  A key challenge for automatic hate-speech detection on social media is the
separation of hate speech from other instances of offensive language. Lexical
detection methods tend to have low precision because they classify all messages
containing particular terms as hate speech and previous work using supervised
learning has failed to distinguish between the two categories. We used a
crowd-sourced hate speech lexicon to collect tweets containing hate speech
keywords. We use crowd-sourcing to label a sample of these tweets into three
categories: those containing hate speech, only offensive language, and those
with neither. We train a multi-class classifier to distinguish between these
different categories. Close analysis of the predictions and the errors shows
when we can reliably separate hate speech from other offensive language and
when this differentiation is more difficult. We find that racist and homophobic
tweets are more likely to be classified as hate speech but that sexist tweets
are generally classified as offensive. Tweets without explicit hate keywords
are also more difficult to classify.
