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dc.contributor.authorYang, Wei
dc.date.accessioned2016-03-18T04:30:18Z
dc.date.available2016-03-18T04:30:18Z
dc.date.issued2015-08
dc.identifier.otheryang_wei_201508_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/yang_wei_201508_phd
dc.identifier.urihttp://hdl.handle.net/10724/34772
dc.description.abstractDepression 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 same-source 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.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectdepression
dc.subjecttweets
dc.subjectclustering
dc.subjectGIS
dc.subjectsocial media
dc.titleGIS analysis of depression using location-based social media data
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentGeography
dc.description.majorGeography
dc.description.advisorLan Mu
dc.description.committeeLan Mu
dc.description.committeeXiaobai Yao
dc.description.committeeYe Shen
dc.description.committeeMarguerite Madden


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