  Abusive language on online platforms is a major societal problem, often
leading to important societal problems such as the marginalisation of
underrepresented minorities. There are many different forms of abusive language
such as hate speech, profanity, and cyber-bullying, and online platforms seek
to moderate it in order to limit societal harm, to comply with legislation, and
to create a more inclusive environment for their users. Within the field of
Natural Language Processing, researchers have developed different methods for
automatically detecting abusive language, often focusing on specific
subproblems or on narrow communities, as what is considered abusive language
very much differs by context. We argue that there is currently a dichotomy
between what types of abusive language online platforms seek to curb, and what
research efforts there are to automatically detect abusive language. We thus
survey existing methods as well as content moderation policies by online
platforms in this light, and we suggest directions for future work.
