H etero C orpus: A Corpus for Heteronormative Language Detection (2022)

Abstract: In recent years, plenty of work has been done by the NLP community regarding gender bias detection and mitigation in language systems. Yet, to our knowledge, no one has focused on the difficult task of heteronormative language detection and mitigation. We consider this an urgent issue, since language technologies are growing increasingly present in the world and, as it has been proven by various studies, NLP systems with biases can create real-life adverse consequences for women, gender minorities and racial minorities and queer people. For these reasons, we propose and evaluate HeteroCorpus; a corpus created specifically for studying heterononormative language in English. Additionally, we propose a baseline set of classification experiments on our corpus, in order to show the performance of our corpus in classification tasks.

## introduction in 1978, the french philosopher monique wittig gave a conference titled the straight mind @xcite , in which she introduced the idea of the straight regimen. wittig declared that heterosexuality is a political system that encompasses all aspects of western societies, and that its basis is the separation of people in binary and opposite categories based on their sex @xcite . the author proposes that the idea of "women" -and that of all sexual minorities-is a generated byproduct of a "superior" category from which every institution should be modelled after. this category is, of course, "men" @xcite . wittig also proposes that language is a system that has established that men, and heterosexuality, are the universals from which every particular derive from. this normalisation of heterosexuality as a political regimen through language -wittig argues-contributes to the continuation of the oppressive systems against everyone who is not a member of the privileged "men" category @xcite . adding to wittig's ideas, judith butler proposed that the subject is itself produced in and as a gendered matrix of relations @xcite , meaning with this that the social and inner processes that construct the "subject" are deeply guided by the ideas of gender. butler even remarks that the matrix of gender is generated prior to the creation of the subject, since this structure defines the limits and possibilities of what the subject can become @xcite . therefore, the boundaries of what can be considered "human", are enforced by the matrix of gender, according to butler. following these ideas, we hypothesize that the majority of the language used in current social media applications must exhibit numerous rules and expressions of heterosexuality as the norm. in recent years, plenty of work has been done by the nlp community regarding gender bias detection and mitigation in language systems. yet, to our knowledge, no one has focused on the difficult task of heteronormative language detection and mitigation. we consider this an urgent issue, since language technologies are growing increasingly present in the world and, as it has been proven by various studies, nlp systems with biases can create real-life adverse consequences for women, gender minorities and racial minorities. for these reasons, we propose and evaluate hete-rocorpus; a corpus created specifically for studying heterononormative language in english. our corpus consists of 7,265 tweets extracted from 2020 to 2022. in order to identify heterononormative lan-guage in our corpus, we manually annotated every tweet, performed agreement experiments among the six annotators, and then evaluated the performance of our corpus in classification tasks using various classification systems. the main contributions of our work are the following: the rest of the paper is structured as follows: section 2 introduces the meaning of heteronormative and the negative impact it has had in society in general and the lgbtqia+ community in particular. it also provides and overview of the work that has been done so far in gender bias detection and mitigation in nlp. section 3 explains the configuration, annotation and challenges on compiling the heterocorpus, a data set especially designed for the detection of heteronormativity. in section 4 we present the pre-processing and classification experiments. the results are discussed in section 5. we close the paper with conclusions and future work (section 6). ## related work in this section we will consider literature that explores what heteronormativity is and how the sense of the word has evolved over time, motivations to challenging heteronormativity, heteronormativity and gender bias as explored in natural language processing (nlp), and how this paper will contribute to this domain. ## heterocorpus in this section we will describe our process for collecting data from twitter and the annotation process, as well as the challenges we faced and the resulting dataset. ## methodology for heteronormativity detection in order to establish a baseline for classification systems trained on our corpus, we performed a set of classification experiments. ## results and discussion since the task of identifying heteronormativity in nlp systems has not been studied yet, we compare our classification experiments with systems that detected gender bias. we decided not to compare with hate speech tasks, since we consider that heteronormative language does not necessarily imply hate speech. we recognize that our baseline can only be vaguely compared with the results obtained by other authors in other classification tasks, since we aim to detect different linguistic phenomena. following those remarks, on it can be observed that bert-large outperforms the supervised classification algorithms. also, the low results shown on ## conclusion and future work in this paper, we presente heterocorpus; a novel human-annotated corpus for heteronormative language detection. this work sets a new precedent in nlp, since, to the best of our knowledge, there has not yet been developed a similar corpus that aims to study heteronormative language in english. we consider that this corpus could be of use in gender bias and sexism detection and mitigation tasks, which have proven to be quite challenging. while gender bias and sexism are not the same as the presence of heteronormativity in language, they all are nocive issues present in current nlp systems. until the nlp community finds an efficient way to minimize these issues, language technologies will continue to amplify the discrimination based gender and sexual identity. the fleiss' kappa obtained on our corpus signals a moderate agreement between our annotators. this indicates that annotating heteronormativity can be complicated. therefore, researchers must take into consideration this extra challenge while creating similar resources, since the quality of the data depends on the expertise of the annotators. we also present a baseline for the task of heteronormative language detection using our corpus, with two supervised algorithms and with four variations of bert. as future work, we plan on expanding this corpus by extracting a larger set of tweets containing more nuanced forms of heteronormative discourses, since heteronormativity is not only associated to lexical properties in the speech, but also to more complex forms of linguistic phenomena. in future projects, we hope to further investigate heteronormative language use in digital spaces, crafting a dataset that better respects the multi-class definition of heterornormativity as discussed in section 2. we propose the creation of similar corpora but for other languages, since heteronormativity is a global issue that requires joint action. also, we encourage researchers to develop further tools for heteronormative language detection and mitigation, since language technologies are rapidly increasing their presence in human lives, and the implicit biases these models have can be very costly and damaging to human lives.