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With ubiquity of social media platforms, millions of people are sharing theironline persona by expressing their thoughts, moods, emotions, feelings, andeven their daily struggles with mental health issues voluntarily and publiclyon social media. Unlike the most existing efforts which study depression byanalyzing textual content, we examine and exploit multimodal big data todiscern depressive behavior using a wide variety of features includingindividual-level demographics. By developing a multimodal framework andemploying statistical techniques for fusing heterogeneous sets of featuresobtained by processing visual, textual and user interaction data, wesignificantly enhance the current state-of-the-art approaches for identifyingdepressed individuals on Twitter (improving the average F1-Score by 5 percent)as well as facilitate demographic inference from social media for broaderapplications. Besides providing insights into the relationship betweendemographics and mental health, our research assists in the design of a newbreed of demographic-aware health interventions.

Fusing Visual, Textual and Connectivity Clues for Studying Mental Health