Modeling Empathy and Distress in Reaction to News Stories

By Sven Buechel, Anneke Buffone, Barry Slaff, Lyle Ungar, João Sedoc. 2018

152 citations recorded by the source corpus.

Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.

Subfields: Artificial Intelligence, Social Psychology, Natural Language Processing

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Citations in the corpus

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (2019, methodology): “Similarly, on the Empathetic Reactions dataset, BERT outperforms our human baseline, where BERT’s predictions have a Pearson correlation of 0.45 on empathy and 0.55 on distress, compared to 0.45 and 0.35 for our human baseline.”
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach (2021, background): “For example, computational research typically defines empathy as reacting with emotions of warmth and compassion [6].”
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support (2020, background): “Also, while previous NLP research has focused predominantly on empathy as reacting with emotions of warmth and compassion (Buechel et al., 2018), a separate but key aspect of empathy is to communicate a cognitive understanding of others (Selman, 1980).”
Can Large Language Models Transform Computational Social Science? (2023, background): “By better understanding community-specific affordances (Zhou and Jurgens, 2020) and the most common triggers for empathetic responses (Buechel et al., 2018; Omitaomu et al., 2022), CSS can reciprocally inform the design of empathetic communities (Coulton et al., 2014; Taylor et al., 2019), as well…”