<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>Although gender identities influence how people present themselves on social media, previous studies have tested pre-specified dimensions of difference, potentially overlooking other differences and ignoring nonbinary users.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>Word association thematic analysis was used to systematically check for fine-grained statistically significant gender differences in Twitter profile descriptions between 409,487 UK-based female, male, and nonbinary users in 2020. A series of statistical tests systematically identified 1,474 differences at the individual word level, and a follow up thematic analysis grouped these words into themes.</p>
</sec>
<sec><h3>Findings</h3>
<p>The results reflect offline variations in interests and in jobs. They also show differences in personal disclosures, as reflected by words, with females mentioning qualifications, relationships, pets, and illnesses much more, nonbinaries discussing sexuality more, and males declaring political and sports affiliations more. Other themes were internally imbalanced, including personal appearance (e.g. male: beardy; female: redhead), self-evaluations (e.g. male: legend; nonbinary: witch; female: feisty), and gender identity (e.g. male: dude; nonbinary: enby; female: queen).</p>
</sec>
<sec><h3>Research limitations</h3>
<p>The methods are affected by linguistic styles and probably under-report nonbinary differences.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>The gender differences found may inform gender theory, and aid social web communicators and marketers.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>The results show a much wider range of gender expression differences than previously acknowledged for any social media site.</p>
</sec>
</abstract>

Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3><p>This article aims to determine the percentage of “Sparking” articles among the work of this year’s Nobel Prize winners in medicine, physics, and chemistry.</p></sec>
<sec><h3>Design/methodology/approach</h3><p>We focus on under-cited influential research among the key publications as mentioned by the Nobel Prize Committee for the 2020 Noble Prize laureates. Specifically, we extracted data from the Web of Science, and calculated the Sparking Indices using the formulas as proposed by Hu and Rousseau in 2016 and 2017. In addition, we identified another type of igniting articles based on the notion in 2017.</p></sec>
<sec><h3>Findings</h3><p>In the fields of medicine and physics, the proportions of articles with sparking characteristics share 78.571% and 68.75% respectively, yet, in chemistry 90% articles characterized by “igniting”. Moreover, the two types of articles share more than 93% in the work of the Nobel Prize included in this study.</p></sec>
<sec><h3>Research limitations</h3><p>Our research did not cover the impact of topic, socio-political, and author’s reputation on the Sparking Indices.</p></sec>
<sec><h3>Practical implications</h3><p>Our study shows that the Sparking Indices truly reflect influence of the best research work, so it can be used to detect under-cited influential articles, as well as identifying fundamental work.</p></sec>
<sec><h3>Originality/value</h3><p>Our findings suggest that the Sparking Indices have good applicability for research evaluation.</p></sec>
</abstract>

Centre for R&D Monitoring (ECOOM) and Dept. MSI, KU Leuven

Department of Neurology of Affiliated Hospital 2

“Sparking” and “Igniting” Key Publications of 2020 Nobel Prize Laureates

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>The ranking lists of highly cited researchers receive much public attention. In common interpretations, highly cited researchers are perceived to have made extraordinary contributions to science. Thus, the metrics of highly cited researchers are often linked to notions of breakthroughs, scientific excellence, and lone geniuses.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>In this study, we analyze a sample of individuals who appear on <italic>Clarivate Analytics</italic>’ Highly Cited Researchers list. The main purpose is to juxtapose the characteristics of their research performance against the claim that the list captures a small fraction of the researcher population that contributes disproportionately to extending the frontier and gaining—on behalf of society—knowledge and innovations that make the world healthier, richer, sustainable, and more secure.</p>
</sec>
<sec><h3>Findings</h3>
<p>The study reveals that the highly cited articles of the selected individuals generally have a very large number of authors. Thus, these papers seldom represent individual contributions but rather are the result of large collective research efforts conducted in research consortia. This challenges the common perception of highly cited researchers as individual geniuses who can be singled out for their extraordinary contributions. Moreover, the study indicates that a few of the individuals have not even contributed to highly cited original research but rather to reviews or clinical guidelines. Finally, the large number of authors of the papers implies that the ranking list is very sensitive to the specific method used for allocating papers and citations to individuals. In the “whole count” methodology applied by <italic>Clarivate Analytics</italic>, each author gets full credit of the papers regardless of the number of additional co-authors. The study shows that the ranking list would look very different using an alternative fractionalised methodology.</p>
</sec>
<sec><h3>Research limitations</h3>
<p>The study is based on a limited part of the total population of highly cited researchers.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>It is concluded that “excellence” understood as highly cited encompasses very different types of research and researchers of which many do not fit with dominant preconceptions.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>The study develops further knowledge on highly cited researchers, addressing questions such as who becomes highly cited and the type of research that benefits by defining excellence in terms of citation scores and specific counting methods.</p>
</sec>
</abstract>

Lone Geniuses or One among Many? An Explorative Study of Contemporary Highly Cited Researchers

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3><p>Building on <xref ref-type="bibr" rid="j_jdis-2021-0014_ref_017">Leydesdorff, Bornmann, and Mingers (2019)</xref>, we elaborate the differences between Tsinghua and Zhejiang University as an empirical example. We address the question of whether differences are statistically significant in the rankings of Chinese universities. We propose methods for measuring statistical significance among different universities within or among countries.</p></sec>
<sec><h3>Design/methodology/approach</h3><p>Based on <italic>z</italic>-testing and overlapping confidence intervals, and using data about 205 Chinese universities included in the Leiden Rankings 2020, we argue that three main groups of Chinese research universities can be distinguished (low, middle, and high).</p></sec>
<sec><h3>Findings</h3><p>When the sample of 205 Chinese universities is merged with the 197 US universities included in Leiden Rankings 2020, the results similarly indicate three main groups: low, middle, and high. Using this data (Leiden Rankings and Web of Science), the <italic>z</italic>-scores of the Chinese universities are significantly below those of the US universities albeit with some overlap.</p></sec>
<sec><h3>Research limitations</h3><p>We show empirically that differences in ranking may be due to changes in the data, the models, or the modeling effects on the data. The scientometric groupings are not always stable when we use different methods.</p></sec>
<sec><h3>Practical implications</h3><p>Differences among universities can be tested for their statistical significance. The statistics relativize the values of decimals in the rankings. One can operate with a scheme of low/middle/high in policy debates and leave the more fine-grained rankings of individual universities to operational management and local settings.</p></sec>
<sec><h3>Originality/value</h3><p>In the discussion about the rankings of universities, the question of whether differences are statistically significant, has, in our opinion, insufficiently been addressed in research evaluations.</p></sec>
</abstract>

Amsterdam School of Communication Research (ASCoR)

Are University Rankings Statistically Significant? A Comparison among Chinese Universities and with the USA

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>Using the metaphor of “unicorn,” we identify the scientific papers and technical patents characterized by the informetric feature of very high citations in the first ten years after publishing, which may provide a new pattern to understand very high impact works in science and technology.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>When we set C<sub>T</sub> as the total citations of papers or patents in the first ten years after publication, with C<sub>T</sub>≥ 5,000 for scientific “unicorn” and C<sub>T</sub>≥ 500 for technical “unicorn,” we have an absolute standard for identifying scientific and technical “unicorn” publications.</p>
</sec>
<sec><h3>Findings</h3>
<p>We identify 165 scientific “unicorns” in 14,301,875 WoS papers and 224 technical “unicorns” in 13,728,950 DII patents during 2001–2012. About 50% of “unicorns” belong to biomedicine, in which selected cases are individually discussed. The rare “unicorns” increase following linear model, the fitting data show 95% confidence with the RMSE of scientific “unicorn” is 0.2127 while the RMSE of technical “unicorn” is 0.0923.</p>
</sec>
<sec><h3>Research limitations</h3>
<p>A “unicorn” is a pure quantitative consideration without concerning its quality, and “potential unicorns” as C<sub>T</sub>≤5,000 for papers and C<sub>T</sub>≤500 for patents are left in future studies.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>Scientific and technical “unicorns” provide a new pattern to understand high-impact works in science and technology. The “unicorn” pattern supplies a concise approach to identify very high-impact scientific papers and technical patents.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>The “unicorn” pattern supplies a concise approach to identify very high impact scientific papers and technical patents.</p>
</sec>
</abstract>

Jiangsu Key Laboratory of Data Engineering and Knowledge Service

Identifying Scientific and Technical “Unicorns”

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3><p>Digital literacy and related fields have received interests from scholars and practitioners for more than 20 years; nonetheless, academic communities need to systematically review how the fields have developed. This study aims to investigate the research trends of digital literacy and related concepts since the year of 2000, especially in education.</p></sec>
<sec><h3>Design/methodology/approach</h3><p>The current study analyzes keywords, co-authorship, and cited publications in digital literacy through the scientometric method. The journal articles have been retrieved from the WoS (Web of Science) using four keywords: “Digital literacy,” “ICT literacy,” “information literacy,” and “media literacy.” Further, keywords, publications, and co-authorship are examined and further classified into clusters for more in-depth investigation.</p></sec>
<sec><h3>Findings</h3><p>Digital literacy is a multidisciplinary field that widely embraces literacy, ICT, the Internet, computer skill proficiency, science, nursing, health, and language education. The participants, or study subjects, in digital literacy research range from primary students to professionals, and the co-authorship clusters are distinctive by countries in America and Europe.</p></sec>
<sec><h3>Research limitations</h3><p>This paper analyzes one fixed chunk of a dataset obtained by searching for all four keywords at once. Further studies will retrieve the data from diverse disciplines and will trace the change of the leading research themes by time spans.</p></sec>
<sec><h3>Practical implications</h3><p>To shed light on the findings, using customized digital literacy curriculums and technology is critical for learners at different ages to nurture digital literacy according to their learning aims. They need to cultivate their understanding of the social impact of exploiting technology and computational thinking. To increase the originality of digital literacy-related studies, researchers from different countries and cultures may collaborate to investigate a broader range of digital literacy environments.</p></sec>
<sec><h3>Originality/value</h3><p>The present study reviews research trends in digital literacy and related areas by performing a scientometric study to analyze multidimensional aspects in the fields, including keywords, journal titles, co-authorship, and cited publications.</p></sec>
</abstract>

University at Albany, State University of New York

Media & Communication, Interdisciplinary Program of Digital Convergence Business

A Scientometric Study of Digital Literacy, ICT Literacy, Information Literacy, and Media Literacy

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>This paper examines factors of payment decision as well as the role each factor plays in casual configurations leading to high payment intention under systematic and heuristic information processing routes.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>Based on heuristic-systematic model (HSM), we propose a configurational analytic framework to investigate complex casual relationships between influencing factors and payment decision. In line with this approach, we use fuzzy-set qualitative comparative analysis (fsQCA) to analyze data crawled from Zhihu.com.</p>
</sec>
<sec><h3>Findings</h3>
<p>The number of previous consultations is a necessary element in all five equivalent configurations which lead to high intention in payment decision. The heuristic processing route plays a core role while the systematic processing route plays a peripheral role in payment decision-making process.</p>
</sec>
<sec><h3>Research limitations</h3>
<p>Research is limited in that moderating effect of professional fields has not been considered in the framework.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>Configurations in results can assist managers of knowledge communities and paid Q&amp;A service providers in the management of information elements to motivate more payment decision.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>This paper is one of the few studies to apply HSM theory and fsQCA method with respect to the payment decision in paid Q&amp;A.</p>
</sec>
</abstract>

A Causal Configuration Analysis of Payment Decision Drivers in Paid Q&amp;A

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>The literatures on single cell research were extracted from Clarivate Analytic's Web of Science Core Collection between 2009 and 2019. Firstly, bibliometric analyses were performed with Thomson Data Analyzer (TDA). Secondly, topic identification and evolution trends of single cell research was conducted through the LDA topic model. Thirdly, taking the post-discretized method which is used for topic evolution analysis for reference, the topics were also be dispersed to countries to detect the spatial distribution.</p>
</sec>
<sec><h3>Findings</h3>
<p>The publication of single cell research shows significantly increasing tendency in the last decade. The topics of single cell research field can be divided into three categories, which respectively refers to single cell research methods, mechanism of biological process, and clinical application of single cell technologies. The different trends of these categories indicate that technological innovation drives the development of applied research. The continuous and rapid growth of the topic strength in the field of <italic>cancer diagnosis and treatment</italic> indicates that this research topic has received extensive attention in recent years. The topic distributions of some countries are relatively balanced, while for the other countries, several topics show significant superiority.</p>
</sec>
<sec><h3>Research limitations</h3>
<p>The analyzed data of this study only contain those were included in the Web of Science Core Collection.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>This study provides insights into the research progress regarding single cell field and identifies the most concerned topics which reflect potential opportunities and challenges. The national topic distribution analysis based on the post-discretized analysis method extends topic analysis from time dimension to space dimension.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>This paper combines bibliometric analysis and LDA model to analyze the evolution trends of single cell research field. The method of extending post-discretized analysis from time dimension to space dimension is distinctive and insightful.</p>
</sec>
</abstract>

Overview of Trends in Global Single Cell Research Based on Bibliometric Analysis and LDA Model (2009–2019)

AHEAD OF PRINT

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Journal of Data and Information Science

Journal of Data and Information Science (JDIS, formerly Chinese Journal of Library and Information Science), sponsored by the Chinese Academy of Sciences (CAS) and published quarterly by the National Science Library of CAS, is the first internationally published English-language academic journal in Library and Information Science and related fields from China.  The Journal of Data and Information Science (JDIS) focuses on data-based research oriented toward the exploration of scientific research and innovation. The main areas of interest are science of science, evidence-based policymaking, research evaluation, computational social science, and scientometrics/bibliometrics/altmetrics/ informetrics. Emphasis is given to research that focuses on data, analytics, and knowledge discovery, and supports decision making and science policy. This includes modeling, innovation, data security, media and communications, and social development. Topics may include studies of metadata or full content data, text or non-textural data, structured or non-structural data, domain-specific or cross-domain data, and dynamic or interactive data.  Specific topic areas may include (but are not limited to):    Knowledge organization  Knowledge discovery and data mining  Knowledge integration and fusion  Semantic Web  Science of science  Bibliometrics and scientometrics  Analytic and diagnostic informetrics  Competitive intelligence  Predictive analysis  Social network analysis and metrics  Semantic and interactively analytic retrieval  Evidence-based policy analysis  Intelligent knowledge production  Knowledge-driven workflow management and decision-making  Knowledge-driven collaboration and its management  Domain knowledge infrastructure with knowledge fusion and analytics  Training for data &amp; information scientists  Development of data and information services    JDIS publishes theoretical and empirical work. Systematic reviews are welcome and applied research in development of advanced methods, services, and best practices is also an important part. But simple application of established informetrics on a specific research field or country is out of the scope.Welcome to submit your papers to JDIS.  Why subscribe and read  JDIS is the first and only English journal from China in Library and Information Science and related fields. With an aim to disseminate the cutting-edge research in these fields, it is devoted to the study and application of the theories, methods, techniques, services, and infrastructural facilities using big data to support knowledge discovery for decision and policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. JDIS has gathered a big body of high profile experts across the world who contribute their research to the journal. The international authors account for around 62% in its first publication year (2016).  Why submit  JDIS is the first and only English journal from China in Library and Information Science and related fields. It owns a number of world front-line scholars as editorial board members or reviewers. The turnaround time on average for a manuscript from submission to final decision is less than two and a half months.  Archiving  Sciendo archives the contents of this journal in Portico- digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 