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