TuEval at SemEval-2019 task 5: LSTM approach to hate speech detection in English and Spanish

Abstract

The detection of hate speech, especially in online platforms and forums, is quickly becoming a hot topic as anti-hate speech legislation begins to be applied to public discourse online. The HatEval shared task was created with this in mind; participants were expected to develop a model capable of determining whether or not input (in this case, Twitter posts in English and Spanish) could be considered hate speech (designated as Subtask A), if they were aggressive, and whether the tweet was targeting an individual, or speaking generally (Subtask B). We approached this Subtask by creating a LSTM model with an embedding layer. We found that our model performed considerably better on English language input when compared to Spanish language input. In English, we achieved an F1-Score of 0.466 for Subtask A and 0.462 for Subtask B; In Spanish, we achieved scores of 0.617 and 0.612 on Subtask A and Subtask B, respectively.