Improving Hate Speech Type and Target Detection with Hateful Metaphor Features

Jens Lemmens, Ilia Markov, Walter Daelemans


Abstract
We study the usefulness of hateful metaphorsas features for the identification of the type and target of hate speech in Dutch Facebook comments. For this purpose, all hateful metaphors in the Dutch LiLaH corpus were annotated and interpreted in line with Conceptual Metaphor Theory and Critical Metaphor Analysis. We provide SVM and BERT/RoBERTa results, and investigate the effect of different metaphor information encoding methods on hate speech type and target detection accuracy. The results of the conducted experiments show that hateful metaphor features improve model performance for the both tasks. To our knowledge, it is the first time that the effectiveness of hateful metaphors as an information source for hatespeech classification is investigated.
Anthology ID:
2021.nlp4if-1.2
Volume:
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
June
Year:
2021
Address:
Online
Venue:
NLP4IF
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–16
Language:
URL:
https://aclanthology.org/2021.nlp4if-1.2
DOI:
10.18653/v1/2021.nlp4if-1.2
Bibkey:
Cite (ACL):
Jens Lemmens, Ilia Markov, and Walter Daelemans. 2021. Improving Hate Speech Type and Target Detection with Hateful Metaphor Features. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 7–16, Online. Association for Computational Linguistics.
Cite (Informal):
Improving Hate Speech Type and Target Detection with Hateful Metaphor Features (Lemmens et al., NLP4IF 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nlp4if-1.2.pdf