  Mental illness is one of the most pressing public health issues of our time.
While counseling and psychotherapy can be effective treatments, our knowledge
about how to conduct successful counseling conversations has been limited due
to lack of large-scale data with labeled outcomes of the conversations. In this
paper, we present a large-scale, quantitative study on the discourse of
text-message-based counseling conversations. We develop a set of novel
computational discourse analysis methods to measure how various linguistic
aspects of conversations are correlated with conversation outcomes. Applying
techniques such as sequence-based conversation models, language model
comparisons, message clustering, and psycholinguistics-inspired word frequency
analyses, we discover actionable conversation strategies that are associated
with better conversation outcomes.
