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In this paper we revisit the problem of automatically identifying hate speechin posts from social media. We approach the task using a system based onminimalistic compositional Recurrent Neural Networks (RNN). We tested ourapproach on the SemEval-2019 Task 5: Multilingual Detection of Hate SpeechAgainst Immigrants and Women in Twitter (HatEval) shared task dataset. Thedataset made available by the HatEval organizers contained English and Spanishposts retrieved from Twitter annotated with respect to the presence of hatefulcontent and its target. In this paper we present the results obtained by oursystem in comparison to the other entries in the shared task. Our systemachieved competitive performance ranking 7th in sub-task A out of 62 systems inthe English track.

UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks