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Gender Differences in Language Use: An Analysis of 14,000 Text Samples
Abstract
Differences in the ways that men and women use language have long been of interest in the study of discourse. Despite extensive theorizing, actual empirical investigations have yet to converge on a coherent picture of gender differences in language. A significant reason is the lack of agreement over the best way to analyze language. In this research, gender differences in language use were examined using standardized categories to analyze a database of over 14,000 text files from 70 separate studies. Women used more words related to psychological and social processes. Men referred more to object properties and impersonal topics. Although these effects were largely consistent across different contexts, the pattern of variation suggests that gender differences are larger on tasks that place fewer constraints on language use.
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“For example, multiple studies report that emoticons (i.e. ‘:)’ ‘:-(‘) are used more often by females [34], [36], [38], but Huffaker & Calvert found males use them more in a sample of 100 teenage bloggers [39].”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [34]
In the citing paper
“Males used more swear words, object references (e.g., ‘xbox’ and swear words) [34], [89].”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [34]
In the citing paper
“they mention more psychological and social processes [34] (e.g., ‘love you’ and ‘3’ –a heart)”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [34]
In the citing paper
“Males used more swear words, object references (e.g., ‘xbox’ and swear words) [34], [89].”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [34]
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A social semiotic analysis of gender power in Nigeria’s newspaper political cartoons
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“Studies of gender and discourse (Newman et al. 2008; Ezeifeka and Osakwe 2013) have focused on the use of language in gender-related matters.”
BackgroundSterling, J., & Jost, J. T. (2018) · Journal of Language and Politics · doi:10.1075/jlp.17034.ste · Cited by 24
Evidentiality in gendered styles in spoken English
Söderqvist, E. B. (2020) · ICAME journal · doi:10.2478/icame-2020-0001 · Cited by 20
“These indicators (or a close approximation in the case of people reference; see discussion in the following paragraph) were found by Newman et al. (2008) to be produced by women more often than men.”
BackgroundMethodResultInfluentialPolítica 2.0 en Ecuador. Análisis del discurso y la comunicación política en Facebook
Altamirano-Benítez, V., Ruiz-Aguirre, P., & Baquerizo-Neira, G. (2022) · Revista Latina de Comunicación Social · doi:10.4185/rlcs-2022-1539 · Cited by 20
Wang, Q. H., & Hu, G. (2023) · English for Specific Purposes · doi:10.1016/j.esp.2022.12.006 · Cited by 20
Valinejad, J., & Mili, L. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3277450 · Cited by 20
N-Gram Approach for Gender Prediction
Reddy, T. R., Vardhan, B. V., & Reddy, P. V. (2017) · IEEE International Advance Computing Conference · doi:10.1109/iacc.2017.0176 · Cited by 20
“Prior works [2, 4] found that the male authors use more 2017 IEEE 7th International Advance Computing Conference”
BackgroundInvestigating the Role of Emotion-Based Features in Author Gender Classification of Text
Montero, C. S., Munezero, M., & Kakkonen, T. (2014) · Lecture notes in computer science · doi:10.1007/978-3-642-54903-8_9 · Cited by 20
“[35], in which they used the Linguistic Inquiry and Word Count (LIWC) on a set of over 14,000 documents, and found differences in emotion expression between genders.”
MethodSentiment variations affected by urban temperature and landscape across China
He, H., & Sun, R. (2024) · Cities · doi:10.1016/j.cities.2024.104933 · Cited by 19
Karlsson, K., Sikström, S., Jönsson, F. U., Sendén, M. G., & Willander, J. (2019) · Journal of Cognitive Psychology · doi:10.1080/20445911.2019.1659281 · Cited by 20
Lexical Patterns in Adolescents’ Online Writing: The Impact of Age, Gender, and Education
Hilte, L., Daelemans, W. M. P., & Vandekerckhove, R. (2020) · Written Communication · doi:10.1177/0741088320917921 · Cited by 24
“This holds, for instance, for the male preference for swear words (Mehl & Pennebaker, 2003, p. 866; Newman et al., 2008, pp. 213–214, 223; Schwartz et al., 2013, p. 8) and for lexemes related to football and gaming (Argamon et al., 2009, p. 121; Schwartz et al., 2013, p. 8) versus the female…”
Influential
Metadata
- Authors
- Newman, M. L., Groom, C. J., Handelman, L. D., & Pennebaker, J. W.
- Affiliations
- Arizona State University (US)
- Department for Work and Pensions (GB)
- Oxford University Press (United Kingdom) (GB)
- The University of Texas at Austin (US)
- ORCID
- Pennebaker, James W.
- Venue
- Discourse Processes
- Publisher
- Informa UK Limited
- Published
- 2008-05-15
- Volume
- 45
- Issue
- 3
- Pages
- 211-236
- ISSN
- 0163-853X (ISSN-L)
- 1532-6950
- OpenAlex
- W2139875525
- Cited by
- 788
- References
- 54
- Citations by year
- 2026: 28 · 2025: 47 · 2024: 48 · 2023: 61 · 2022: 91 · 2021: 57 · 2020: 63 · 2019: 53 · 2018: 59 · 2017: 61 · 2016: 46 · 2015: 38 · 2014: 59 · 2013: 21 · 2012: 20
- FWCI
- 27.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Discourse Processes (publishedVersion)
- Topics
- Social Sciences › Gender Studies › Gender Studies in Language
- Social Sciences › Linguistics and Language › Discourse Analysis in Language Studies
- Social Sciences › Linguistics and Language › Linguistic Variation and Morphology
- Keywords
- gender differences in language, language use, women's language, men's language, text analysis, topic categories, object properties, discourse analysis
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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.
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