Gender Differences in Language Use: An Analysis of 14,000 Text Samples

By Matthew L. Newman, Carla J. Groom, Lori D. Handelman, James W. Pennebaker. 2008

801 citations recorded by the source corpus.

Subfields: Language and Linguistics, Literature and Literary Theory, Human-Computer Interaction

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Citations in the corpus

Getting Gender Right in Neural Machine Translation (2019, background): “…linguistic studies, which have shown that gender does not only have an effect on morphological agreement, but also manifests itself in other ways as males and females have different preferences when it comes to different types of constructions, word choices etc. (Newman et al., 2008; Coates, 2015).”
Motivating Personality-aware Machine Translation (2015, background): “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.”
Demographics of News Sharing in the U.S. Twittersphere (2017, methodology): “, the LIWC software ﬁnds the occurrence of the words in each category. The output is the proportion of the words in each category to the total words in the text. LIWC Dimension Our data Newman et al. [30] Pronouns First-person singular M<F M<F Third-person M<F M<F Linguistic dimensions N”
Alexa, Google, Siri: What are Your Pronouns? Gender and Anthropomorphism in the Design and Perception of Conversational Assistants (2021, methodology): “Following West et al. (2019), we argue that designing conversational assistants with young, subservient female personas can perpetuate negative gender stereotypes, and lead to abusive, misogynistic behaviour in the real world.”
Personalized Machine Translation: Preserving Original Author Traits (2016, background): “We provide here a brief summary of research relevant to our work.”
Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style (2017, background): “…male class is non-alphabetical symbols including symbols for euro ( e ) and percent (%), and numerals (as digits) – the latter were also found to be more indicative of male authors and speakers in an English corpus of various genres (Newman et al., 2008) and the spoken part of BNC (Baker, 2014).”
Gender Bias in Machine Translation (2021, result): “For instance, it opts for think rather than believe, which is in concordance with corpus studies claiming a tendency for women to use less assertive speech (Newman et al., 2008).”
These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution (2017, background): “Women write more about emotions, men use more numbers (Newman et al., 2008).”
Stylometric Analysis of Parliamentary Speeches: Gender Dimension (2017, background): “Understanding gender differences in professional environment would assist in a more balanced atmosphere (Herring and Paolillo, 2006; Mullany, 2007), however results on extent of variation depending on context of communication in professional setting are inconclusive(Newman et al., 2008).”
Gendered Language in Resumes and its Implications for Algorithmic Bias in Hiring (2022, background): “Scholars have since studied linguistic gender differences in a wide range of contexts including articles, social media posts, emails, grants (Newman et al., 2008; Argamon et al., 2003; Streib et al., 2019; Urquhart-Cronish and Otto, 2019; Colley and Todd, 2002).”
Are There Necessary Conditions for Inducing a Sense of Sarcastic Irony? (2012, background): “The authors of the LIWC argued that the word count “: : : fails to appreciate sarcasm or irony” (Newman et al., 2008, p. 217).”
PoKi: A Large Dataset of Poems by Children (2020, result): “Gender differences in VAD over development: Although the overall higher valence of female poems is consistent with previous linguistic analyses (Newman et al., 2008), contrary to our hypothesis, there appeared to be a stronger decrease in valence in poems written by females.”