IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i11d10.1007_s11192-021-04137-0.html
   My bibliography  Save this article

Modelling trend life cycles in scientific research using the Logistic and Gompertz equations

Author

Listed:
  • E. Tattershall

    (The University of Manchester)

  • G. Nenadic

    (The University of Manchester)

  • R. D. Stevens

    (The University of Manchester)

Abstract

Scientific topics vary in popularity over time. In this paper, we model the life cycles of 200 trending topics by fitting the Logistic and Gompertz models to their frequency over time in published abstracts. Unlike other work, the topics we use are algorithmically extracted from large datasets of abstracts covering computer science, particle physics, cancer research, and mental health. We find that the Gompertz model produces lower median error, leading us to conclude that it is the more appropriate model. Since the Gompertz model is asymmetric, with a steep rise followed a long tail, this implies that scientific topics follow a similar trajectory. We also explore the case of double-peaking curves and find that in some cases, topics will peak multiple times as interest resurges. Finally, when looking at the different scientific disciplines, we find that the lifespan of topics is longer in some disciplines (e.g. cancer research and mental health) than it is others, which may indicate differences in research process and culture between these disciplines.

Suggested Citation

  • E. Tattershall & G. Nenadic & R. D. Stevens, 2021. "Modelling trend life cycles in scientific research using the Logistic and Gompertz equations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9113-9132, November.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:11:d:10.1007_s11192-021-04137-0
    DOI: 10.1007/s11192-021-04137-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04137-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-021-04137-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. E. Tattershall & G. Nenadic & R. D. Stevens, 2020. "Detecting bursty terms in computer science research," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 681-699, January.
    2. Bettencourt, Luís M.A. & Cintrón-Arias, Ariel & Kaiser, David I. & Castillo-Chávez, Carlos, 2006. "The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 513-536.
    3. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    4. Luís M. A. Bettencourt & David I. Kaiser & Jasleen Kaur & Carlos Castillo-Chávez & David E. Wojick, 2008. "Population modeling of the emergence and development of scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 495-518, June.
    5. Adamuthe, Amol C. & Thampi, Gopakumaran T., 2019. "Technology forecasting: A case study of computational technologies," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 181-189.
    6. Ho, Jonathan C. & Saw, Ewe-Chai & Lu, Louis Y.Y. & Liu, John S., 2014. "Technological barriers and research trends in fuel cell technologies: A citation network analysis," Technological Forecasting and Social Change, Elsevier, vol. 82(C), pages 66-79.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    2. Xia Gao & Jiancheng Guan, 2012. "Network model of knowledge diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(3), pages 749-762, March.
    3. Bettencourt, Luís M.A. & Kaiser, David I. & Kaur, Jasleen, 2009. "Scientific discovery and topological transitions in collaboration networks," Journal of Informetrics, Elsevier, vol. 3(3), pages 210-221.
    4. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    5. Wang, Haiying & Wang, Jun & Small, Michael & Moore, Jack Murdoch, 2019. "Review mechanism promotes knowledge transmission in complex networks," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 113-125.
    6. Frédéric Goulet & Matthieu Hubert, 2020. "Making a Place for Alternative Technologies: The Case of Agricultural Bio‐Inputs in Argentina," Review of Policy Research, Policy Studies Organization, vol. 37(4), pages 535-555, July.
    7. M. Laura Frigotto & Massimo Riccaboni, 2011. "A few special cases: scientific creativity and network dynamics in the field of rare diseases," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 397-420, October.
    8. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    9. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    10. Wang, Haiying & Wang, Jun & Small, Michael, 2018. "Knowledge transmission model with differing initial transmission and retransmission process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 478-488.
    11. Cristian Mejia & Yuya Kajikawa, 2018. "Using acknowledgement data to characterize funding organizations by the types of research sponsored: the case of robotics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 883-904, March.
    12. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    13. Xiaoyu Liu & Alan L. Porter, 2020. "A 3-dimensional analysis for evaluating technology emergence indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 27-55, July.
    14. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analysis knowledge transmission process in complex networks by considering internalization mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    15. Kiss, Istvan Z. & Broom, Mark & Craze, Paul G. & Rafols, Ismael, 2010. "Can epidemic models describe the diffusion of topics across disciplines?," Journal of Informetrics, Elsevier, vol. 4(1), pages 74-82.
    16. Simone Turchetti & Roberto Lalli, 2020. "Envisioning a “science diplomacy 2.0”: on data, global challenges, and multi-layered networks," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-9, December.
    17. Wang, Haiying & Wang, Jun & Ding, Liting & Wei, Wei, 2017. "Knowledge transmission model with consideration of self-learning mechanism in complex networks," Applied Mathematics and Computation, Elsevier, vol. 304(C), pages 83-92.
    18. Jiancheng Guan & Wenjia Zhu, 2014. "How knowledge diffuses across countries: a case study in the field of management," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 2129-2144, March.
    19. Bhattacharya, Saumik & Gaurav, Kumar & Ghosh, Sayantari, 2019. "Viral marketing on social networks: An epidemiological perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 478-490.
    20. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:126:y:2021:i:11:d:10.1007_s11192-021-04137-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.