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You look like my 14-year-old daughter

A corpus-based study of sexist language in everyday sexism Twitter stories

Bibliographic Data

ID7159798
AuthorsWanwen Wang (0000-0002-8026-5556, Hong Kong Polytechnic University, corresponding author), Jonathan Ngai (0009-0002-2883-5184, City University of Hong Kong)
Year2025
Volume13
Issue2
Pages155-181
Publication date2025-08-26
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Language Aggression and Conflict (JOURNAL)
Journal identifiersISSN: 2213-1272 • E-ISSN: 2213-1280
PublisherJohn Benjamins Publishing Company (PUBLISHER • NL)
DOI10.1075/jlac.00090.wan
OpenAlexW4390114146
LanguageEN
Citations received1
References cited41

The main purpose of this corpus-based study is to examine the different types of sexist language women are subjected to in their daily interactions with men, together with their hidden ideologies. To this end, we analysed a total of 1,118 English tweets posted on the hashtag #everydaysexism on Twitter over a year. Results indicate that women experience both overt and indirect verbal aggression in different domains of life, expressed through a range of sexist linguistic markers, and that such aggressions often reflect the users’ beliefs and values about men and women. By using a category-based model to examine a feminist narrative hashtag where women’s experiences of sexism are shared, our study offers a robust and principled approach to conducting a corpus-based, cross-domain discourse analysis of sexism in daily communication

Aggression · Daughter · Ideology · Linguistics · Narrative · Political science · Politics · Discourse Analysis in Language Studies · Gender Studies in Language · Gender, Feminism, and Media · Psychology · Social Psychology

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Unique citing works1
Citations per year1
Citation span2026 - 2026 (1)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 1

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