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Predicting highly cited papers: A Method for Early Detection of Candidate Breakthroughs

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  • Ponomarev, Ilya V.
  • Williams, Duane E.
  • Hackett, Charles J.
  • Schnell, Joshua D.
  • Haak, Laurel L.

Abstract

Scientific breakthroughs are rare events, and usually recognized retrospectively. We developed methods for early detection of candidate breakthroughs, based on dynamics of publication citations and used a quantitative approach to identify typical citation patterns of known breakthrough papers and a larger group of highly cited papers. Based on these analyses, we proposed two forecasting models that were validated using statistical methods to derive confidence levels. These findings can be used to inform research portfolio management practices.

Suggested Citation

  • Ponomarev, Ilya V. & Williams, Duane E. & Hackett, Charles J. & Schnell, Joshua D. & Haak, Laurel L., 2014. "Predicting highly cited papers: A Method for Early Detection of Candidate Breakthroughs," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 49-55.
    • Handle: RePEc:eee:tefoso:v:81:y:2014:i:c:p:49-55
    DOI: 10.1016/j.techfore.2012.09.017
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    Cited by:

    1. Haydar Yalcin & Tugrul Daim, 2021. "Mining research and invention activity for innovation trends: case of blockchain technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3775-3806, May.
    2. Holly N. Wolcott & Matthew J. Fouch & Elizabeth R. Hsu & Leo G. DiJoseph & Catherine A. Bernaciak & James G. Corrigan & Duane E. Williams, 2016. "Modeling time-dependent and -independent indicators to facilitate identification of breakthrough research papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 807-817, May.
    3. Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "Can we predict ESI highly cited publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 109-125, January.
    4. J. J. Winnink & Robert J. W. Tijssen, 2015. "Early stage identification of breakthroughs at the interface of science and technology: lessons drawn from a landmark publication," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 113-134, January.
    5. Kai Li & Jason Rollins & Erjia Yan, 2018. "Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 1-20, April.
    6. Shiyun Wang & Yaxue Ma & Jin Mao & Yun Bai & Zhentao Liang & Gang Li, 2023. "Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 150-167, February.
    7. Xian Li & Ronald Rousseau & Liming Liang & Fangjie Xi & Yushuang Lü & Yifan Yuan & Xiaojun Hu, 2022. "Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2105-2122, April.
    8. 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.
    9. Alonso Rodríguez-Navarro & Francis Narin, 2018. "European Paradox or Delusion—Are European Science and Economy Outdated?," Science and Public Policy, Oxford University Press, vol. 45(1), pages 14-23.
    10. Xue Wang & Xuemei Yang & Jian Du & Xuwen Wang & Jiao Li & Xiaoli Tang, 2021. "A deep learning approach for identifying biomedical breakthrough discoveries using context analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5531-5549, July.
    11. Luo, Zhuoran & Lu, Wei & He, Jiangen & Wang, Yuqi, 2022. "Combination of research questions and methods: A new measurement of scientific novelty," Journal of Informetrics, Elsevier, vol. 16(2).
    12. Lachance, Christian & Larivière, Vincent, 2014. "On the citation lifecycle of papers with delayed recognition," Journal of Informetrics, Elsevier, vol. 8(4), pages 863-872.
    13. Nguyen, Ai Linh & Liu, Wenyuan & Khor, Khiam Aik & Nanetti, Andrea & Cheong, Siew Ann, 2020. "The golden eras of graphene science and technology: Bibliographic evidences from journal and patent publications," Journal of Informetrics, Elsevier, vol. 14(4).
    14. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    15. Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).
    16. Ilya V. Ponomarev & Brian K. Lawton & Duane E. Williams & Joshua D. Schnell, 2014. "Breakthrough paper indicator 2.0: can geographical diversity and interdisciplinarity improve the accuracy of outstanding papers prediction?," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 755-765, September.
    17. Jianhua Hou & Xiucai Yang, 2019. "Patent sleeping beauties: evolutionary trajectories and identification methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 187-215, July.
    18. Ho Fai Chan & Malka Guillot & Lionel Page & Benno Torgler, 2015. "The inner quality of an article: Will time tell?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 19-41, July.
    19. Bornmann, Lutz & Tekles, Alexander & Zhang, Helena H. & Ye, Fred Y., 2019. "Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data," Journal of Informetrics, Elsevier, vol. 13(4).
    20. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    21. Winnink, J.J. & Tijssen, Robert J.W. & van Raan, A.F.J., 2019. "Searching for new breakthroughs in science: How effective are computerised detection algorithms?," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 673-686.
    22. Andrea Bonaccorsi & Nicola Melluso & Francesco Alessandro Massucci, 2022. "Exploring the antecedents of interdisciplinarity at the European Research Council: a topic modeling approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6961-6991, December.
    23. Jos J. Winnink & Robert J. W. Tijssen & Anthony F. J. van Raan, 2016. "Theory‐changing breakthroughs in science: The impact of research teamwork on scientific discoveries," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1210-1223, May.
    24. Sepideh Fahimifar & Khadijeh Mousavi & Fatemeh Mozaffari & Marcel Ausloos, 2023. "Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3685-3712, August.

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