UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks
This work addresses the problem of detecting hate speech online, which is important for moderators and researchers, but it is incremental as it applies an existing method to a new dataset.
The paper tackled hate speech identification in social media posts using a minimalistic compositional recurrent neural network, achieving competitive performance by ranking 7th out of 62 systems in the English track of the SemEval-2019 shared task.
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.