Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage (2020)

Abstract: Abusive language classifiers have been shown to exhibit bias against women and racial minorities. Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives. As a result, comments written by victims of abuse are frequently labelled as hateful, even if they discuss or reclaim slurs. Any attempt to address bias in keyword-based corpora requires a better understanding of pejorative language, as well as an equitable representation of targeted users in data collection. We make two main contributions to this end. First, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 sub-categories. Second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from Reddit. This corpus was annotated by a diverse cohort of coders, with Shannon equitability indices of 0.90, 0.92, and 0.87 across sexuality, ethnicity, and gender. Taken together, our taxonomy and corpus allow researchers to evaluate classifiers on a wider range of speech containing slurs.

## introduction detecting abusive language is important for two substantive reasons. first is the mitigation of harm to individuals. exposure to hate speech can result in a wide range of psychological effects, including degradation of mental health, depression, reduced self-esteem, and greater stress expression @xcite @xcite . second is the broader impact of unregulated speech on the participation gap in social media @xcite . overexposure to hateful language results in user desensitization @xcite and radicalization (norman and † these authors made equal contributions. mikhael, 2017), both of which have been shown to worsen racial relations @xcite . moreover, hateful echo-chambers promote a "spiral of silence" that discourages counter-speech in conversations online @xcite . access to large-scale training data is the first step towards robust automated systems for abusive language detection. while industry researchers can access moderator logs and user reports, proprietary data is not the standard for academics. instead, pejorative keywords are commonly used as filters in the data collection process. these include, but are not limited to, slurs and other curated lists of profane language @xcite @xcite , terms borrowed from hatebase, a multilingual repository for hate speech @xcite @xcite , offensive hashtags @xcite , and manually selected threads or subreddits @xcite @xcite . although the drawbacks of keyword-based approaches are known to researchers, there are currently no clear alternatives to this technique @xcite @xcite . there has been a recent focus on how technical choices involving data curation can introduce systemic bias in the resultant corpus. for instance, @xcite discover that terms like football, announcer, and sport have the strongest correlation to abusive posts in @xcite . furthermore, @xcite , @xcite and @xcite reveal how classifiers trained on data with systemic racial bias have a higher tendency to label text written in african-american english as abusive. cited examples include: "wussup, nigga!", and "i saw his ass yesterday". left unaddressed, bias has a real impact on users. automated recruiting tools used by amazon.com were shown to discriminate against women @xcite . similarly, microsoft released a public chatbot that learned to share racist content on twitter @xcite . a common solution is to debias language representations @xcite . however, these methods conceal but do not remove systemic bias in the overall data @xcite . a way of beginning to address the issue of racial and gender bias is therefore to understand the implications of forced sampling. our paper focuses specifically on data that is collected using derogatory keywords and we make two main contributions to this end. first, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 subcategories. second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from reddit. we also propose an approach to data collection and annotation that prioritizes inclusivity both by design and application: inclusivity by design: data selection and annotation achieves weighted group representation. we sample from a variety of subreddits in order to capture non-derogatory slur usage. we then hire a diverse set of coders under strict ethical standards as a means of engaging the perspectives of various target communities. we encourage opinion diversity by pairing annotators into teams based on maximum demographic differences. ## inclusive design process random sampling of slur-based data allows for proportional representation because the share of each usage in the corpus is reflective of its probability of occurrence online. however, this approach is not equitable. less common usages, such as reclamation, discussion, and counter-speech, are not captured. consequently, language models can overfit on pejoratives and further codify institutional biases @xcite . a top-down approach to debiasing is simply insufficient. we advocate in favor of affirmative action during data collection and make an effort to represent a wider range of slur usages through community targeting. we also tailor our study to include individuals that belong to targeted communities, both as authors and annotators. ## conclusion we present a comprehensive taxonomy and largescale annotated corpus for online slur usage. our findings are an attempt at integrating a qualitative understanding of slurs into their usage in natural language. we believe that they provide a significant contribution to the hate speech research community, not only as resources for training machine and deep learning models, but also as a means of achieving a nuanced understanding of the phenomenon of slurs. we encourage researchers to replicate and expand our efforts by studying language that targets other marginalized communities. with that said, our corpus is a challenging benchmark that will help expose over-fitting on pejoratives and our taxonomy introduces a systematic approach for dealing with derogatory keywords and epithets. our corpus can be accessed by emailing the authors.