An Empirical Analysis of Big Scholarly Data to Find the Increase in Citations
01 Jan 2019-pp 41-51
TL;DR: In this article, the influence of social media and abstract views in the increase of citations was evaluated on the top cited research articles of cloud computing area and more research focus is needed to analyze the social media influence score.
Abstract: The research quality and productivity of a research area are decided by the number of research articles and citations. Several factors affect the citation count of a research article. The objective of this paper is to find the influences of social media and abstract views in the increase of citations. The relationship between social media influence and abstract count on the overall citations is evaluated on the top cited research articles of cloud computing area. More research focus is needed to analyze the social media influence score. The research scholars, research organizations, funding agencies, and various communities can increase their research productivity and research impact through this analysis.
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TL;DR: A weak positive correlation between a novel author-level complementary metric and the h-index is found, and the findings point toward a bridge between the two domains.
Abstract: Background: The development of an author-level complementary metric could play a role in the process of academic promotion through objective evaluation of scholars’ influence and impact.
Objective: The objective of this study was to evaluate the correlation between the Healthcare Social Graph (HSG) score, a novel social media influence and impact metric, and the h-index, a traditional author-level metric.
Methods: This was a cross-sectional study of health care stakeholders with a social media presence randomly sampled from the Symplur database in May 2020. We performed stratified random sampling to obtain a representative sample with all strata of HSG scores. We manually queried the h-index in two reference-based databases (Scopus and Google Scholar). Continuous features (HSG score and h-index) from the included profiles were summarized as the median and IQR. We calculated the Spearman correlation coefficients (ρ) to evaluate the correlation between the HSG scores and h-indexes obtained from Google Scholar and Scopus.
Results: A total of 286 (31.2%) of the 917 stakeholders had a Google Scholar h-index available. The median HSG score for these profiles was 61.1 (IQR 48.2), and the median h-index was 14.5 (IQR 26.0). For the 286 subjects with the HSG score and Google Scholar h-index available, the Spearman correlation coefficient ρ was 0.1979 (P<.001), indicating a weak positive correlation between these two metrics. A total of 715 (78%) of 917 stakeholders had a Scopus h-index available. The median HSG score for these profiles was 57.6 (IQR 46.4), and the median h-index was 7 (IQR 16). For the 715 subjects with the HSG score and Scopus h-index available, ρ was 0.2173 (P<.001), also indicating a weak positive correlation.
Conclusions: We found a weak positive correlation between a novel author-level complementary metric and the h-index. More than a chiasm between traditional citation metrics and novel social media–based metrics, our findings point toward a bridge between the two domains.
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Book•
25 Mar 2010
TL;DR: This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.
Abstract: The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas. Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.
10,567 citations
TL;DR: The index h, defined as the number of papers with citation number ≥h, is proposed as a useful index to characterize the scientific output of a researcher.
Abstract: I propose the index h, defined as the number of papers with citation number ≥h, as a useful index to characterize the scientific output of a researcher.
8,996 citations
TL;DR: The journal impact factor was created to help select additional source journals and is based on the number of citations in the current year to items published in the previous 2 years, which allows for the inclusion of many small but influential journals.
Abstract: IFIRST MENTIONED THE IDEA OF AN IMPACT FACTOR IN Science in 1955. With support from the National Institutes of Health, the experimental Genetics Citation Index was published, and that led to the 1961 publication of the Science Citation Index. Irving H. Sher and I created the journal impact factor to help select additional source journals. To do this we simply re-sorted the author citation index into the journal citation index. From this simple exercise, we learned that initially a core group of large and highly cited journals needed to be covered in the new Science Citation Index (SCI). Consider that, in 2004, the Journal of Biological Chemistry published 6500 articles, whereas articles from the Proceedings of the National Academy of Sciences were cited more than 300 000 times that year. Smaller journals might not be selected if we rely solely on publication count, so we created the journal impact factor (JIF). The TABLE provides a selective list of journals ranked by impact factor for 2004. The Table also includes the total number of articles published in 2004, the total number of articles published in 2002 plus 2003 (the JIF denominator), the citations to everything published in 2002 plus 2003 (the JIF numerator), and the total citations in 2004 for all articles ever published in a given journal. Sorting by impact factor allows for the inclusion of many small (in terms of total number of articles published) but influential journals. Obviously, sorting by total citations or other provided data would result in a different ranking. The term “impact factor” has gradually evolved to describe both journal and author impact. Journal impact factors generally involve relatively large populations of articles and citations. Individual authors generally produce smaller numbers of articles, although some have published a phenomenal number. For example, transplant surgeon Tom Starzl has coauthored more than 2000 articles, while Carl Djerassi, inventor of the modern oral contraceptive, has published more than 1300. Even before the Journal Citation Reports (JCR) appeared, we sampled the 1969 SCI to create the first published ranking by impact factor. Today, the JCR includes every journal citation in more than 5000 journals—about 15 million citations from 1 million source items per year. The precision of impact factors is questionable, but reporting to 3 decimal places reduces the number of journals with the identical impact rank. However, it matters very little whether, for example, the impact of JAMA is quoted as 24.8 rather than 24.831. A journal’s impact factor is based on 2 elements: the numerator, which is the number of citations in the current year to items published in the previous 2 years, and the denominator, which is the number of substantive articles and reviews published in the same 2 years. The impact factor could just as easily be based on the previous year’s articles alone, which would give even greater weight to rapidly changing fields. An impact factor could also take into account longer periods of citations and sources, but then the measure would be less current.
2,345 citations
TL;DR: In this paper, the authors examined the distribution of citations for papers published in 1981 in journals which were cataloged by the Institute for Scientific Information (IISI) and 20 years of publications in Physical Review D, vol. 11-50 (24,296 papers).
Abstract: Numerical data for the distribution of citations are examined for: (i) papers published in 1981 in journals which are catalogued by the Institute for Scientific Information (783,339 papers) and (ii) 20 years of publications in Physical Review D, vols. 11-50 (24,296 papers). A Zipf plot of the number of citations to a given paper versus its citation rank appears to be consistent with a power-law dependence for leading rank papers, with exponent close to -1/2. This, in turn, suggests that the number of papers with x citations, N(x), has a large-x power law decay $$N(x) \sim {x^{ - a}}$$
, with $$a \approx 3$$
.
1,476 citations
TL;DR: The general tendency of the results of the empirical studies makes it clear that citing behavior is not motivated solely by the wish to acknowledge intellectual and cognitive influences of colleague scientists, since the individual studies reveal also other, in part non‐scientific, factors that play a part in the decision to cite.
Abstract: Purpose – The purpose of this paper is to present a narrative review of studies on the citing behavior of scientists, covering mainly research published in the last 15 years. Based on the results of these studies, the paper seeks to answer the question of the extent to which scientists are motivated to cite a publication not only to acknowledge intellectual and cognitive influences of scientific peers, but also for other, possibly non‐scientific, reasons.Design/methodology/approach – The review covers research published from the early 1960s up to mid‐2005 (approximately 30 studies on citing behavior‐reporting results in about 40 publications).Findings – The general tendency of the results of the empirical studies makes it clear that citing behavior is not motivated solely by the wish to acknowledge intellectual and cognitive influences of colleague scientists, since the individual studies reveal also other, in part non‐scientific, factors that play a part in the decision to cite. However, the results of t...
1,182 citations