  The proliferation of harmful content on online platforms is a major societal
problem, which comes in many different forms including hate speech, offensive
language, bullying and harassment, misinformation, spam, violence, graphic
content, sexual abuse, self harm, and many other. Online platforms seek to
moderate such content to limit societal harm, to comply with legislation, and
to create a more inclusive environment for their users. Researchers have
developed different methods for automatically detecting harmful content, often
focusing on specific sub-problems or on narrow communities, as what is
considered harmful often depends on the platform and on the context. We argue
that there is currently a dichotomy between what types of harmful content
online platforms seek to curb, and what research efforts there are to
automatically detect such content. We thus survey existing methods as well as
content moderation policies by online platforms in this light and we suggest
directions for future work.
