@inproceedings{li-etal-2023-understanding,
    title = "Understanding Client Reactions in Online Mental Health Counseling",
    author = "Li, Anqi  and
      Ma, Lizhi  and
      Mei, Yaling  and
      He, Hongliang  and
      Zhang, Shuai  and
      Qiu, Huachuan  and
      Lan, Zhenzhong",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.577",
    doi = "10.18653/v1/2023.acl-long.577",
    pages = "10358--10376",
    abstract = "Communication success relies heavily on reading participants{'} reactions. Such feedback is especially important for mental health counselors, who must carefully consider the client{'}s progress and adjust their approach accordingly. However, previous NLP research on counseling has mainly focused on studying counselors{'} intervention strategies rather than their clients{'} reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encompasses counselors{'} strategies and client reaction behaviors. The framework has been tested against a large-scale, high-quality text-based counseling dataset we collected over the past two years from an online welfare counseling platform. Our study show how clients react to counselors{'} strategies, how such reactions affect the final counseling outcomes, and how counselors can adjust their strategies in response to these reactions. We also demonstrate that this study can help counselors automatically predict their clients{'} states.",
}

Communication success relies heavily on reading participants{'} reactions. Such feedback is especially important for mental health counselors, who must carefully consider the client{'}s progress and adjust their approach accordingly. However, previous NLP research on counseling has mainly focused on studying counselors{'} intervention strategies rather than their clients{'} reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encompasses counselors{'} strategies and client reaction behaviors. The framework has been tested against a large-scale, high-quality text-based counseling dataset we collected over the past two years from an online welfare counseling platform. Our study show how clients react to counselors{'} strategies, how such reactions affect the final counseling outcomes, and how counselors can adjust their strategies in response to these reactions. We also demonstrate that this study can help counselors automatically predict their clients{'} states.
Li, Anqi Ma, Lizhi Mei, Yaling He, Hongliang Zhang, Shuai Qiu, Huachuan Lan, Zhenzhong
Understanding Client Reactions in Online Mental Health Counseling