IDEAS home Printed from https://ideas.repec.org/a/nas/journl/v118y2021pe2011695118.html
   My bibliography  Save this article

How quantifying the shape of stories predicts their success

Author

Listed:
  • Olivier Toubia

    (Marketing Division, Columbia Business School, Columbia University, New York, NY 10027)

  • Jonah Berger

    (Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104)

  • Jehoshua Eliashberg

    (Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104)

Abstract

Narratives, and other forms of discourse, are powerful vehicles for informing, entertaining, and making sense of the world. But while everyday language often describes discourse as moving quickly or slowly, covering a lot of ground, or going in circles, little work has actually quantified such movements or examined whether they are beneficial. To fill this gap, we use several state-of-the-art natural language-processing and machine-learning techniques to represent texts as sequences of points in a latent, high-dimensional semantic space. We construct a simple set of measures to quantify features of this semantic path, apply them to thousands of texts from a variety of domains (i.e., movies, TV shows, and academic papers), and examine whether and how they are linked to success (e.g., the number of citations a paper receives). Our results highlight some important cross-domain differences and provide a general framework that can be applied to study many types of discourse. The findings shed light on why things become popular and how natural language processing can provide insight into cultural success.

Suggested Citation

  • Olivier Toubia & Jonah Berger & Jehoshua Eliashberg, 2021. "How quantifying the shape of stories predicts their success," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(26), pages 2011695118-, June.
    • Handle: RePEc:nas:journl:v:118:y:2021:p:e2011695118
    as

    Download full text from publisher

    File URL: http://www.pnas.org/content/118/26/e2011695118.full
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rubin, Dan & Mohr, Iris & Kumar, V., 2022. "Beyond the box office: A conceptual framework for the drivers of audience engagement," Journal of Business Research, Elsevier, vol. 151(C), pages 473-488.
    2. Jingda Ding & Yifan Chen & Chao Liu, 2023. "Exploring the research features of Nobel laureates in Physics based on the semantic similarity measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5247-5275, September.
    3. Jonah Berger & Grant Packard & Reihane Boghrati & Ming Hsu & Ashlee Humphreys & Andrea Luangrath & Sarah Moore & Gideon Nave & Christopher Olivola & Matthew Rocklage, 2022. "Marketing insights from text analysis," Marketing Letters, Springer, vol. 33(3), pages 365-377, September.
    4. Mikaela Irene Fudolig & Thayer Alshaabi & Kathryn Cramer & Christopher M. Danforth & Peter Sheridan Dodds, 2023. "A decomposition of book structure through ousiometric fluctuations in cumulative word-time," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    5. Liu, Jialin & Chen, Hongkan & Liu, Zhibo & Bu, Yi & Gu, Weiye, 2022. "Non-linearity between referencing behavior and citation impact: A large-scale, discipline-level analysis," Journal of Informetrics, Elsevier, vol. 16(3).
    6. Jiyeon Hong & Paul R. Hoban, 2022. "Writing More Compelling Creative Appeals: A Deep Learning-Based Approach," Marketing Science, INFORMS, vol. 41(5), pages 941-965, September.
    7. Shengzhi Huang & Jiajia Qian & Yong Huang & Wei Lu & Yi Bu & Jinqing Yang & Qikai Cheng, 2022. "Disclosing the relationship between citation structure and future impact of a publication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1025-1042, July.

    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:nas:journl:v:118:y:2021:p:e2011695118. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Eric Cain (email available below). General contact details of provider: http://www.pnas.org/ .

    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.