Social media is a large and growing feature of teen life across the world. While some research suggests that social media are partlyto blame for growing rates of mental illness among teens, social media can also play a positive role in promoting teen mental healthby giving teens new ways to socialize and feel part of a community. In this work, we propose a framework for developing system that can further enhance the upsides of social media use: a computational model that uses social media data to predict depression, as part of a detection-and-intervention loop that engages the user in positive conversations when dynamic indicators of depression present themselves in their social media activity. Our framework uses three pillars of multimodal Content, Behavioral, and Contextual data drawn from users’ social media feeds in order to provide timely detection and intervention services via a chatbot. This multimodal architecture allows us to envision chat features that are precise and responsive to the behavior that triggers the detection. We present a review of the state of the art in depression detectionsystems, and then proceed to explain our system, which builds upon successes in deep learning-based detection systems, as well asplacing these tools in the new dynamic setting of online depression detection that enables our chatbot to initiate therapeutic interactions with social media users.