GIS analysis of depression among Twitter users |
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Affiliation: | 1. AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar;2. Institute of Digital Healthcare, WMG University of Warwick, Warwick, UK;3. College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar;4. Jordan University Hospital, Amman, Jordan |
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Abstract: | 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 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. |
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Keywords: | Depression Tweets Clustering GIS Social media |
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