Leveraging World Knowledge in Implicit Hate Speech Detection
This addresses the problem of identifying subtle, coded hate speech for social media platforms and content moderators, representing an incremental improvement by adapting existing techniques to a specific domain.
The paper tackled the challenge of detecting implicit hate speech by applying Entity Linking techniques to both explicit and implicit hate speech detection, showing that incorporating real-world knowledge about entity mentions improves detection, with benefits more pronounced when explicit entity triggers are present.
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.