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A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support

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arxiv 2009.08441 v1 pith:UKTHFUEP submitted 2020-09-17 cs.CL cs.SI

Ashish Sharma , Adam S. Miner , David C. Atkins , Tim Althoff This is my paper
classification cs.CLcs.SI
keywords empathyhealthmentaltext-basedapproachconversationssupportunderstanding
verification ladder T0 review T1 audit T2 compute T3 formal
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Empathy is critical to successful mental health support. Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous, text-based contexts. Because millions of people use text-based platforms for mental health support, understanding empathy in these contexts is crucial. In this work, we present a computational approach to understanding how empathy is expressed in online mental health platforms. We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations. We collect and share a corpus of 10k (post, response) pairs annotated using this empathy framework with supporting evidence for annotations (rationales). We develop a multi-task RoBERTa-based bi-encoder model for identifying empathy in conversations and extracting rationales underlying its predictions. Experiments demonstrate that our approach can effectively identify empathic conversations. We further apply this model to analyze 235k mental health interactions and show that users do not self-learn empathy over time, revealing opportunities for empathy training and feedback.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Empirical Modeling of Therapist-Client Dynamics in Psychotherapy Using LLM-Based Assessments

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    LLM-based scoring of 1,610 therapy sessions finds therapist empathy and exploration are followed by more client disclosure, while prior-session rapport is associated with less self-directed negative emotion—but the cl...

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    General health queries can be labeled in advance for whether they call for emotional reactions or interpretive empathy, and classifiers trained on these labels beat simple baselines.

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