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

TL;DR

The paper addresses how empathy is expressed in asynchronous text-based mental health support and introduces Epitome, a framework outlining three mechanisms of empathy (Emotional Reactions, Interpretations, Explorations) with graded intensity. It builds a 10k annotated corpus of (seeker post, response) pairs and a RoBERTa-based bi-encoder model that jointly identifies empathy and extracts rationales, achieving strong gains over baselines. Domain-adaptive pre-training on large social-communication data and seeker-context attention enable robust performance across TalkLife and Reddit datasets. Applying the model to 235k TalkLife interactions reveals that peer supporters rarely self-learn empathy over time, while empathic exchanges correlate with positive feedback and relationship formation, highlighting the need for real-time, rationale-grounded feedback to train lay responders.

Abstract

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.

Figures (4)

• Figure 1: Our framework of empathic conversations contains three empathy communication mechanisms -- Emotional Reactions, Interpretations, and Explorations. We differentiate between no communication, weak communication, and strong communication of these factors. Our computational approach simultaneously identifies these mechanisms and the underlying rationale phrases (highlighted portions). All examples in this paper have been anonymized using best practices in privacy and security matthews2017stories.
• Figure 2: We use two independently pre-trained RoBERTa-based encoders for encoding seeker post and response post respectively. We leverage attention between them for generating seeker-context aware representation of the response post, used to perform the two tasks of empathy identification and rationale extraction.
• Figure 3: (a) Peer-supporters do not self-learn empathy over time. Only users who joined in 2015 were included but similar trends hold for other user groups; (b) Stronger communications of emotional reactions and interpretations are received positively by seekers. Stronger explorations get 47% more replies; (c) A lot more seekers follow peers after empathic interactions; (d) Females are more empathic towards females.
• Figure 4: Empathy over time analysis of various user groups. We find similar trends across multiple groups.