“You look like my 14-year-old daughter” : a corpus-based study of sexist language in everyday sexism Twitter stories

Abstract

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.