IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v87y2011i3d10.1007_s11192-011-0366-1.html
   My bibliography  Save this article

Mining typical features for highly cited papers

Author

Listed:
  • Mingyang Wang

    (Harbin Institute of Technology
    Northeast Forestry University)

  • Guang Yu

    (Harbin Institute of Technology)

  • Daren Yu

    (Harbin Institute of Technology)

Abstract

In this paper, we discuss the application of the data mining tools to identify typical features for highly cited papers (HCPs). By integrating papers’ external features and quality features, the feature space used to model HCPs was established. Then, a series of predictor teams were extracted from the feature space with rough set reduction framework. Each predictor team was used to construct a base classifier. Then the five base classifiers with the highest classification performance and larger diversity on whole were selected to construct a multi-classifier system (MCS) for HCPs. The combination prediction model obtained better performance than models of a single predictor team. 11 typical prediction features for HCPs were extracted on the basis of the MCS. The findings show that both the papers’ inner quality and external features, mainly represented as the reputation of the authors and journals, contribute to generation of HCPs in future.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:scient:v:87:y:2011:i:3:d:10.1007_s11192-011-0366-1
    DOI: 10.1007/s11192-011-0366-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-011-0366-1
    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/s11192-011-0366-1?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. Hendrik P. van Dalen & K?ne Henkens, 2005. "Signals in science - On the importance of signaling in gaining attention in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(2), pages 209-233, August.
    2. Robert F. Bordley, 1982. "A Multiplicative Formula for Aggregating Probability Assessments," Management Science, INFORMS, vol. 28(10), pages 1137-1148, October.
    3. Laband, David N & Piette, Michael J, 1994. "Favoritism versus Search for Good Papers: Empirical Evidence Regarding the Behavior of Journal Editors," Journal of Political Economy, University of Chicago Press, vol. 102(1), pages 194-203, February.
    4. 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.
    5. Hendrik P. Van Dalen & Kène Henkens, 2001. "What makes a scientific article influential? The case of demographers," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 455-482, March.
    6. Wolfgang Glänzel & Balázs Schlemmer & Bart Thijs, 2003. "Better late than never? On the chance to become highly cited only beyond the standard bibliometric time horizon," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(3), pages 571-586, November.
    7. Donald O. Case & Georgeann M. Higgins, 2000. "How can we investigate citation behavior? A study of reasons for citing literature in communication," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(7), pages 635-645.
    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. Yifan Qian & Wenge Rong & Nan Jiang & Jie Tang & Zhang Xiong, 2017. "Citation regression analysis of computer science publications in different ranking categories and subfields," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1351-1374, March.
    2. Yang, Jinqing & Liu, Zhifeng, 2022. "The effect of citation behaviour on knowledge diffusion and intellectual structure," Journal of Informetrics, Elsevier, vol. 16(1).
    3. Jiayin Pei & Guang Yu & Xianyun Tian & Maureen Renee Donnelley, 2017. "A new method for early detection of mass concern about public health issues," Journal of Risk Research, Taylor & Francis Journals, vol. 20(4), pages 516-532, April.
    4. 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.
    5. Vahid Garousi & João M. Fernandes, 2017. "Quantity versus impact of software engineering papers: a quantitative study," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 963-1006, August.
    6. 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.
    7. Jefferson Seide Molléri & Kai Petersen & Emilia Mendes, 2018. "Towards understanding the relation between citations and research quality in software engineering studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1453-1478, December.
    8. Martorell Cunil, Onofre & Otero González, Luis & Durán Santomil, Pablo & Mulet Forteza, Carlos, 2023. "How to accomplish a highly cited paper in the tourism, leisure and hospitality field," Journal of Business Research, Elsevier, vol. 157(C).
    9. Mingyang Wang & Shi Li & Guangsheng Chen, 2017. "Detecting latent referential articles based on their vitality performance in the latest 2 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1557-1571, September.
    10. 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.
    11. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    12. Bin Wang & Feng Wu & Lukui Shi, 2023. "AGSTA-NET: adaptive graph spatiotemporal attention network for citation count prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 511-541, January.
    13. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
    14. 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.
    15. Leo Egghe & Raf Guns & Ronald Rousseau, 2013. "Measuring co-authors’ contribution to an article’s visibility," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 55-67, April.

    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. 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.
    2. 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.
    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.
    4. Basma Albanna & Julia Handl & Richard Heeks, 2021. "Publication outperformance among global South researchers: An analysis of individual-level and publication-level predictors of positive deviance," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8375-8431, October.
    5. Jianhua Hou & Xiucai Yang & Yang Zhang, 2023. "The effect of social media knowledge cascade: an analysis of scientific papers diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5169-5195, September.
    6. Meyer, Matthias & Waldkirch, Rüdiger W. & Duscher, Irina & Just, Alexander, 2018. "Drivers of citations: An analysis of publications in “top” accounting journals," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 51(C), pages 24-46.
    7. Jianhua Hou & Xiucai Yang, 2019. "Patent sleeping beauties: evolutionary trajectories and identification methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 187-215, July.
    8. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    9. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2011. "Assessing the varying level of impact measurement accuracy as a function of the citation window length," Journal of Informetrics, Elsevier, vol. 5(4), pages 659-667.
    10. 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.
    11. Li, Jiang & Shi, Dongbo & Zhao, Star X. & Ye, Fred Y., 2014. "A study of the “heartbeat spectra” for “sleeping beauties”," Journal of Informetrics, Elsevier, vol. 8(3), pages 493-502.
    12. Onodera, Natsuo, 2016. "Properties of an index of citation durability of an article," Journal of Informetrics, Elsevier, vol. 10(4), pages 981-1004.
    13. Elizabeth S. Vieira, 2023. "The influence of research collaboration on citation impact: the countries in the European Innovation Scoreboard," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3555-3579, June.
    14. Jonathan M. Levitt & Mike Thelwall, 2009. "The most highly cited Library and Information Science articles: Interdisciplinarity, first authors and citation patterns," Scientometrics, Springer;Akadémiai Kiadó, vol. 78(1), pages 45-67, January.
    15. Marcel Clermont & Johanna Krolak & Dirk Tunger, 2021. "Does the citation period have any effect on the informative value of selected citation indicators in research evaluations?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1019-1047, February.
    16. You Song & Fangling Situ & Hongjun Zhu & Jinzhi Lei, 2018. "To be the Prince to wake up Sleeping Beauty: the rediscovery of the delayed recognition studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 9-24, October.
    17. Hendrik P. van Dalen & Kène Henkens, 2012. "What is on a Demographer’s Mind?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(16), pages 363-408.
    18. Stremersch, Stefan & Camacho, Nuno & Vanneste, Sofie & Verniers, Isabel, 2015. "Unraveling scientific impact: Citation types in marketing journals," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 64-77.
    19. Hendrik P. van Dalen & K?ne Henkens, 2005. "Signals in science - On the importance of signaling in gaining attention in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(2), pages 209-233, August.
    20. David Michayluk & Ralf Zurbruegg, 2014. "Do lead articles signal higher quality in the digital age? Evidence from finance journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 961-973, February.

    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:87:y:2011:i:3:d:10.1007_s11192-011-0366-1. 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.