Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda 2021
DOI: 10.18653/v1/2021.nlp4if-1.2
|View full text |Cite
|
Sign up to set email alerts
|

Improving Hate Speech Type and Target Detection with Hateful Metaphor Features

Abstract: We study the usefulness of hateful metaphors as 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 e… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
10
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
3
3
2

Relationship

1
7

Authors

Journals

citations
Cited by 12 publications
(10 citation statements)
references
References 21 publications
0
10
0
Order By: Relevance
“…Some studies aim at answering the research question how various target groups are referred to. As an example, Lemmens et al [54] analyzed the language of hateful Dutch comments regarding classes of metaphoric terms, including body parts, products, animals, or mental conditions. Such a closed world approach, however, does not permit the identification of targets that were not known at the development time of the HOF detection system.…”
Section: Target Classificationmentioning
confidence: 99%
See 1 more Smart Citation
“…Some studies aim at answering the research question how various target groups are referred to. As an example, Lemmens et al [54] analyzed the language of hateful Dutch comments regarding classes of metaphoric terms, including body parts, products, animals, or mental conditions. Such a closed world approach, however, does not permit the identification of targets that were not known at the development time of the HOF detection system.…”
Section: Target Classificationmentioning
confidence: 99%
“…Such a closed world approach, however, does not permit the identification of targets that were not known at the development time of the HOF detection system. This is to some degree addressed by Silva et al [55], who developed a rule-based method to identify target mentions that are then, similarly to Lemmens et al [54], compared regarding the expressions that are used.…”
Section: Target Classificationmentioning
confidence: 99%
“…[Zampieri et al, 2019] and [Chuang et al, 2021]) as well as accurately determining the category of different types of hate speech / offensive language (e.g. [Zampieri, 2021] and [Lemmens et al, 2021]), or even identifying the targets of offensive language (e.g. [Lemmens et al, 2021], [Shvets et al, 2021]).…”
Section: Introductionmentioning
confidence: 99%
“…[Zampieri, 2021] and [Lemmens et al, 2021]), or even identifying the targets of offensive language (e.g. [Lemmens et al, 2021], [Shvets et al, 2021]). Usually this involves supervised machine learning and even the creation of language models fine-tuned for hate-speech (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…To our knowledge, only metaphorical and stereotyped information have been exploited for abusive language detection. Interestingly, Lemmens et al [2021] proved the contribution of hateful metaphors as features for the identification of the type and target of hate speech in Dutch Facebook comments in models based on classical machine learning and transformers. Whereas Lavergne et al [2020] exploit the multi-annotation proposed in HaSpeeDe2020 about the presence of hate speech and stereotype in tweets to train a multi-task learning-based model reaching the best score in hate speech detection in tweets.…”
Section: Open Challenge: Implicit Abusementioning
confidence: 98%