there any underlying common themes among depressed individuals generated using multimodal content that can be used to detect depression reliably?

![Self-disclosure on Twitter from likely depressed users discovered by matching depressive-indicative terms](self){width="53.00000%"}

\[self\_reported\]

Related Work
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**Mental Health Analysis using Social Media:** Several efforts have attempted to automatically detect depression from social media content utilizing machine/deep learning and natural language processing approaches. Conducting a retrospective study over tweets, [@de2013social] characterizes depression based on factors such as language, emotion, style, ego-network, and user engagement. They built a classifier to predict the likelihood of depression in a post [@de2013social; @shuai2016mining] or in an individual [@de2013predicting; @nguyen2014affective; @yazdavar2016analyzing; @bajaj2017identifying]. Moreover, there have been significant advances due to the shared task [@coppersmith2015clpsych] focusing on methods for identifying depressed users on Twitter at the Computational Linguistics and Clinical Psychology Workshop (CLP 2015). A corpus of nearly 1,800 Twitter users was built for evaluation, and the best models employed topic modeling [@resnik2015beyond], Linguistic Inquiry and Word Count (LIWC) features, and other metadata [@preotiuc2015role]. More recently, a neural network architecture introduced by [@yates2017depression] combined posts into a representation of user’s activities for detecting depressed users. Another active line of research has focused on capturing suicide and self-harm 