@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Understanding Client Reactions in Online Mental Health Counseling
%A Li, Anqi
%A Ma, Lizhi
%A Mei, Yaling
%A He, Hongliang
%A Zhang, Shuai
%A Qiu, Huachuan
%A Lan, Zhenzhong
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F li-etal-2023-understanding
%X 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.
%R 10.18653/v1/2023.acl-long.577
%U https://aclanthology.org/2023.acl-long.577
%U https://doi.org/10.18653/v1/2023.acl-long.577
%P 10358-10376
Markdown (Informal)
[Understanding Client Reactions in Online Mental Health Counseling](https://aclanthology.org/2023.acl-long.577) (Li et al., ACL 2023)
ACL
- Anqi Li, Lizhi Ma, Yaling Mei, Hongliang He, Shuai Zhang, Huachuan Qiu, and Zhenzhong Lan. 2023. Understanding Client Reactions in Online Mental Health Counseling. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10358–10376, Toronto, Canada. Association for Computational Linguistics.