  In this paper we examine methods to detect hate speech in social media, while
distinguishing this from general profanity. We aim to establish lexical
baselines for this task by applying supervised classification methods using a
recently released dataset annotated for this purpose. As features, our system
uses character n-grams, word n-grams and word skip-grams. We obtain results of
78% accuracy in identifying posts across three classes. Results demonstrate
that the main challenge lies in discriminating profanity and hate speech from
each other. A number of directions for future work are discussed.
