Web of Science with the Conference Proceedings Citation Indexes: the case of computer science

Bar‐Ilan, Judit

doi:10.1007/s11192-009-0145-4

Cited by 64 publications

(53 citation statements)

References 12 publications

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“…For instance, comparing the 254 TRECVid papers which Scopus and Scholar both located in 2007, we found that 1/3 of the Scopus papers had located no citations at all, and Figure 1 shows the dramatic drop in citations per papers for both databases. Normally what matters are the documents that have been cited a large number of times, and it is interesting and important for the field to find out why, what they are about, who wrote them, and where they were published [1,9]. Thus, in general, more effort should be put into carefully cleaning and checking this data rather than the long tail of non-cited documents These highly cited documents can be analysed in detail.…”

Section: Conclusion On Data Accuracymentioning

confidence: 99%

A bibliometric study of Video Retrieval Evaluation Benchmarking (TRECVid): A methodological analysis

Thornley

McLoughlin

Johnson

et al. 2011

Journal of Information Science

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This paper provides a discussion and analysis of methodological issues encountered during a scholarly impact and bibliometric study within the field of computer science (TRECVid Text Retrieval and Evaluation Conference, Video Retrieval Evaluation). The purpose of this paper is to provide a reflection and analysis of the methods used to provide useful information and guidance for those who may wish to undertake similar studies, and is of particular relevance for the academic disciplines which have publication and citation norms that may not perform well using traditional tools. Scopus and Google Scholar are discussed and a detailed comparison of the effects of different search methods and cleaning methods within and between these tools for subject and author analysis is provided. The additional database capabilities and usefulness of "Scopus More" in addition to "Scopus General" is discussed and evaluated. Scopus paper coverage is found to favourably compare to Google Scholar but Scholar consistently has superior performance at finding citations to those papers. These additional citations significantly increase the citation totals and also change the relative ranking of papers. Publish or Perish (PoP), a software wrapper for Google Scholar, is also examined and its limitations and some possible solutions are described. Data cleaning methods, including duplicate checks, expert domain checking of bibliographic data, and content checking of retrieved papers are compared and their relative effects on paper and citation count discussed. Google Scholar and Scopus are also compared as tools for collecting bibliographic data for visualisations of developing trends and, due to the comparative ease of collecting abstracts, Scopus is found far more effective.

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Section: Conclusion On Data Accuracymentioning

confidence: 99%

A bibliometric study of Video Retrieval Evaluation Benchmarking (TRECVid): A methodological analysis

Thornley

McLoughlin

Johnson

et al. 2011

Journal of Information Science

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show abstract

“…Since WoS does not enable the science domain to be specified in a straightforward way, we were forced to limit ourselves to publications appearing in journals classified as computer science sources. The data collection we have chosen has an obvious limitation: it is biased towards computer scientists who prefer publishing their research in journals, although it is well known that computer science research is presented at conferences to a greater extent than other fields of science (Bar-Ilan, 2010;Franceschet, 2010;Wainer et al, 2011). On the other hand, computer science journal articles receive more citations on average than conference papers (Franceschet, 2010) and we can expect that with a growing pressure on the visibility of papers and a faster journal editorial process, both of which we have been witnessing in recent years, the need for publishing computer science research in journals will increase.…”

Section: Datamentioning

confidence: 99%

Time-aware PageRank for bibliographic networks

Fiala

2012

Journal of Informetrics

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Abstract:In the past, recursive algorithms, such as PageRank originally conceived for the Web, have been successfully used to rank nodes in the citation networks of papers, authors, or journals. They have proved to determine prestige and not popularity, unlike citation counts.However, bibliographic networks, in contrast to the Web, have some specific features that enable the assigning of different weights to citations, thus adding more information to the process of finding prominence. For example, a citation between two authors may be weighed according to whether and when those two authors collaborated with each other, which is information that can be found in the co-authorship network. In this study, we define a couple of PageRank modifications that weigh citations between authors differently based on the information from the co-authorship graph. In addition, we put emphasis on the time of publications and citations. We test our algorithms on the Web of Science data of computer

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“…The research performance of global universities in computer science has been explored too [9]. Other investigations have been more concerned with the role of computer science conferences and their lower impact compared to journals [10][11][12][13] while some research has also been devoted to the study of the citedness of computer science journals [14,15]. Some works have been very specific and have inspected the evolution of the number of authors [16] or of the age of cited references [17] in computer science publications.…”

Section: Introductionmentioning

confidence: 99%

Computer Science Papers in Web of Science: A Bibliometric Analysis

Fiala

Tutoky

2017

Publications

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Abstract:In this article we present a bibliometric study of 1.9 million computer science papers published from 1945 to 2014 and indexed in Web of Science. We analyze both the quantity and the impact of these publications according to document types, languages, disciplines, countries, institutions, and publication sources. The most frequent author keywords, cited references, and cited papers as well as the distribution of the number of references and citations per paper and of the age of cited references are also explored. Since conference proceedings play a tremendous role in this scientific field, we investigate the time and place of computer science conferences in terms of the most prolific months and locations. And, last but not least, the production of journal articles and conference papers over the whole time period and the level of collaboration in different computer science disciplines are inspected. One of the main results is the finding that "Artificial Intelligence" is the most productive subfield of computer science, but "Interdisciplinary Applications" has the highest relative impact.

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Web of Science with the Conference Proceedings Citation Indexes: the case of computer science

Cited by 64 publications

References 12 publications

A bibliometric study of Video Retrieval Evaluation Benchmarking (TRECVid): A methodological analysis

A bibliometric study of Video Retrieval Evaluation Benchmarking (TRECVid): A methodological analysis

Time-aware PageRank for bibliographic networks

Computer Science Papers in Web of Science: A Bibliometric Analysis

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