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Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature

Author

Listed:
  • Lawrence D. Fu

    (New York University Medical Center)

  • Constantin F. Aliferis

    (New York University Medical Center)

Abstract

The most popular method for judging the impact of biomedical articles is citation count which is the number of citations received. The most significant limitation of citation count is that it cannot evaluate articles at the time of publication since citations accumulate over time. This work presents computer models that accurately predict citation counts of biomedical publications within a deep horizon of 10 years using only predictive information available at publication time. Our experiments show that it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and their statistical analysis provides greater insight into citation behavior.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:85:y:2010:i:1:d:10.1007_s11192-010-0160-5
    DOI: 10.1007/s11192-010-0160-5
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    References listed on IDEAS

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    1. T. J. Phelan, 1999. "A compendium of issues for citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 45(1), pages 117-136, May.
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