Empathy in Text-based Mental Health Support

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Online peer-to-peer support platforms such as TalkLife enable conversations between millions of people who seek and provide mental health support. If successful, web-based mental health conversations could improve access to treatment and reduce the global disease burden. Psychologists have repeatedly demonstrated that empathy, the ability to understand and feel the emotions and experiences of others, is a key component leading to positive outcomes in supportive conversations. While peer supporters on mental health platforms are motivated and well-intentioned to help others seeking support (seekers), they are untrained in conducting empathic conversations online. NLP systems that understand conversational empathy could empower peer supporters with feedback and training.

Towards this goal, we present a computational approach to understanding how empathy is expressed in online mental health platforms. Our contributions are:

  • A new conceptual framework of expressed empathy: We develop EPITOME, a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based mental health support.
  • A dataset of empathic interactions: We create a corpus of 10k (post, response) pairs annotated based on EPITOME with supporting evidence for annotations (rationales).
  • A model for identifying empathy: We design a multi-task RoBERTa-based bi-encoder model for identifying empathy in conversations and extracting rationales underlying its predictions.
  • Model-based insights into mental health platforms: We demonstrate that empathy is associated with positive feedback from seekers and the forming of relationships. Importantly, our results suggest that most peer supporters do not self-learn empathy with time, revealing opportunities for empathy training and feedback.

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).

Citation

Ashish Sharma, Adam S Miner, David C Atkins, & Tim Althoff (2020).
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support. EMNLP

Bibtex

@inproceedings{sharma2020empathy,
title={A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support},
author={Sharma, Ashish and Miner, Adam S and Atkins, David C and Althoff, Tim},
year={2020},
booktitle={EMNLP}
}

Team



Acknowledgements


We would like to thank TalkLife and Jamie Druitt for their support and for providing us access to a TalkLife dataset. We also thank the members of UW Behavioral Data Science group, UW NLP group, Zac E. Imel, and the anonymous reviewers for their feedback on this work. A.S. and T.A. were supported in part by NSF grant IIS-1901386, Bill & Melinda Gates Foundation (INV-004841), an Adobe Data Science Research Award, the Allen Institute for Artificial Intelligence, and a Microsoft AI for Accessibility grant. A.S.M. was supported by grants from the National Institutes of Health, National Center for Advancing Translational Science, Clinical and Translational Science Award (KL2TR001083 and UL1TR001085) and the Stanford Human-Centered AI Institute. D.C.A. was supported in part by a NIAAA K award (K02AA023814).

Conflict of Interest Disclosure: Dr. Atkins is a cofounder with equity stake in a technology company, Lyssn.io, focused on tools to support training, supervision, and quality assurance of psychotherapy and counseling.