detect depressed users on Twitter [@shen2017depression]. They provide a sparse user representations by defining a feature set consisting of social network features, user profile features, visual features, emotional features [@ebrahimi2017challenges], topic-level features, and domain-specific features. Particularly, our choice of multi-model prediction framework is intended to improve upon the prior works involving use of images in multimodal depression analysis [@shen2017depression] and prior works on studying Instagram photos [@ahsan2017towards; @andalibi2016understanding].

**Demographic information inference on Social Media:** There is a growing interest in understanding online user’s demographic information due to its numerous applications in healthcare [@mislove2011understanding; @lerman2016emotions]. A supervised model developed by [@burger2011discriminating] for determining users’ gender by employing features such as screen-name, full-name, profile description and content on external resources (e.g., personal blog). Employing features including emoticons, acronyms, slangs, punctuations, capitalization, sentence length and included links/images, along with online behaviors such as number of friends, post time, and commenting activity, a supervised model was built for predicting user’s age group [@rosenthal2011age]. Utilizing users life stage information such as secondary school student, college student, and employee, [@nguyen2013old] builds age inference model for Dutch Twitter users. Similarly, relying on profile descriptions while devising a set of rules and patterns, a novel model introduced for 