public health challenge and a major cause of disability worldwide. Depression affects 6.7% (i.e., about 16 million) Americans each year [^1]. According to the World Mental Health Survey conducted in 17 countries, on average, about 5% of people reported having an episode of depression in 2011 [@marcus2012depression]. Untreated or under-treated clinical depression can lead to suicide and other chronic risky behaviors such as drug or alcohol addiction[^2].

Global efforts to curb clinical depression involve identifying depression through survey-based methods employing phone or online questionnaires. These approaches suffer from under-representation as well as sampling bias (with very small group of respondents.) In contrast, the widespread adoption of social media where people voluntarily and publicly express their thoughts, moods, emotions, and feelings, and even share their daily struggles with mental health problems has not been adequately tapped into studying mental illnesses, such as depression. The visual and textual content shared on different social media platforms like Twitter offer new opportunities for a deeper understanding of self-expressed depression both at an individual as well as community-level. Previous research efforts have suggested that language style, sentiment, users’ activities, and engagement expressed in social media posts can predict the likelihood of depression [@de2013predicting; @de2016discovering]. However, 