# Understanding Client Reactions in Online Mental Health Counseling

arXiv: https://arxiv.org/abs/2306.15334 · HTML: https://mlsanity.com/paper/2306.15334/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 59 references that are in the map.
- First posted on arXiv 27 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013)
- [PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support](https://mlsanity.com/paper/2106.01702/) (2021)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [On the State of Social Media Data for Mental Health Research](https://mlsanity.com/paper/2011.05233/) (2020)
- [Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes](https://mlsanity.com/paper/1907.00326/) (2019)
- [Quantifying Intimacy in Language](https://mlsanity.com/paper/2011.03020/) (2020)
- [Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards](https://mlsanity.com/paper/2005.04245/) (2020)
- [Finding Your Voice: The Linguistic Development of Mental Health Counselors](https://mlsanity.com/paper/1906.07194/) (2019)
- [Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues](https://mlsanity.com/paper/1904.00350/) (2019)
- [Towards Automated Real-time Evaluation in Text-based Counseling](https://mlsanity.com/paper/2203.03442/) (2022)

## Built on by (most cited first, 9 of 9)

- [Rethinking Large Language Models in Mental Health Applications](https://mlsanity.com/paper/2311.11267/) (2023)
- [A Benchmark for Understanding Dialogue Safety in Mental Health Support](https://mlsanity.com/paper/2307.16457/) (2023)
- [TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration](https://mlsanity.com/paper/2309.16090/) (2023)
- [PsyChat: A Client-Centric Dialogue System for Mental Health Support](https://mlsanity.com/paper/2312.04262/) (2023)
- [ES-MemEval: Benchmarking Conversational Agents on Personalized Long-Term Emotional Support](https://mlsanity.com/paper/2602.01885/) (2026)
- [Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies](https://mlsanity.com/paper/2408.06527/) (2024)
- [Towards Reliable and Empathetic Depression-Diagnosis-Oriented Chats](https://mlsanity.com/paper/2404.05012/) (2024)
- [Multi-dimensional Assessment and Explainable Feedback for Counselor Responses to Client Resistance in Text-based Counseling with LLMs](https://mlsanity.com/paper/2602.21638/) (2026)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)

## Abstract (arXiv)

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.