Abstract
While a large majority of scientific publications get most of their citations within the initial few years after publication, there is an interesting number of papers—termed as sleeping beauties—which do not get much cited for several years after being published, but then suddenly start getting cited heavily. In this work, we focus on sleeping beauties (SBs) in the domain of Computer Science. We identify more than 5,000 sleeping beauties in Computer Science, and characterise them based on their sub-field and their citation profile after awakening. We also reveal some interesting factors which led to their awakening long after publication. Furthermore, we also propose a methodology for early identification of sleeping beauties, and develop a machine learning-based classification approach that attempts to classify publications based on whether they are likely to be SBs. The classifier achieves a precision of 0.73 and a recall of 0.45 in identifying SBs immediately after their year of publications, and the performance significantly improves with time. To our knowledge, this is the first study on sleeping beauties in Computer Science.
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References
Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.
Braun, T., Glänzel, W., & Schubert, A. (2010). On sleeping beauties, princes and other tales of citation distributions. Research Evaluation, 19(3), 195–202. doi:10.3152/095820210x514210.
Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., & Mukherjee, A. (2014). Towards a stratified learning approach to predict future citation counts. In Proceedings of ACM/IEEE-CS joint conference on digital libraries (pp. 351–360).
Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., & Mukherjee, A. (2015). On the categorization of scientific citation profiles in computer science. Communications of the ACM, 58(9), 82–90.
Chakraborty, T., Kumar, S., Reddy, M. D., Kumar, S., Ganguly, N., & Mukherjee, A. (2013). Automatic classification and analysis of interdisciplinary fields in computer sciences. In Proceedings of international conference on social computing (SocialCom) (pp. 180–187).
Chakraborty, T., & Nandi, S. (2017). Universal trajectories of scientific success. Knowledge and Information Systems. doi:10.1007/s10115-017-1080-y.
Garfield, E. (1989). Delayed recognition in scientific discovery: Citation frequency analysis aids the search for case history. Current Contents, 23, 3–9.
Garfield, E. (1999). Journal impact factor: A brief review. Canadian Medical Association Journal, 161(8), 979–980.
Garfield, E. (2001). Impact factors, and why they won’t go away. Nature, 411(6837), 522.
Glänzel, W., Schlemmer, B., & Thijs, B. (2003). Better late than never? On the chance to become highly cited only beyond the standard bibliometric time horizon. Scientometrics, 58(3), 571–586.
Ke, Q., Ferrara, E., Radicchi, F., & Flammini, A. (2015). Defining and identifying sleeping beauties in science. PNAS, 112(24), 7426–7431.
Li, J. (2014). Citation curves of all-elements-sleeping-beauties: Flash in the pan first and then delayed recognition. Scientometrics, 100(2), 595–601. doi:10.1007/s11192-013-1217-z.
Li, J., & Shi, D. (2015). Sleeping beauties in genius work: When were they awakened? Journal of the Association for Information Science and Technology, 67(2), 745–757. http://dblp.uni-trier.de/db/journals/scientometrics/scientometrics107.html#SunML16.
Li, J., Shi, D., Zhao, S. X., & Ye, F. Y. (2014). A study of the heartbeat spectra for sleeping beauties. Journal of Informetrics, 8(3), 493–502. doi:10.1016/j.joi.2014.04.002.
Li, J., & Ye, F. Y. (2012). The phenomenon of all-elements-sleeping-beauties in scientific literature. Scientometrics, 92(3), 795–799. doi:10.1007/s11192-012-0643-7.
Li, J., & Ye, F. Y. (2016). Distinguishing sleeping beauties in science. Scientometrics, 108(2), 821–828. doi:10.1007/s11192-016-1977-3.
Min, C., Sun, J., Pei, L., & Ding, Y. (2016). Measuring delayed recognition for papers: Uneven weighted summation and total citations. Journal of Informetrics, 10(4), 1153–1165. doi:10.1016/j.joi.2016.10.001.
Raan, A. F. J. V. (2004). Sleeping beauties in science. Scientometrics, 59(3), 461–466.
Redner, S. (2005). Citation statistics from 110 years of physical review. Physics Today, 58(6), 49–54.
Ruiz-Castillo, J. (2013). The role of statistics in establishing the similarity of citation distributions in a static and a dynamic context. Scientometrics, 96(1), 173–181. doi:10.1007/s11192-013-0954-3.
Solomon, D. J., Laakso, M., & Bjrk, B. C. (2013). A longitudinal comparison of citation rates and growth among open access journals. Journal of Informetrics, 7(3), 642–650. doi:10.1016/j.joi.2013.03.008. http://www.sciencedirect.com/science/article/pii/S175115771300028X.
Sun, J., Min, C., & Li, J. (2015). A vector for measuring obsolescence of scientific articles. In Proceedings of international society of scientometrics and informetrics conference.
Sun, J., Min, C., & Li, J. (2016). A vector for measuring obsolescence of scientific articles. Scientometrics, 107(2), 745–757. http://dblp.uni-trier.de/db/journals/scientometrics/scientometrics107.html#SunML16.
van Raan, A. F. J. (2015). Dormitory of physical and engineering sciences: Sleeping beauties may be sleeping innovations. PLOS ONE, 10(10), 1–38. doi:10.1371/journal.pone.0139786.
Wallace, M. L., Larivire, V., & Gingras, Y. (2009). Modeling a century of citation distributions. Journal of Informetrics, 3(4), 296–303. http://dblp.uni-trier.de/db/journals/joi/joi3.html#WallaceLG09
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Dey, R., Roy, A., Chakraborty, T. et al. Sleeping beauties in Computer Science: characterization and early identification. Scientometrics 113, 1645–1663 (2017). https://doi.org/10.1007/s11192-017-2543-3
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DOI: https://doi.org/10.1007/s11192-017-2543-3