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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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“Another interesting study is that of Newman et al. (2008) which examined texts from essays, diaries, descriptions of pictures, and transcripts of everyday conversations; a total of 46 million words were analysed (from 11609 respondents).”
BackgroundMultiplicity and uncertainty: Media coverage of autism causation
Zhai, Y., Sun, S., Wang, F., & Ding, Y. (2017) · Journal of Informetrics · doi:10.1016/j.joi.2017.07.005 · Cited by 4
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“In contrast, numeric characters have been previously correlated with male-authored texts in [47,48].”
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MethodEvaluation and Sociolinguistic Analysis of Text Features for Gender and Age Identification
Simaki, V., Mporas, I., & Megalooikonomou, V. (2016) · American Journal of Engineering and Applied Sciences · doi:10.3844/ajeassp.2016.868.876 · Cited by 7
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“Using LIWC, researchers have found differences in word usage between high-power and low-power individuals [10] and be-tween females and males [19], amongst many other identity group differences.”
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BackgroundLeFebvre, L. E., Brody, N., & Blackburn, K. G. (2019) · Discourse Processes · doi:10.1080/0163853x.2019.1627647 · Cited by 4
Gender variation in the discourse of online hotel reviews: A corpus-based semantic analysis
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Additional evidence on gender and language in academic economics research
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“…offered researchers the possibility of linking spontaneous language use to a wide range of behavioral data and have led to crucial insights (Tausczik and Pennebaker, 2010; Newman et al., 2003; Kahn et al., 2007; Holmes et al., 2007; Leshed et al., 2007; Newman et al., 2008; Kacewicz et al., 2014).”
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“The differences have been observed in many previous studies, the most notable of which is the study by Newman et al. (2008), who enumerated the linguistic features typically used by either males or females.”
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“As Newman et al. (2008: 233) point out: ‘ [I]t is important to note that our analyses merely identify how men and women communicate differently, without addressing the issue of why these differences exist.”
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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
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.
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