  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.
