Spring 2016 Brown Bag Speaker Series
GIS Analysis of Depression Among Twitter Users
Dr. Wei Yang
Lecturer, Spatial Sciences Institute
12:00 – 1:00 p.m.
Friday, April 8, 2016
Allan Hancock Foundation (AHF) B57J
Due to limited diagnosis methods, depression often goes undetected. As delays in diagnosis can result in serious harm to personal and public health, methods to accelerate detection of depressed behavior can be useful in the diagnosis and treatment of chronically depressed individuals. Previous research which detected geographic pattern for depression using questionnaires or self-reported measures of mental health may reflect same source bias. Other recent studies analyzed social media content for depression detection, but none of them examined the geographic patterns. In my work with Dr. Lan Mu, I designed a process rapidly detects key words or phrases typically used by depressed individuals in their Twitter feeds, and analyzed their spatial patterns using GIS technology. This method which combines rapid data collection with analyzed results can be useful in improving diagnosis techniques for depression and can provide new perspectives for public health research. Further applications of this method are possible in detecting real-time patterns in human behavior during emergencies and events such as the aftermath of earthquakes and disease outbreaks.
April 08, 2016 @ 12:00 pm - 1:00 pm