Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language (2023)

Abstract: We address the task of identifying euphemistic abuse (e.g. “You inspire me to fall asleep”) paraphrasing simple explicitly abusive utterances (e.g. “You are boring”). For this task, we introduce a novel dataset that has been created via crowdsourcing. Special attention has been paid to the generation of appropriate negative (non-abusive) data. We report on classification experiments showing that classifiers trained on previous datasets are less capable of detecting such abuse. Best automatic results are obtained by a classifier that augments training data from our new dataset with automatically-generated GPT-3 completions. We also present a classifier that combines a few manually extracted features that exemplify the major linguistic phenomena constituting euphemistic abuse.

## introduction abusive language is commonly defined as hurtful, derogatory or obscene utterances made by one person to another person. 1 examples are (1) and ( 2 ). due to the increasing amount of abusive language on the web, nlp methods are required that are able to detect such content automatically. currently, only explicit abuse can be reliably detected @xcite @xcite . by explicit abuse we understand abusive language that is conveyed by unambiguously abusive words (e.g. bimbo, scum, tosser). the automatic detection of more implicit forms of abusive language (3)-(5) remains challenging @xcite @xcite . despite some recent research efforts introducing new datasets for implicitly abusive language @xcite @xcite , there are still some subtypes that have not been explored. this particularly concerns implicit abuse that does not target identity groups (e.g. jews, immigrants, women, black people etc.). in this paper, we address such a form of implicit abuse. we address implicitly abusive sentences such as (6) that paraphrase some common explicitly abusive utterances (7). such paraphrases are a form of euphemism @xcite since they are perceived as less harsh than their explicit counterparts. therefore, we call them euphemistic abuse. in their roadmap on implicit abuse, @xcite identify euphemistic abuse as one subtype next to abuse towards identity groups, dehumanization, call for action, multimodal abuse and comparisons. the authors establish euphemistic abuse as a rare phenomenon, with a frequency similar to dehumanization or comparisons, which have been examined in previous work @xcite . we focus on euphemistic sentences that are perceived as abusive without any additional context when being uttered in an everyday situation. the target of our sentences is a generic individual addressed by the second person pronoun. the task is a binary (sentence-level) classification problem in which abusive euphemisms are to be distinguished from similar but non-abusive sentences. we introduce a novel dataset for this task that has been created via crowdsourcing. a special property of that dataset is that the negative instances, i.e. the non-abusive sentences, are contrast sets @xcite . they represent sentences that are syntactically and semantically similar to the instances of euphemistic abuse. this makes them very difficult to distinguish from euphemistic abuse for automatic classifiers. apart from detailing the creation of our dataset, we report the performances of various automated classifiers. the most effective is an approach that augments the data by automatically generated completions by gpt-3. we also introduce a featurebased approach that exemplifies the major linguistic phenomena that constitute euphemistic abuse. our contributions are the following: all data and annotation guidelines created as part of this research are publicly available. 2 2 https://github.com/miwieg/euphemistic_abuse 2 related work much of the previous work on abusive language detection follows a one-size-fits-all approach @xcite . surveys on existing datasets do not address implicit abuse @xcite . quite recently, several new datasets for supervised classification have been introduced that focus on implicitly abusive language @xcite @xcite @xcite . @xcite report that most of these datasets suffer from heavy biases. moreover, all of these datasets focus on identity groups. to the best of our knowledge, the only exception is the dataset by @xcite . like our dataset, it has been constructed via crowdsourcing. yet it includes only abusive sentences in the form of like-comparisons (e.g. your hair looks like you have been electrocuted). our dataset of euphemistic abuse is much more heterogeneous in terms of sentence structure. another work that is also closely related to ours is the collection of microaggressions introduced in @xcite which are subtle, often veiled, manifestations of abusive language. however, that dataset exclusively comprises abusive instances. that is, there are no negative (non-abusive) data, which is a prerequisite for supervised classification. moreover, in that dataset, each example typically represents a larger discourse in which a situation involving abusive language is described. on average, instances are 7 times longer than the instances in our dataset ( our instances of euphemistic abuse can be considered as paraphrases of explicit abuse. paraphrases in abusive language detection have been studied for the task of translating abusive sentences to non-abusive (civil) paraphrases @xcite . in this paper, we address paraphrases that remain abusive. ## data our dataset was produced via crowdsourcing. as a platform we used prolific academic. 3 using existing datasets for our task was not an option as instances of euphemistic abuse are too rare in them and have also a very limited lexical variability. fol-lowing previous work on creating a dataset for a rare subtype of implicit abuse @xcite , we therefore asked crowdworkers to invent instances of euphemistic abuse. all crowdworkers admitted to any of our tasks had to be native speakers of english with some basic academic education (at least undergraduate level) without dyslexia and an overall approval rate of 95% or higher. we had to enforce these criteria since, without them, we had far too many crowdworkers that failed to follow our annotation instructions (e.g. they did not understand the concept of implicitly abusive language; they provided many ungrammatical sentences, etc.). in general, our aim was to recruit a sample of crowdworkers that represents a wide spectrum of the english-speaking society. this was achieved by dividing the creation of the dataset into many smaller tasks, thus allowing many different crowdworkers to participate in the creation process. we did not specifically sample from a narrow range of age or a specific subset of countries (other than those in which english is spoken as a first language). figure 1 illustrates the order of the individual tasks that we set up. each of these tasks is described in the following. we repeatedly ran through this pipeline until no more significantly new sentences were obtained. cue phrases. the cue phrases represent the input given to the crowdworkers. they were selected by the authors and consist of explicitly abusive sentences (e.g. you are ugly) that the crowdworkers are to paraphrase. many common abusive words, such as slurs (e.g. cunt, tosser), do not have a sufficiently specific semantics that can be paraphrased. therefore, they are unsuitable for being used in cue phrases. we consulted the lexicon of abusive words from @xcite that contains a large number of common nouns and adjectives with a specific semantics (e.g. wannabe, boring, stupid). we aimed for a list with a wide semantic spectrum of abuse. therefore, we avoided including multiple similar words. we identified 97 abusive words that complied with the above criteria and used them for our cue phrases. the cue phrases were selected manually since we did not see any possibility to identify abusive words with a sufficiently specific semantics automatically (i.e. one of the criteria these words have to fulfil). some of our cue phrases may only be mildly abusive. however, each paraphrase would be validated at a later stage as being abusive. ## classifiers in the following, we always used roberta @xcite for supervised classification unless stated otherwise. we considered this model since it is regarded as a robust transformer for text classification. we fine-tuned the pretrained model on the given training data using the flair-framework @xcite for some approaches we had to identify explicit abuse. in order to detect this kind of abuse accurately, we fine-tuned roberta on the dataset by @xcite . this dataset was chosen since it comprises short text instances like our dataset and has been reported to contain a high degree of explicit abuse @xcite . ## evaluation as evaluation measures, we use macro-average precision, recall and f1-score. for withindataset classification, we carry out a 5-fold crossvalidation. unless stated otherwise, we arrange the folds in such a way that the euphemistic abusive sentences for the same cue phrase are restricted to one fold. thus, the test data will always contain euphemistic abuse for unseen cue phrases. the alternative in which sentences are assigned to the folds at random is less strict since euphemistic abuse may originate from cue phrases observed in training. for all classifiers that we trained on a nondeterministic model, e.g. roberta, we report the average over 5 training runs (+ standard deviation). as an upper bound, we also tested a human classifier. we randomly sampled the judgment of one individual annotator from the crowdsourced gold-standard annotation. this individual judgement may notably differ from the gold standard label which is the majority label of 5 annotators. ## discussion the performance of our best automated classifier (i.e. gpt-3::inclusive) is still notably below that of the manual but very predictive feature-based classifier. since the latter classifier is an explainable classifier we measured the overlap of our best automated classifier with the feature-based classifier in order to find out which linguistic aspects that classifier manages to detect to what specific degree. ## conclusion we presented the task of identifying euphemistic abuse. we introduced a novel dataset that has been created via crowdsourcing and also includes suitable negative data. our classification experiments revealed that classifiers trained on previous datasets are insufficient. best results are obtained by a classifier that augments training data from our new dataset with automatically-generated completions. as an explainable upper bound, we also presented a classifier that combines a set of manually extracted features that helps us better understand what linguistic concepts are involved in that task.