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

By Ashish Sharma, Adam S. Miner, David C. Atkins, Tim Althoff. Conference on Empirical Methods in Natural Language Processing, 2020

381 citations recorded by the source corpus.

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.

Subfields: Applied Psychology, Artificial Intelligence, Social Psychology

View Paper: https://www.semanticscholar.org/paper/15c9bbc6de95fbff176b6cb76530785146da81eb

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Citations in the corpus

Towards conversational diagnostic artificial intelligence (2025, neutral): “The doctor agent played the role of an empathetic clinician seeking to comprehend the patient’s medical history within the online chat environment 51 .”
Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support (2022, background): “However, as demonstrated repeatedly in the clinical psychology literature 25,27–29 , empathy is a critical, foundational approach to all evidence-based mental health support, plays an important role in building alliance and relationship between people and is highly correlated with symptom…”
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach (2021, methodology): “Yet, highly empathic conversations are rare on online support platforms [53].”
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021, methodology): “Following recent work on social bias inference and commonsense reasoning (Sap et al., 2020; Forbes et al., 2020; Sharma et al., 2020; Bosselut et al., 2019), we ﬁne-tune Open-AI’s GPT (Radford et al., 2018) and GPT-2 (Radford et al., 2019) pre-trained language models to the task and evaluate using…”
Towards Emotional Support Dialog Systems (2021, background): “Without trained individuals, existing online conversation datasets(Sharma et al., 2020a; Rashkin et al., 2019; Zhong et al., 2020; Sun et al., 2021) do not naturally exhibit examples or elements of supportive conversations.”
Can Large Language Models Transform Computational Social Science? (2023, methodology): “Specifically, we evaluate on empathy detection in online mental health platforms (Sharma et al., 2020) in §3.”
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2021, background): “Also, Sharma et al. (2020) releases some contexts related to mental health and corresponding empathetic responses from Reddit, which we regarded as safe data candidates in Risk Ignorance .”
Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing (2021, background): “trigger warnings they add to their own papers (e.g., Sap et al., 2020; Nangia et al., 2020; Sharma et al., 2020; Han and Tsvetkov, 2020).”