Linguistic Indicators of Severity and Progress in Online Text-based Therapy for Depression

Abstract

Mental illnesses such as depression andanxiety are highly prevalent, and therapyis increasingly being offered online. Thisnew setting is a departure from face-to-face therapy, and offers both a challengeand an opportunity – it is not yet knownwhat features or approaches are likely tolead to successful outcomes in such a dif-ferent medium, but online text-based ther-apy provides large amounts of data for lin-guistic analysis. We present an initial in-vestigation into the application of compu-tational linguistic techniques, such as topicand sentiment modelling, to online ther-apy for depression and anxiety. We findthat important measures such as symptomseverity can be predicted with compara-ble accuracy to face-to-face data, usinggeneral features such as discussion topicand sentiment; however, measures of pa-tient progress are captured only by finer-grained lexical features, suggesting thataspects of style or dialogue structure mayalso be important