Predicting gender from electronic discourse
Abstract
There is substantial evidence of gender differences in face-to-face communication, and we suspect that similar differences are present in electronic communication. We designed three studies to examine gender-preferential language style in electronic discourse. In Expt 1, participants sent electronic messages to a designated 'netpal'. A discriminant analysis showed that it was possible to successfully classify the participants by gender with 91.4% accuracy. In Expts 2 and 3, we wanted to determine whether readers of e-mails could accurately identify author gender. We gave participants a selection of messages from Expt 1 and asked them to predict the author's gender. It was found that for 14 of the 16 messages used, the gender of author was correctly predicted. In the third experiment, six messages about gender-neutral topics were composed. Using a subset of the variables identified in Expt 1, female and male versions of each message were created. When participants were asked to rate whether a female or a male wrote these messages, their ratings differed as a function of the message version. These findings establish that people use gender-preferential language in informal electronic discourse. Furthermore, readers of these messages can use these gender-linked language differences to identify the author's gender.
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Females used more emotion words.
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From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [87]
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Menachem, E. T.-B., & Livnat, Z. (2018) · Journal of Pragmatics · doi:10.1016/j.pragma.2018.08.014 · Cited by 12 · see citing works
In Search of the Cartesian Self
Young, G., & Whitty, M. T. (2010) · Theory & Psychology · doi:10.1177/0959354309345633 · Cited by 12 · see citing works
“Thomson and murachver (2001) found that women are more likely to make references to emotional and personal information online. moreover, these researchers found that participants in their experimental study were able to identify the gender of the person they were communicating with online.”
BackgroundGender and Technology: Social Context and Intersectionality
Campbell, K. (2020) · Springer eBooks · doi:10.1007/978-3-030-36119-8_7 · Cited by 15 · see citing works
Ferguson, S. A., & Olechowski, A. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610173 · Cited by 11 · see citing works
“Men communicate in an adversarial way, by expressing more critical and opposed views [13, 49, 51], disagreeing with others [39, 103], using aggressive [65] or hostile [89] tones, and using swear words [36, 53, 100], insults [114, 115], or threats [51].”
BackgroundInfluentialvan der Meij, H. (2007) · Sex Roles · doi:10.1007/s11199-007-9270-9 · Cited by 10 · see citing works
“Several gender studies with adults in face-to-face settings report a similar alignment for language use (e.g., Hanna and Murachver 1999; Thomson et al. 2001).”
BackgroundGender Clues and Cues: Online Interactions as Windows into Lay Theories about Men and Women
Williams, M. J., & Mendelsohn, G. A. (2008) · Basic and Applied Social Psychology · doi:10.1080/01973530802375136 · Cited by 10 · see citing works
“Two studies (Savicki, Kelley, & Oesterreich, 1999 ; Thomson & Murachver, 2001 ) demonstrated that the gender of e-mail authors could be identified at rates above chance.”
BackgroundInfluentialGender-based Study of Interactive Metadiscourse Markers in EFL Academic Writing
Alqahtani, S. N., & Abdelhalim, S. M. (2020) · Theory and Practice in Language Studies · doi:10.17507/tpls.1010.20 · Cited by 10 · see citing works
The Role of Gender in New Zealand Literature
Janssen, A. L., & Murachver, T. (2004) · Journal of Language and Social Psychology · doi:10.1177/0261927x04263822 · Cited by 10 · see citing works
“…focus to their communication (Holmes, 1995; Lang-Takac & Osterweil, 1992; Maltz & Borker, 1982) whereas men are more likely to focus on clarity, facts, and conveying their opinions (Mulac, Bradac, & Gibbons, 2001; Mulac, Studley, & Blau, 1990; Tannen, 1990; Thomson & Murachver, 2001).”
Self-citationGender rating gap in online reviews
Bayerl, A., Dover, Y., Riemer, H., & Shapira, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02003-6 · Cited by 9 · see citing works
“Women and men use different vocabulary in self-presentation 20,21 , and women tend more to limit the visibility of their profiles 22 .”
Gender Effects in Student Technical and Scientific Writing—A Corpus-Based Study
Boettger, R. K., & Wulff, S. (2019) · IEEE Transactions on Professional Communication · doi:10.1109/tpc.2019.2920029 · Cited by 9 · see citing works
Clinton stated, Trump exclaimed!
McDonnell, A. M. (2020) · Journal of Language and Politics · doi:10.1075/jlp.19085.mcd · Cited by 9 · see citing works
How Different Genders Use Profanity on Twitter?
Wong, S. C., Teh, P. L., & Cheng, C.-B. (2020) · International Conference on Compute and Data Analysis (ICCDA) · doi:10.1145/3388142.3388145 · Cited by 9 · see citing works
- Authors
- Thomson, R., & Murachver, T.
- Affiliations
- University of Otago (NZ)
- Corresponding author
- Murachver, Tamar
- ORCID
- Thomson, Rob
- Venue
- British Journal of Social Psychology
- Publisher
- Wiley
- Published
- 2001-06
- Volume
- 40
- Issue
- 2
- Pages
- 193-208
- ISSN
- 0144-6665 (ISSN-L)
- 2044-8309
- OpenAlex
- W2030498115
- Cited by
- 179
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 6 · 2023: 9 · 2022: 6 · 2021: 7 · 2020: 13 · 2019: 5 · 2018: 2 · 2017: 5 · 2016: 7 · 2015: 11 · 2014: 9 · 2013: 12 · 2012: 7
- FWCI
- 23.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- British Journal of Social Psychology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Gender Studies › Gender Studies in Language
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Human-Computer Interaction › Digital Communication and Language
- Keywords
- gender differences, digital communication, email, gendered language, informal discourse, gender stereotypes
- MeSH terms
- Adolescent, Adult, Affect, Communication, Female, Gender Identity, Humans, Male, Random Allocation, Social Perception, Verbal Behavior, Discriminant Analysis, Internet
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