Offensive Language and Hate Speech Detection for Danish

By Gudbjartur Ingi Sigurbergsson, Leon Derczynski. 2019

186 citations recorded by the source corpus.

The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modern society. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this type of content. Until now, most of the research has focused on solving the problem for the English language, while the problem is multilingual. We construct a Danish dataset DKhate containing user-generated comments from various social media platforms, and to our knowledge, the first of its kind, annotated for various types and target of offensive language. We develop four automatic classification systems, each designed to work for both the English and the Danish language. In the detection of offensive language in English, the best performing system achieves a macro averaged F1-score of 0.74, and the best performing system for Danish achieves a macro averaged F1-score of 0.70. In the detection of whether or not an offensive post is targeted, the best performing system for English achieves a macro averaged F1-score of 0.62, while the best performing system for Danish achieves a macro averaged F1-score of 0.73. Finally, in the detection of the target type in a targeted offensive post, the best performing system for English achieves a macro averaged F1-score of 0.56, and the best performing system for Danish achieves a macro averaged F1-score of 0.63. Our work for both the English and the Danish language captures the type and targets of offensive language, and present automatic methods for detecting different kinds of offensive language such as hate speech and cyberbullying.

Subfields: Artificial Intelligence, Communication, Natural Language Processing, AI Ethics, Fairness & Society

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Citations in the corpus

SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) (2020, methodology): “A full description of the dataset and an accompanying data statement (Bender and Friedman, 2018) is in Sigurbergsson and Derczynski (2020).”
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages (2020, unknown): “A non-exhaustive list includes Arabic (Mubarak et al., 2017; Chowdhury et al., 2020), Danish (Sigurbergsson and Derczynski, 2020), German (Jaki and De Smedt, 2019; Wiegand et al., 2018b), Hindi (Saroj and Pal, 2020), Italian (Bosco et al., 2018; Fersini et al., 2018), Polish (Ptaszynski et al.,…”
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective (2020, background): “Sigurbergsson and Derczynski (2020) demonstrated that although language and user behaviour vary between platforms, sharing information across languages and platforms improves the performance of automatic systems.”
HateBERT: Retraining BERT for Abusive Language Detection in English (2020, background): “…of datasets in multiple languages (Waseem and Hovy, 2016; Poletto et al., 2017; Founta et al., 2018; Zampieri et al., 2019a; Ibrohim and Budi, 2019; Sigurbergsson and Derczynski, 2020; C¸¨oltekin, 2020; Pitenis et al., 2020), dedicated workshops 1 and evaluation campaigns (Wiegand et al., 2018;…”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “Common examples of group-directed abuse include sexism, which is included in datasets provided by [32, 43, 62, 101, 111] and racism, which is directly targeted in [99, 111].”
Directions in abusive language training data, a systematic review: Garbage in, garbage out (2020, background): “Common examples of group-directed abuse include sexism, which is included in datasets provided by [32, 56, 59–61] and racism, which is included in [32, 62].”
Towards multidomain and multilingual abusive language detection: a survey (2021, background): “com/articles/dataset/Danish Hate Speech Abusive Language data/12220805 [128]”
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification (2020, methodology): “The taxonomy was successfully adopted for several languages (Mubarak et al., 2021; Pitenis et al., 2020; Sigurbergsson and Derczynski, 2020; Çöl-tekin, 2020), and it was used in a series of shared tasks (Zampieri et al., 2019b; Mandl et al., 2019).”