He said “who’s gonna take care of your children when you are at ACL ?”: Reported Sexist Acts are Not Sexist (2020)

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 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.

## 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, behaviours, practices, insults to rape or murder. sexist 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" @xcite @xcite . as defined by the council of europe, "the aim of sexist hate speech is to humiliate or objectify women, to undervalue their skills and opinions, to destroy their reputation, to make them feel vulnerable and fearful, and to control and punish them for not following a certain behaviour" foot_0 . its psychological, emotional and/or physical impacts can be severe. in several countries, sexist behaviours are now prohibited. see for example the french law of 27 january 2017 related to equality and citizenship, where penalties due to discrimination are doubled (sexism is now considered 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, @xcite 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 ideology 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 people's beliefs, values, and theories, and can be seen as a mechanism that has the role of upholding the social norms of patriarchies (i.e., represents the "law enforcement" branch of a patriarchal order) by differentiating between good women and bad women and punishing those who take (or attempt to take) a man's place in society. considering these definitions, misogyny is a type of sexism. in this paper, as we target french sexist messages detection, we consider sexism in its common french usage, i.e. discrimination or hate speech against women. social media and web platforms have offered a large space to sexist hate speech (in france, 10% of sexist abuses come from social media @xcite ) but also allow to share stories of sexism experienced by women (see "the everyday sexism project" 2 available in many languages, "paye ta shnek" 3 in french, or hashtags such as #metoo or #balancetonporc). in this context, it is important to automatically detect sexist messages on social platforms and possibly to prevent the wide-spreading of gender stereotypes, especially towards young people, which is a first step towards offensive content moderation (see the recommendations of the european commission @xcite . however, we believe that it is important not only to be able to automatically detect messages with a sexist content but also to distinguish between real sexist messages that are addressed to a woman or describing a woman or women in general (e.g., the goalkeeper has no merit in stopping this pregnant woman shooting), and messages which relate sexism experiences (e.g., he said "who's gonna take care of your children when you are at acl?"). indeed, whereas messages could be reported and moderated in the first case as recommended by european laws, messages relating sexism experiences should not be moderated. as far as we are aware, the distinction between reports/denunciations of sexism experience and real sexist messages has not been addressed. previous work considers sexism either as a type of hate speech, along with racism, homophobia, or hate speech against immigrants @xcite @xcite @xcite or study it as such. in this latter case, detection is casted as a binary classification problem (sexist vs. nonsexist) or a multi-label classification by identifying the type of sexist behaviours @xcite @xcite @xcite . english is dominant, although italian and spanish have already been studied (see the ibereval 2018 @xcite , evalita 2018 @xcite and hateeval 2019 (basile et al., 2019) shared tasks). this paper proposes the first approach to detect different types of reports/denunciations of sexism experiences in french tweets, based on their impact on the target. our contributions are: gender bias in word embeddings. @xcite have shown that word embeddings trained on news articles exhibit female/male gender stereotypes. several algorithms have then been proposed to attenuate this bias @xcite or to make embeddings gender-neutral @xcite , although @xcite consider that bias removal techniques are insufficient. debiased embeddings were used by @xcite observing a decrease in sexism detection performance compared to the non-debiased model. to overcome this limitation, @xcite propose neural methods for stereotypical bias removal for hate speech detection (i.e., hateful vs. non-hateful). they first identify a set of bias sensitive words, then mitigate their impact by replacing them with their pos, ner tags, k-nearest neighbours and hypernyms obtained via wordnet. ## characterizing sexist content propositional content can be introduced in discourse by acts of varying forces @xcite : it can be asserted (e.g., paul is cleaning up his room), questioned (e.g., is paul cleaning up his room?), or asked to be performed as with imperatives (e.g., paul, clean up your room!). in philosophy of language, on the one hand, and feminist philosophy on the other, speech acts have already been advocated in a variety of manners. most accounts however either focus on the type of act (assault-like, propaganda, authoritative, etc.) that derogatory language performs @xcite or concentrate on the analytical level at which the derogatory content is interpreted, whether it provides meaning at the level of the presupposition (or more largely non at-issue content @xcite ) or of the assertion @xcite . we have chosen to distinguish cases where the addressee is directly addressed from those in which she is not, as done in hate speech analysis. for example, @xcite and @xcite consider that directed hate speech is explicitly directed at a person while generalized hate speech targets a group. for @xcite , a hateful tweet is direct when the target is explicitly named, or indirect when "less easily discernible". unlike these approaches and the definitions of target used in @xcite , we do not consider the number of targets of a sexist message (it can indifferently be a woman, a group of women or all women) but rather distinguish the target from the addressee. our use of the notions of directness and indirectness are also transverse to the ones used in @xcite or @xcite , who resort to the label indirectness for subtle forms of sexism that perpetuate gender stereotypes through humor, presuppositions, metaphors, etc. we newly consider three different stages in the scale of 'directedness' of an assertion: assertions directed to the addressee, descriptive assertions not directed to a particular addressee and reported assertions. all these three types of acts can contain subtle and non-subtle sexist content. the main goal of our classification is thus to focus on the impact of the content by resorting to the force of the act and not only to its content. sexist content in directed assertions is explicitly addressed at a target, but contrary to other approaches cited above, the target can be a woman, a group of women or all women. across the different classifications of speech acts @xcite , 'direct' speech acts such as imperatives are addresseeoriented and they require that the addressee performs an action (responding (with questions) or acting (with imperatives)). indirect speech acts are not addressee-oriented. assertions themselves can be direct or indirect. they are direct when they are in the second person ('you'), as shown in ( 1 ) and (2) (linguistic clues are underlined) foot_4 . they require that the addressee be committed to the truthfulness of their content. since a direct sexist assertion is a type of speech act that immediately involves the addressee and triggers a request of commitment, direct assertions of sexism have been ranked as the most prominent expressions of sexism with a greater impact on the victim. most prominently, with assertions, directedness is the trigger of perlocutionary content, rendering the assertion an 'insult'. descriptive assertions are not directed to an addressee: the target can be a woman, a group of women, or all women, it can be named but is not the addressee. descriptive assertions are in the third person and thus may have a lower impact on the receiver in comparison with second person assertions. they do not commit the addressee to the truth of the content by soliciting a response. they report generic content @xcite . linguistic clues can be the presence of a named entity as the target or use of generalizing terms, as shown in (3) and ( 4 ). finally, in reported assertions, the sexist content is a report of an experience or a denunciation of a sexist behaviour. they may elicit an even lower commitment on behalf of the addressee. the speaker is not committed to the truth of a reported content (as in i heard that you were coming too). however, when reporting sexist content, the speaker is still conveying a lack of commitment, and a general sense of disapproval or dismissal may emerge. in these messages, we observe the presence of reporting verbs, quotation, locations (as reports often mention public spaces where the experience happened) or specific hashtags, as shown in ( 5 ), ( 6 ) and ( 7 ). as it appears, the three types of assertions have a sexist content, but only the first two ones are really sexist. indeed, direct and descriptive assertions are first-hand information, whereas reported ones are second-hand information. as such, they may trigger a different reaction from the receiver: in the first two cases, a female receiver can be immediately involved as the target of the sexist dismissal; in the third case, she is the witness of a sexist report. ## data and annotation our corpus is new and contains french tweets collected between october 2017 and may 2018. in order to collect sexist and non sexist tweets, we followed @xcite approach using: (i) a set of representative keywords: femme, fille (woman, girl), enceinte (pregnant), some activities (cuisine (cooking), football, ...), insults, etc., (ii) the names of women/men potentially victims or guilty of sexism (mainly politicians), (iii) specific hashtags to collect stories of sexism experiences foot_5 : #balancetonporc, #sexisme, #sexiste, #sexismeordinaire, #ensemblecontrelesexisme, #payetashnek, #payetontaf, etc. the tweets collected with these hashtags may contain reported sexist acts towards both men and women. thus, we collected around 205, 000 tweets, among which about 70, 000 contain the specific hashtags. given a tweet, annotation consists in assigning it one of the following five categories: direct, descriptive, reporting (as defined in the previous section), non-sexist and no decision. a tweet is non sexist when it has no sexist content (it may contain a specific hashtag, but the content is not sexist), as in ( 8 ). no decision refers to cases where the tweet lacks context, or when the sexist content is not in the text but only in a photo, video, or url (because we cannot process them). ## identifying reports of sexist acts to identify reported assertions, we performed three classification tasks: (bin) sexist content vs. noncnn. this model has already been used in @xcite . it uses pre-trained on wikipedia and common crawl fasttext french word vectors and three 1d convolutional layers, each one using 100 filters and a stride of 1, but different window sizes (2, 3, and 4 respectively) with a relu activation function. we further downsample the output of these layers by a 1d max pooling layer (with a pool size of 4), and we feed its output to the final softmax layer. ## conclusion in this paper, we have presented the first approach to detect reports/denunciations of sexism from real sexist content that are directly addressed to a target or describes a target. we proposed a new dataset of about 12, 000 french tweets annotated according to a new characterization of sexist content inspired from both speech act theory and discourse studies in gender. we then experimented with several deep learning models in binary, three classes and a cascade classifier configurations, showing that bert trained on word embeddings, linguistic features and generalization strategies (i.e., place and hypernym replacements) achieved the best results for all the configurations, and that cascade classification allows to successfully correct misclassified non-sexist messages. these results are encouraging and demonstrate that detecting reporting assertions of sexism is possible, which is a first step towards automatic offensive content moderation. in the future, we plan to develop more complex models to be added in the next stages of the cascade classifier as well as automatically identify irony, gender stereotypes and sexist vocabulary.