ArticleProceedings of the National Academy of Sciences of the United States of America2021
How quantifying the shape of stories predicts their success.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Emotional Dialogue Predicts the Success of Prestige Television Episodes.Journal of psycholinguistic research · 2026Article
- Optimally sequencing semantic search predicts creativity.PloS one · 2026Article
- Neural representations of noncentral events during narrative encoding predict subsequent story ending originality.Science advances · 2025Article
- Narrative reversals and story success.Science advances · 2024Article
- The topography of thought.PNAS nexus · 2024Article
- Article
- Word embedding for social sciences: an interdisciplinary survey.PeerJ. Computer science · 2024Review
- Characterizing the mechanisms of social connection.Neuron · 2023Review
- Divergent semantic integration (DSI): Extracting creativity from narratives with distributional semantic modeling.Behavior research methods · 2023Article
- Quantifying the narrative flow of imagined versus autobiographical stories.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Linguistic, cultural, and narrative capital: computational and human readings of transfer admissions essays.Journal of computational social science · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.