Empath: Tracing Multi-Level Emotion Dynamics in Crisis Counseling Dialogues

2026-09-24

AI Summary

This study addresses the limitation of static emotion labeling in crisis intervention dialogues, which fails to capture dynamic emotional evolution. To this end, we propose EMPATH, a framework integrating textual affective computing with statistical models to achieve turn-level, transition-probability, and global-prototype emotion dynamics modeling across three granularities—the first of its kind. Our analysis reveals the complex trajectories through which sustained negative emotions transition toward hope during crisis support, while identifying that African American help-seekers exhibit persistent negative affect and heterogeneous recovery pathways. This work overcomes the constraints of traditional static analyses and demonstrates the critical value of multi-granularity dynamic modeling for understanding psychological mechanisms such as grief expression.

Abstract

Emotion dynamics are critical for understanding crisis-support conversations, yet most computational work treats emotion as static utterance-level labels. We introduce EMPATH, a framework for understanding affective dynamics in mental health dialogues across three granularities: turn-level labels, transition probabilities, and global conversation archetypes. Applying EMPATH to text-based crisis conversations with self-identified Black texters discussing grief, we find persistent negative affect, gradual hope-ward transitions, distinct texter-volunteer emotional roles, and heterogeneous recovery trajectories. These results highlight the informative patterns that emerge from computationally understanding crisis support and expressions of grief as dynamic processes within conversations, as well as the overall value of emotion-dynamic analysis for analyzing and comparing affect in dialogues.

Problem

• emotion dynamics
• crisis counseling
• mental health dialogues
• affective analysis
• grief

Innovation

• Emotion Dynamics
• Crisis Counseling
• Multi-Granularity Modeling
• Computational Framework
• Mental Health Dialogues