He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist

37711

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nan

Abstract

AbstractIn a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automatically detect sexist content but also to identify if a message with a sexist content is really sexist or is a story of sexism experienced by a woman. We propose: (1) a new characterization of sexist content inspired by speech acts theory and discourse analysis studies, (2) the first French dataset annotated for sexism detection, and (3) a set of deep learning experiments trained on top of a combination of several tweet’s vectorial representations (word embeddings, linguistic features, and various generalization strategies). Our results are encouraging and constitute a first step towards offensive content moderation.

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nan

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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4055–4066 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 4055 He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist Patricia Chiril1, V´eronique Moriceau1, Farah Benamara1 Alda Mari2, Gloria Origgi2, Marl`ene Coulomb-Gully3 (1) IRIT, Universit´e de Toulouse, Universit´e Toulouse III - UPS, France {firstname.lastname}@irit.fr (2) Institut Jean Nicod, CNRS, ENS, EHESS, Paris, France {firstname.lastname}@ens.fr (3) LERASS, Universit´e de Toulouse, UT2J, France marlene.coulomb@univ-tlse2.fr Abstract In a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automat- ically detect sexist content but also to iden- tify if a message with a sexist content is re- ally sexist or is a story of sexism experienced by a woman. We propose: (1) a new charac- terization of sexist content inspired by speech acts theory and discourse analysis studies, (2) the first French dataset annotated for sexism detection, and (3) a set of deep learning ex- periments trained on top of a combination of several tweet’s vectorial representations (word embeddings, linguistic features, and various generalization strategies). Our results are en- couraging and constitute a first step towards offensive content moderation. 1 Introduction Sexism is prejudice or discrimination based on a person’s gender. It is based on the belief that one sex or gender is superior to another. It can take several forms from sexist remarks, gestures, be- haviours, practices, insults to rape or murder. Sex- ist hate speech is a message of inferiority usually directed against women at least in part because they are women, some authors refer to it as: ”words that wound” (Matsuda et al., 1993; Waldron, 2012; Del- gado et al., 2015). As defined by the Council of Europe, ”The aim of sexist hate speech is to hu- miliate or objectify women, to undervalue their skills and opinions, to destroy their reputation, to make them feel vulnerable and fearful, and to con- trol and punish them for not following a certain behaviour”1. Its psychological, emotional and/or physical impacts can be severe. In several coun- tries, sexist behaviours are now prohibited. See for example the French law of 27 January 2017 related to equality and citizenship, where penalties due to 1https://rm.coe.int/1680651592 discrimination are doubled (sexism is now consid- ered as an aggravating factor), law that extends to the internet and social media. Although overall misogyny and sexism share the common purpose of maintaining or restoring a patriarchal social order, Manne (2017) illustrates the contrast between the two ideologies. A sexist ideology (which often ”consists of assumptions, beliefs, theories, stereotypes and broader cultural narratives that represent men and women”) will tend to discriminate between men and women and has the role of justifying these norms via an ide- ology that involves believing in men’s superiority in highly prestigious domains (i.e., represents the ”justificatory” branch of a patriarchal order). A misogynistic ideology does not necessarily rely on …

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Labeled Text

AbstractIn a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automatically detect sexist content but also to identify if a message with a sexist content is really sexist or is a story of sexism experienced by a woman. We propose: (1) a new characterization of sexist content inspired by speech acts theory and discourse analysis studies, (2) the first French dataset annotated for sexism detection, and (3) a set of deep learning experiments trained on top of a combination of several tweet’s vectorial representations (word embeddings, linguistic features, and various generalization strategies). Our results are encouraging and constitute a first step towards offensive content moderation.

Publication Details

Paper ID:
37711
Year:
2020
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan