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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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    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. 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).
    5. 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.
    6. 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.
    7. 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.

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