Major depressive disorder is one of common mental disorders globally. It is best to be early detected and cured. This work introduces a method to detect depressive disorder at risk via a behavior made on Facebook platform. A set of features related to Facebook main functions including amount of posting, sharing, commenting and replying is designed to represent users’ activities in a numerical value form. The collected data with periodic and consecutive aspects are gathered without interpreting content. Thus, the data are easier to be collected with less privacy issue. To distinct between positive and negative depression-at risk, PHQ-9 questionnaire, a standard tool commonly used to screen depression patient in Thailand, was used. These features hence are used in supervised learning classification algorithm for detecting a risk of being depressive disorder. From the experiment of 160 Thai Facebook users, the statistical result indicated that depression-positive users tend to do consecutive actions and rarely reply to other comments. Moreover, they often have activities in late night. The classification experiment shows that the designed features based on users’ activities from Facebook with deep learning algorithm yields about 87% in terms of F-measure. After analyzing the data, we thus split data regarding users’ gender and removed obviously low active data, and the F-measure from classification went up to 91.4 which improves for 4 points.