Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health

Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health Tim Althoff∗, Kevin Clark∗, Stanford UniversityJure Leskovec {althoff, kevclark, jure}@cs.stanford.edu Abstract Mental illness is one of the most pressing pub- lic health issues of our time. While counsel- ing and psychotherapy can be effective treat- ments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with la- beled 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 meth- ods to measure how various linguistic aspects of conversations are correlated with conver- sation outcomes. Applying techniques such as sequence-based conversation models, lan- guage model comparisons, message cluster- ing, and psycholinguistics-inspired word fre- quency analyses, we discover actionable con- versation strategies that are associated with better conversation outcomes. 1 Introduction Mental illness is a major global health issue. In the U.S. alone, 43.6 million adults (18.1%) experience mental illness in a given year (National Institute of Mental Health, 2015). In addition to the person di- rectly experiencing a mental illness, family, friends, and communities are also affected (Insel, 2008). In many cases, mental health conditions can be treated effectively through psychotherapy and coun- seling (World Health Organization, 2015). However, it is far from obvious how to best conduct counsel- ing conversations. Such conversations are free-form without strict rules, and involve many choices that *Both authors contributed equally to the paper. could make a difference in someone’s life. Thus far, quantitative evidence for effective conversation strategies has been scarce, since most studies on counseling have been limited to very small sample sizes and qualitative observations (e.g., Labov and Fanshel, (1977); Haberstroh et al., (2007)). How- ever, recent advances in technology-mediated coun- seling conducted online or through texting (Haber- stroh et al., 2007) have allowed counseling ser- vices to scale with increasing demands and to col- lect large-scale data on counseling conversations and their outcomes. Here we present the largest study on counseling conversation strategies published to date. We use data from an SMS texting-based counseling service where people in crisis (depression, self-harm, sui- cidal thoughts, anxiety, etc.), engage in therapeutic conversations with counselors. The data contains millions of messages from eighty thousand counsel- ing conversations conducted by hundreds of coun- selors over the course of one year. We develop a set of computational methods suited for large-scale dis- course analysis to study how various linguistic as- pects of conversations are correlated with conversa- tion outcomes (collected via a follow-up survey). We focus our analyses on counselors instead of individual conversations because we are interested in general conversation strategies rather than prop- erties of specific issues. We find that there are sig- nificant, quantifiable differences between more suc- cessful and less successful counselors in how they conduct conversations. Our findings suggest actionable strategies that are associated with successful counseling: 463 Transactions of the Association for Computational Linguistics, vol. 4, pp. 463–476, …

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