“You look like my 14-year-old daughter”
A corpus-based study of sexist language in everyday sexism Twitter stories
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.
Article outline
- 1.Introduction
- 2.Literature review
- 2.1Hashtag feminism
- 2.2Gendered verbal aggression
- 2.2.1Overt sexist language
- 2.2.2Indirect sexist language
- 3.Data and methodology
- 3.1Data collection
- 3.2Data analysis
- 3.2.1Extraction of verbal aggression
- 3.2.2Identification of linguistic markers of overt and indirect verbal aggression
- 3.2.3Identification of prevalent domains
- 4.Findings
- 4.1Distribution of sexist markers in the #everydaySexism Twitter corpus
- 4.2Sexist markers used as forms of verbal aggression against women in the public sphere
- 4.3Sexist markers used as forms of verbal aggression against women in street harassment
- 4.4Sexist markers used as forms of verbal aggression against women in the private sphere
- 5.Discussion
- 6.Conclusion
- Acknowledgements
- Notes
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References