  The task of automatically detecting hate speech in social media is gaining
more and more attention. Given the enormous volume of content posted daily,
human monitoring of hate speech is unfeasible. In this work, we propose new
word-level features for automatic hate speech detection (HSD): multiword
expressions (MWEs). MWEs are lexical units greater than a word that have
idiomatic and compositional meanings. We propose to integrate MWE features in a
deep neural network-based HSD framework. Our baseline HSD system relies on
Universal Sentence Encoder (USE). To incorporate MWE features, we create a
three-branch deep neural network: one branch for USE, one for MWE categories,
and one for MWE embeddings. We conduct experiments on two hate speech tweet
corpora with different MWE categories and with two types of MWE embeddings,
word2vec and BERT. Our experiments demonstrate that the proposed HSD system
with MWE features significantly outperforms the baseline system in terms of
macro-F1.
