Improving Cross-Domain Hate Speech Generalizability with Emotion Knowledge

TL;DR

This paper introduces an emotion knowledge-based framework that enhances the cross-domain generalizability of hate speech detection systems, demonstrating significant improvements across multiple datasets and domains.

Contribution

It proposes a novel multitask architecture leveraging emotion knowledge to improve hate speech detection generalization across diverse online domains.

Findings

Improved cross-domain F1 performance by up to 18.1%.

Consistent generalization gains across six datasets.

Effective use of emotion corpora with varying scopes.

Abstract

Reliable automatic hate speech (HS) detection systems must adapt to the in-flow of diverse new data to curtail hate speech. However, hate speech detection systems commonly lack generalizability in identifying hate speech dissimilar to data used in training, impeding their robustness in real-world deployments. In this work, we propose a hate speech generalization framework that leverages emotion knowledge in a multitask architecture to improve the generalizability of hate speech detection in a cross-domain setting. We investigate emotion corpora with varying emotion categorical scopes to determine the best corpus scope for supplying emotion knowledge to foster generalized hate speech detection. We further assess the relationship between using pretrained Transformers models adapted for hate speech and its effect on our emotion-enriched hate speech generalization model. We perform extensive experiments on six publicly available datasets sourced from different online domains and show that our emotion-enriched HS detection generalization method demonstrates consistent generalization improvement in cross-domain evaluation, increasing generalization performance up to 18.1% and average cross-domain performance up to 8.5%, according to the F1 measure.