Citation analysis

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Citation metrics

Citation analysis is the examination of the frequency, patterns, and graphs of citations in documents. It uses the directed graph of citations — links from one document to another document — to reveal properties of the documents. A typical aim would be to identify the most important documents in a collection. A classic example is that of the citations between academic articles and books.[1][2] For another example, judges of law support their judgements by referring back to judgements made in earlier cases (see citation analysis in a legal context). An additional example is provided by patents which contain prior art, citation of earlier patents relevant to the current claim. The digitization of patent data and increasing computing power have led to a community of practice that uses these citation data to measure innovation attributes, trace knowledge flows, and map innovation networks.[3]

Documents can be associated with many other features in addition to citations, such as authors, publishers, journals as well as their actual texts. The general analysis of collections of documents is known as bibliometrics and citation analysis is a key part of that field. For example, bibliographic coupling and co-citation are association measures based on citation analysis (shared citations or shared references). The citations in a collection of documents can also be represented in forms such as a citation graph, as pointed out by Derek J. de Solla Price in his 1965 article "Networks of Scientific Papers".[4] This means that citation analysis draws on aspects of social network analysis and network science.

An early example of automated citation indexing was CiteSeer, which was used for citations between academic papers, while Web of Science is an example of a modern system which includes more than just academic books and articles reflecting a wider range of information sources. Today, automated citation indexing[5] has changed the nature of citation analysis research, allowing millions of citations to be analyzed for large-scale patterns and knowledge discovery. Citation analysis tools can be used to compute various impact measures for scholars based on data from citation indices.[6][7][note 1] These have various applications, from the identification of expert referees to review papers and grant proposals, to providing transparent data in support of academic merit review, tenure, and promotion decisions. This competition for limited resources may lead to ethically questionable behavior to increase citations.[8][9]

A great deal of criticism has been made of the practice of naively using citation analyses to compare the impact of different scholarly articles without taking into account other factors which may affect citation patterns.[10] Among these criticisms, a recurrent one focuses on "field-dependent factors", which refers to the fact that citation practices vary from one area of science to another, and even between fields of research within a discipline.[11]

  1. ^ Rubin, Richard (2010). Foundations of library and information science (3rd ed.). New York: Neal-Schuman Publishers. ISBN 978-1-55570-690-6.
  2. ^ Garfield, E. Citation Indexing - Its Theory and Application in Science, Technology and Humanities Philadelphia:ISI Press, 1983.
  3. ^ Jaffe, Adam; de Rassenfosse, Gaétan (2017). "Patent citation data in social science research: Overview and best practices". Journal of the Association for Information Science and Technology. 68 (6): 1360–1374. doi:10.1002/asi.23731.
  4. ^ Derek J. de Solla Price (July 30, 1965). "Networks of Scientific Papers" (PDF). Science. 149 (3683): 510–515. Bibcode:1965Sci...149..510D. doi:10.1126/science.149.3683.510. PMID 14325149.
  5. ^ Giles, C. Lee; Bollacker, Kurt D.; Lawrence, Steve (1998), "CiteSeer", Proceedings of the third ACM conference on Digital libraries - DL '98, New York: Association for Computing Machinery, pp. 89–98, doi:10.1145/276675.276685, ISBN 978-0-89791-965-4, S2CID 514080
  6. ^ Kaur, Jasleen; Diep Thi Hoang; Xiaoling Sun; Lino Possamai; Mohsen JafariAsbagh; Snehal Patil; Filippo Menczer (2012). "Scholarometer: A Social Framework for Analyzing Impact across Disciplines". PLOS ONE. 7 (9): e43235. Bibcode:2012PLoSO...743235K. doi:10.1371/journal.pone.0043235. PMC 3440403. PMID 22984414.
  7. ^ Hoang, D.; Kaur, J.; Menczer, F. (2010), "Crowdsourcing Scholarly Data", Proceedings of the WebSci10: Extending the Frontiers of Society On-Line, April 26-27th, 2010, Raleigh, NC: US, archived from the original on 2015-04-17, retrieved 2015-08-09
  8. ^ Anderson, M.S. van; Ronning, E.A. van; de Vries, R.; Martison, B.C. (2007). "The perverse effects of competition on scientists' work and relationship". Science and Engineering Ethics. 4 (13): 437–461. doi:10.1007/s11948-007-9042-5. PMID 18030595. S2CID 2994701.
  9. ^ Wesel, M. van (2016). "Evaluation by Citation: Trends in Publication Behavior, Evaluation Criteria, and the Strive for High Impact Publications". Science and Engineering Ethics. 22 (1): 199–225. doi:10.1007/s11948-015-9638-0. PMC 4750571. PMID 25742806.
  10. ^ Bornmann, L.; Daniel, H. D. (2008). "What do citation counts measure? A review of studies on citing behavior". Journal of Documentation. 64 (1): 45–80. doi:10.1108/00220410810844150. hdl:11858/00-001M-0000-0013-7A94-3. S2CID 17260826.
  11. ^ Anauati, Maria Victoria and Galiani, Sebastian and Gálvez, Ramiro H., Quantifying the Life Cycle of Scholarly Articles Across Fields of Economic Research (November 11, 2014). Available at SSRN: https://ssrn.com/abstract=2523078


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