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In recent years, social media platforms have hosted an explosion of hatespeech and objectionable content. The urgent need for effective automatic hatespeech detection models have drawn remarkable investment from companies andresearchers. Social media posts are generally short and their semantics coulddrastically be altered by even a single token. Thus, it is crucial for thistask to learn context-aware input representations, and consider relevancyscores between input embeddings and class representations as an additionalsignal. To accommodate these needs, this paper introduces CRAB (ClassRepresentation Attentive BERT), a neural model for detecting hate speech insocial media. The model benefits from two semantic representations: (i)trainable token-wise and sentence-wise class representations, and (ii)contextualized input embeddings from state-of-the-art BERT encoder. Toinvestigate effectiveness of CRAB, we train our model on Twitter data andcompare it against strong baselines. Our results show that CRAB achieves 1.89%relative improved Macro-averaged F1 over state-of-the-art baseline. The resultsof this research open an opportunity for the future research on automatedabusive behavior detection in social media

CRAB: Class Representation Attentive BERT for Hate Speech Identification in Social Media