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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Multiple studies report that emoticons are used more often by females, but Huffaker & Calvert found males use them more in a sample of 100 teenage bloggers.
“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]
Males used more swear words.
“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]
Females mention more psychological and social processes.
“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]
Males used more object references.
“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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Do women (still) use more intensifiers than men?
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Barnett, M. D., Maciel, I. V., Johnson, D. M., & Ciepluch, I. (2020) · Psychological Reports · doi:10.1177/0033294119900975 · Cited by 102 · see citing works
Personalized Machine Translation: Preserving Original Author Traits
Rabinovich, E., Patel, R. N., Mirkin, S., Specia, L., & Wintner, S. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1101 · Cited by 95 · see citing works
“We provide here a brief summary of research relevant to our work.”
Backgroundvan der Vegt, I., & Kleinberg, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60975-7_29 · Cited by 94 · see citing works
Who Gossips and How in Everyday Life?
Robbins, M. L., & Karan, A. (2019) · Social Psychological and Personality Science · doi:10.1177/1948550619837000 · Cited by 93 · see citing works
Exploiting sentiment analysis to track emotions in students' learning diaries
Munezero, M., Montero, C. S., Mozgovoy, M., & Sutinen, E. (2013) · Koli Calling International Conference on Computing Education Research · doi:10.1145/2526968.2526984 · Cited by 91 · see citing works
“[17] corpus 1 .”
Crossley, S. A., Cobb, T., & McNamara, D. S. (2013) · System · doi:10.1016/j.system.2013.08.002 · Cited by 89 · see citing works
Male and Female Users’ Differences in Online Technology Community Based on Text Mining
Sun, B., Mao, H., & Yin, C. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00806 · Cited by 93 · see citing works
Zimmermann, J., Wolf, M., Bock, A., Peham, D., & Benecke, C. (2013) · Journal of Research in Personality · doi:10.1016/j.jrp.2013.01.008 · Cited by 82 · see citing works
Age and gender in language, emoji, and emoticon usage in instant messages
Koch, T. K., Romero, P., & Stachl, C. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106990 · Cited by 83 · see citing works
It Takes a Motive: Communal and Agentic Articulated Interest and Candidate Emergence
Conroy, M., & Green, J. (2020) · Political Research Quarterly · doi:10.1177/1065912920933668 · Cited by 83 · see citing works
Representation Learning for Natural Language Processing
Liu, Z., Lin, Y., & Sun, M. (2020) · Springer eBooks · doi:10.1007/978-981-15-5573-2 · Cited by 84 · see citing works
Language and gender in Congressional speech
Yu, B. (2013) · Literary and Linguistic Computing · doi:10.1093/llc/fqs073 · Cited by 81 · see citing works
“to what extent do the differences depend on the communicative context (Krauss and Chiu, 1998; Newman et al., 2008).”
BackgroundMethodInfluentialDalton, S. G., & Richardson, J. D. (2015) · American Journal of Speech-Language Pathology · doi:10.1044/2015_ajslp-14-0161 · Cited by 81 · see citing works
Exploring Future Paths for Historical Sociolinguistics
(2017) · Advances in historical sociolinguistics · doi:10.1075/ahs.7 · Cited by 79 · see citing works
Grysman, A., Fıvush, R., Merrill, N. A., & Graci, M. E. (2016) · Memory & Cognition · doi:10.3758/s13421-016-0610-2 · Cited by 78 · see citing works
“Affect Robust findings indicate that women generally express more affect (see Leaper & Ayres, 2007, and Newman et al., 2008, for reviews), and that they express more affect in autobiographical recall (see, e.g., Bauer, Stennes, & Haight, 2003; Boals, 2010; Buckner & Fivush, 1998; Davis, 1999; Dunn,…”
Background‘The good old days’: An examination of nostalgia in Facebook posts
Davalos, S. V., Merchant, A., Rose, G. M., Lessley, B. J., & Teredesai, A. M. (2015) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2015.05.009 · Cited by 76 · see citing works
Fong, A., Roozenbeek, J., Goldwert, D., Rathje, S., & van der Linden, S. (2021) · Group Processes & Intergroup Relations · doi:10.1177/1368430220987596 · Cited by 68 · see citing works
“Having said this, it is important to note that conspiracy influencers generally have fewer followers, and the accounts were not balanced on gender, ethnicity, or other potential criteria which in and of themselves have shown to correlate with language use (Newman et al., 2008).”
BackgroundText-Based Detection of the Risk of Depression
Havigerová, J. M., Haviger, J., Kučera, D., & Hoffmannová, P. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00513 · Cited by 72 · see citing works
“The literature has repeatedly described that men and women generally differ in the preference of using some linguistic morpho-syntactic elements in their texts (e.g., Koppel et al., 2002; Argamon et al., 2003; Herring and Paolillo, 2006; Newman et al., 2008; Tausczik and Pennebaker, 2010; Rafi, 2019).”
BackgroundBurke, M., Kraut, R. E., & Joyce, E. (2010) · Small Group Research · doi:10.1177/1046496409351936 · Cited by 84 · see citing works
Robinson, M. D., Cassidy, D. M., Boyd, R. L., & Fetterman, A. K. (2015) · Journal of Applied Social Psychology · doi:10.1111/jasp.12306 · Cited by 69 · see citing works
Sentiment Analysis and Gender Differences in Earnings Conference Calls
Amicis, C. D., Falconieri, S., & Tastan, M. (2020) · Journal of Corporate Finance · doi:10.1016/j.jcorpfin.2020.101809 · Cited by 68 · see citing works
Pearl, R. L., Puhl, R. M., Himmelstein, M. S., Pinto, A. M., & Foster, G. D. (2020) · Annals of Behavioral Medicine · doi:10.1093/abm/kaaa026 · Cited by 72 · see citing works
“10) were interpreted with caution [59, 60].”
Research on gender differences in online health communities
Liu, X., Sun, M., & Li, J. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.12.019 · Cited by 66 · see citing works
Food for thought: Exploring how people think and talk about food online
Blackburn, K. G., Yılmaz, G., & Boyd, R. L. (2018) · Appetite · doi:10.1016/j.appet.2018.01.022 · Cited by 66 · see citing works
Sbarra, D. A., Law, R. W., Lee, L. A., & Mason, A. E. (2009) · Psychosomatic Medicine · doi:10.1097/psy.0b013e3181a23eee · Cited by 65 · see citing works
“In terms of the observed sex difference, women are more adroit at discussing emotional topics than men (45), and even when women report higher degrees of perceived negative emotion, the DMAT may not constitute an emotional challenge that requires a corresponding BP response.”
BackgroundMcIntyre, M. M., & Graziano, W. G. (2016) · Personality and Social Psychology Bulletin · doi:10.1177/0146167216653937 · Cited by 64 · see citing works
Emotions under Discussion: Gender, Status and Communication in Online Collaboration
Iosub, D., Laniado, D., Castillo, C., Morell, M. F., & Kaltenbrunner, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104880 · Cited by 64 · see citing works
“This is in line with previous literature, regarding both the online and offline context [37,38,44,66].”
BackgroundResultInfluentialLee, L. A., Sbarra, D. A., Mason, A. E., & Law, R. W. (2011) · Personal Relationships · doi:10.1111/j.1475-6811.2011.01360.x · Cited by 63 · see citing works
Text Analysis for Psychology: Methods, Principles, and PracticesPreprint
Kennedy, B., Ashokkumar, A., Boyd, R. L., & Dehghani, M. (2021) · doi:10.31234/osf.io/h2b8t · Cited by 57 · see citing works
Linguistic predictors of post-traumatic stress disorder symptoms following 11 September 2001
D'Andrea, W., Chiu, P. H., Casas, B. R., & Deldin, P. (2012) · Applied Cognitive Psychology · doi:10.1002/acp.1830 · Cited by 62 · see citing works
Weight bias internalization in a commercial weight management sample: prevalence and correlates
Pearl, R. L., Himmelstein, M. S., Puhl, R. M., Wadden, T. A., Wojtanowski, A. C., & Foster, G. D. (2019) · Obesity Science & Practice · doi:10.1002/osp4.354 · Cited by 62 · see citing works
Do (women’s) words matter? The influence of gendered language in entrepreneurial pitching
Balachandra, L., Fischer, K., & Brush, C. (2021) · Journal of Business Venturing Insights · doi:10.1016/j.jbvi.2021.e00224 · Cited by 61 · see citing works
Harjoto, M. A., Laksmana, I., & Lee, W. E. (2020) · Advances in Accounting · doi:10.1016/j.adiac.2020.100475 · Cited by 61 · see citing works
User Demographics and Language in an Implicit Social Network
Filippova, K. (2012) · Empirical Methods in Natural Language Processing · Cited by 60 · see citing works
Abercrombie, G., Curry, A. C., Pandya, M., & Rieser, V. (2021) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2021.gebnlp-1.4 · Cited by 66 · see citing works
“Following analysis and criticism of the effects of the genderised and anthropomorphic design of conversational agents (Cercas Curry and Rieser, 2018; West et al., 2019), the producers of some commercial conversational assistant systems have been at pains to claim that their products do not perpetuate negative stereoty…”
InfluentialGelfand, M. J., Severance, L. E., Lee, T., Bruss, C. B., Lun, J., Abdel-Latif, A.-H., Al‐Moghazy, A. A., & Ahmed, S. M. (2015) · Journal of Organizational Behavior · doi:10.1002/job.2026 · Cited by 60 · see citing works
Teso, E., Olmedilla, M., Martínez-Torres, M. D. R., & Toral, S. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.12.018 · Cited by 53 · see citing works
Motivating Personality-aware Machine Translation
Mirkin, S., Nowson, S., Brun, C., & Pérez, J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1130 · Cited by 59 · see citing works
“In English, for example, women tend to use firstperson pronouns such as “I” more than men (Newman et al., 2008); but this does not guarantee a gender-based usage difference for, say, “je” in French.”
BackgroundLinguistic analysis of municipal twitter feeds: Factors influencing frequency and engagement
Stone, J. A., & Can, S. H. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101468 · Cited by 57 · see citing works
Male and female politicians on Twitter: A machine learning approach
Beltran, J., Gallego, A., Huidobro, A., Romero, E. E., & Padró, L. (2020) · European Journal of Political Research · doi:10.1111/1475-6765.12392 · Cited by 57 · see citing works
Gender Differences in Family Dinnertime Conversations
Merrill, N. A., Gallo, E., & Fıvush, R. (2014) · Discourse Processes · doi:10.1080/0163853x.2014.958425 · Cited by 55 · see citing works
The Applicability of the Verifiability Approach to the Real World
Nahari, G. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812729-2.00014-8 · Cited by 55 · see citing works
Friend matters: sex differences in social language during autism diagnostic interviews
Cola, M., Yankowitz, L. D., Tena, K. G., Russell, A. R. B., Bateman, L., Knox, A., Plate, S. N., Cubit, L. S., Zampella, C., Pandey, J., et al. (2022) · Molecular Autism · doi:10.1186/s13229-021-00483-1 · Cited by 65 · see citing works
“Friendship in autism Humans use language to achieve a complex set of social goals, including meeting diverse situational demands and conforming to societal expectations – which often differ by sex [48].”
BackgroundTelling a convincing story: Richness in detail as a function of gender and information.
Nahari, G., & Pazuelo, M. (2015) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2015.08.005 · Cited by 55 · see citing works
Gender and emotion in everyday event memory
Wang, Q. (2012) · Memory · doi:10.1080/09658211.2012.743568 · Cited by 56 · see citing works
Analyzing Biases in Human Perception of User Age and Gender from Text
Flek, L., Carpenter, J. M., Giorgi, S., Ungar, L., & Preoțiuc-Pietro, D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1080 · Cited by 52 · see citing works
Narrative and gender as mutually constituted meaning-making systems
Fıvush, R., & Grysman, A. (2021) · Memory Mind & Media · doi:10.1017/mem.2021.4 · Cited by 53 · see citing works
“Using both big data analyses of extremely large databases of language use (Newman et al 2008; Schwartz et al 2013), and close qualitative study of conversations (Bischoping 1993; Burleson 2003; Tannen 2003), research indicates that women use more affiliative language than men (e.g., language aimed…”
MethodPerson-Centered Emotional Support and Gender Attributions in Computer-Mediated Communication
Spottswood, E. L., Walther, J. B., Holmstrom, A. J., & Ellison, N. B. (2013) · Human Communication Research · doi:10.1111/hcre.12006 · Cited by 52 · see citing works
Women and Men Have Different Discourse Biases for Pronoun Interpretation
Arnold, J. E. (2014) · Discourse Processes · doi:10.1080/0163853x.2014.946847 · Cited by 51 · see citing works
McDavitt, B., & Mutchler, M. G. (2014) · Journal of Adolescent Research · doi:10.1177/0743558414528974 · Cited by 51 · see citing works
Gender differences in communicative abstraction.
Joshi, P., Wakslak, C. J., Appel, G., & Huang, L. (2020) · Journal of Personality and Social Psychology · doi:10.1037/pspa0000177 · Cited by 40 · see citing works
Sturrock, A., Chilton, H., Foy, K., Freed, J., & Adams, C. (2021) · Autism · doi:10.1177/13623613211002047 · Cited by 49 · see citing works
“This may be due to naturally occurring differences in female conversation, favouring topics of emotions and other people demonstrated in TD (Newman et al., 2008) and autistic populations (Sedgewick et al., 2019).”
BackgroundWalkington, C., Clinton‐Lisell, V., & Shivraj, P. (2017) · American Educational Research Journal · doi:10.3102/0002831217737028 · Cited by 48 · see citing works
Gender differences in grief narrative construction: a myth or reality?
Stelzer, E., Atkinson, C., O'Connor, M., & Croft, A. (2019) · European Journal of Psychotraumatology · doi:10.1080/20008198.2019.1688130 · Cited by 47 · see citing works
“This is puzzling as it fails to replicate previous research reporting gender differences in various linguistic categories (Mehl & Pennebaker, 2003; Newman et al., 2008).”
BackgroundResultWhat's in a word? Linguistic characteristics of Adult Attachment Interviews
Cassidy, J. A., Sherman, L. J., & Jones, J. D. (2011) · Attachment & Human Development · doi:10.1080/14616734.2012.636649 · Cited by 47 · see citing works
“…is possible that the present finding that preoccupied participants swear more than dismissing participants may not have emerged in a sample containing men (for additional discussion of gender differences [e.g., women use more negations than men; Handelman & Lester, 2007], see Newman et al., 2008).”
Background
- 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 › Arts and Humanities › Literature and Literary Theory › 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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