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Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field
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Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.
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“However, such a dictionary-based approach oversimplifies older people's interactions with SVA to a one-way initiative by the user and fails to capture the communicative nature of such interactions—the transmission and coordination of complex information with others (7).”
From Understanding older people's voice interactions with smart voice assistants: a new modified rule-based natural language processing model with human input (2024) · cites this paper as [7]
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Why do some academic articles receive more citations from policy communities?
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Qian, X., & Xu, K. (2024) · Habitat International · doi:10.1016/j.habitatint.2024.103257 · Cited by 11
Rethinking PowerPoint Slide Design for Multimedia Learning
LeFebvre, L., Parsons, M. M., Entwistle, C., Boyd, R. L., & Allen, M. R. (2022) · Journal of Educational Technology Systems · doi:10.1177/00472395221077628 · Cited by 15
Self-citationPolitical ideology shapes risk and benefit judgments of COVID‐19 vaccines
Rubaltelli, E., Dickert, S., Markowitz, D. M., & Slovic, P. (2023) · Risk Analysis · doi:10.1111/risa.14150 · Cited by 9
Text classification models for personality disorders identification
Jain, D., Arora, S., Jha, C. K., & Malik, G. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01219-8 · Cited by 11
Metadata
- Authors
- Boyd, R. L., & Schwartz, H. A.
- Affiliations
- Lancaster University (GB)
- Stony Brook University (US)
- Corresponding author
- Boyd, Ryan L.
- Venue
- Journal of Language and Social Psychology
- Publisher
- SAGE Publications
- Published
- 2020-10-28
- Volume
- 40
- Issue
- 1
- Pages
- 21-41
- ISSN
- 0261-927X (ISSN-L)
- 1552-6526
- PMC
- PMC8373026
- OpenAlex
- W3094938719
- Cited by
- 317
- References
- 83
- Citations by year
- 2026: 47 · 2025: 52 · 2024: 82 · 2023: 60 · 2022: 43 · 2021: 29 · 2020: 4
- FWCI
- 37.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Language and Social Psychology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (196255)
- National Institute on Alcohol Abuse and Alcoholism (R01AA028032)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- natural language analysis, verbal behavior, language and personality, computational text analysis, digital data sources, interdisciplinary research, methodological integration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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