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

## TL;DR

This paper presents a minimalistic RNN-based system for hate speech detection on social media, achieving competitive results in the SemEval-2019 HatEval shared task for English and Spanish tweets.

## Contribution

The paper introduces a simple RNN approach for multilingual hate speech identification and demonstrates its effectiveness on a large Twitter dataset.

## Key findings

- Achieved 7th place in English sub-task out of 62 systems
- Effective use of minimalistic RNN for multilingual hate speech detection
- Competitive performance compared to state-of-the-art methods

## Abstract

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.