Kenneth Resnicow


2021

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Exploring Self-Identified Counseling Expertise in Online Support Forums
Allison Lahnala | Yuntian Zhao | Charles Welch | Jonathan K. Kummerfeld | Lawrence C An | Kenneth Resnicow | Rada Mihalcea | Verónica Pérez-Rosas
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Expressive Interviewing: A Conversational System for Coping with COVID-19
Charles Welch | Allison Lahnala | Veronica Perez-Rosas | Siqi Shen | Sarah Seraj | Larry An | Kenneth Resnicow | James Pennebaker | Rada Mihalcea
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolation and mental health. We introduce Expressive Interviewing – an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their lives. We present relevant aspects of the system’s design and implementation as well as quantitative and qualitative analyses of user interactions with the system. In addition, we conduct a comparative evaluation with a general purpose dialogue system for mental health that shows our system potential in helping users to cope with COVID-19 issues.

2019

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What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations
Verónica Pérez-Rosas | Xinyi Wu | Kenneth Resnicow | Rada Mihalcea
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects of the collaboration process occurring during counseling conversations. Specifically, we address the differences between high-quality and low-quality counseling. Our approach examines participants’ turn-by-turn interaction, their linguistic alignment, the sentiment expressed by speakers during the conversation, as well as the different topics being discussed. Our results suggest important language differences in low- and high-quality counseling, which we further use to derive linguistic features able to capture the differences between the two groups. These features are then used to build automatic classifiers that can predict counseling quality with accuracies of up to 88%.

2018

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Analyzing the Quality of Counseling Conversations: the Tell-Tale Signs of High-quality Counseling
Verónica Pérez-Rosas | Xuetong Sun | Christy Li | Yuchen Wang | Kenneth Resnicow | Rada Mihalcea
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Predicting Counselor Behaviors in Motivational Interviewing Encounters
Verónica Pérez-Rosas | Rada Mihalcea | Kenneth Resnicow | Satinder Singh | Lawrence An | Kathy J. Goggin | Delwyn Catley
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

As the number of people receiving psycho-therapeutic treatment increases, the automatic evaluation of counseling practice arises as an important challenge in the clinical domain. In this paper, we address the automatic evaluation of counseling performance by analyzing counselors’ language during their interaction with clients. In particular, we present a model towards the automation of Motivational Interviewing (MI) coding, which is the current gold standard to evaluate MI counseling. First, we build a dataset of hand labeled MI encounters; second, we use text-based methods to extract and analyze linguistic patterns associated with counselor behaviors; and third, we develop an automatic system to predict these behaviors. We introduce a new set of features based on semantic information and syntactic patterns, and show that they lead to accuracy figures of up to 90%, which represent a significant improvement with respect to features used in the past.

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Understanding and Predicting Empathic Behavior in Counseling Therapy
Verónica Pérez-Rosas | Rada Mihalcea | Kenneth Resnicow | Satinder Singh | Lawrence An
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Counselor empathy is associated with better outcomes in psychology and behavioral counseling. In this paper, we explore several aspects pertaining to counseling interaction dynamics and their relation to counselor empathy during motivational interviewing encounters. Particularly, we analyze aspects such as participants’ engagement, participants’ verbal and nonverbal accommodation, as well as topics being discussed during the conversation, with the final goal of identifying linguistic and acoustic markers of counselor empathy. We also show how we can use these findings alongside other raw linguistic and acoustic features to build accurate counselor empathy classifiers with accuracies of up to 80%.

2016

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Building a Motivational Interviewing Dataset
Verónica Pérez-Rosas | Rada Mihalcea | Kenneth Resnicow | Satinder Singh | Lawrence An
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology