“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification? (2021)

Abstract: In this paper, we focus on the detection of sexist hate speech against women in tweets studying for the first time the impact of gender stereotype detection on sexism classification. We propose: (1) the first dataset annotated for gender stereotype detection, (2) a new method for data augmentation based on sentence similarity with multilingual external datasets, and (3) a set of deep learning experiments first to detect gender stereotypes and then, to use this auxiliary task for sexism detection. Although the presence of stereotypes does not necessarily entail hateful content, our results show that sexism classification can definitively benefit from gender stereotype detection.

## introduction stereotypes were originally defined by @xcite as "pictures in our heads", contending that our imagination is shaped by the pictures we see. this definition explains the way in which opinions are formed and manipulated because of what we trust, that in consequence "leads to stereotypes that are hard to shake". stereotypes provide information about what a group is like (they are descriptive), but also about why group members are the way they are (they are explanatory). although stereotypes can be positive or negative, these generalizations are often linked to negative attitudes towards members of certain social groups @xcite . as such, stereotypes represent the root cause of sexism, racism and other inter-group tensions because they convey attributional information that model the way in which stereotyped social group members are being treated by others, as well as the way in which they perceive themselves. in this paper, we focus on: (1) gender stereotypes (gs hereafter) defined by the office of the high commissioner for human rights as "a generalised view or preconception about attributes, or characteristics that are or ought to be possessed by women and men or the roles that are or should be performed by men and women", and (2) sexist hate speech which aims according to the council of europe 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". 1 in particular, as 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 ), it is important to automatically detect sexist messages and possibly to prevent the wide-spreading of gs as they may be used in sexist messages to make generalizations about women, most of the time negative (e.g., women can't drive). gs have been widely studied in psychology, communication studies and social science @xcite @xcite . in nlp, they have been studied mainly to detect or remove gender bias in word embeddings or word association graphs @xcite @xcite @xcite as well as to identify disparity across gender in various applications like co-reference resolution @xcite , sentiment analysis @xcite . in addition to gs, other types of stereotypes have been investigated, such as in the haspeede 2 shared task @xcite which focused on racist stereotypes with tasks for stereotypes and hate speech detection against minority groups. @xcite conducted an error analysis on the haspeede 2018 evaluation campaign @xcite concluding that there is a significant correlation between the usage of racist stereotypes and hate speech and that the false positive rate of hateful tweets is slightly higher for tweets that also contain stereotypes. although sim-ilar correlations have been observed between gs and hate speech from a psychological perspective @xcite , to our knowledge, no one has empirically measured the impact of gs detection for sexist hate speech classification. in this paper, we aim to bridge the gap by proposing for the first time an approach for gs detection in tweets as well as a method to inject stereotype information to improve sexism classification. in particular, our contributions are: ## conclusion in this paper, we proposed the first approach for gender stereotype detection in tweets as well as several deep learning strategies to inject appropriate knowledge about how stereotypes are expressed in language into sexism hate speech classification. our main results are: (1) a new dataset for gs detection, (2) a method to counter class imbalance based on sentence similarity from multilingual external datasets, (3) different strategies to incorporate gs triggers as input into the learning process based on automatically extended lexicon via a multilingual knowledge graph, and finally, (4) an empirical evaluation of the positive impact of multiclass gs detection on improving hate speech against women based on multitask architectures, beating several strong state of the art baselines. although our approach is specific to gender stereotyping, we believe it is generic enough to detect other types of stereotypes like the ones related to racism through the use of other resources (e.g., concept-net, babelnet, hurtlex, etc.), without presuming performances. gs is an understudied problem and we believe it should not only be viewed as a type of sexism/misogyny but considered instead as an independent task to be used in other applications as well. among them, education is a promising future direction for selecting which digital media/books are being given to children, as previous research has indicated that the stereotypes children encounter in their environment can impact their motivational dispositions and attitudes. in the future, we plan on addressing these issues, as well as developing approaches for leveraging the gs information in other datasets annotated for sexism. ethical approval. this article does not contain any studies with human participants carried out by any of the authors. in addition, the data that was used is composed of textual content from the public domain taken from datasets publicly available to the research community. these datasets also conform to the twitter developer agreement and policy that allows unlimited distribution of the numeric identification number of each tweet. for the gs corpus, the data have been annotated with respect to certain types of stereotypical language, however, we are not making any claims about the authors of the tweets, neither share a large numbers of tweets from the same users. additionally, if any of the users want to opt out from having their data being used for research, they can request that they be removed from the dataset by sending an email to the authors of this paper. this work offers several positive societal benefits. sexism is a well-known problem, and countering it via automatic methods can have a big impact on people's lives. this challenge is meant to spur innovation and encourage new developments for both sexism detection and stereotype detection which can have positive effects for an extremely wide variety of tasks and applications. with these advantages also come potential downsides. the gs dataset is not intended to be used for collecting user information which could potentially raise ethical issues. relying on models flagging posts as sexist/conveying stereotypes based on user statistics might be biased towards certain users which eventually could limit freedom of speech on the platform.