2019/04/04 by Jing Qian, Mai ElSherief, Qian, Jing +5
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Internet Traffic Analysis and Secure E-voting #Spam and Phishing Detection #cs.AI #cs.CL #cs.CY
paper · pdf · doi:10.48550/arxiv.1904.02418
arxiv created 2019/04/04 · openalex publication_date 2019/04/04 · arxiv updated 2019/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Existing computational models to understand hate speech typically frame the problem as a simple classification task, bypassing the understanding of hate symbols (e.g., 14 words, kigy) and their secret connotations. In this paper, we propose a novel task of deciphering hate symbols. To do this, we leverage the Urban Dictionary and collected a new, symbol-rich Twitter corpus of hate speech. We investigate neural network latent context models for deciphering hate symbols. More specifically, we study Sequence-to-Sequence models and show how they are able to crack the ciphers based on context. Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.