@inproceedings{albrecht-etal-2026-oncoco,
    title = "{O}n{C}o{C}o 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Conversations",
    author = "Albrecht, Jens  and
      Lehmann, Robert  and
      Poltermann, Aleksandra  and
      Rudolph, Eric  and
      Steigerwald, Philipp  and
      Stieler, Mara",
    editor = "Stranisci, Marco Antonio  and
      Falk, Neele  and
      Labat, Sofie  and
      Lo, Soda Marem  and
      Velutharambath, Aswathy  and
      Weber, Sabine  and
      Damiano, Rossana  and
      Frenda, Simona  and
      Hoste, Veronique  and
      Kleinberg, Bennett  and
      Klinger, Roman  and
      Patti, Viviana  and
      Plaza-del-Arco, Flor Miriam  and
      Sap, Maarten  and
      Yimam, Seid Muhie",
    booktitle = "Proceedings of the 1st Workshop on Social Context ({S}o{C}on) and the 2nd Workshop on Integrating {NLP} and Psychology to Study Social Interactions ({NLPSI}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/cawl-year/2026.socon-1.9/",
    doi = "10.63317/3fv7ej9tcxf9",
    pages = "85--94",
    abstract = "This paper presents OnCoCo 1.0, a new public dataset for fine-grained message classification in online counseling. It is based on a new, integrative system of categories, designed to improve the automated analysis of psychosocial online counseling conversations. Existing category systems, predominantly based on Motivational Interviewing (MI), are limited by their narrow focus and dependence on datasets derived mainly from face-to-face counseling. This limits the detailed examination of textual counseling conversations. In response, we developed a comprehensive new coding scheme that differentiates between 38 types of counselor and 28 types of client utterances, and created a labeled dataset consisting of about 2.800 messages from counseling conversations. We fine-tuned several models on our dataset to demonstrate its applicability. The data and models are publicly available to researchers and practitioners. Thus, our work contributes a new type of fine-grained conversational resource to the language resources community, extending existing datasets for social and mental-health dialogue analysis."
}