# Learning to Decipher Hate Symbols

## TL;DR

This paper introduces a new task of deciphering hate symbols in social media, leveraging neural network models and a novel Twitter corpus to improve understanding of hidden hate speech connotations.

## Contribution

It proposes a new deciphering task, creates a symbol-rich Twitter dataset, and develops neural models including a Variational Decipher for better generalization.

## Key findings

- Sequence-to-Sequence models can decode hate symbols based on context.
- The Variational Decipher outperforms other models on unseen hate symbols.
- The approach enhances understanding of covert hate speech in social media.

## 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.