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Predicting the long-term citation impact of recent publications

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  • Stegehuis, Clara
  • Litvak, Nelly
  • Waltman, Ludo

Abstract

A fundamental problem in citation analysis is the prediction of the long-term citation impact of recent publications. We propose a model to predict a probability distribution for the future number of citations of a publication. Two predictors are used: the impact factor of the journal in which a publication has appeared and the number of citations a publication has received one year after its appearance. The proposed model is based on quantile regression. We employ the model to predict the future number of citations of a large set of publications in the field of physics. Our analysis shows that both predictors (i.e., impact factor and early citations) contribute to the accurate prediction of long-term citation impact. We also analytically study the behavior of the quantile regression coefficients for high quantiles of the distribution of citations. This is done by linking the quantile regression approach to a quantile estimation technique from extreme value theory. Our work provides insight into the influence of the impact factor and early citations on the long-term citation impact of a publication, and it takes a step toward a methodology that can be used to assess research institutions based on their most recently published work.

Suggested Citation

  • Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:3:p:642-657
    DOI: 10.1016/j.joi.2015.06.005
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    1. H. P. F. Peters & A. F. J. van Raan, 1994. "On determinants of citation scores: A case study in chemical engineering," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 45(1), pages 39-49, January.
    2. Cirillo, Pasquale, 2013. "Are your data really Pareto distributed?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5947-5962.
    3. Bornmann, Lutz, 2013. "The problem of citation impact assessments for recent publication years in institutional evaluations," Journal of Informetrics, Elsevier, vol. 7(3), pages 722-729.
    4. Bornmann, Lutz & Leydesdorff, Loet & Wang, Jian, 2013. "Which percentile-based approach should be preferred for calculating normalized citation impact values? An empirical comparison of five approaches including a newly developed citation-rank approach (P1," Journal of Informetrics, Elsevier, vol. 7(4), pages 933-944.
    5. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    6. Glenn D. Walters, 2006. "Predicting subsequent citations to articles published in twelve crime-psychology journals: Author impact versus journal impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(3), pages 499-510, December.
    7. 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.
    8. Didegah, Fereshteh & Thelwall, Mike, 2013. "Which factors help authors produce the highest impact research? Collaboration, journal and document properties," Journal of Informetrics, Elsevier, vol. 7(4), pages 861-873.
    9. Quentin L. Burrell, 2003. "Predicting future citation behavior," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(5), pages 372-378, March.
    10. Jonathan M. Levitt & Mike Thelwall, 2008. "Patterns of annual citation of highly cited articles and the prediction of their citation ranking: A comparison across subjects," Scientometrics, Springer;Akadémiai Kiadó, vol. 77(1), pages 41-60, October.
    11. Nick Haslam & Lauren Ban & Leah Kaufmann & Stephen Loughnan & Kim Peters & Jennifer Whelan & Sam Wilson, 2008. "What makes an article influential? Predicting impact in social and personality psychology," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(1), pages 169-185, July.
    12. Pedro Albarrán & Juan A. Crespo & Ignacio Ortuño & Javier Ruiz-Castillo, 2011. "The skewness of science in 219 sub-fields and a number of aggregates," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 385-397, August.
    13. Mingyang Wang & Guang Yu & Daren Yu, 2011. "Mining typical features for highly cited papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 695-706, June.
    14. Anthony F. J. van Raan, 2004. "Sleeping Beauties in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 59(3), pages 467-472, March.
    15. Jonathan Adams, 2005. "Early citation counts correlate with accumulated impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(3), pages 567-581, June.
    16. J Mingers, 2008. "Exploring the dynamics of journal citations: Modelling with s-curves," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1013-1025, August.
    17. Jian Wang, 2013. "Citation time window choice for research impact evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 851-872, March.
    18. Tim Brody & Stevan Harnad & Leslie Carr, 2006. "Earlier Web usage statistics as predictors of later citation impact," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(8), pages 1060-1072, June.
    19. 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.
    20. Weimao Ke, 2013. "A fitness model for scholarly impact analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 981-998, March.
    21. Mingers, John & Xu, Fang, 2010. "The drivers of citations in management science journals," European Journal of Operational Research, Elsevier, vol. 205(2), pages 422-430, September.
    22. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    23. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.
    24. Daniel E. Acuna & Stefano Allesina & Konrad P. Kording, 2012. "Predicting scientific success," Nature, Nature, vol. 489(7415), pages 201-202, September.
    25. Beirlant, Jan & Glänzel, Wolfgang & Carbonez, An & Leemans, Herlinde, 2007. "Scoring research output using statistical quantile plotting," Journal of Informetrics, Elsevier, vol. 1(3), pages 185-192.
    26. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    27. Wallace, Matthew L. & Larivière, Vincent & Gingras, Yves, 2009. "Modeling a century of citation distributions," Journal of Informetrics, Elsevier, vol. 3(4), pages 296-303.
    28. Natsuo Onodera & Fuyuki Yoshikane, 2015. "Factors affecting citation rates of research articles," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(4), pages 739-764, April.
    29. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    30. David I Stern, 2014. "High-Ranked Social Science Journal Articles Can Be Identified from Early Citation Information," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-11, November.
    31. 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.
    32. Derek De Solla Price, 1976. "A general theory of bibliometric and other cumulative advantage processes," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(5), pages 292-306, September.
    33. Rodrigo Costas & Zohreh Zahedi & Paul Wouters, 2015. "Do “altmetrics” correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(10), pages 2003-2019, October.
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