2019/04/16 by Gustavo Henrique Paetzold, Paetzold, Gustavo Henrique, Shervin Malmasi +3 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #cs.CL
paper · pdf · doi:10.48550/arxiv.1904.07839
Proceedings of SemEval
arxiv created 2019/04/16 · openalex publication_date 2019/04/16 · arxiv updated 2019/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.