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Understanding the onset of hot streaks across artistic, cultural, and scientific careers

Author

Listed:
  • Lu Liu

    (Northwestern University
    Northwestern University
    Northwestern University
    Pennsylvania State University)

  • Nima Dehmamy

    (Northwestern University
    Northwestern University
    Northwestern University)

  • Jillian Chown

    (Northwestern University
    Northwestern University)

  • C. Lee Giles

    (Pennsylvania State University
    Pennsylvania State University)

  • Dashun Wang

    (Northwestern University
    Northwestern University
    Northwestern University
    Northwestern University)

Abstract

Across a range of creative domains, individual careers are characterized by hot streaks, which are bursts of high-impact works clustered together in close succession. Yet it remains unclear if there are any regularities underlying the beginning of hot streaks. Here, we analyze career histories of artists, film directors, and scientists, and develop deep learning and network science methods to build high-dimensional representations of their creative outputs. We find that across all three domains, individuals tend to explore diverse styles or topics before their hot streak, but become notably more focused after the hot streak begins. Crucially, hot streaks appear to be associated with neither exploration nor exploitation behavior in isolation, but a particular sequence of exploration followed by exploitation, where the transition from exploration to exploitation closely traces the onset of a hot streak. Overall, these results may have implications for identifying and nurturing talents across a wide range of creative domains.

Suggested Citation

  • Lu Liu & Nima Dehmamy & Jillian Chown & C. Lee Giles & Dashun Wang, 2021. "Understanding the onset of hot streaks across artistic, cultural, and scientific careers," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25477-8
    DOI: 10.1038/s41467-021-25477-8
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    Citations

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    Cited by:

    1. Feng Shi & James Evans, 2023. "Surprising combinations of research contents and contexts are related to impact and emerge with scientific outsiders from distant disciplines," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Oleg Sobchuk & Artjoms Šeļa, 2024. "Computational thematics: comparing algorithms for clustering the genres of literary fiction," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    3. Li, Heyang & Wu, Meijun & Wang, Yougui & Zeng, An, 2022. "Bibliographic coupling networks reveal the advantage of diversification in scientific projects," Journal of Informetrics, Elsevier, vol. 16(3).
    4. Guo, Liying & Wang, Yang & Li, Meiling, 2024. "Exploration, exploitation and funding success: Evidence from junior scientists supported by the Chinese Young Scientists Fund," Journal of Informetrics, Elsevier, vol. 18(2).
    5. Tokmachev, Andrey M., 2023. "Hidden scales in statistics of citation indicators," Journal of Informetrics, Elsevier, vol. 17(1).
    6. Zhang, Lin & Qi, Fan & Sivertsen, Gunnar & Liang, Liming & Campbell, David, 2023. "Gender differences in the patterns and consequences of changing specialization in scientific careers," SocArXiv ep5bx, Center for Open Science.
    7. Weihua Li & Sam Zhang & Zhiming Zheng & Skyler J. Cranmer & Aaron Clauset, 2022. "Untangling the network effects of productivity and prominence among scientists," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    8. Liu, Meijun & Jaiswal, Ajay & Bu, Yi & Min, Chao & Yang, Sijie & Liu, Zhibo & Acuña, Daniel & Ding, Ying, 2022. "Team formation and team impact: The balance between team freshness and repeat collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    9. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.

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