@inproceedings{chen-etal-2025-hassles,
    title = "Hassles and Uplifts Detection on Social Media Narratives",
    author = "Chen, Jiyu  and
      Karimi, Sarvnaz  and
      Molla, Diego  and
      Duenser, Andreas  and
      Kangas, Maria  and
      Paris, Cecile",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.28/",
    pages = "475--489",
    ISBN = "979-8-89176-298-5",
    abstract = "Hassles and uplifts are psychological constructs of individuals' positive or negative responses to daily minor incidents, with cumulative impacts on mental health. These concepts are largely overlooked in NLP, where existing tasks and models focus on identifying general sentiment expressed in text. These, however, cannot satisfy targeted information needs in psychological inquiry. To address this, we introduce Hassles and Uplifts Detection (HUD), a novel NLP application to identify these constructs in social media language.We evaluate various language models and task adaptation approaches on a probing dataset collected from a private, real-time emotional venting platform. Some of our models achieve F scores close to 80{\%}. We also identify open opportunities to improve affective language understanding in support of studies in psychology."
}