Leveraging World Knowledge in Implicit Hate Speech Detection

TL;DR

This paper explores the use of Entity Linking to incorporate world knowledge into hate speech detection, improving identification of implicit and explicit hate speech, especially when explicit entity triggers are present.

Contribution

It is the first to apply Entity Linking techniques to both explicit and implicit hate speech detection, demonstrating the benefits of real-world knowledge integration.

Findings

Entity Linking improves hate speech detection accuracy.

Knowledge addition is more effective with explicit entity triggers.

Real-world knowledge sometimes does not enhance detection.

Abstract

While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present. We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech.