UA at SemEval-2019 task 5: Setting a strong linear baseline for hate speech detection

Abstract

This paper describes the system developed at the University of Alicante (UA) for the SemEval 2019 Task 5: Multilingual detection of hate speech against immigrants and women in Twitter. The purpose of this work is to build a strong baseline for hate speech detection by means of a traditional machine learning approach with standard textual features, which could serve as a reference to compare with deep learning systems. We participated in both task A (Hate Speech Detection against Immigrants and Women) and task B (Aggressive behavior and Target Classification) for both English and Spanish. Given the text of a tweet, task A consists of detecting hate speech against women or immigrants in the text, whereas task B consists of identifying the target harassed as individual or generic, and to classify hateful tweets as aggressive or not aggressive. Despite its simplicity, our system obtained a remarkable macro-F1 score of 72.5 (sixth highest) and an accuracy of 73.6 (second highest) in Spanish (task A), outperforming more complex neural models from a total of 40 participant systems.