Understanding Client Reactions in Online Mental Health Counseling

Anqi Li, Lizhi Ma, Yaling Mei, Hongliang He, Shuai Zhang +2 more
6/27/2023
cs.CL

No official code found. Best implementation: dll-wu/Client-Reactions (38 stars, last commit 2023-10-15).

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 shows 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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Code Implementations(1)

dll-wu/Client-ReactionsCommunity100%

The official repository of ACL2023 paper: Understanding Client Reactions in Online Mental Health Counseling

381Shell, PythonMay 15, 20233 years ago

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Cite this paper

@article{li2023understanding,
  title  = {Understanding Client Reactions in Online Mental Health Counseling},
  author = {Anqi Li and Lizhi Ma and Yaling Mei and Hongliang He and Shuai Zhang and Huachuan Qiu and Zhenzhong Lan},
  year   = {2023},
  eprint = {2306.15334},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.15334v1}
}

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