Verbal behavior and the future of social science.


Journal

The American psychologist
ISSN: 1935-990X
Titre abrégé: Am Psychol
Pays: United States
ID NLM: 0370521

Informations de publication

Date de publication:
30 May 2024
Historique:
medline: 30 5 2024
pubmed: 30 5 2024
entrez: 30 5 2024
Statut: aheadofprint

Résumé

Natural language processing (NLP)-previously the domain of a select few language and computer scientists-is undergoing an unprecedented surge in popularity across disciplines. The ubiquity of language data, alongside extremely rapid methodological innovations, has magnetized the field, attracting researchers with the promise of measuring, forecasting, and understanding the most central questions in business, psychology, biology, sociology, the humanities, and beyond. The power of language analysis to reveal insights into human thought, feeling, and behavior has become a core interest emerging from recent technological advances, which are being probed to unearth deeply embedded truths about the human condition. However, NLP research has reached a critical juncture, sitting at the cusp of societal transformation in many aspects of daily life. The details of how NLP research develops over the next 3-5 years will define this transformation. In this emerging, near-infinite space of NLP-driven research, we provide a critical frame of reference for how, when, and why these technologies should evolve in a particularly transdisciplinary manner. Specifically, we discuss (a) the urgency of pairing existing and emerging NLP research with existing scientific knowledge, theory, and principles from the behavioral sciences; (b) the coevolution of NLP technologies; and (c) the practical implications and ethical consequences of expanding language analysis using broader psychosocial theories of the human condition. While our discussion focuses principally on using language as a window in the

Identifiants

pubmed: 38815063
pii: 2024-87978-001
doi: 10.1037/amp0001319
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Ryan L Boyd (RL)

Department of Computer Science, Stony Brook University.

David M Markowitz (DM)

Department of Communication, Michigan State University.

Classifications MeSH