@inproceedings{dragos-etal-2024-exploring,
    title = "Exploring the Emotional Dimension of {F}rench Online Toxic Content",
    author = "Dragos, Valentina  and
      Battistelli, Delphine  and
      Sow, Fatou  and
      Etienne, Aline",
    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.608",
    pages = "6945--6954",
    abstract = "One of the biggest hurdles for the effective analysis of data collected on social platforms is the need for deeper insights on the content and meaning of this data. Emotion annotation can bring new perspectives on this issue and can enable the identification of content{--}specific features. This study aims at investigating the ways in which variation in online content can be explored through emotion annotation and corpus-based analysis. The paper describes the emotion annotation of three data sets in French composed of extremist, sexist and hateful messages respectively. To this end, first a fine-grained, corpus annotation scheme was used to annotate the data sets and then several empirical studies were carried out to characterize the content in the light of emotional categories. Results suggest that emotion annotations can provide new insights for online content analysis and stronger empirical background for automatic content detection.",
}

One of the biggest hurdles for the effective analysis of data collected on social platforms is the need for deeper insights on the content and meaning of this data. Emotion annotation can bring new perspectives on this issue and can enable the identification of content{--}specific features. This study aims at investigating the ways in which variation in online content can be explored through emotion annotation and corpus-based analysis. The paper describes the emotion annotation of three data sets in French composed of extremist, sexist and hateful messages respectively. To this end, first a fine-grained, corpus annotation scheme was used to annotate the data sets and then several empirical studies were carried out to characterize the content in the light of emotional categories. Results suggest that emotion annotations can provide new insights for online content analysis and stronger empirical background for automatic content detection.
Dragos, Valentina Battistelli, Delphine Sow, Fatou Etienne, Aline
Exploring the Emotional Dimension of French Online Toxic Content