Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder

Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing (NLP) techniques have been used to predict depression, with pronoun and emotion usage being identified as important features. However, it is unclear how depressed individuals use positive and negative words when writing about themselves. Individuals with MDD (N=258) completed ecological momentary assessments (EMAs) three times a day (including the Patient Health Questionnaire-9 (PHQ-9) and a free-text diary entry) and weekly EMAs (including a free-text response to a life events prompt) over a 90-day study period. Using NLP techniques, we generated 20 model features to detect and predict average weekly depression from diary entries. Two regression models detected and predicted total PHQ-9 and two classification models detected and predicted moderate to severe depression. The models classified current (AUC=0.68) and future depression (AUC=0.63), and suggest that lower valence increased usage of “I”/“me”/“my”, and lower valence of passages with “I”/“me” as the subject, influenced model predictions towards more severe depression, supporting prior research. These findings highlight that depressed individuals use less positive and more negative words when referring to themselves. Treatments targeting positive affect and digital interventions that written components may be beneficial for targeting MDD.