Leveraging World Knowledge in Implicit Hate Speech Detection

Jessica Lin


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.
Anthology ID:
2022.nlp4pi-1.4
Volume:
Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Laura Biester, Dorottya Demszky, Zhijing Jin, Mrinmaya Sachan, Joel Tetreault, Steven Wilson, Lu Xiao, Jieyu Zhao
Venue:
NLP4PI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
31–39
Language:
URL:
https://aclanthology.org/2022.nlp4pi-1.4
DOI:
10.18653/v1/2022.nlp4pi-1.4
Bibkey:
Cite (ACL):
Jessica Lin. 2022. Leveraging World Knowledge in Implicit Hate Speech Detection. In Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI), pages 31–39, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Leveraging World Knowledge in Implicit Hate Speech Detection (Lin, NLP4PI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.nlp4pi-1.4.pdf