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

arXiv: https://arxiv.org/abs/1904.07839 · HTML: https://mlsanity.com/paper/1904.07839/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech](https://mlsanity.com/paper/2109.00591/) (2021)
- [Selecting and combining complementary feature representations and classifiers for hate speech detection](https://mlsanity.com/paper/2201.06721/) (2022)
- [Improved two-stage hate speech classification for twitter based on Deep Neural Networks](https://mlsanity.com/paper/2206.04162/) (2022)
- [Confidence Composition for Multiagent Language Model Systems](https://mlsanity.com/paper/2606.13591/) (2026)

## Abstract (arXiv)

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.