Unsupervised Detection of Microaggressions in Textual Data
Authors: Karolina Stańczak, Valerio Basile (Università di Torino, 2021)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Microaggressions are subtle manifestations of bias <cit.>. These demonstrations of bias can often be classified as a subset of abusive language. However, not as much focus has been placed on the recognition of these instances. As a result, limited data is available on the topic, and only in English. Being able to detect microaggressions without the need for labeled data would be advantageous since it would allow content moderation also for languages lacking annotated data. In this study, we introduce an unsupervised method to detect microaggressions in natural language expressions. The algorithm relies on pre-trained word-embeddings, leveraging the bias encoded in the model in order to detect microaggressions in unseen textual instances. We test the method on a dataset of racial and gender-based microaggressions, reporting promising results. We further run the algorithm on out-of-domain unseen data with the purpose of bootstrapping corpora of microaggressions “in the wild”, and discuss the benefits and drawbacks of our proposed method.

Introduction
The growth of Social Media platforms has been accompanied by an increased visibility of expressions of socially unacceptable language online. In a 2016 Eurobarometer survey, 75% of people who follow or participate in online discussions have witnessed or experienced abuse or hate speech. With this umbrella term, different phenomena can be identified ranging from offensive language to more complex and dangerous ones, such as hate speech or doxing. Recently, there has been a growing interest by the Natural Language Processing community in the development of language resources and systems to counteract socially unacceptable language online. Most previous work has focused on few, easy to model phenomena, ignoring more subtle and complex ones, such as microaggressions <cit.>. Microaggressions are brief, everyday exchanges that denigrate stigmatised and culturally marginalised groups <cit.>. They are not always perceived as hurtful by either party, and they can often be detected as positive statements by current hate-speech detection systems <cit.>. The occasionally unintentional hurt caused by such comments is a reflection of how certain stereotypes of others are baked into society. sue2007 define microaggressions in the racial context, particularly when directed toward people of color, as “brief and commonplace daily verbal, behavioral, or environmental indignities”, such as: “you are a credit to your race.” (intended message: it is unusual for someone of your race to be intelligent) or “do you think you're ready for college?” (indented message: it is unusual for people of color to succeed). The need for moderation of hateful content has previously been explored. For instance, mathew2019temporal analyses the temporal effects of allowing hate speech on Gab, and finds that the language of users tends to become more and more similar to that of hateful users over time. mathew2019spread further highlights that the spreading speed and reach of hateful content is much higher than with the non-hateful content. As a result, being able to remove instances of hateful language, such as microaggressions, is of great importance.Previous work on microaggressions with computational methods is quite recent. breitfeller2019finding is one of the first work to address microaggressions in a systematic way, also introducing a first dataset, SelfMA. A further contribution specifically focused on racial microaggression is ali2020automated, where the authors focus on the development of machine learning systems. In this study we introduce an unsupervised method for microaggression detection. Our method utilizes the existing bias in word-embeddings to detect words with biased connotations in the message.Although unsupervised approaches tend to be less competitive than their supervised counterparts, our method is language-independent and thus it can be applied to any language for which embedding representations exist. Furthermore, the reliance of our methods on specific lexical items and their context of occurrence makes transparent the flagging of a message as an instance of a microaggression.In addition to the usefulness of our method in languages with no labeled data, the reliance of our model on words in the sentences would make it interpretable as it allow human moderators to understand what the system has based its decision on.Our contributions can be summarised as follows:   * we introduce a new unsupervised method for the detection of microaggressions which builds on top of pre-trained word embeddings;   * we compare the performance of our model using different pre-trained word embeddings (Glove, FastText, and Word2Vec) and discuss the potential reasons behind the differences;   * we test the proposed algorithm on unseen data from a different domain (i.e., Twitter), in order to qualitatively evaluate its efficacy in discovering new instances of microaggression. The rest of this paper is structured as follows: we introduce our method in Section <ref>. The data and our results are reported in Section <ref>. We deploy our model and discuss its limitations in Section <ref>. Finally, we present the conclusion and future work in Section <ref>.

Conclusion
In this paper we introduce a novel algorithm that exploits the existing bias in pre-trained word embeddings to detect subtly abusive language phenomena such as microagressions.While supervised methods of detection in the field of natural language processing are plentiful, these methods are only viable for languages and topics with available labeled datasets. That is however not the case for many languages. As a result, the unsupervised method of detection introduced in this study could help address the need for the moderation of microaggressions in languages other than English. This is further helped by the availability of multilingual word-embeddings as they would allow the method to be used in any of the languages supported by the embedding.The method is unsupervised and only needs a small list of seed words. Considering its simplicity, the results obtained from an experiment on a dataset of manually annotated microaggressions are very promising. Further, the method is transparent, explicitly identifying the words triggering a microaggression, and thus paving the way for explainable microaggression detection.Although the preliminary results are promising, an experiment on unseen data from a different domain shows that there is leeway for improvement.Given that we are looking at the explicit words used in each message, our method is not sensitive to implicit expressions like “you people" or “your kind", often occurring in microaggressions. We would have to add further steps to our algorithm to catch expressions like these.Polysemy is another known issue, e.g., in words like “black" and “white" whose relatedness to certain identified trigger words could not necessarily be due to race. While a careful composition of the seed word lists helps to minimize this issue, a systematic approach to polysemy would certainly be desirable.The seed word list may also be expanded, either manually or exploiting existing lexicons such as HurtLex <cit.> for offensive terms (including stereotypes for several categories of individuals) or specialized lists of identity-related terms[See for instance this compendium of LGBTQIA+ terminology: <https://www.umass.edu/stonewall/sites/default/files/documents/allyship_term_handout.pdf>].In future work, we plan on improving our model to account for lexical ambiguity, and the complexity derived from the interference between pragmatic phenomena and aggression, e.g., in humorous and ironic messages, following the intuition in recent literature <cit.> about the interconnection between irony or sarcasm and abusive language online.Our current plan is to apply the algorithm presented in this paper to bootstrap the creation of a multilingual resource of online verbal microaggressions and release it to the research community.