Learning to Decipher Hate Symbols

ACL 2019

Learning to Decipher Hate Symbols

Jan 19, 2021
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Abstract: 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. Authors: Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang (University of California, Santa Barbara)

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