Relationality and Offensive Speech: A Research Agenda (2023)

Abstract: We draw from the framework of relationality as a pathway for modeling social relations to address gaps in text classification, generally, and offensive language classification, specifically. We use minoritized language, such as queer speech, to motivate a need for understanding and modeling social relations–both among individuals and among their social communities. We then point to socio-ethical style as a research area for inferring and measuring social relations as well as propose additional questions to structure future research on operationalizing social context.

## introduction in this paper, we build on nlp-based approaches to defining and classifying offensive speech to lay out research directions for robustly incorporating social context into the ways text classification tasks are conceptualized and operationalized. our motivation lies in classifying sociolinguistic norms of minoritized communities, such as the use of reclaimed slurs, which current classification approaches often fail to distinguish from language which is abusive, toxic, or hateful. to achieve a robust understanding of social context, we consider offensive speech in terms of relationality-or the social relations that inform how language is used and interpreted. at a conceptual level we defined offensiveness as a property of social relations rather than as a property of specific language terms. at an operational level, we discuss initial insights and open research directions for how social relations can be measured in practice. reclaimed language use and other aspects of minoritized language, such as queer speech and black american vernacular have proven challenging for text classification @xcite . this language use reflects a plurality of language meaning and non-normative use * authors contributed equally that many nlp approaches currently fail to capture. the research directions we propose are oriented toward text classification for potentially harmful or undesirable speech, such as toxicity detection or hate speech detection. while we consider offensive speech to be distinct from hate speech or toxic language, they have important similarities that help to clarify a definition of offensive speech as well as point to approaches for improving classification tasks @xcite . that is to say, while we use a definition of offensive speech that overlaps with definitions of hate speech and other abusive language, a sociological understanding of offensive speech indicates that it is distinct in ways that current classification approaches do not reflect. our overarching goal is to provide research directions toward contextually-informed modeling and annotation to appropriately capture sociolinguistic norms used within minoritized groups. a key underlying postulate of our research is that speech, and in particular offensive speech, is not divorced from "doing". on the contrary, offensive speech has practical effects that enact and perform subjective formations @xcite . although a range of definitions and labels have been used to operationalize offensive language, they share a goal of classifying undesirable language that stands to harm or deteriorate discourse. concepts for classification have included, "abusive language" @xcite , "harmful speech" @xcite , and "hate speech" @xcite , among others. these tasks do not use identical definitions of offensiveness but often use similar labels and share similar goals. definitional differences can be observed in the label schema for each task. for example, @xcite define 'racist' and 'sexist' as subsets of 'insults @xcite include a specific label for personal attacks. importantly, researchers have identified issues and challenges related to the variety of social contexts in which classification tools are applied, namely those involving satire and nonstandard use of language, such as reclaimed speech @xcite . these challenges are rooted in nuanced use and understanding of language that rely heavily on aspects of social context including, culture, place, and power. additionally, they point to a need for better incorporation of social context in the ways that nlp tasks are conceptualized and operationalized @xcite . we refine this line of research by emphasizing that a socio-ethical account of offensive speech should be attentive to a diversity of contextual uses and the variety of forms it can take. this requires a basic understanding that the offensiveness of speech is dependent upon 1) the background of social and cultural conditions that surround it; 2) the social dynamics between the subjects and objects of offense; 3) the in-group/out-group language norms surrounding language use; and 4) the different types of outcomes of offensive speech, including the resulting potential and actual harms associated with the previous considerations. our approach expands from @xcite , who use conceptual analysis to evaluate specific components of how hate speech and toxicity are defined in order to form the basis for an expanded definition of offensive speech. rather than an exhaustive review of definitions, they identify those which help to build a more robust approach to defining offensiveness, with specific attention toward identifying and operationalizing its relational qualities. building from their work, we propose relationality as a conceptual bridge to more robustly operationalize social context and, in particular, the social relations that differentiate minoritized speech from antagonistic forms of speech. in addition, we point to existing work on style measurement as an avenue to do so. in the following sections we draw from the framework of relationality to motivate a need for modeling social relations to address gaps in text classification, generally, and offensive language classification, specifically. second, we propose research domains and questions to structure future research on operationalizing social context. third, we point to and discuss examples for how we can begin to better model social relations. we do not provide a closed or exhaustive set of techniques for applying a relational lens, however we discuss style and its use in nlp as a jumping off point for addressing ethical concerns surrounding offensive language classification that others have raised (e.g., @xcite . ## a relational framework for contextual analysis of offensive speech relationality operates as a general analytic tool that helps to unveil and disambiguate specific contextual uses of offensive speech from others. a relational lens in the context of nlp refers to a focus on the social relations that influence the production, meaning, reception, and outcomes of language among interlocutors. in this way, relationality is a means of analysis to conceptually organize social context. @xcite propose to shift nlp analysis toward a contextual understanding of speech that consists in the following seven factors: 1) speaker and 2) receiver, 3) social relations, 4) context, 5) social norms, 6) culture and ideology, and 7) communicative goals @xcite . we argue that contextual analysis of offensive speech can be achieved through a focus on the social relations inherent in language, its use, and its outcomes. @xcite point out a distinction between treating offensiveness as a property of an utterance rather than as a relation between individuals or communities and that utterance. treating offensiveness as a property of a linguistic token, such as by registering a term to a blocklist, ignores the very real ways in which language meaning is not fixed or inherent to its orthography but rather is constructed socially via a network of meanings among social actors. for this reason, when we refer to "offensive speech" we refer not only to the content of an utterance, but also the confluence of social relations and context that surround the production of that utterance. in other words, "offensive speech" entails time, place, by whom, and to whom, in addition to orthography. relationality also reflects a move away from locating offensiveness exclusively at the level of words and instead locates offensiveness in an individual or group's relation to a word or concept. this, in turn, helps to distinguish why a term might produce offense when used between members of different communities but not when used between members of the same community, as in the example of reclaimed slurs. through relationality our focus is on accounting for patterns inherent in the social relations that pro-duce offensive speech. in this respect, our work overlaps with prior work that effectively operationalizes aspects of relationality through analyses of interactional patterns and discourse (e.g., @xcite ). relationality itself does not provide a comprehensive list of all the contextual elements that influence how communication is understood between social actors, however, it emphasizes how to conceptually organize social context-namely around social relations between and surrounding subjects. as such, applying relationality rests on further research and validation of the relevant aspects of social relations that must be accounted for across text classification tasks. while @xcite have laid important groundwork for addressing this question and @xcite explore social context more specifically in the context of offensive language classification tasks, we propose several research directions for bringing relationality into practice for classification tasks. there has not been explicit work on detailing the aspects of social context most operative for distinguishing the range and differential impacts of offensive language. each of these directions has overlapping components but address open questions about what a relational lens means for 1) how offensive language can be conceptualized in a way that is responsive to minoritized speech and 2) how offensive language is operationalized through annotation task design and language modeling. ## relationality through style in response to the challenges posed by minoritized speech, we turn to linguistic style and its measurement in nlp as a means of both describing and applying relationality. in doing so, we draw from style as an artifact of social context that specifies how social relations are structured. work on linguistic style in nlp has typically focused on individual communication style, such as in investigating author attribution @xcite or making inferences about author psychological state and demographics @xcite . notably, measurements of style are usually pursued in contrast to explorations of language content. @xcite bridge the gap between structure and content by applying style measurement to understand an individual's relationship to a broader community. the authors use style to explicate a social relation that is not necessarily explicit in an utterance itself. this moves from simply applying style to characterize individuals to understanding a broader social relation and orientation to community language norms. our contention is that nlp accounts of style must explicitly contend with the social, historical, and practical conditions from which styles of speech emerge. thus, work on style in nlp needs to be attuned to the underlying ethical questions associated with the technical measurement of styles of speech. first and foremost, this means that style needs to be understood as embedded in specific contexts of production with distinct practical outcomes. for example, at an ethical level, style can be understood: (1) as the reflexive practice of styles of existence via the exercise of specific technologies of the self @xcite , such as practices of self writing @xcite and practices of truthful speech @xcite ; (2) as a work of forming and transforming one's existence @xcite via somatoaesthetic projects that are not reducible to the purely individual and voluntaristic manifestations of heroic self-distinction (heyes, 2007) and moral quests for universal wisdom predicated on self-possession @xcite . however, an ethical grounding of style is related yet distinct from a strictly sociological @xcite , historical @xcite and an epistemological account of styles of thinking @xcite . from an ethical standpoint style is conceptualized as a practice of the self and others while at a sociological level, style is the product of community language norms that reflect hierarchical patterns of discourse that are interwoven with social identity formation and relational dynamics @xcite . in both cases, a socio-ethical account of style is context-dependent, "relational and dynamic" @xcite and a key feature of an individual or a group's selfexpression. one aspect that we emphasize here is that style has irreducible ethical, social and political conditions, expressions and manifestations which refer to speech that an individual or a group produce in relation to others, rather than as a fixed property of an individual, their words or given images. in this way, analyses of style can be robust to code-switching or the range of styles individuals may use in changing social contexts. thus, given the contingent and contextual production of style we propose relationality through style as an analytic or a mode of analysis that seeks to account for the historically and socially constituted matrices of power relations where style works as an interactive feature which opens to spaces of contestation in the formation of both individuals and collectives. for our purposes, while style provides general indications of social context, its relational significance lies in its potential for disentangling minoritized forms of speech from abusive language. for example, mock impoliteness, which features in drag queen speech, plays a central role in group identity formation and resistance against oppressive social systems @xcite . style's significance for minoritized communities emerges through "contextualized repertoires of speaking and behaving through which identities and sociocultural affiliations are claimed and communicated" @xcite . a key takeaway is that a common style among interlocutors can suggest shared norms or social or cultural proximity. because style is an artifact of social norms, and thus social relations, it can be used to infer shared context among individuals involved in an interaction being assessed for offensive content. in this way, comparisons of linguistic form can be a tool to unveil the relations among which offensive language is couched. while style can vary from individual to individual, @xcite show that style can reliably predict group membership, independent of language content @xcite . indeed, earlier work has shown that style can indicate social demographic information about a speaker @xcite . in the context of offensive speech classification, this means that style provides useful information for assessing whether individuals share a sociolect or dialect. this carries significance not only for disambiguating language use within a given minoritized sociolect and improving upon weaknesses in offensive speech classification. ## operationalizing relational context in practice, the primary challenge of applying a relational approach to offensive language lies in defining its scope and operationalizing its component parts. as others have pointed out, identifying social context and integrating it into nlp models is both needed for more robust and successful nlp as well as nontrivial @xcite . measuring linguistic style provides one way of applying a relational lens, however other features may be leveraged to infer relational context. a relational focus in classification tasks requires determining a set of measurable features that provide information about social relations, as well as work to prioritize features that most improve task performance, particularly with respect to language norms missed by current techniques. identifying and predicting aspects of social context as a part of classifying offensive speech brings its own, deep set of research questions and challenges. although offensive speech detection and related tasks have largely been framed as text classification tasks, we break down research questions for future work into those that focus on linguistic features and those that focus on extra-linguistic features surrounding text and its production. in doing so, we implicitly shift offensive language classification from a text classification task to one that expands to include non-textual inferences in addition to linguistic content. through a relational lens, language is one artifact produced by the social relation of offense between social actors. framing language in this way allows us to consider other artifacts that result or shift as a consequence of offense. this broadens the range of features at our disposal to infer social context, including user behavior (e.g., "liking" comments), networks of user accounts, the post structure of dialogue, and histories of interactions. taking advantage of this broadened set of features, we propose areas for research that build both from established approaches in language modeling, such as text annotation, as well as modeling approaches focused on non-text data, such as conversation structure, can serve as a clue to the nature of the relationship between two social actors @xcite . ## conclusion our chief claim is that relationality and its sociological and ethical formulations of linguistic style are a promising guiding analytic for achieving a more robust contextual analysis of offensive speech. motivated by the challenges posed by minoritized language norms, we propose avenues for research that take aim at operationalizing it in practice. because style patterns can be used to unveil social relations among individuals and communities, we point to its measurement as an example for operationalizing our approach. ultimately, offense is produced through social relations that must be ethically and sociologically understood in order to accurately model and classify language content. focusing on social relations and their potential to help distinguish sociolinguistic norms generates the following research questions: in the context of data annotation: with respect to modeling language and social interactions: importantly, we must also explore the limitations of a relational approach rooted in style. while style has important connections to the formation of individual and collective identities, it has different uses, such as to comply with institutional (e.g., workplace) norms, which may not necessarily align with community norms or social identification processes. in addition, style can be deployed in adversarial ways, such as with mockery, intent to impersonate, exploit trust, or arguably 'inauthentic' uses of accent or dialect (e.g., appropriative use of a "blaccent" @xcite ). it is unclear, at least at an operational level, how relationality might account for these uses of style. another complication lies in the fact that in many digital contexts, one's "true" identity is often not verifiable. for our purposes, this means that a person can communicate online using styles of speech that may align with offline specific manners of speaking (e.g., aae) but that may not align with the styles they use in other contexts. underlining all of the above limitations is a greater tension regarding the ethical risks of inferring social identity and the extent to which inferring an individual's social identity is meaningful for classification. @xcite studied trans* and gender nonconforming individuals' perceptions of automatic gender recognition systems, demonstrating how automated systems can contribute to misgendering harms and undermine individual autonomy. thus, inferences about social context that rely on further inferences about social identity still, relationality can work as a frame of analysis for the design of nlp approaches, including annotation practices and modeling decisions that can unveil specific relational context. we have identified minoritized speech as a motivating example to show how current, generalized approaches are inadequate for classifying language that deviates from dominant sociolinguistic norms. providing sound criteria to disambiguate and classify a plurality of modes of speech grounded in a deep social understanding of their formation is key to ensure a more just and ethical approach to offensive speech.