Depression is a common chronic disorder. It often goes undetected due to limited diagnosis methods and
brings serious results to public and personal health. Former research detected geographic pattern for
depression using questionnaires or self-reported measures of mental health, this may induce samesource
bias. Recent studies use social media for depression detection but none of them examines the
geographic patterns. In this paper, we apply GIS methods to social media data to provide new perspectives
for public health research. We design a procedure to automatically detect depressed users in
Twitter and analyze their spatial patterns using GIS technology. This method can improve diagnosis
techniques for depression. It is faster at collecting data and more promptly at analyzing and providing
results. Also, this method can be expanded to detect other major events in real-time, such as disease
outbreaks and earthquakes.
© 2014 Elsevier Ltd. All rights reserved.
Introduction1
Depression is a common chronic disorder with adverse effects
for well-being and daily functioning, and is associated with high
suicide rates (Barlow & Durand, 2011). Major depressive disorder
(MDD) (Barlow & Durand, 2005) is the most common type. The
centers for disease control and prevention has reported that an
estimated 3.4 percent U.S. adults report MDD (MMWR, 2010).
Depression often goes undetected due to the absence of reliable
laboratory test, and therefore new methodology for its
diagnosis is
Depression is a common chronic disorder. It often goes undetected due to limited diagnosis methods andbrings serious results to public and personal health. Former research detected geographic pattern fordepression using questionnaires or self-reported measures of mental health, this may induce samesourcebias. Recent studies use social media for depression detection but none of them examines thegeographic patterns. In this paper, we apply GIS methods to social media data to provide new perspectivesfor public health research. We design a procedure to automatically detect depressed users inTwitter and analyze their spatial patterns using GIS technology. This method can improve diagnosistechniques for depression. It is faster at collecting data and more promptly at analyzing and providingresults. Also, this method can be expanded to detect other major events in real-time, such as diseaseoutbreaks and earthquakes.© 2014 Elsevier Ltd. All rights reserved.Introduction1Depression is a common chronic disorder with adverse effectsfor well-being and daily functioning, and is associated with highsuicide rates (Barlow & Durand, 2011). Major depressive disorder(MDD) (Barlow & Durand, 2005) is the most common type. Thecenters for disease control and prevention has reported that anestimated 3.4 percent U.S. adults report MDD (MMWR, 2010).Depression often goes undetected due to the absence of reliablelaboratory test, and therefore new methodology for itsdiagnosis is
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