Directions for NLP Practices Applied to Online Hate Speech Detection (2022)

Abstract: Addressing hate speech in online spaces has been conceptualized as a classification task that uses Natural Language Processing (NLP) techniques. Through this conceptualization, the hate speech detection task has relied on common conventions and practices from NLP. For instance, inter-annotator agreement is conceptualized as a way to measure dataset quality and certain metrics and benchmarks are used to assure model generalization. However, hate speech is a deeply complex and situated concept that eludes such static and disembodied practices. In this position paper, we critically reflect on these methodologies for hate speech detection, we argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task.

## introduction online hate speech is the cause for growing concern, due to its social impacts @xcite . in response, automatic detection of hate speech has become a popular research area in natural language processing (nlp), with a substantial number of papers at leading conferences, targeted workshops and shared tasks dedicated to it. simultaneously, content moderation infrastructures that rely on machine learning technologies have been proposed to address the propagation of online harms. the increase in research interest to hate speech detection has spurred on a growth and variety in annotated resources for the task created within the academy and industry. however, at the same time, critical work on hate speech detection has found that there are significant challenges related to the published research outcome with respect to the construction of data @xcite , model generalizability @xcite , and socially biased effects of models @xcite . these challenges indicate that despite a techno-optimist attitude from the nlp community to the task @xcite , the acceleration in research on the topic has impeded a maturation of the basic conventions, principles and practices which this research is based on. hence, it seems timely to assess whether the nlp conventions and practices that have been followed so far are indeed appropriate for the classification of hate speech. in contrast to prior work, which has sought to address specific shortcomings of machine learning algorithms for hate speech detection (e.g., @xcite , our work presents a comprehensive analysis of the inadequacies of some of nlp's methodological conventions for algorithmic hate speech detection. that is, in this position paper we reflect on contemporary methodological challenges that arise from applying common nlp methodologies to hate speech detection. more specifically, we a) reflect on frequently used conventions for classification of text and explore their inadequacies with reference to hate speech detection; and b) discuss the future of current methodologies for this task. on the basis of our analysis, we conclude that current models are incapable of detecting hate speech without harms to marginalized communities. we therefore call for the scientific community to adapt nlp methodologies such that future developments center the impacts that used methodologies may have on marginalized communities. we believe that by critically reflecting on the potential real-world impacts of the methodologies for hate speech detection on marginalized communities, the scientific community can come to identify methodologies that result in more just futures. ## reflections on hate speech detection hate speech detection is commonly conceptualized as a supervised classification task, with the goal to determine whether content is hateful or not @xcite . this setting requires that we i) define the problem; ii) collect, sample, and annotate data to obtain labels; foot_0 and iii) apply optimization technologies (i.e. machine learning algorithms) to the labeled data. finally, the resulting methods and models are evaluated using specific metrics and techniques. in this section, we review these stages through the lens of hate speech detection. ## conclusion in this paper, we have argued that current nlp practices for hate speech detection are unlikely to address the core concerns of hate speech detection, i.e. identify hate with minimal errors and protect marginalized communities. we therefore call for the nlp community to rethink its methodologies such that future developments reduce risk for marginalized communities. one avenue for future work is to follow the principles of design justice (costanza-chock, 2018), which emphasizes community inclusion and ownership of (technological) solutions. following the principles of design justice, researchers would decenter their own expertise in favor of the lived expertise of affected communities. we strongly believe that future steps must center a multidisciplinary approach in close communication with affected groups. by taking steps to document and address the limitations of contemporary methods for hate speech, researchers can identify new avenues for improving hate speech detection models. moreover, researchers can take steps towards ensuring that content moderation technologies provide safer online spaces for marginalized communities by mitigating the prevalence of online hate speech.