Towards Assessing Changes in Degree of Depression through Facebook

Depression is typically diagnosed as be-ing present or absent. However, depres-sion severity is believed to be continu-ously distributed rather than dichotomous. Severity may vary for a given patient daily and seasonally as a function of many vari-ables ranging from life events to environ-mental factors. Repeated population-scale assessment of depression through ques-tionnaires is expensive. In this paper we use survey responses and status updates from 28,749 Facebook users to develop a regression model that predicts users ’ de-gree of depression based on their Face-book status updates. Our user-level pre-dictive accuracy is modest, significantly outperforming a baseline of average user sentiment. We use our model to estimate user changes in depression across seasons, and find, consistent with literature, users’ degree of depression most often increases from summer to winter. We then show the potential to study factors driving individ-uals ’ level of depression by looking at its most highly correlated language features.