@inproceedings{korre-etal-2023-harmful,
    title = "Harmful Language Datasets: An Assessment of Robustness",
    author = "Korre, Katerina  and
      Pavlopoulos, John  and
      Sorensen, Jeffrey  and
      Laugier, L{\'e}o  and
      Androutsopoulos, Ion  and
      Dixon, Lucas  and
      Barr{\'o}n-cede{\~n}o, Alberto",
    editor = {Chung, Yi-ling  and
      R{{\textbackslash}"ottger}, Paul  and
      Nozza, Debora  and
      Talat, Zeerak  and
      Mostafazadeh Davani, Aida},
    booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.woah-1.24",
    doi = "10.18653/v1/2023.woah-1.24",
    pages = "221--230",
    abstract = "The automated detection of harmful language has been of great importance for the online world, especially with the growing importance of social media and, consequently, polarisation. There are many open challenges to high quality detection of harmful text, from dataset creation to generalisable application, thus calling for more systematic studies. In this paper, we explore re-annotation as a means of examining the robustness of already existing labelled datasets, showing that, despite using alternative definitions, the inter-annotator agreement remains very inconsistent, highlighting the intrinsically subjective and variable nature of the task. In addition, we build automatic toxicity detectors using the existing datasets, with their original labels, and we evaluate them on our multi-definition and multi-source datasets. Surprisingly, while other studies show that hate speech detection models perform better on data that are derived from the same distribution as the training set, our analysis demonstrates this is not necessarily true.",
}

The automated detection of harmful language has been of great importance for the online world, especially with the growing importance of social media and, consequently, polarisation. There are many open challenges to high quality detection of harmful text, from dataset creation to generalisable application, thus calling for more systematic studies. In this paper, we explore re-annotation as a means of examining the robustness of already existing labelled datasets, showing that, despite using alternative definitions, the inter-annotator agreement remains very inconsistent, highlighting the intrinsically subjective and variable nature of the task. In addition, we build automatic toxicity detectors using the existing datasets, with their original labels, and we evaluate them on our multi-definition and multi-source datasets. Surprisingly, while other studies show that hate speech detection models perform better on data that are derived from the same distribution as the training set, our analysis demonstrates this is not necessarily true.
Korre, Katerina Pavlopoulos, John Sorensen, Jeffrey Laugier, L{\'e}o Androutsopoulos, Ion Dixon, Lucas Barr{\'o}n-cede{\~n}o, Alberto
Harmful Language Datasets: An Assessment of Robustness