  Existing work on automated hate speech detection typically focuses on binary
classification or on differentiating among a small set of categories. In this
paper, we propose a novel method on a fine-grained hate speech classification
task, which focuses on differentiating among 40 hate groups of 13 different
hate group categories. We first explore the Conditional Variational Autoencoder
(CVAE) as a discriminative model and then extend it to a hierarchical
architecture to utilize the additional hate category information for more
accurate prediction. Experimentally, we show that incorporating the hate
category information for training can significantly improve the classification
performance and our proposed model outperforms commonly-used discriminative
models.
