C o RAL : a Context-aware C roatian Abusive Language Dataset (2022)

Abstract: In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderators by either automatically classifying the examples or allowing the moderators to prioritize which comments to consider first. However, the concept of inappropriate content is often subjective, and such content can be conveyed in many subtle and indirect ways. In this work, we propose CoRAL – a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context. We show experimentally that current models degrade when comments are not explicit and further degrade when language skill and context knowledge are required to interpret the comment.

## introduction the growing volume of user-generated contentfrom social media to online forums and comments under news articles -implies a growing need for moderation of this content to counter abuse and the spread of misinformation. automatic and semiautomatic moderation systems can greatly aid human moderators, making their work quicker, easier, and more accurate; however, most of this work focuses on english, ignoring smaller, less-resourced languages @xcite . this situation is improving with the advent of multilingual contextual language models, as they enable cross-lingual transfer learning: recent work shows that comment moderation models with reasonable performance for less-resourced languages can be produced using zero-or few-shot transfer learning after pre-training on majority language datasets @xcite . 1 the coral dataset can be found here. it is not always sufficient to identify whether a comment is inappropriate or not; further subcategorization helps build measures to counter it. previous work has taken a range of approaches to sub-categorizing inappropriate content. @xcite divided abusive language into two orthogonal categories -directed/generalized and implicit/explicit. a very similar approach is taken by @xcite . more fine-grained approaches include very specific topics such as homophobia, cyberbullying or racism (e.g., @xcite , and the annotation of communityspecific extreme hate speech with targets from multiple countries @xcite ; we refer to @xcite for a comprehensive list. recently, a unified taxonomy of abusive language categories has been proposed by @xcite , a systematic division of slurs by @xcite , and another taxonomy by @xcite . @xcite provide a detailed empirical analysis of model performance across different example categories. all of these approaches divide comments primarily on the basis of how/whom they insult. in contrast, we are interested in categorizing how such comments can be difficult to classify or interpret automatically due to their use of linguistic and cultural context. our goal is to create a dataset and accompanying annotation schema to quantify what categories (primarily related to linguistic and cultural context) of abuse are being used by people and how well nlp models handle these different categories. to this end, we identified three context dependency categories (cdc): implicitness, global context, and local context. these cdcs are further sub-divided according to implicitness (explicit/implicit), use of (global/local) language alterations, and use of (global/local) external knowledge; see section 2 for details. the closest related work in this vein is that of @xcite , who give a systematic overview of various ways in which examples can be difficult (e.g., sarcasm, dehumanization, inference required, multimodality, etc.). however, @xcite only focused on implicit abuse in english without any empirical analysis. we focus on the croatian language, a lessrepresented language in natural language processing research. we annotated 2,240 croatian comments from the 24sata newspaper foot_0 with our proposed cdcs. we experimented with four transformer-based models @xcite @xcite . our experimentation shows that models do not perform equally well on all cdcs. the easiest cdc is explicit expression (e.g., cursing or using slurs), confirming the findings of @xcite . more difficult cdcs are those that require global or local context for their interpretation, via language disguise or external knowledge. the contribution of this paper is twofold. first, we present a publicly available schema and the context-aware croatian abusive language dataset (coral) comprised of croatian news comments annotated for different cdcs. second, we provide a quantitative and qualitative comparison of comment moderation models, revealing the limitations of different cross-lingual models when handling difficult examples and which cdcs are generally the most challenging. ## dataset when building coral, we aim to have annotated examples with the cdc's they exhibit. moreover, we focus on devising cdcs that would reflect the challenges models face when accounting for cultural context (global or local). by manual inspection, we identified three main cdcs of blocked comments on which cross-lingual models tend to fail: implicitness, global context, and localcontext, which are further divided as follows. to the best of our knowledge, coral is the first dataset with annotations on which category of local/global context is required for interpretation. 3 dataset annotation: we use the publicly available 24sata newspaper comment dataset @xcite . 4 the dataset contains comments moderated by 24sata's moderators based on the newspaper's policy: rules include the removal of hate ## conclusion we present the context-aware croatian abusive language dataset (coral), a dataset annotated with context dependency categories (cdc) of problematic examples for croatian comment moderation. we annotated 2240 blocked comments for explicitness, implicitness, language independent disguise, world knowledge-based, croatian specific disguise, and croatia knowledge-based. we found that only 58.44% had explicit expressions of abuse. this indicates that almost half the remaining examples are challenging (croatian specific disguise alone accounting for ≈ 24%). this shows that addressing these categories of examples is very practically relevant. we tested four transformerbased models and found that explicit comments are the easiest and local context ones are hardest. we also found that language-specific multilingual language models better identify croatianspecific blocked comments. finally, we believe that coral will help design better models for croatian comment moderation, build a foundation for creating similar datasets in other languages, and develop novel methods by incorporating local context.