The DOI matches its Crossref record; 136 of the 137 references it cites were found in a registry or an index.
Psychological Aspects of Natural Language Use: Our Words, Our Selves
Abstract
The words people use in their daily lives can reveal important aspects of their social and psychological worlds. With advances in computer technology, text analysis allows researchers to reliably and quickly assess features of what people say as well as subtleties in their linguistic styles. Following a brief review of several text analysis programs, we summarize some of the evidence that links natural word use to personality, social and situational fluctuations, and psychological interventions. Of particular interest are findings that point to the psychological value of studying particles—parts of speech that include pronouns, articles, prepositions, conjunctives, and auxiliary verbs. Particles, which serve as the glue that holds nouns and regular verbs together, can serve as markers of emotional state, social identity, and cognitive styles.
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“From a psycholinguistic perspective, there is a relationship between word usage and personality, social conditions, and consequently emotional states [120].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [120]
In the citing paper
“Even if such words might not be useful for certain analyses, for other applications they may embed useful signals about e.g., authorship and/or emotional states (Pennebaker et al., 2003; Saif et al., 2014), threatening as a result the research validity (Denny and Spirling, 2016).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Pennebaker et al., 2003
In the citing paper
“Language can re fl ect these affective, cognitive, perceptual, and other psychological processes of individuals, including changes over time around speci fi c events (school shooter drills in our case) (Pennebaker et al., 2003).”
From Impacts of school shooter drills on the psychological well-being of American K-12 school communities: a social media study (2021) · cites this paper as Pennebaker et al., 2003
In the citing paper
“We take this as a proxy for social integration [19], suggesting the increasing importance of friendships and relationships as people age.”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [19]
In the citing paper
“It remains unclear to what extent the results of such studies generalize to less constrained real-world situations where people's personalities can influence not only how they write or talk about specific topics, but also what topics they choose to write or talk about (cf. Pennebaker, Mehl, & Niederhoffer, 2003).”
From Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers (2010) · cites this paper as Pennebaker, Mehl, & Niederhoffer, 2003
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Metadata
- Authors
- Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G.
- Affiliations
- The University of Texas at Austin (US)
- Venue
- Annual Review of Psychology
- Publisher
- Annual Reviews
- Published
- 2003-02
- Volume
- 54
- Issue
- 1
- Pages
- 547-577
- ISSN
- 0066-4308 (ISSN-L)
- 1545-2085
- OpenAlex
- W2100772444
- Cited by
- 2,624
- References
- 137
- Citations by year
- 2026: 158 · 2025: 149 · 2024: 167 · 2023: 174 · 2022: 166 · 2021: 159 · 2020: 165 · 2019: 163 · 2018: 157 · 2017: 139 · 2016: 151 · 2015: 129 · 2014: 136 · 2013: 97 · 2012: 116
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Psychology (publishedVersion)
- PubMed (publishedVersion)
- CLOK (University of Central Lancashire) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH052391, MH52391)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- natural language use, text analysis, personality, language style, particles, parts of speech, pronouns, prepositions, auxiliary verbs, emotional state, social identity, cognitive style, psychological intervention, self-representation
- MeSH terms
- Artificial Intelligence, Emotions, Health Status, Humans, Individuality, Interpersonal Relations, Mental Health, Natural Language Processing, Personality, Psycholinguistics, Semantics, Social Environment, Verbal Behavior
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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