  Hate speech is one type of harmful online content which directly attacks or
promotes hate towards a group or an individual member based on their actual or
perceived aspects of identity, such as ethnicity, religion, and sexual
orientation. With online hate speech on the rise, its automatic detection as a
natural language processing task is gaining increasing interest. However, it is
only recently that it has been shown that existing models generalise poorly to
unseen data. This survey paper attempts to summarise how generalisable existing
hate speech detection models are, reason why hate speech models struggle to
generalise, sums up existing attempts at addressing the main obstacles, and
then proposes directions of future research to improve generalisation in hate
speech detection.
