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PurposeHow to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and s of articles.Design/methodology/approachThe authors conduct a comparison study of topic modeling on full-text paper and corresponding to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.FindingsThe authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding is higher when more documents are analyzed.Originality/valueFirst, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.
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Infection with SARS-CoV-2 initiates an immune-hemostatic response. While both systems are intimately connected and necessary for an efficient immune response to contain the infection, excessive coagulation activation might exceed the valuable benefits by causing thrombotic consequences and excessive inflammation. This biological response is new to clinicians and researchers, and accordingly, tremendous studies have been conducted on coagulopathy and its relationship to COVID-19 disease during this pandemic. Therefore, it takes a research insight from a bibliometric perspective to determine research hotspots and trends of COVID-19 associated coagulopathy (C19-CA). The analysis relies on the Scopus database for bibliographic content and Visualization of Similarities viewer software to map bibliometric data of C19-CA. Our study finds the most eminent authors, journals, institutions, funding organizations, and countries that publish in the C19-CA. Additionally; this research employs bibliometric analysis of co-authorship, co-citations, bibliographic coupling, and co-occurrence of keywords. A total of 2242 studies were retrieved, and the number of annual publications of C19-CA showed remarkable growth. The top-publishing authors on C19-CA are Smadja, D.M., Diehl, J.L., and Gendron, N (France). The total number of articles published in English in these three years was 1241, with the original article accounting for 99.8% and conference papers accounting for 0.2%. Huazhong University of Science and Technology (China) is the top-productive institution, with the US being the top-publishing country. Journal of Thrombosis and Thrombolysis received the highest number of original articles. The research results were mainly published in the fields of Medicine, Biochemistry, Genetics, and Molecular Biology, Immunology and Microbiology. Yuanyuan Li, who is (China), is the top-collaborating author. China and its authors have the highest number of citations. Keywords' co-occurrence analyses of the authors and all keywords revealed the following themes in C19-CA; abnormal coagulation parameters, pulmonary coagulopathy, venous and arterial thrombotic disorders, distinct features of coagulopathy, inflammation, and thrombosis in COVID-19, and anticoagulants and thrombolytic therapies. By combining bibliometric analysis with VOSviewer software, we identified C19-CA's leaders, collaborating institutions, and research hotspots, as well as give references for future research paths.
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Submissions to the Journal have stabilised since the Covid‐related surge in 2020, and continue their strong international pattern. Our response times continue to meet or exceed our targets, with a few regrettable exceptions, for which our sincere apologies. The JAE's citation impact factor increased again in 2021 to 4.16, a modest increase from the 2020 score. Our total 2‐year citations, however, show a worrying decline since last year. Our sincere thanks are due to our authors and our many reviewers for their contributions. Wiley continue to provide a strong publishing platform with our full archive, generating continuing growth in downloads.
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Introduction: The aim of this research is to analyze how COVID-19 was studied by the academic discipline of journalism, regarding its impact, methodology, thematic and source, and their repercussions on sites. Methodology: A universe of 124 articles is obtained through algorithmic grouping by InCites (journalism micro topic, Spanish affiliation, and COVID-19 keyword). A bibliometric analysis is performed, accompanied by a qualitative content analysis to generate common codes in methodology, themes, and use of sources. Quantitative analysis of co-occurrence and descriptive correlations between the three variables studied and their citations are carried out. Results: Articles on COVID-19 received five times more citations than the rest. The majority of cites (86%) are concentrated in the first-published articles. Classic methodologies were mostly used (49% content analysis, 16% surveys). Bibliographic review (13 cites/article) and advanced automated analysis techniques (10.75 cites/article) are the ones that receive the most citations. The main theme is disinformation (26%, 11,07 cites/article) and the most common source is the press (27%, 6,15 cites/article), although social networks (22%, 9.12 cites/article) and fact-checkers (10%, 8.50 cites/article) generated a greater impact. Discussion and Conclusions: The articles that were published during the first months generated the highest volume of citations. In journalism research, a recurrent use of classic strategies (content analysis, press) was found, although the slightly more novel approaches (advanced automated analysis techniques) are the ones that produced the most citations. Misinformation becomes one of the key issues in journalism studies. Unusual methodologies and themes receive practically no citations.Alternate :Introduction: Se analiza el impacto y el modo en el que la disciplina académica del periodismo investigó sobre el COVID-19 y su repercusión metodológica, temática y de fuentes. Metodología: Se obtiene un universo de 124 artículos mediante agrupación algorítmica por InCites (micro tópico periodismo, afiliación española y palabra clave COVID-19). Se procede a un análisis bibliométrico, acompañado por un análisis de contenido cualitativo para generar códigos comunes en metodología, temática y uso de fuentes. Se realizan análisis cuantitativos de co-ocurrencia y correlaciones descriptivas entre las tres variables estudiadas y sus citas. Resultados: Los artículos sobre COVID-19 recibieron cinco veces más citas que el resto, y la mayoría (86%) se concentran en los primeros artículos. Se emplearon mayormente metodologías clásicas (49% análisis de contenido, 16% encuestas). La revisión bibliográfica (13 citas/ artículo) y las técnicas avanzadas de análisis automático (10,75 citas/artículo) son las que reciben más citas. La temática principal es la desinformación (26%, 11,07 citas/artículo) y la fuente más común la prensa (27%, 6,15 citas/artículo), si bien generan más impacto las redes sociales (22%, 9,12 citas/ artículo) y los fact-checkers (10%, 8,50 citas/artículo). Discusión y Conclusiones: Los artículos que primero se publicaron generaron más citas. Se identificó un uso recurrente de estrategias clásicas (análisis de contenido, prensa) si bien son las aproximaciones ligeramente más novedosas (técnicas avanzadas de análisis automático) las que producen más citas. La desinformación deviene uno de los temas claves. Las metodologías y temáticas poco comunes no reciben prácticamente citaciones.
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Thematically, the book concentrates on the intellectual, cultural, and public health contexts of epidemics, with frequent attention to the interplay of war and disease. The main disappointment (for me) in his choices of what to include and what to leave out is the extremely thin treatment of the disease experience of the Americas in the wake of Columbus. For readers interested primarily in Europe's cultural, scientific, and public health engagement with epidemics, this book will serve admirably.
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Background and Aims: One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods: On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results: The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion: The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.
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COVID-19 has greatly impacted science. It has become a global research front that constitutes a unique phenomenon of interest for the scientometric community. Accordingly, there has been a proliferation of descriptive studies on COVID-19 papers using altmetrics. Social media metrics serve to elucidate how research is shared and discussed, and one of the key points is to determine which factors are well-conditioned altmetric values. The main objective of this study is to analyze whether the altmetric mentions of COVID-19 medical studies are associated with the type of study and its level of evidence. Data were collected from the PubMed and Altmetric.com databases. A total of 16,672 publications by study types (e.g., case reports, clinical trials, or meta-analyses) that were published in the year 2021 and that had at least one altmetric mention were retrieved. The altmetric indicators considered were Altmetric Attention Score (AAS), news mentions, Twitter mentions, and Mendeley readers. Once the dataset of COVID-19 had been created, the first step was to carry out a descriptive study. Then, a normality hypothesis was evaluated by means of the Kolmogorov–Smirnov test, and since this was significant in all cases, the overall comparison of groups was performed using the nonparametric Kruskal–Wallis test. When this test rejected the null hypothesis, pairwise comparisons were performed with the Mann– Whitney U test, and the intensity of the possible association was measured using Cramer's V coefficient. The results suggest that the data do not fit a normal distribution. The Mann–Whitney U test revealed coincidences in five groups of study types: The altmetric indicator with most coincidences was news mentions, and the study types with the most coincidences were the systematic reviews together with the meta-analyses, which coincided with four altmetric indicators. Likewise, between the study types and the altmetric indicators, a weak but significant association was observed through the chi-square and Cramer's V. It can thus be concluded that the positive association between altmetrics and study types in medicine could reflect the level of the "pyramid” of scientific evidence. © 2023, El Profesional de la Informacion. All rights reserved.
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PurposeThis study aims to comprehend the application of analytics in the supply chain during the ongoing COVID-19 crisis and identify the emerging themes.Design/methodology/approachThe author downloaded a list of research articles on the application of analytics to the supply chain from SCOPUS, conducted a systematic literature review for exploratory analysis and proposed a framework. Notably, the author used the topic modeling technique to identify research themes published during the ongoing COVID-19 crisis and thereby underscore some future research directions.FindingsThe author found that artificial intelligence, machine learning, internet of thing and blockchain are trending topics. Additionally, the author identified five themes by topic modeling, including the theme "Social Media information in Supply chain.”Research limitations/implicationsThe results were derived from a data set extracted from SCOPUS. Thus, the author excluded all studies not listed in SCOPUS from the analysis. Future research with articles indexed in other databases should be investigated to get a more holistic perspective of specific themes.Practical implicationsThis study provides a deeper understanding and proposes a framework for applications of analytics in the supply chain that researchers could use for future research and industry practitioners to implement in their organizations to make a more sustainable and resilient supply chain.Originality/valueThis study provides exploratory information from published articles on the use of analytics in the supply chain during the COVID-19 crisis and generates themes that help understand the emerging and underpinned area of research.
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The literature on high-performance athletes has developed in a not-so-considerable way in the last two years, in the same way, few productions of a systematic review of the literature are evidenced, as well as no study has been examined under the use of the technique of citation mapping called: bibliometrics. Therefore, it is the first time that this study is performed by means of scientific mapping and bibliometric analysis having a total of 130 documents published in different databases such as Scopus, PubMed, and Web of Science;the cut of years that were considered for this research was 2019-2022 since the period where the SARS-CoV-2 virus spread worldwide, causing several collateral effects in athletes with high performance around the world. For the analysis of the obtained data and mapping preparation, the VoS Viewer software, the lens.org bibliometric platform is implemented. The results of the scientific mapping, bibliometric clustering and co-occurrence networks have allowed the identification of possible fields of research that can be developed in the future. The results show that the literature on the high performance of athletes during the Covid-19 pandemic has advanced over these 4 years, as well as the most influential parts of the literature in terms of authors, scientific production, most cited topics, and keywords are related and from which lines of research can be innovated. Finally, there is no review of the literature regarding the collateral effects produced by Covid-19, which is why a line of research focused on biomechanical studies and collateral effects should be focused on. © 2023 IEEE.
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Journalistic papers published in high impact scientific journals can be very influential, especially in hot fields. This meta-research analysis aimed to evaluate the publication profiles, impact, and disclosures of conflicts of interest of non-research authors who had published > 200 Scopus-indexed papers in Nature, Science, PNAS, Cell, BMJ, Lancet, JAMA or New England Journal of Medicine. 154 prolific authors were identified, 148 of whom had published 67,825 papers in their main affiliated journal in a non-researcher capacity. Nature, Science, and BMJ have the lion's share of such authors. Scopus characterized 35% of the journalistic publications as full articles and another 11% as short surveys. 264 papers had received more than 100 citations. 40/41 most-cited papers in 2020-2022 were on hot COVID-19 topics. Of 25 massively prolific authors with > 700 publications in one of these journals, many were highly-cited (median citations 2273), almost all had published little or nothing in the Scopus-indexed literature other than in their main affiliated journal, and their influential writing covered diverse hot topics over the years. Of the 25, only 3 had a PhD degree in any subject matter, and 7 had a Master's degree in journalism. Only the BMJ offered conflicts of interest disclosures for prolific science writers in its website, but even then only 2 of the 25 massively prolific authors disclosed potential conflicts with some specificity. The practice of assigning so much power to non-researchers in shaping scientific discourse should be further debated and disclosures of potential conflicts of interest should be emphasized.
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PurposeToday, the rapid development and expansion of advanced technologies have created many changes in society and industry and motivate businesses to use digital transformation strategy (DTS) to create significant changes in the business environment. Therefore, it is necessary to define a roadmap and a vision that will determine the steps forward in this direction. In line with this, the purpose of this study is a comprehensive review of past and present studies in this field to identify future research guidelines and gaps related to the implementation of this concept.Design/methodology/approachThis study is a bibliometric analysis using VOSviewer software for all documents published in the Scopus database in the field of DTS from 2011 (the emergence of Industry 4.0) to 2021. It should also be noted that the data for this study have been collected and analyzed in September 2021.FindingsThe current study presents the basic bibliometric results for DTS, and it focuses on DTS performance analysis and its science mapping during the past 10 years. This study first shows the publication process, types and languages of published documents, and the most influential authors, institutions, sources and countries in terms of publishing documents and receiving citations in the field of DTS. Then, by using the VOSviewer software, it shows the bibliographic coupling of top authors, institutions, sources and countries. Finally, it reports the co-occurrence of authors' frequently occurring keywords and the timeline of their publications.Originality/valueThe study presents the results of the first attempt to conduct a comprehensive bibliometric analysis of DTS-related documents. Its contribution lies in the fact that it has categorized the most frequently co-occurring keywords into specific clusters so that researchers will know which keywords have co-occurred with each other the most. Also, the most influential keywords in each cluster in terms of having total link strength and the number of its co-occurrence with others were identified. Finally, it became clear that the process of publishing documents over time has been concentrated on topics such as acceptance of digital culture, strategic renewal and digital transformation of business models, as well as presentation of a research agenda on the applications and barriers of DTS in critical situations such as COVID-19, which leads researchers to some awareness and insights for conducting new research.
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Many altmetric studies have analyzed which papers were mentioned how often on Twitter (one of the most important altmetrics sources). In order to study the potential relevance of tweets from another perspective, we investigate which tweets were cited in papers. If many tweets were cited in publications, this might demonstrate that tweets have substantial and useful content. Overall, a rather low number of citations to tweets (n=13,149) by less than 7,000 papers was found. Most tweets do not seem to be cited because of any cognitive influence they might have had on studies;they rather were study objects. Thus, this study does not support a high relevance of tweets (for research). Most of the papers that cited tweets are from the subject areas Social Sciences, Arts and Humanities, and Medicine. Most of the papers cited only one tweet. Up to 65 tweets cited in a single paper were found. An author keyword analysis revealed that the single largest topic seems to be the COVID-19/corona pandemic. © 2023 Elsevier Ltd
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PurposePerformance in virtual teams, which faces cultural and demographic differences, is a relevant phenomenon that has been widely investigated in recent decades, but with opportunities in exploring other levels of analysis as individual and project. This current research aims to understand how multicultural virtual teams affect individual, team and project performance.Design/methodology/approachThe authors conducted a systematic literature review (SLR) and bibliometric analysis to capture 273 papers from the Web of Science (WoS) database using a snowball approach. In a second approach, the authors selected 130 papers to conduct a content analysis.FindingsThe authors presented a longitudinal overview regarding the adoption of virtual teams in project management (PM) literature. A conceptual framework was proposed to explore the relationship between multicultural virtual teams and performance with three levels of analysis: individual, teams and project. The authors contributed with research hypotheses to be explored in future empirical studies not only at the team perspective but also at the project and individual levels. The thematic analysis suggested that the literature focus has shifted from hard to soft aspects faced by virtual teams. Social identity/categorization theory was the most prominent theory in this body, but it is not fully explored in PM literature. Other opportunities of future studies are to understand the impact of cultural diversity, the sense of belongingness, the project life cycle and the development of a knowledge management program.Originality/valueThe authors developed a 3-level conceptual framework for future empirical studies and demonstrated that cultural differences are mainly approached at the national level in the literature, bringing suggestions for future empirical research.
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Much of today's data are represented as graphs, ranging from social networks to bibliographic citations. Nodes in such graphs correspond to records that generally represent entities, while edges represent relationships between these entities. Both nodes and edges in a graph can have attributes that characterize the entities and their relationships. Relationships are either explicitly known (like friends in a social network), or they are inferred using link prediction (such as two babies are siblings because they have the same mother). Any graph representing real-world data likely contains nodes and edges that are abnormal, and identifying these can be important for outlier detection in applications ranging from crime and fraud detection to viral marketing. We propose a novel approach to the unsupervised detection of abnormal nodes and edges in graphs. We first characterize nodes and edges using a set of features, and then employ a one-class classifier to identify abnormal nodes and edges. We extract patterns of features from these abnormal nodes and edges, and apply clustering to identify groups of patterns with similar characteristics. We finally visualize these abnormal patterns to show co-occurrences of features and relationships between those features that mostly influence the abnormality of nodes and edges. We evaluate our approach on datasets from diverse domains, including historical birth certificates, COVID patient records, e-mails, books, and movies. This evaluation demonstrates that our approach is well suited to identify both abnormal nodes and edges in graphs in an unsupervised way, and it can outperform several baseline anomaly detection techniques. © 2022 Copyright held by the owner/author(s).
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Compare is a leading journal in the comparative and international education research field. To assess this journal's productivity and influence, we conducted a bibliometric analysis of 428 papers published in Compare between 2010 and 2019. The findings show that in the past decade, Compare experienced significant growth in the number of publications and citations. This growth primarily stemmed from England, which yielded over half of the top 20 most productive authors and institutions. Among the numerous research topics discussed in Compare, the disciplinary development of comparative and international education, the internationalisation of education, gender studies in education, and citizenship education were the most frequently addressed. A detailed analysis of these four topics reveals that despite having published many papers falling within the scope of international education, Compare is encouraged to publish more papers about this subfield in the post-COVID-19 era.
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The sudden spread of the monkeypox virus has been accompanied by an increase in the scientific interest in the virus. More than 1,400 PubMed-indexed documents have been authored by about 5,800 unique authors, averaging around 120 publications per month. This sheer rise in the number led us to explore the content published in the literature. We discovered more than 30% of the documents are Quantitative Productivity (QP) i.e., papers that illustrate the emerging trends of parachute concerns, modified salami tactics, cyclic recycling, and excellence in redundancy. In addition, we found few common hyper-prolific authors previously identified in the COVID-19 literature. Further, we share our experience in publishing monkeypox literature and highlight the growing readership and citation interest in editorials, commentaries, and correspondences that were thought to be uncitable in the medical literature. As long as the scientific community and public demand, the supply of such papers will continue, with no responsibility on the authors, journals, or the reader. Since overhauling the current system is an arduous task, we propose the optimization of existing retrieval services that would selectively filter documents based on article type (requires standardization of definitions) to dilute the crowding out effects of quantitative productivity.
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Objective: The number of citations to a paper represents the weight of that work in a particular area of interest. Several highly cited papers are listed in the bibliometric analysis. This study aimed to identify and analyze the 100 most cited papers in insomnia research that might appeal to researchers and clinicians. Methods: We reviewed the Web of Science (WOS) Core Collection database to identify articles from 1985 to 24 March 2022. The R bibliometric package was used to further analyze citation counts, authors, year of publication, source journal, geographical origin, subject, article type, and level of evidence. Word co-occurrence in 100 articles was visualized using VOS viewer software. Results: A total of 44,654 manuscripts were searched on the Web of Science. Between 2001 and 2021, the top 100 influential manuscripts were published, with a total citation frequency of 38,463. The top countries and institutions contributing to the field were the U.S. and Duke University. Morin C.M. was the most productive author, ranking first in citations. Sleep had the highest number of manuscripts published in the top 100 (n = 31), followed by Sleep Medicine Reviews (n = 9). The most cited manuscript (Bastien et al., Sleep Medicine, 2001; 3,384 citations) reported clinical validation of the Insomnia Severity Index (ISI) as a brief screening indicator for insomnia and as an outcome indicator for treatment studies. Co-occurrence analyses suggest that psychiatric disorders combined with insomnia and cognitive behavioral therapy remain future research trends. Conclusion: This study provides a detailed list of the most cited articles on insomnia. The analysis provides researchers and clinicians with a detailed overview of the most cited papers on insomnia over the past two decades. Notably, COVID-19, anxiety, depression, CBT, and sleep microstructure are potential areas of focus for future research.
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Background: Since December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2),causative pathogen of coronavirus disease 2019 (COVID-19), has triggered a pandemic with challenges for health care systems around the world. Researchers have studied and published on the subject of SARS-CoV-2 and the disease extensively. What is the significance of articles published, shared and cited in the early stages of such a pandemic? Materials and methods: A systematic literature search in a time frame of 12 months and analysis rating using Principle Component Analysis (PCA) and Multiple Factor Analysis (MFA) were performed. Results: The 100 most cited COVID-19 articles were identified. The majority of these articles were from China (n = 54), followed by United States of America (USA) (n = 21) and United Kingdom (UK) (n = 8). All articles were published in high-ranked, peer-reviewed journals, with research focusing onthe the diagnosis, transmission and therapy of COVID-19. The level of evidence of the 100 most cited COVID-19 articles on average was low. Conclusion: In the early stages of a pandemic, new and innovative research can emerge and be highly cited, regardless of the level of evidence.