@inproceedings{krenn-etal-2024-germs,
    title = "{GERMS}-{AT}: A Sexism/Misogyny Dataset of Forum Comments from an {A}ustrian Online Newspaper",
    author = "Krenn, Brigitte  and
      Petrak, Johann  and
      Kubina, Marina  and
      Burger, Christian",
    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.683",
    pages = "7728--7739",
    abstract = "Brigitte Krenn, Johann Petrak, Marina Kubina, Christian Burger This paper presents a sexism/misogyny dataset extracted from comments of a large online forum of an Austrian newspaper. The comments are in Austrian German language, and in some cases interspersed with dialectal or English elements. We describe the data collection, the annotation guidelines and the annotation process resulting in a corpus of approximately 8 000 comments which were annotated with 5 levels of sexism/misogyny, ranging from 0 (not sexist/misogynist) to 4 (highly sexist/misogynist). The professional forum moderators (self-identified females and males) of the online newspaper were involved as experts in the creation of the annotation guidelines and the annotation of the user comments. In addition, we also describe first results of training transformer-based classification models for both binarized and original label classification of the corpus.",
}

Brigitte Krenn, Johann Petrak, Marina Kubina, Christian Burger This paper presents a sexism/misogyny dataset extracted from comments of a large online forum of an Austrian newspaper. The comments are in Austrian German language, and in some cases interspersed with dialectal or English elements. We describe the data collection, the annotation guidelines and the annotation process resulting in a corpus of approximately 8 000 comments which were annotated with 5 levels of sexism/misogyny, ranging from 0 (not sexist/misogynist) to 4 (highly sexist/misogynist). The professional forum moderators (self-identified females and males) of the online newspaper were involved as experts in the creation of the annotation guidelines and the annotation of the user comments. In addition, we also describe first results of training transformer-based classification models for both binarized and original label classification of the corpus.
Krenn, Brigitte Petrak, Johann Kubina, Marina Burger, Christian
GERMS-AT: A Sexism/Misogyny Dataset of Forum Comments from an Austrian Online Newspaper