The topography of thought.

academic success automated textual analysis language natural language processing thought

Journal

PNAS nexus
ISSN: 2752-6542
Titre abrégé: PNAS Nexus
Pays: England
ID NLM: 9918367777906676

Informations de publication

Date de publication:
May 2024
Historique:
received: 17 11 2023
accepted: 02 04 2024
medline: 8 5 2024
pubmed: 8 5 2024
entrez: 8 5 2024
Statut: epublish

Résumé

Whether speaking, writing, or thinking, almost everything humans do involves language. But can the semantic structure behind how people express their ideas shed light on their future success? Natural language processing of over 40,000 college application essays finds that students whose writing covers more semantic ground, while moving more slowly (i.e. moving between more semantically similar ideas), end up doing better academically (i.e. have a higher college grade point average). These relationships hold controlling for dozens of other factors (e.g. SAT score, parents' education, and essay content), suggesting that essay topography encodes information that goes beyond family background. Overall, this work sheds light on how language reflects thought, demonstrates that how people express themselves can provide insight into their future success, and provides a systematic, scalable, and objective method for quantifying the topography of thought.

Identifiants

pubmed: 38715729
doi: 10.1093/pnasnexus/pgae163
pii: pgae163
pmc: PMC11075530
doi:

Types de publication

Journal Article

Langues

eng

Pagination

pgae163

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.

Auteurs

Jonah Berger (J)

Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA.

Olivier Toubia (O)

Columbia Business School, Columbia University, New York, NY 10027, USA.

Classifications MeSH