Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments (2023)

Abstract: The definitions of abusive, offensive, toxic and uncivil comments used for annotating corpora for automated content moderation are highly intersected and researchers call for their disambiguation. We summarize the definitions of these terms as they appear in 23 papers across different fields. We compare examples given for uncivil, offensive, and toxic comments, attempting to foster more unified scientific resources. Additionally, we stress that the term incivility that frequently appears in social science literature has hardly been mentioned in the literature we analyzed that focuses on computational linguistics and natural language processing.

## introduction the current low to toxic quality of online discussions and the massive amount of user-generated content lead to the need of automatic content moderation @xcite . but the definitions of which comments are actually in need of moderation are not standardized, resulting in a clutter of inconsistent annotated data sets which makes it difficult to build models using multiple data sources @xcite . phenomena such as hate speech and offensiveness cannot be distinguished by classification models and rare or subtle forms of abusive language are not detected @xcite . @xcite analyzed the similarity of classes of six distinct hate speech data sets and compared the predicted labels for these data sets with the perspective api toxicity classifier. they came to the conclusion that many definitions are used for equivalent concepts. they called for avoidance of creating new categories and for referring to categories already existing in the literature. furthermore, they stated that if a new category is defined it should be justified and clearly defined. we analyze and compare prominent papers across languages and fields focusing on online abusiveness, incivility, offensiveness and toxicity. concretely, we contribute the following insights: in order to be able to detect nuances of abusive language and to provide well-defined classes for classification models, more fine grained annotations were proposed: directed towards an individual / a generalized group @xcite targeted (to an individual or a group), not we analyzed prominent papers across fields and languages treating the terms abusiveness (abusive language / speech), offensiveness (offensive language / speech), toxicity (toxic language / speech) and incivility (uncivil language / speech). the analyzed sources contain six overviews of shared tasks (germeval and semeval) on abusive, offensive or toxic comment classification in german and english, two toxicity classification challenges by google jigsaw, a survey paper on hate speech detection, two resource papers on annotated hate speech corpora, one resource paper on an annotated corpus on offensive comments, five papers on different aspects of hate speech and toxic comment detection and six papers from the social science domain. only three of the analyzed papers have less than 30 citations (they are all from 2021). only @xcite referred to annotation guidelines which were not entirely documented in the paper. we analyzed the annotation guidelines documented in the papers. we summarized the definitions for the concepts in we summarized the verbally expressed statements of how the concepts relate to each other in the papers ( the implications of all these statements clearly lead to several contradictions, which point once more to the fact that there do not exist generally accepted definitions of these concepts. ## discussion we found considerable overlap of instances considered as offensive, toxic and uncivil in distinct papers (figure 2 ). additionally, we verified inconsistencies regarding the percieved relations of abusive, offensive, toxic or uncivil speech ( therefore, we propose that literature and annotated data sets on all four concepts should be taken into account when working with one of them. tables 1 and 2 serve as initial pointers to distinct sources. the research community would benefit from exact working definitions and from listings of data and models with compatible concepts and labels. @xcite point out that fine grained labels representing distinct aspects of a broader phenomenon such as abusive, offensive and toxic speech inherently allow for the classification model to learn more nuanced appearances of this phenomenon. they furthermore state that future annotations should be based on existing annotation guidelines in order to make data sets compatible. this is not a trivial task given that existing annotations are based on distinct perceptions of related phenomena ( ## incivility from communication scientists' perspectives we noticed a considerable overlap of instances considered as uncivil and instances considered as offensive or toxic (figure 2 ). at the same time, the term incivility did not appear in most of the papers published at venues for natural language processing and computational linguistics we screened (tables 1 and 2 ). we provide examples of works originating from communication science exhibiting potential relevance for automated classification of abusive, offensive, toxic and uncivil speech. @xcite found that incivility is associated with contextual factors such as the topic of the article and the sources quoted within the article. moreover, they state that frequent users are more civil than infrequent users. the scheme is an expansion of a proposed scheme for hate speech annotation by @xcite . aspects proposed in referenced papers in the table of section 2 and instances found in the analyzed papers (section 5) were used for expanding the framework. note that it does not guarantee to cover all cases of offensive, toxic and uncivil language, it rather presents a summary of the 23 papers we scanned. @xcite found that personal-level incivility (impoliteness) is perceived as more uncivil than public-level incivility (e.g. lack of deliberativeness). otto et al. (2020) showed that political conflict has negative effects on political participation intention in a homogeneous manner across the netherlands, uk and spain. classification models across certain languages could rely on similar annotation guidelines. furthermore, they show that people with low tolerance for disagreement are more affected by uncivil conflict. these insights can be related to approaches where distinct classification models are trained for distinct groups of people @xcite . ## conclusion and future work we provided an overview of definitions of the terms abusiveness, incivility, offensiveness and toxicity as they appear in the context of (automated) content moderation in 23 papers across fields. furthermore, we compared examples given for these concepts and reflected on a more unified usage of these terms in the scientific literature on automated content moderation. based on existing annotation guidelines, we proposed aspects which can be taken into account when designing annotation guidelines for one of the four concepts. lastly, we introduced some examples of scientific literature on incivility from communication scientists' perspectives. this paper should provoke initial thoughts on a framework for designing annotation guidelines for classifying abusive, offensive, toxic and uncivil comments that can be tailored to different tasks. there are more concepts similar to these four terms such as intolerant speech / talk and dark participation which could be analyzed as well.