  We present a neural-network based approach to classifying online hate speech
in general, as well as racist and sexist speech in particular. Using
pre-trained word embeddings and max/mean pooling from simple, fully-connected
transformations of these embeddings, we are able to predict the occurrence of
hate speech on three commonly used publicly available datasets. Our models
match or outperform state of the art F1 performance on all three datasets using
significantly fewer parameters and minimal feature preprocessing compared to
previous methods.
