INF-HatEval at SemEval-2019 Task 5: Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on Twitter
Alison Ribeiro, Nádia Félix Felipe da Silva · 2019
In this paper, we describe our approach to detect hate speech against women and immigrants on Twitter in a multilingual context, English and Spanish.This challenge was proposed by the SemEval-2019 Task 5, where participants should develop models for hate speech detection, a two-class classification where systems have to predict whether a tweet in English or in Spanish with a given target (women or immigrants) is hateful or not hateful (Task A), and whether the hate speech is directed at a specific person or a group of individuals (Task B).For this, we implemented a Convolutional Neural Networks (CNN) using pre-trained word embeddings (GloVe and FastText) with 300 dimensions.Our proposed model obtained in Task A 0.488 and 0.696 F1-score for English and Spanish, respectively.For Task B, the CNN obtained 0.297 and 0.430 EMR for English and Spanish, respectively.