@inproceedings{lemmens-etal-2021-improving,
    title = "Improving Hate Speech Type and Target Detection with Hateful Metaphor Features",
    author = "Lemmens, Jens  and
      Markov, Ilia  and
      Daelemans, Walter",
    editor = "Feldman, Anna  and
      Da San Martino, Giovanni  and
      Leberknight, Chris  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.nlp4if-1.2",
    doi = "10.18653/v1/2021.nlp4if-1.2",
    pages = "7--16",
    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.",
}