  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.
