Mining Symptoms of Severe Mood Disorders in Large Internet Communities

Internet communities have become an important source of support for people with chronic illnesses such as diabetes and obesity, both of which have been associated with depression. In this paper, we argue text classification as promising tool for mining mood disorder cues from Internet chat messages. We created a minimal corpus of 200 chat profiles, based on a disease classification system, ICD-10 diagnostic criteria, and DSM-IV depression definitions. Using significant grams, we trained and tested multiple classifiers on the corpus, with additional evaluation on unlabelled data. Current findings demonstrate the feasibility of scalable flagging of patients who areat risk of developing severe depression in large Internet health communities.