  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.
