Empathic Conversations: A Multi-level Dataset of Contextualized Conversations

TL;DR

This paper introduces the Empathic Conversations dataset, capturing multi-level, annotated dialogues about news to analyze how empathy manifests and varies across individuals, with baseline models for prediction.

Contribution

It provides the first multi-faceted dataset of empathy in conversations, including personality, demographics, and third-party assessments, enabling comprehensive analysis.

Findings

Differences in perceived empathy relate to personality and demographics.

Baseline models can predict empathy-related features from dialogue data.

The dataset includes diverse forms of empathy and personal traits.

Abstract

Empathy is a cognitive and emotional reaction to an observed situation of others. Empathy has recently attracted interest because it has numerous applications in psychology and AI, but it is unclear how different forms of empathy (e.g., self-report vs counterpart other-report, concern vs. distress) interact with other affective phenomena or demographics like gender and age. To better understand this, we created the {\it Empathic Conversations} dataset of annotated negative, empathy-eliciting dialogues in which pairs of participants converse about news articles. People differ in their perception of the empathy of others. These differences are associated with certain characteristics such as personality and demographics. Hence, we collected detailed characterization of the participants' traits, their self-reported empathetic response to news articles, their conversational partner other-report, and turn-by-turn third-party assessments of the level of self-disclosure, emotion, and empathy expressed. This dataset is the first to present empathy in multiple forms along with personal distress, emotion, personality characteristics, and person-level demographic information. We present baseline models for predicting some of these features from conversations.