Year: 2014
Type: conference-paper
Abstract: 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.
DOI: 10.3115/v1/w14-3202
Authors: Christine Howes, Matthew Purver, Rose McCabe
Institutions: Queen Mary University of London, University of Exeter
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Funder: European Commission
Award: Concept Creation Technology
OpenAlex ID: W2113669296