Finding Your Voice: The Linguistic Development of Mental Health Counselors
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Abstract (arXiv)
Mental health counseling is an enterprise with profound societal importance where conversations play a primary role. In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients. However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear. For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior. This has led some to even argue that counseling is a profession without expertise. In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution. We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure. We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors. Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jun 2019.
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Citing papers per year: 2021: 4; 2022: 0; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
9 papers.
- 2021PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Supportcited by 92
- 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interactioncited by 77
- 2021Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approachcited by 75
- 2021Exploring Self-Identified Counseling Expertise in Online Support Forumscited by 10
- 2023Understanding Client Reactions in Online Mental Health Counselingcited by 9
- 2021Modeling Language Usage and Listener Engagement in Podcastscited by 2
- 2024Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Modelscited by 1
- 2025Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communitiescited by 1
- 2026Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselorscited by 0
- 2026Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors3 Sep 2026
- 2025Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities22 Aug 2025
- 2024Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models22 Feb 2024
- 2023Understanding Client Reactions in Online Mental Health Counseling27 Jun 2023
- 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction4 May 2023
- 2021Exploring Self-Identified Counseling Expertise in Online Support Forums24 Jun 2021
- 2021Modeling Language Usage and Listener Engagement in Podcasts11 Jun 2021
- 2021PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support3 Jun 2021
- 2021Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach19 Jan 2021