Multimodal mental health analysis in social media

TitleMultimodal mental health analysis in social media
Publication TypeJournal Article
Year of Publication2020
AuthorsYazdavar, AHossein, Mahdavinejad, MSaeid, Baja, G, Romine, W, Sheth, A, Monadjemi, AHassan, Thirunarayan, K, Meddar, JM, Myers, A, Pathak, J, Hitzler, P
JournalPLoS ONE
KeywordsExplainable Machine Learning, Hypothesis Testing, National Language Processing, Prediction, Regression

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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.