Mental illnesses such as depression and anxiety are highly prevalent, and therapy is increasingly being offered online. This new setting is a departure from face-toface therapy, and offers both a challenge and an opportunity -it is not yet known what features or approaches are likely to lead to successful outcomes in such a different medium, but online text-based therapy provides large amounts of data for linguistic analysis. We present an initial investigation into the application of computational linguistic techniques, such as topic and sentiment modelling, to online therapy for depression and anxiety. We find that important measures such as symptom severity can be predicted with comparable accuracy to face-to-face data, using general features such as discussion topic and sentiment; however, measures of patient progress are captured only by finergrained lexical features, suggesting that aspects of style or dialogue structure may also be important.
Howes et al. REF find that symptom severity can be predicted from transcript data with comparable accuracy to face-to-face data but suggest that insights into style and dialogue structure are needed to predict measures of patient progress.
Linguistic Indicators of Severity and Progress in Online Text-based Therapy for Depression
CLPsych@ACL
Psychology Computer Science