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

Improving Automatic Hate Speech Detection with Multiword Expression Features