CoRAL: a Context-aware Croatian Abusive Language Dataset
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Abusive LanguageMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights
Detecting abusive language in social media conversations poses significant challenges, as identifying abusiveness often depends on the conversational context, characterized by the content and topology of preceding commen…
Abusive LanguageAbuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection
The datasets most widely used for abusive language detection contain lists of messages, usually tweets, that have been manually judged as abusive or not by one or more annotators, with the annotation performed at message…
Abusive LanguageGeneral ClassificationEnriching Abusive Language Detection with Community Context
Uses of pejorative expressions can be benign or actively empowering. When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized gr…
Abuse DetectionAbusive LanguageContext Matters: Incorporating Target Awareness in Conversational Abusive Language Detection
Abusive language detection has become an increasingly important task as a means to tackle this type of harmful content in social media. There has been a substantial body of research developing models for determining if a…
User-Aware Multilingual Abusive Content Detection in Social Media
Despite growing efforts to halt distasteful content on social media, multilingualism has added a new dimension to this problem. The scarcity of resources makes the challenge even greater when it comes to low-resource lan…