@inproceedings{chen-etal-2023-uncivil,
    title = "The uncivil empathy: Investigating the relation between empathy and toxicity in online mental health support forums",
    author = "Chen, Ming-Bin  and
      Lau, Jey Han  and
      Frermann, Lea",
    editor = "Muresan, Smaranda  and
      Chen, Vivian  and
      Casey, Kennington  and
      David, Vandyke  and
      Nina, Dethlefs  and
      Koji, Inoue  and
      Erik, Ekstedt  and
      Stefan, Ultes",
    booktitle = "Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association",
    month = nov,
    year = "2023",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2023.alta-1.16/",
    pages = "136--147",
    abstract = "We explore the relationship between empathy and toxicity in the context of online mental health forums. Despite the common assumption of a negative correlation between these concepts, it has not been empirically examined. We augment the EPITOME mental health empathy dataset with toxicity labels using two widely employed toxic/harmful content detection APIs: Perspective API and OpenAI moderation API. We find a notable presence of toxic/harmful content (17.77{\%}) within empathetic responses, and only a very weak negative correlation between the two variables. Qualitative analysis revealed contributions labeled as empathetic often contain harmful content such as promotion of suicidal ideas. Our results highlight the need for reevaluating empathy independently from toxicity in future research and encourage a reconsideration of empathy`s role in natural language generation and evaluation."
}