@inproceedings{greschner-etal-2025-qolas,
    title = "{Q}o{LAS}: A {R}eddit Corpus of Health-Related Quality of Life Aspects of Mental Disorders",
    author = {Greschner, Lynn  and
      W{\"u}hrl, Amelie  and
      Klinger, Roman},
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Tsujii, Junichi",
    booktitle = "ACL 2025",
    month = aug,
    year = "2025",
    address = "Viena, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-1.18/",
    pages = "201--216",
    ISBN = "979-8-89176-275-6",
    abstract = "Quality of Life (QoL) refers to a person{'}s subjective perception of various aspects of their life. For medical practitioners, it is one of the most important concepts for treatment decisions. Therefore, it is essential to understand in which aspects a medical condition affects a patient{'}s subjective perception of their life. With this paper, we focus on the under-resourced domain of mental health-related QoL, and contribute the first corpus to study and model this concept: We (1) annotate 240 Reddit posts with a set of 11 QoL aspects (such as `independence', `mood', or `relationships') and their sentiment polarity. Based on this novel corpus, we (2) evaluate a pipeline to detect QoL mentions and classify them into aspects using open-domain aspect-based sentiment analysis. We find that users frequently discuss health-related QoL in their posts, focusing primarily on the aspects `relationships' and `selfimage'. Our method reliably predicts such mentions and their sentiment, however, detecting fine-grained individual aspects remains challenging. An analysis of a large corpus of automatically labeled data reveals that social media content contains novel aspects pertinent to patients that are not covered by existing QoL taxonomies."
}