Advertisement

Scientometrics

, Volume 109, Issue 3, pp 1989–2005 | Cite as

Detecting impact factor manipulation with data mining techniques

  • Dong-Hui Yang
  • Xin Li
  • Xiaoxia Sun
  • Jie Wan
Article

Abstract

Disingenuously manipulating impact factor is the significant way to harm the fairness of impact factor. That behavior should be banned with effective means. In this paper, data mining techniques are used to solve this problem. Firstly, ten features are collected into feature set for nine normal journals and nine abnormal journals from 2005 to 2014. Then, three types of strong classification methods, k-nearest neighbor, decision tree and support vector machine are adopted to learn the well classification models. Moreover, eight algorithms are run on the data set to find out suitable methods for detecting impact factor manipulation in our experiment. Finally, two excellent algorithms in performance with precisions higher than 85 % are picked out and used to predict new journal samples. According to the results, random forest and one type of support vector machine are relatively more suitable than k-nearest neighbor in this case of detecting abnormal journals. When using those two methods to recognize other 90 journals in the field of nine disciplines from 2007 to 2014, they are verified to be broadly applicable. Unfortunately, four journals are recognized to be manipulated in some years. Therefore, in this paper, two data mining methods are discovered to be intelligent and automatic ways to detect and ban impact factor manipulation for journal managers.

Keywords

Impact factor Manipulation Data mining Classification Prediction 

Notes

Acknowledgments

The authors would like to thank the editor and anonymous referees for their constructive comments that substantially helped improve the quality and presentation of this paper. This work was supported by the National Natural Science Foundation of China (Grant Nos. 71501040, 71473034), and the Fundamental Research Funds for the Central Universities (2242014K10020).

References

  1. Billington, J., & Smith, A. T. (2015). Neural mechanisms for discounting head-roll-induced retinal motion. Journal of Neuroscience, 35(12), 4851–4856.CrossRefGoogle Scholar
  2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRefzbMATHGoogle Scholar
  3. Campanario, J. M. (2014). The effect of citations on the significance of decimal places in the computation of journal impact factors. Scientometrics, 99(2), 289–298.CrossRefGoogle Scholar
  4. Campanario, J. M. (2015). Providing impact: The distribution of JCR journals according to references they contribute to the 2-year and 5-year journal impact factors. Journal of Informetrics, 9(2), 398–407.CrossRefGoogle Scholar
  5. Carrizosa, E., & Morales, D. R. (2013). Supervised classification and mathematical optimization. Computers and Operations Research, 40(1), 150–165.MathSciNetCrossRefzbMATHGoogle Scholar
  6. Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27.CrossRefGoogle Scholar
  7. Cortes, C., & Vapnik, V. (1995). Suppot-vector networks. Machine Learning, 20(3), 273–297.zbMATHGoogle Scholar
  8. Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., & Hess, K. T. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792.CrossRefGoogle Scholar
  9. Davis, P. (2012). The emergence of a citation cartel. The Scholarly Kitchen, 10, 15–17.Google Scholar
  10. Diaz-Uriarte, R., & de Andres, S. A. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7, 1.CrossRefGoogle Scholar
  11. Ding, H., Takigawa, I., Mamitsuka, H., & Zhu, S. F. (2014). Similarity-based machine learning methods for predicting drug-target interactions: A brief review. Briefings in Bioinformatics, 15(5), 734–747.CrossRefGoogle Scholar
  12. Falagas, M. E., & Alexiou, V. G. (2008). The top-ten in journal impact factor manipulation. Archivum Immunologiae Et Therapiae Experimentalis, 56(4), 223–226.CrossRefGoogle Scholar
  13. Fowler, J. H., & Aksnes, D. W. (2007). Does self-citation pay? Scientometrics, 72(3), 427–437.CrossRefGoogle Scholar
  14. Garfield, E. (1955). Citation indexse for science-new dimension in documentation through association of ideas. Science, 122(3159), 108–111.CrossRefGoogle Scholar
  15. Garfield, E. (2006). The history and meaning of the journal impact factor. JAMA-Journal of the American Medical Association, 295(1), 90–93.CrossRefGoogle Scholar
  16. Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300.CrossRefGoogle Scholar
  17. Haghdoost, A., Zare, M., & Bazrafshan, A. (2014). How variable are the journal impact measures? Online Information Review, 38(6), 723–737.CrossRefGoogle Scholar
  18. Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques. New York: Elsevier.zbMATHGoogle Scholar
  19. Hemmingsson, A., Mygind, T., Skjennald, A., & Edgren, J. (2002). Manipulation of impact factors by editors of scientific journals. American Journal of Roentgenology, 178(3), 767.CrossRefGoogle Scholar
  20. Heneberg, P. (2014). Parallel worlds of citable documents and others: Inflated commissioned opinion articles enhance scientometric indicators. Journal of the Association for Information Science and Technology, 65(3), 635–643.CrossRefGoogle Scholar
  21. Heneberg, P. (2016). From excessive journal self-cites to citation stacking: Analysis of journal self-citation kinetics in search for journals, which boost their scientometric indicators. PLoS One, 11(4), e0153730.CrossRefGoogle Scholar
  22. Henriksson, J., Piasecki, B. P., Lend, K., Burglin, T. R., & Swoboda, P. (2013). Finding ciliary genes: A computational approach. Method in Enzymology, 525, 327–350.CrossRefGoogle Scholar
  23. Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2), 415–425.CrossRefGoogle Scholar
  24. Jacso, P. (2009). Five-year impact factor data in the Journal Citation Reports. Online Information Review, 33(3), 603–614.CrossRefGoogle Scholar
  25. Jain, A. K., Duin, R. P. W., & Mao, J. C. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37.CrossRefGoogle Scholar
  26. Khabsa, M., Elmagarmid, A., Ilyas, I., Hammady, H., & Ouzzani, M. (2016). Learning to identify relevant studies for systematic reviews using random forest and external information. Machine Learning, 102(3), 465–482.MathSciNetCrossRefzbMATHGoogle Scholar
  27. Krauss, J. (2007). Journal self-citation rates in ecological sciences. Scientometrics, 73(1), 79–89.CrossRefGoogle Scholar
  28. Kuo, W., & Rupe, J. (2007). R-impact: Reliability-based citation impact factor. IEEE Transactions on Reliability, 56(3), 366–367.CrossRefGoogle Scholar
  29. Lynch, J. G. (2012). Business journals combat coercive citation. Science, 335(6073), 1169.CrossRefGoogle Scholar
  30. Martin, B. R. (2016). Editors’ JIF-boosting stratagems-which are appropriate and which not? Research Policy, 45(1), 1–7.CrossRefGoogle Scholar
  31. Miller, J. B. (2002). Impact factors and publishing research. Scientist, 16(18), 11.Google Scholar
  32. Mongeon, P., Waltman, L., & Rijcke, S. (2016). https://www.cwts.nl/blog?article=n-q2w2b4.
  33. Seok, J. H., & Kim, J. H. (2015). Scene text recognition using a Hough forest implicit shape model and semi-Markov conditional random fields. Pattern Recognition, 48(11), 3584–3599.CrossRefGoogle Scholar
  34. Smith, R. (1997). Journal accused of manipulating impact factor. British Medical Journal, 314(7079), 463.CrossRefGoogle Scholar
  35. Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14(4), 323–348.CrossRefGoogle Scholar
  36. Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003). Random forest: A classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Sciences, 43(6), 1947–1958.CrossRefGoogle Scholar
  37. Thombs, B. D., Levis, A. W., Razykov, I., Syamchandra, A., Leentjens, A. F., Levenson, J. L., et al. (2015). Potentially coercive self-citation by peer reviewers: A cross-sectional study. Journal of Psychosomatic Research, 78(1), 1–6.CrossRefGoogle Scholar
  38. Tort, A. B. L., Targino, Z. H., & Amaral, O. B. (2012). Rising publication delays inflate journal impact factors. PLoS One, 7(12), e53374.CrossRefGoogle Scholar
  39. van Nierop, E. (2010). The introduction of the 5-year impact factor: does it benefit statistics journals? Statistica Neerlandica, 64(1), 71–76.MathSciNetCrossRefGoogle Scholar
  40. Van Noorden, R., & Tollefson, J. (2013). Brazilian citation scheme outed. Nature, 500(7464), 510–511.CrossRefGoogle Scholar
  41. Vens, C., Struyf, J., Schietgat, L., Dzeroski, S., & Blockeel, H. (2008). Decision trees for hierarchical multi-label classification. Machine Learning, 73(2), 185–214.CrossRefGoogle Scholar
  42. Wallner, C. (2009). Ban impact factor manipulation. Science, 323(5913), 461.CrossRefGoogle Scholar
  43. Wan, X. J., & Liu, F. (2014). Are all literature citations equally important? Automatic citation strength estimation and its applications. Journal of the Association for Information Science and Technology, 65(9), 1929–1938.CrossRefGoogle Scholar
  44. Wilhite, A. W., & Fong, E. A. (2012). Coercive citation in academic publishing. Science, 335(6068), 542–543.CrossRefGoogle Scholar
  45. Wu, X. D., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.CrossRefGoogle Scholar
  46. Yu, G., & Wang, L. (2007). The self-cited rate of scientific journals and the manipulation of their impact factors. Scientometrics, 73(3), 321–330.CrossRefGoogle Scholar
  47. Yu, G., Yang, D. H., & He, H. X. (2011). An automatic recognition method of journal impact factor manipulation. Journal of Information Science, 37(3), 235–245.CrossRefGoogle Scholar
  48. Yu, T., Yu, G., & Wang, M.-Y. (2014). Classification method for detecting coercive self-citation in journals. Journal of Informetrics, 8(1), 123–135.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.School of Economics and ManagementSoutheast UniversityNanjingPeople’s Republic of China
  2. 2.School of Energy Science and EngineeringHarbin Institute of TechnologyHarbinPeople’s Republic of China
  3. 3.Nanjing Qiuya Power Horizon Information Technology Company LimitedNanjingPeople’s Republic of China

Personalised recommendations