  With ubiquity of social media platforms, millions of people are sharing their
online persona by expressing their thoughts, moods, emotions, feelings, and
even their daily struggles with mental health issues voluntarily and publicly
on social media. Unlike the most existing efforts which study depression by
analyzing textual content, we examine and exploit multimodal big data to
discern depressive behavior using a wide variety of features including
individual-level demographics. By developing a multimodal framework and
employing statistical techniques for fusing heterogeneous sets of features
obtained by processing visual, textual and user interaction data, we
significantly enhance the current state-of-the-art approaches for identifying
depressed individuals on Twitter (improving the average F1-Score by 5 percent)
as well as facilitate demographic inference from social media for broader
applications. Besides providing insights into the relationship between
demographics and mental health, our research assists in the design of a new
breed of demographic-aware health interventions.
