@inproceedings{ollagnier-2024-cyberagressionado,
    title = "{C}yber{A}gression{A}do-v2: Leveraging Pragmatic-Level Information to Decipher Online Hate in {F}rench Multiparty Chats",
    author = "Ollagnier, Anais",
    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://preview.aclanthology.org/fix-sig-urls/2024.lrec-main.383/",
    pages = "4287--4298",
    abstract = "As a part of the release of the \textit{CyberAgressionAdo-V2} dataset, this paper introduces a new tagset that includes tags marking pragmatic-level information occurring in cyberbullying situations. The previous version of this dataset, \textit{CyberAgressionAdo-V1}, consists of aggressive multiparty chats in French annotated using a hierarchical tagset developed to describe bullying narrative events including the participant roles, the presence of hate speech, the type of verbal abuse, among others. In contrast, \textit{CyberAgressionAdo-V2} uses a multi-label, fine-grained tagset marking the discursive role of exchanged messages as well as the context in which they occur {---} for instance, attack (ATK), defend (DFN), counterspeech (CNS), abet/instigate (AIN), gaslight (GSL), etc. This paper provides a comprehensive overview of the annotation tagset and presents statistical insights derived from its application. Additionally, we address the challenges encountered when annotating pragmatic-level information in this context, conducting a thorough analysis of annotator disagreements. The resulting dataset comprises 19 conversations that have been manually annotated and is now available to facilitate further research in the field."
}