@inproceedings{dehghan-yanikoglu-2024-multi,
    title = "Multi-domain Hate Speech Detection Using Dual Contrastive Learning and Paralinguistic Features",
    author = "Dehghan, Somaiyeh  and
      Yan{\i}ko{\u{g}}lu, Berrin",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1025",
    pages = "11745--11755",
    abstract = "Social networks have become venues where people can share and spread hate speech, especially when the platforms allow users to remain anonymous. Hate speech can have significant social and cultural effects, especially when it targets specific groups of people in terms of religion, race, ethnicity, culture or a specific social situation such as immigrants and refugees. In this study, we propose a hate speech detection model, BERTurk-DualCL, using a mixed objective with contrastive learning loss that is combined with the traditional cross-entropy loss used for classification. In addition, we study the effects of paralinguistic features, namely emojis and hashtags, on the performance of our model. We trained and evaluated our model on tweets in four different topics with heated discussions from two separate datasets, ranging from discussions about migrants to the Israel-Palestine conflict. Our multi-domain model outperforms comparable results in literature and the average results of four domain-specific models, achieving a macro-F1 score of 81.04{\%} and 58.89{\%} on two- and five-class tasks respectively.",
}

Social networks have become venues where people can share and spread hate speech, especially when the platforms allow users to remain anonymous. Hate speech can have significant social and cultural effects, especially when it targets specific groups of people in terms of religion, race, ethnicity, culture or a specific social situation such as immigrants and refugees. In this study, we propose a hate speech detection model, BERTurk-DualCL, using a mixed objective with contrastive learning loss that is combined with the traditional cross-entropy loss used for classification. In addition, we study the effects of paralinguistic features, namely emojis and hashtags, on the performance of our model. We trained and evaluated our model on tweets in four different topics with heated discussions from two separate datasets, ranging from discussions about migrants to the Israel-Palestine conflict. Our multi-domain model outperforms comparable results in literature and the average results of four domain-specific models, achieving a macro-F1 score of 81.04{\%} and 58.89{\%} on two- and five-class tasks respectively.
Dehghan, Somaiyeh Yan{\i}ko{\u{g}}lu, Berrin
Multi-domain Hate Speech Detection Using Dual Contrastive Learning and Paralinguistic Features