmic Images, Photo Essay, Surgical Techniques, and All India Ophthalmic Society Meeting Papers. The data was comprehensively analyzed for specialty-wise correlations, distribution, citation trend, and reasons for the same.

Results:

A total of 231 COVID-19-related articles were published during the study period. The maximum articles were [82 (35.49%)] letters to the editor, followed by [51 (22.08%)] original articles, [30 (12.99%)] commentaries, and [20 (8.66%)] editorials. The least were perspectives, consensus, images, and photo assay with [1 (0.43%)] each. The maximum publications were in July [44 (19.05%)] and least in April [1 (0.43%)]. Considering specialty, the maximum articles were related to general ophthalmology [124 (53.68%)] and least were in refractive surgery and community ophthalmology with [1 (0.43%)] each. The maximum citations were for original articles [352 (34.65%)], which was 2.3 times higher than review articles and letters to editor [150 (14.76%)]. General ophthalmology had 740 (72.83%) citations, which were nearly five times that of cornea [140 (13.78%)].

Conclusion:

The IJO showed a trough and crest pattern of COVID-19 publications month wise. Letter to editor and general ophthalmology COVID-19 articles had maximum publications with maximum citations for general ophthalmology owing to practice patterns and COVID-19 challenges.

A critical analysis of COVID-19 research literature: Text mining approach

OBJECTIVE

Among the stakeholders of COVID-19 research, clinicians particularly experience difficulty keeping up with the deluge of SARS-CoV-2 literature while performing their much needed clinical duties. By revealing major topics, this study proposes a text-mining approach as an alternative to navigating large volumes of COVID-19 literature.

MATERIALS AND METHODS

We obtained 85,268 references from the NIH COVID-19 Portfolio as of November 21. After the exclusion based on inadequate abstracts, 65,262 articles remained in the final corpus. We utilized natural language processing to curate and generate the term list. We applied topic modeling analyses and multiple correspondence analyses to reveal the major topics and the associations among topics, journal countries, and publication sources.

RESULTS

In our text mining analyses of NIH's COVID-19 Portfolio, we discovered two sets of eleven major research topics by analyzing abstracts and titles of the articles separately. The eleven major areas of COVID-19 research based on abstracts included the following topics: 1) Public Health, 2) Patient Care & Outcomes, 3) Epidemiologic Modeling, 4) Diagnosis and Complications, 5) Mechanism of Disease, 6) Health System Response, 7) Pandemic Control, 8) Protection/Prevention, 9) Mental/Behavioral Health, 10) Detection/Testing, 11) Treatment Options. Further analyses revealed that five (2,3,4,5, and 9) of the eleven abstract-based topics showed a significant correlation (ranked from moderate to weak) with title-based topics.

CONCLUSION

By offering up the more dynamic, scalable, and responsive categorization of published literature, our study provides valuable insights to the stakeholders of COVID-19 research, particularly clinicians.

Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted include ACE2, IL6, TMPRSS2, and TNF. Emerging genes to the COVID-19 consist of FURIN, CXCL10, OAS1, OAS2, OAS3, and ISG15. This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers’ decision-making on science policy and public health administration.

Scaling Scientometrics: Dimensions on Google BigQuery as an Infrastructure for Large-Scale Analysis

Cloud computing has the capacity to transform many parts of the research ecosystem, from particular research areas to overall strategic decision making and policy. Scientometrics sits at the boundary between research and the decision-making, policy-making, and evaluation processes that underpin research. One of the biggest challenges in research policy and strategy is having access to data in a way that allows for analysis that can respond in an iterative way to inform decisions. Many decisions are based on "global" measures such as benchmark metrics that are hard to source and hence are often nonspecific or outdated. The use of cloud technologies may be promising in addressing this area of providing data for research strategy and policy decisions. A novel visualisation technique is introduced and used as a means to explore the potential for scaling scientometrics by democratising both access to data and compute capacity using the cloud.

Topic evolution, disruption and resilience in early COVID-19 research

The COVID-19 pandemic presented a challenge to the global research community as scientists rushed to find solutions to the devastating crisis. Drawing expectations from resilience theory, this paper explores how the trajectory of and research community around the coronavirus research was affected by the COVID-19 pandemic. Characterizing epistemic clusters and pathways of knowledge through extracting terms featured in articles in early COVID-19 research, combined with evolutionary pathways and statistical analysis, the results reveal that the pandemic disrupted existing lines of coronavirus research to a large degree. While some communities of coronavirus research are similar pre- and during COVID-19, topics themselves change significantly and there is less cohesion amongst early COVID-19 research compared to that before the pandemic. We find that some lines of research revert to basic research pursued almost a decade earlier, whilst others pursue brand new trajectories. The epidemiology topic is the most resilient among the many subjects related to COVID-19 research. Chinese researchers in particular appear to be driving more novel research approaches in the early months of the pandemic. The findings raise questions about whether shifts are advantageous for global scientific progress, and whether the research community will return to the original equilibrium or reorganize into a different knowledge configuration.

Iranian researchers' contributions to research on COVID-19: A bibliometric analysis and visualization

Background: COVID-19 pandemic crisis motivated researchers worldwide to deeply investigate it from different perspectives. As Iran is one of the highly-affected countries by Covid-19, Iranian researchers have focused on studying it. This study aimed at analyzing and visualizing Iranian researchers' papers on COVID-19 from a bibliometric perspective. Methods: By searching MeSH-selected keywords related to COVID-19 in Scopus, Iranian researchers' papers on COVID-19 were extracted in a CSV format and underwent bibliometric techniques, such as coauthorship analysis, citation, and co-citation analysis, keyword and term co-occurrence mapping and etc. in the Microsoft Excel and VOSviewer software package. Results: A total of 405 papers were authored by Iranian researchers on COVID-19 during the study period, with the average number of citations per paper of 2.60 and a mean h-index of 15. The majority of papers were original articles in English. Archives of Clinical Infectious Diseases and Archives of Iranian Medicine and Medical Hypotheses were highly ranked publishing journals, respectively. The most productive institute and author were Tehran University of Medical Sciences with 119 papers and Rezaei, N. with 12 papers. Iranian researchers collaborated with the researchers of 73 countries, with the USA ranking first in Covid-19 research, followed by Italy, Canada, and United Kingdom. In publishing papers on COVID-19, Iran ranked first among the Middle Eastern countries and thirteenth internationally. Conclusion: Iranian researchers were active in 5 main areas of COVID-19 research, including epidemiology, diagnosis, treatment, virology, and systematic review.

Topic evolution, disruption and resilience in early COVID-19 research

The COVID-19 pandemic presented a challenge to the global research community as scientists rushed to find solutions to the devastating crisis. Drawing expectations from resilience theory, this paper explores how the trajectory of and research community around the coronavirus research was affected by the COVID-19 pandemic. Characterizing epistemic clusters and pathways of knowledge through extracting terms featured in articles in early COVID-19 research, combined with evolutionary pathways and statistical analysis, the results reveal that the pandemic disrupted existing lines of coronavirus research to a large degree. While some communities of coronavirus research are similar pre- and during COVID-19, topics themselves change significantly and there is less cohesion amongst early COVID-19 research compared to that before the pandemic. We find that some lines of research revert to basic research pursued almost a decade earlier, whilst others pursue brand new trajectories. The epidemiology topic is the most resilient among the many subjects related to COVID-19 research. Chinese researchers in particular appear to be driving more novel research approaches in the early months of the pandemic. The findings raise questions about whether shifts are advantageous for global scientific progress, and whether the research community will return to the original equilibrium or reorganize into a different knowledge configuration.

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

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.

Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature

On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data. It is imperative and only ethical for modern scientists to be vigilant in inspecting and be prepared to mitigate the potential biases when working with any datasets. This article describes a framework to examine biases in scientific document collections like CORD-19 by comparing their properties with those derived from the citation behaviors of the entire scientific community. In total, three expanded sets are created for the analyses: 1) the enclosure set CORD-19E composed of CORD-19 articles and their references and citations, mirroring the methodology used in the renowned "A Century of Physics" analysis; 2) the full closure graph CORD-19C that recursively includes references starting with CORD-19; and 3) the inflection closure CORD-19I, that is, a much smaller subset of CORD-19C but already appropriate for statistical analysis based on the theory of the scale-free nature of the citation network. Taken together, all these expanded datasets show much smoother trends when used to analyze global COVID-19 research. The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary. A question-answering system with such expanded scope of knowledge may perform better in understanding the literature and answering related questions. However, while CORD-19 appears to have topical coverage biases compared to the expanded sets, the collaboration patterns, especially in terms of team sizes and geographical distributions, are captured very well already in CORD-19 as the raw statistics and trends agree with those from larger datasets.

Tracking and Mining the COVID-19 Research Literature

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.