Automatic Detection of Psychological Distress Indicators and Severity Assessment from Online Forum Posts

Abstract

Psychological disorders are frequently under-diagnosed and consequently have an irreversible impact on individuals and society. The stigma associated with such disorders makes face-to-face discussions with family members and clinicians difficult for many individuals. In contrast, people openly relate experiences on Internet forums. This paper describes a novel system that analyses forum posts to: (1) detect distress indicators that directly map to the Diagnostic and Statistical Manual of Mental Disorders (DSM) IV constructs, and (2) assess the severity of distress for prioritizing individuals who should seek clinical help (i.e. triage). For distress indicator detection, we use support vector machines (SVMs) trained on a suite of innovative intra- and inter-message features. We show significant improvements in multi-label classification accuracy using humangenerated rationales in support of annotated distress labels. For triage assessment, we demonstrate the effectiveness of Markov Logic Networks (MLNs) in dealing with noisy distress label detections and encoding expert rules.

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