Abstract
In this paper, we propose a methodology to detect latent referential articles through a universal, citation-based investigation. We discuss articles’ dynamic vitality performance, concealed in their citation distributions, in order to understand the mechanisms that govern which articles are likely to be referenced in the future. Articles have diverse vitality performances expressed in the amount of citations obtained in different time periods. Through an examination of the correlation between articles’ future citation count and their past citations, we establish the optimal time period during which it is best to forecast articles’ future referential possibilities. The results show that the latest 2 years is the optimal time period. In other words, the correlation between the articles’ future citation count and their past citation count reaches a maximum value in the most recent 2-year period. The articles with a higher vitality performance in the most recent 2 years have a higher ratio of being cited as references in the future. These results help, not only, in understanding mechanisms of generating references, but also provide an additional indicator for decision makers to evaluate the academic performance of individuals according to their citation performance in the latest 2 years.
Similar content being viewed by others
References
Adam, D. (2002). The counting house. Nature, 415(6873), 726–729.
Aksnes, D. W. (2003). Characteristics of highly cited papers. Research Evaluation, 12(3), 159–170.
Beel, J., & Gipp, B. (2009). Google Scholar’s ranking algorithm: The impact of citation counts (an empirical study). In Research challenges in information science, third international conference on IEEE (pp. 439–446).
Bornmann, L. (2013). The problem of citation impact assessments for recent publication years in institutional evaluations. Journal of Informetrics, 7(3), 722–729.
Bornmann, L., & Daniel, H. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.
Bornmann, L., & Leydesdorff, L. (2012). Which are the best performing regions in information science in terms of highly cited papers? Some improvements of our previous mapping approaches. Journal of Informetrics, 6(2), 336–345.
Bornmann, L., & Leydesdorff, L. (2015). Does quality and content matter for citedness? A comparison with para-textual factors and over time. Journal of Informetrics, 9(3), 419–429.
Burrell, Q. L. (2002a). Will this paper ever be cited? Journal of the American Society for Information Science and Technology, 53(3), 232–235.
Burrell, Q. L. (2002b). On the nth-citation distribution and obsolescence. Scientometrics, 53(3), 309–323.
Burrell, Q. L. (2003). Predicting future citation behavior. Journal of the American Society for Information Science and Technology, 54(5), 372–378.
Camacho, M. M. D. M., & Nunez, N. M. (2009). The multilayered nature of reference selection. Journal of the American Society for Information Science and Technology, 60(4), 754–777.
Cano, V., & Lind, N. C. (1991). Citation life-cycles of 10 citation-classics. Scientometrics, 22(2), 297–312.
Chen, C. (2012). Predictive effects of structural variation on citation counts. Journal of the American Society for Information Science and Technology, 63(3), 431–449.
Cozzens, S. E. (1985). Comparing the sciences—Citation context analysis of papers from neuropharmacology and the sociology of science. Social Studies of Science, 15(1), 127–153.
Didegah, F., & Thelwall, M. (2013). Which factors help authors produce the highest impact research? Collaboration, journal and document properties. Journal of Informetrics, 7(4), 861–873.
Ding, Y., Liu, X., Guo, C., & Cronin, B. (2013). The distribution of references across texts: Some implications for citation analysis. Journal of Informetrics, 7(3), 583–592.
Ding, Y., Zhang, G., Chambers, T., Song, M., Wang, X., & Zhai, C. (2014). Content-based citation analysis: The next generation of citation analysis. Journal of the Association for Information Science and Technology, 65(9), 1820–1833.
Egghe, L., & Rousseau, R. (2000). Aging, obsolescence, impact, growth, and utilization: Definitions and relations. Journal of the American Society for Information Science and Technology, 51(11), 1004–1017.
Fu, L., & Aliferis, C. (2010). Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature. Scientometrics, 85(1), 257–270.
Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.
Garfield, E. (1981). Citation classics—Four years of the human side of science. Current Contents, 22, 5–16.
Garfield, E. (2000). Use of Journal Citation Reports and Journal Performance Indicators in measuring short and long term journal impact. Croatian Medical Journal, 41(4), 368–374.
Garfield, E. (2002). Highly cited authors. Scientist, 16(7), 10.
Haslam, N., Ban, L., Kaufmann, L., Loughnan, S., Peters, K., Whelan, J., et al. (2008). What makes an article influential? Predicting impact in social and personality psychology. Scientometrics, 76(1), 169–185.
Haslam, N., & Koval, P. (2010). Predicting long-term citation impact of articles in social and personality psychology. Psychological Reports, 106(3), 891–900.
Hu, Z., Chen, C., & Liu, Z. (2013). Where are citations located in the body of scientific articles? A study of the distributions of citation locations. Journal of Informetrics, 7(4), 887–896.
Huber, J. C. (1998). Cumulative advantage and success-breeds-success: The value of time pattern analysis. Journal of the American Society for Information Science and Technology, 49(5), 471–476.
Kim, K. (2004). The motivation for citing specific references by social scientists in Korea: The phenomenon of co-existing references. Scientometrics, 59(1), 79–93.
King, D. A. (2004). The scientific impact of nations what different countries get for their research spending. Nature, 430, 311–316.
Kostoff, R. (2007). The difference between highly and poorly cited medical articles in the journal Lancet. Scientometrics, 72(3), 513–520.
Lercher, A. (2013). Correlation over time for citations to mathematics articles. Journal of the American Society for Information Science and Technology, 64(3), 455–463.
Li, R., Chambers, T., Ding, Y., Zhang, G., & Meng, L. (2014). Patent citation analysis: Calculating science linkage based on citing motivation. Journal of the Association for Information Science and Technology, 65(5), 1007–1017.
Li, Z., Peng, Q. K., & Liu, C. (2016). Two citation-based indicators to measure latent referential value of papers. Scientometrics, 108(3), 1299–1313.
Marx, W., & Bornmann, L. (2015). On the causes of subject-specific citation rates in Web of Science. Scientometrics, 102(2), 1823–1827.
May, R. M. (1997). The scientific wealth of nations. Science, 275, 793–796.
Nicholas, D., Huntington, P., Dobrowolski, T., Rowlands, I., Jamali, H. R., & Polydoratou, P. (2005). Revisiting ‘obsolescence’ and journal article ‘decay’ through usage data: An analysis of digital journal use by year of publication. Information Processing and Management, 41(6), 1441–1461.
Onodera, N., & Yoshikane, F. (2015). Factors affecting citation rates of research articles. Journal of the Association for Information Science and Technology, 66(4), 739–764.
Peters, H., & Van Raan, A. (1994). On determinants of citation scores: A case study in chemical engineering. Journal of the American Society for Information Science, 45(1), 39–49.
Pislyakov, V., & Shukshina, E. (2014). Measuring excellence in Russia: Highly cited papers, leading institutions, patterns of national and international collaboration. Journal of the Association for Information Science and Technology, 65(11), 2321–2330.
Price, D. J. de Solla. (1963). Little science, big science. NewYork: Columbia University Press.
Price, D. J. de Solla. (1965). Networks of scientific papers: The pattern of bibliographic references indicates the nature of the scientific research front. Science, 149(3685), 510–515.
Price, D. J. de Solla. (1976). A general theory of bibliometric and other cumulative advantage processes. Journal of the American Society for Information Science, 27(5), 292–306.
Rabow, H. (2005). The discovery of discoveries: Exploring the dissemination of major findings in the life sciences. In Proceedings of the 10th international conference of the international society for scientometrics and informetrics, Karolinska University Press, Stockholm.
Rodríguez-Navarro, A. (2011). Measuring research excellence number of Nobel Prize achievements versus conventional bibliometric indicators. Journal of Documentation, 67(4), 582–600.
Song, Y., Ma, F., & Yang, S. (2015). Comparative study on the obsolescence of humanities and social sciences in China: Under the new situation of web. Scientometrics, 102(1), 365–388.
Spitz, A., & Horvát, E.-Á. (2014). Measuring long-term impact based on network centrality: Unraveling cinematic citations. PLoS ONE, 9(10), e108857.
Stegehuis, C., Litvak, N., & Waltman, L. (2015). Predicting the long-term citation impact of recent publications. Journal of Informetrics, 9, 642–657.
Sun, J., Min, C., & Li, J. (2016). A vector for measuring obsolescence of scientific articles. Scientometrics, 107(2), 745–757.
Tahamtan, I., Afshar, A. S., & Ahamdzadeh, K. (2016). Factors affecting number of citations: A comprehensive review of the literature. Scientometrics, 107(3), 1195–1225.
Van Dalen, H. P., & Henkens, K. (1999). How influential are demography journals? Population and Development Review, 25(2), 229–251.
Van Dalen, H. P., & Kenkens, K. (2005). Signals in science: On the importance of signaling in gaining attention in science. Scientometrics, 64(2), 209–233.
Ventura, O., & Mombrú, A. W. (2006). Use of bibliometric information to assist research policy making. A comparison of publication and citation profiles of Full and Associate Professors at a School of Chemistry in Uruguay. Scientometrics, 69(2), 287–313.
Walters, G. D. (2006). Predicting subsequent citations to articles published in twelve crime-psychology journals: Author impact versus journal impact. Scientometrics, 69(3), 499–510.
Wang, M., Yu, G., Xu, J., He, H., Yu, D., & An, S. (2012). Development a case-based classifier for predicting highly cited papers. Journal of Informetrics, 6(4), 586–599.
Wang, M., Yu, G., & Yu, D. (2011). Mining typical features for highly cited papers. Scientometrics, 87(3), 695–706.
Yamashita, Y., & Yoshinaga, D. (2014). Influence of researchers’ international mobilities on publication: A comparison of highly cited and uncited papers. Scientometrics, 101(2), 1475–1489.
Yang, S., & Han, R. (2015). Breadth and depth of citation distribution. Information Processing and Management, 51(2), 130–140.
Zhang, J. J., & Guan, J. C. (2017). Scientific relatedness and intellectual base: A citation analysis of un-cited and highly-cited papers in the solar energy field. Scientometrics, 110(1), 141–162.
Acknowledgements
This work was supported by the special funds of Central College Basic Scientific Research Bursary (Grant No. 2572014DB05), the National Natural Science Foundation of China (Grant No. 71473034), and the financial assistance from Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province (Grant No. LBH-Q16003).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, M., Li, S. & Chen, G. Detecting latent referential articles based on their vitality performance in the latest 2 years. Scientometrics 112, 1557–1571 (2017). https://doi.org/10.1007/s11192-017-2433-8
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-017-2433-8