Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach

TL;DR

This paper introduces an unsupervised domain adaptation method that enhances hate speech detection models by augmenting data, significantly improving recall and precision across multiple models and datasets.

Contribution

It presents a novel data augmentation approach for unsupervised domain adaptation in hate speech detection, addressing vocabulary and disguise challenges.

Findings

Improved AUPRC by up to 42%

Recall increased by up to 278%

No loss in precision, sometimes improved

Abstract

Online harassment in the form of hate speech has been on the rise in recent years. Addressing the issue requires a combination of content moderation by people, aided by automatic detection methods. As content moderation is itself harmful to the people doing it, we desire to reduce the burden by improving the automatic detection of hate speech. Hate speech presents a challenge as it is directed at different target groups using a completely different vocabulary. Further the authors of the hate speech are incentivized to disguise their behavior to avoid being removed from a platform. This makes it difficult to develop a comprehensive data set for training and evaluating hate speech detection models because the examples that represent one hate speech domain do not typically represent others, even within the same language or culture. We propose an unsupervised domain adaptation approach to augment labeled data for hate speech detection. We evaluate the approach with three different models (character CNNs, BiLSTMs and BERT) on three different collections. We show our approach improves Area under the Precision/Recall curve by as much as 42% and recall by as much as 278%, with no loss (and in some cases a significant gain) in precision.