  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.
