signals [@coppersmith2018natural; @thompson2014predicting; @de2017language; @wang2017understanding; @de2016discovering; @coppersmith2016exploratory]. Moreover, the CLP 2016 [@milne2016clpsych] defined a shared task on detecting the severity of the mental health from forum posts. *All* of these studies derive discriminative features to classify depression in user-generated content at message-level, individual-level or community-level. Recent emergence of photo-sharing platforms such as Instagram, has attracted researchers attention to study people’s behavior from their visual narratives – ranging from mining their emotions [@wang2015unsupervised], and happiness trend [@abdullah2015collective], to studying medical concerns [@garimella2016social]. Researchers show that people use Instagram to engage in social exchange and storytelling about their difficult experiences [@andalibi2017sensitive]. The role of visual imagery as a mechanism of self-disclosure by relating visual attributes to mental health disclosures on Instagram was highlighted by [@manikonda2017modeling; @reece2017instagram] where individual Instagram profiles were utilized to build a prediction framework for identifying markers of depression. The importance of data modality to understand user behavior on social media was highlighted by [@duong2017multimodal]. More recently, a deep neural network sequence modeling approach that marries audio and text data modalities to analyze question-answer style interviews between an individual and an agent has been developed to study mental health [@duong2017multimodal]. Similarly, a multimodal depressive dictionary learning was proposed to 