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Hate speech detection is a critical, yet challenging problem in NaturalLanguage Processing (NLP). Despite the existence of numerous studies dedicatedto the development of NLP hate speech detection approaches, the accuracy isstill poor. The central problem is that social media posts are short and noisy,and most existing hate speech detection solutions take each post as an isolatedinput instance, which is likely to yield high false positive and negativerates. In this paper, we radically improve automated hate speech detection bypresenting a novel model that leverages intra-user and inter-userrepresentation learning for robust hate speech detection on Twitter. Inaddition to the target Tweet, we collect and analyze the user's historicalposts to model intra-user Tweet representations. To suppress the noise in asingle Tweet, we also model the similar Tweets posted by all other users withreinforced inter-user representation learning techniques. Experimentally, weshow that leveraging these two representations can significantly improve thef-score of a strong bidirectional LSTM baseline model by 10.1%.

Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection