Understanding Client Reactions in Online Mental Health Counseling

TL;DR

This paper develops a framework to analyze client reactions in online mental health counseling, enabling better understanding and prediction of client responses to improve counseling outcomes.

Contribution

It introduces a theoretically grounded annotation framework for client reactions and demonstrates its application on a large counseling dataset to enhance counselor strategies.

Findings

Clients' reactions influence counseling outcomes

Counselors can predict client states automatically

The framework helps tailor counseling strategies

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.