@inproceedings{wiegand-etal-2021-implicitly,
    title = "Implicitly Abusive Comparisons {--} A New Dataset and Linguistic Analysis",
    author = "Wiegand, Michael  and
      Geulig, Maja  and
      Ruppenhofer, Josef",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.eacl-main.27/",
    doi = "10.18653/v1/2021.eacl-main.27",
    pages = "358--368",
    abstract = "We examine the task of detecting implicitly abusive comparisons (e.g. ``Your hair looks like you have been electrocuted''). Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g. ``dumbass'' or ``scum'') are absent. We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons. We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons."
}