Multimodal mental health analysis in social media. Academic Article uri icon

Overview

abstract

  • 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 individual-level 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.

publication date

  • April 10, 2020

Research

keywords

  • Depression
  • Mental Health
  • Social Media

Identity

PubMed Central ID

  • PMC7147779

Scopus Document Identifier

  • 85083232650

Digital Object Identifier (DOI)

  • 10.18637/jss.v036.i11

PubMed ID

  • 32275658

Additional Document Info

volume

  • 15

issue

  • 4