TY - JOUR T1 - Multimodal mental health analysis in social media JF - PLoS ONE Y1 - 2020 A1 - Amir Hossein Yazdavar A1 - Mohammad Saeid Mahdavinejad A1 - Goonmeet Baja A1 - William Romine A1 - Amit Sheth A1 - Amir Hassan Monadjemi A1 - Krishnaprasad Thirunarayan A1 - John M. Meddar A1 - Annie Myers A1 - Jyotishman Pathak A1 - Pascal Hitzler KW - Explainable Machine Learning KW - Hypothesis Testing KW - National Language Processing KW - Prediction KW - Regression AB -

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Depression is a major public health concern in the U.S. and globally. While successful early

identification and treatment can lead to many positive health and behavioral outcomes,

depression, remains undiagnosed, untreated or undertreated due to several reasons,

including denial of the illness as well as cultural and social stigma. With the ubiquity of social

media platforms, millions of people are now sharing their online persona by expressing their

thoughts, moods, emotions, and even their daily struggles with mental health on social

media. Unlike traditional observational cohort studies conducted through questionnaires

and self-reported surveys, we explore the reliable detection of depressive symptoms from

tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)

data to discern depressive behaviors using a wide variety of features including individuallevel

demographics. By developing a multimodal framework and employing statistical techniques

to fuse heterogeneous sets of features obtained through the processing of visual,

textual, and user interaction data, we significantly enhance the current state-of-the-art

approaches for identifying depressed individuals on Twitter (improving the average F1-

Score by 5 percent) as well as facilitate demographic inferences from social media. Besides

providing insights into the relationship between demographics and mental health, our

research assists in the design of a new breed of demographic-aware health interventions.

UR - https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&type=printable ER -