“Zo Grof !”: A Comprehensive Corpus for Offensive and Abusive Language in D utch (2022)

Abstract: This paper presents a comprehensive corpus for the study of socially unacceptable language in Dutch. The corpus extends and revise an existing resource with more data and introduces a new annotation dimension for offensive language, making it a unique resource in the Dutch language panorama. Each language phenomenon (abusive and offensive language) in the corpus has been annotated with a multi-layer annotation scheme modelling the explicitness and the target(s) of the message. We have conducted a new set of experiments with different classification algorithms on all annotation dimensions. Monolingual Pre-Trained Language Models prove as the best systems, obtaining a macro-average F1 of 0.828 for binary classification of offensive language, and 0.579 for the targets of offensive messages. Furthermore, the best system obtains a macro-average F1 of 0.667 for distinguishing between abusive and offensive messages.

## introduction social media platforms have become an intrinsic part of the lives of lots of people. a phenomenon that accompanies social media platforms, with serious impacts on society, is the presence of socially unacceptable language. socially unacceptable language is to be regarded as a generic umbrella term comprehending many different user-generated language phenomena such as toxic language @xcite , offensive language @xcite @xcite , abusive language @xcite @xcite , hate speech @xcite @xcite , among others. while manually monitoring and flagging these phenomena is impossible, there has been a growing interest in the computational linguistics (cl) and natural language processing (nlp) communities to develop automatic systems to flag messages containing these phenomena. besides the limitations of this type of reactive interventions, previous work @xcite has shown the necessity of language specific resources for these phenomena to properly train systems. this work contributes in this direction by presenting a comprehensive dataset to identify socially unacceptable language in twitter messages in dutch. we integrate and extend dalc v1.0 @xcite by introducing a new annotation layer for offensive language and expanding the size of the dataset from 8,156 messages to 11,292. the main contribution of this paper can be summarised as follows: the annotation guidelines, the data, and the code for the reported experiments, and a data statement (bender and friedman, 2018) are publicly available. 1 examples of offensive messages have been redacted to preserve privacy and explicit offensive lexical items have been obfuscated. ## offensive language: why and how offensive language is a broader language phenomenon when compared to other phenomena and behaviours (e.g., abusive language, hate speech or cyberbullying) and, most importantly, more subjective @xcite . in offensive language @xcite abusive language @xcite posts containing any form of non-acceptable language (profanity) or a targeted offence, which can be veiled or direct. this includes insults, threats, and posts containing profane language or swear words. impolite, harsh, or hurtful language (that may contain profanities or vulgar language) that result in a debasement, harassment, threat, or aggression of an individual or a (social) group, but not necessarily of an entity, an institution, an organisations, or a concept. while we can identify and list parameters and details that help us to narrow down whether a message is abusive or not, the offensiveness of a message is only partially dependent on its content. other variables such as the context of occurrence, the background and experience of the reader/annotator play a relevant role. despite these difficulties, offensive language datasets have been developed in different languages @xcite @xcite and used in recent shared tasks @xcite . to maximise resource interoperability and foster the study of offensive language from a multilingual perspective, we adopt the definition of offensive language from @xcite . in a key element distinguishing these two language phenomena is the level of detail used to describe them, the different emphasis on the intentions of the producers, the presence/absence of a target, and the effects on the receivers. in particular, target is an essential and compulsory element of abusive language, while it is not the case for offensive messages. on the other hand, given its more generic nature, offensive language can be identified in messages that do not contain any target. this is particularly evident in the use of profanities to express strong (positive or negative) emotions. to better clarify the difference between the two phenomena consider the following examples from dalc v2.0: example 1 instantiate an offensive message, due to the presence of a profanity. its perception of being offensive can vary according to the context of use and the receivers of the message. at the same time, the message does not fully comply with the definition of abusive language for multiple reasons: there is not a (human) target and there is no intention to debase or harass an individual/group. example 2, on the contrary, it is a clear case of abusive language. here the abusive is express via a stereotype and a debasing act, and with an explicit target realised via a specific identity term. the message is abusive and also offensive. in this work, we have maintained the multi-layer annotation approach of dalc v1.0, distinguishing between the explicitness of the message and its target. the explicitness and the target layers for the offensive dimension have been refined with subclasses along the existing annotation of abusive language. the explicitness layer distinguishes three subclasses: (i.) explicit; (ii.) implicit; and (iii.) not. while not is used to annotate not offensive messages, the difference between the explicit and implicit subclasses mainly rely on the surface forms of the message. explicit offensive content refers to the presence of profanities or combination of words that unambiguously make the message offensive. implicit messages are more subtle, lacking any surface markers, thus making the offence hidden @xcite . the target layer, on the other hand, extends the classes used for abusive language allowing for the absence of a target. in particular, we have four subclasses defined as follows: (i.) individual, for messages that are addressed or target a specific id text explicitness target 1. dat gebeurt in het park en veel jongeren bij elkaar [that happens in a park and many young people together] not not 2. en daar trap jij in. echt slim [and you fall for that. really smart] imp. ind. 3. s*** worden ze niet gemaakt [they don't get any d****] exp. grp. 4. j*** dat was wel schrikken geweest [j**** that was scary] exp. not 5. ons geld vervangen door die sh** euro [ replace our money by that sh** euro ] exp. oth. data collection and annotation dalc v1.0 is a corpus of 8,156 messages from twitter in dutch obtained by applying three different collection methods: keywords extraction, message geolocation, and seed users. we have extracted a total of 10k messages using only the keywords and seed users data from dalc v1.0, since these two sources proved to be denser and more suitable for the language phenomenon of interest. following the settings of dalc v1.0, there is no overlap of messages concerning topic and authors between train and test distributions. consequently, the 10k messages are equally and independently extracted from the train and test candidates -resulting in 5k messages per distribution. we divided the messages of each distribution in batches of 1k each for the annotation. given the highly subjective nature of offensive language, all annotations for both layers have been conducted in parallel by four annotators. 2 annotators were asked to apply the definition of offensive 2 the annotators are also authors of this paper. language as reported in to better understand these results, we have analysed the pairwise confusion matrices of all the annotators. 4 for the explicitness layer, it clearly appears that the biggest source of disagreement is the offensive status of the message rather than the distinction between explicit or implicit, further supporting the claim that offensiveness is subjective. this has also an impact on the target layer: if a message is not annotated as offensive, the target annotation is ignore. ## data overview the annotated corpus contains 11,292 twitter messages in dutch, covering a time period between november 2015 and august 2020. for completeness, all messages marked as offensive and containing a target have also been further annotated for abusiveness. for abusive language, we applied the same annotation procedure used in dalc v1.0. overall, 2,097 messages have been annotated as abusive, with an increase of 208 messages when compared to dalc v1.0. on the other hand, 3,783 messages have been marked as offensive. in both dimensions, the explicit subclass represents the majority, with 62.47% of cases for the abusive dimension and 58.71% for the offensive one. the difference in the distribution of the implicit subclass is striking, with implicit offensive messages being almost the double of the abusive counterpart. a possible explanation can be found in the definitions of the two phenomena and their annotations: offensive messages have been labelled as such either because they contained a profanity, or because the annotators subjectively perceived them as offensive. as for the targets, we observe that only a minority of offensive messages does not have a target (6.95%). when compared to other datasets for offensive language, the amount of messages associated with this class varies -for instance, being the majority class in sigurbergsson and derczynski (2020) but not the minority in @xcite -suggesting that there may be a dependency of this subclass on the method(s) used for collecting the data. on the other hand, differences in the realisation of the targets are more evident when focusing on the ind and grp subclasses. offensive messages have a balanced distribution between these two subclasses corresponding to 28.25% and 28.60% of all the targets, respectively. on the contrary, abusive messages see a majority of cases (55.22%) for the ind subclass, and relatively fewer cases for grp (34.09%). lastly, the oth subclass has been selected more often (19.53%) with offensive messages than with the abusive ones (only 10.68%). this difference can be again explained in the light of the definitions of the two phenomena. no significant difference in length has been found between abusive and offensive messages (average length abusive 28.79 words; average length offensive 28.44), foot_2 while this is not the case for offensive and not offensive messages (average length not offensive 21.93 words; average length offensive 28.44). 6 similarly to dalc v1.0, significant differences in length between implicit and explicit messages appear only in the test distribution, where implicit offensive messages have an average of 30.04 words compared to the 23.55 words of the explicit messages. to gain better insights into the data and the differences between the two dimensions, we have extracted and compared the top-50 keywords between the train and test distributions by collapsing the subclass in the explicitness layer, resulting in of-fensive, abusive, not ( ## experiments we ran a set of experiments to validate the newly annotated corpus. we first focused on the iden-tification of the offensiveness dimension ( § 4.1), and then on the target layer ( § 4.2). we also investigate the ability of systems to distinguish between offensive and abusive dimensions ( § 4.3). we tested four different architectures: a linear svm combining character and word n-gram tf-idf vectors, a bi-lstm model initialised coosto pre-trained word embeddings, 7 and two monolingual transformer-based pre-trained language models (ptlms), namely bertje (de vries et al., 2019) and robbert @xcite . the two ptlms differ with respect to their architectures (bert vs. roberta), the size (12gb vs. 39gb) and origin of the data used to generate the models (manually selected data vs. the dutch section of the automatically derived oscar corpus @xcite ). all models are trained on the train split and evaluated against the heldout, non-overlapping test split. the dev split is used for tuning of the systems' (hyper)parameters. models are compared using the macro-average f1. however, given the imbalance among the subclasses in the different layers, for each subclass, we also report precision and recall. for the offensiveness and the offensive target dimensions, systems are compared against a dummy classifier based on the majority class. in all experiments, a common preprocessing approach is applied. all preprocessing steps and (hyper)parameters are detailed in appendix a for replicability. ## error analysis we have conducted an error analysis for the offensive dimension and the offensive target layer since they represents the new annotations in the dataset. the error analysis has been conducted on the dev set using the best performing system for each dimension. offensive language for the offensive language dimension, we have used the predictions by rob-bert in the binary settings. the system wrongly classifies 179 messages, with the majority (101 messages) being off messages wrongly labelled as not. to gain better insights, we have classified all the errors into six categories: target for targets, 127 messages are wrongly classified. when analysing the confusion matrices across all systems, it appears that the most confounded class is oth. the class tends to be wrongly assigned to the ind and the grp subclasses. on the contrary, the errors for the not subclass are limited and they seem to be due to lack of training data. the large part of the errors (31.49%) are due to different elements such as mixture of pronouns in the message (e.g., "jij" and "ze"), presence of collective nouns, or presence of a user's placeholder (i.e., mention) but no direct address in the text, and even mentions of concepts. the second largest block of errors, 23.62%, is due to the presence of multiple placeholders in the message, often happening in twitter when replying to a long conversation but not necessarily addressing all the users involved. 18.11% of the errors could have been avoided by correctly processing the verb form. given the larger amount of classes, 15.74% of the messages present some errors in the gold datanote, however, that these messages also include the errors in the gold standard for the offensive language dimension. finally, 11.02% of the targets could have been correctly assigned if some form of commonsense knowledge was available to the system. ## related work the interest for the development of datasets and systems for the detection of abusive and offensive language phenomena has seen a steep growth in recent years. different phenomena have been investigated including racism @xcite @xcite , hate speech @xcite @xcite , toxicity foot_7 , verbal aggression @xcite , and misogyny @xcite @xcite . offensive language, as we have detailed in § 2, is a more general and subjective phenomenon than abusive language. founta et al. ( 2018 ) provides an extensive analysis of the correlations between different phenomena and decide to collapse messages labelled as abusive, offensive and aggressive into a single category, namely abusive. early attempts to annotate offensive language have been conducted in german as part of broader evaluation on hate speech @xcite . the semeval 2019 task 6: offenseval @xcite ) has set up a common reference framework for the definition and the annotation of offensive language. the follow-up edition of the task @xcite applied the original definition and annotation approach to four additional languages other than english, namely turkish, danish, arabic, greek. this corpus complements these annotation efforts with a further compatible dataset to fill a gap in the dutch language resource panorama and to promote the advancement of multilingual approaches. a different direction to the development of multilingual offensive language datasets has been presented with xhate-99 @xcite . in this case, the authors have semi-automatically translated selected messages from three english datasets into five target languages (albanian, croatian, german, russian, and turkish). by working with translations, the authors have managed to better disentangle the impact of language versus domain shift in a transfer learning setting. as a matter of fact, the language alignments have ensured that losses observed in the cross-lingual setting are solely due to language shift rather than domain. ## conclusion and future work this paper has presented dalc v2.0, a corpus for detecting offensive and abusive language in social media for dutch. the corpus is composed of 11,292 messages manually annotated and it currently represents the largest available resource for these language phenomena in dutch. offensive language captures a more subjective dimension when compared to abusive language. for this reason, the data have been annotated in parallel by all annotators. we have applied a multi-layered annotation scheme targeting two key dimensions: the explicitness of the message and the presence of a potential target. for both annotation layers, the final labels have been assigned by means of majority voting. however, in the release of the corpus, we also distribute the disaggregated labels for both layers. we have conducted a series of experiments by applying different algorithms. we have obtained the best results by using two monolingual ptlms, namely robbert for the offensive dimension, and bertje for the targets. for the offensive dimension, we have found that a bi-lstm architecture is very competitive when compared to the ptlms also when using non-domain specific embeddings. we have also experimented on the ability of the models to distinguish between abusive and offensive language, obtaining promising results, showing that the distinction between offensive and abusive language is a more complex task than targeting each phenomenon individually. our error analysis has indicated limits of the systems and of the dataset. in particular, it seems that systems heavily rely on surface cues to assign a label to the message, showing a lack of "comprehension" of the content of the message and a high sensitivity to the distribution of the data in the training split. future work will focus on further testing the abilities of the dataset to train robust system by applying trained models to dynamic benchmark on the line of the hatecheck approach @xcite . furthermore, given the presence of multiple compatible corpora in different languages, we plan to explore the application of multilingual systems to address this task.