IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v97y2013i3d10.1007_s11192-013-0983-y.html
   My bibliography  Save this article

Computer models for identifying instrumental citations in the biomedical literature

Author

Listed:
  • Lawrence D. Fu

    (New York University Medical Center)

  • Yindalon Aphinyanaphongs

    (New York University Medical Center)

  • Constantin F. Aliferis

    (New York University Medical Center)

Abstract

The most popular method for evaluating the quality of a scientific publication is citation count. This metric assumes that a citation is a positive indicator of the quality of the cited work. This assumption is not always true since citations serve many purposes. As a result, citation count is an indirect and imprecise measure of impact. If instrumental citations could be reliably distinguished from non-instrumental ones, this would readily improve the performance of existing citation-based metrics by excluding the non-instrumental citations. A citation was operationally defined as instrumental if either of the following was true: the hypothesis of the citing work was motivated by the cited work, or the citing work could not have been executed without the cited work. This work investigated the feasibility of developing computer models for automatically classifying citations as instrumental or non-instrumental. Instrumental citations were manually labeled, and machine learning models were trained on a combination of content and bibliometric features. The experimental results indicate that models based on content and bibliometric features are able to automatically classify instrumental citations with high predictivity (AUC = 0.86). Additional experiments using independent hold out data and prospective validation show that the models are generalizeable and can handle unseen cases. This work demonstrates that it is feasible to train computer models to automatically identify instrumental citations.

Suggested Citation

  • Lawrence D. Fu & Yindalon Aphinyanaphongs & Constantin F. Aliferis, 2013. "Computer models for identifying instrumental citations in the biomedical literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 871-882, December.
    • Handle: RePEc:spr:scient:v:97:y:2013:i:3:d:10.1007_s11192-013-0983-y
    DOI: 10.1007/s11192-013-0983-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-013-0983-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-013-0983-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. T. J. Phelan, 1999. "A compendium of issues for citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 45(1), pages 117-136, May.
    2. Lawrence D. Fu & Constantin F. Aliferis, 2010. "Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 257-270, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ashkan Ebadi & Andrea Schiffauerova, 2016. "iSEER: an intelligent automatic computer system for scientific evaluation of researchers," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 477-498, May.
    2. Federica Bologna & Angelo Iorio & Silvio Peroni & Francesco Poggi, 2023. "Do open citations give insights on the qualitative peer-review evaluation in research assessments? An analysis of the Italian National Scientific Qualification," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 19-53, January.
    3. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ashkan Ebadi & Andrea Schiffauerova, 2016. "iSEER: an intelligent automatic computer system for scientific evaluation of researchers," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 477-498, May.
    2. Lina Xu & Steven Dellaportas & Zhiqiang Yang & Jin Wang, 2023. "More on the relationship between interdisciplinary accounting research and citation impact," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 4779-4803, December.
    3. Weimao Ke, 2013. "A fitness model for scholarly impact analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 981-998, March.
    4. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    5. Wen-Yau Cathy Lin & Mu-Hsuan Huang, 2012. "The relationship between co-authorship, currency of references and author self-citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 343-360, February.
    6. Zaggl, Michael A., 2017. "Manipulation of explicit reputation in innovation and knowledge exchange communities: The example of referencing in science," Research Policy, Elsevier, vol. 46(5), pages 970-983.
    7. van den Besselaar, Peter & Heyman, Ulf & Sandström, Ulf, 2017. "Perverse effects of output-based research funding? Butler’s Australian case revisited," Journal of Informetrics, Elsevier, vol. 11(3), pages 905-918.
    8. Mingyang Wang & Guang Yu & Shuang An & Daren Yu, 2012. "Discovery of factors influencing citation impact based on a soft fuzzy rough set model," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 635-644, December.
    9. Na Liu & Philip Shapira & Xiaoxu Yue, 2021. "Tracking developments in artificial intelligence research: constructing and applying a new search strategy," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3153-3192, April.
    10. Guan Jiancheng & Wang Junxia, 2004. "Evaluation and interpretation of knowledge production efficiency," Scientometrics, Springer;Akadémiai Kiadó, vol. 59(1), pages 131-155, January.
    11. Shaun Goldfinch & Tony Dale & Karl DeRouen, 2003. "Science from the periphery: Collaboration, networks and 'Periphery Effects' in the citation of New Zealand Crown Research Institutes articles, 1995-2000," Scientometrics, Springer;Akadémiai Kiadó, vol. 57(3), pages 321-337, July.
    12. Wang, Mingyang & Yu, Guang & Xu, Jianzhong & He, Huixin & Yu, Daren & An, Shuang, 2012. "Development a case-based classifier for predicting highly cited papers," Journal of Informetrics, Elsevier, vol. 6(4), pages 586-599.
    13. Zhao, Qihang & Feng, Xiaodong, 2022. "Utilizing citation network structure to predict paper citation counts: A Deep learning approach," Journal of Informetrics, Elsevier, vol. 16(1).
    14. Tahamtan, Iman & Bornmann, Lutz, 2018. "Core elements in the process of citing publications: Conceptual overview of the literature," Journal of Informetrics, Elsevier, vol. 12(1), pages 203-216.
    15. Teodora Diana Corsatea, 2010. "Measuring science: Spatial investigation of academic opportunities in Belgium," Papers in Regional Science, Wiley Blackwell, vol. 89(2), pages 373-387, June.
    16. James H. Fowler & Dag W. Aksnes, 2007. "Does self-citation pay?," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(3), pages 427-437, September.
    17. Roberto Fernández‐Gago & Laura Cabeza‐García & José‐Luis Godos‐Díez, 2020. "How significant is corporate social responsibility to business research?," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(4), pages 1809-1817, July.
    18. Marinova, Dora & Newman, Peter, 2008. "The changing research funding regime in Australia and academic productivity," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 283-291.
    19. Babak Sohrabi & Hamideh Iraj, 2017. "The effect of keyword repetition in abstract and keyword frequency per journal in predicting citation counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 243-251, January.
    20. Maite Barrios & Angel Borrego & Andreu Vilaginés & Candela Ollé & Marta Somoza, 2008. "A bibliometric study of psychological research on tourism," Scientometrics, Springer;Akadémiai Kiadó, vol. 77(3), pages 453-467, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:97:y:2013:i:3:d:10.1007_s11192-013-0983-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.