I will begin my research by creating a subset of tweets believed to be indicative of depressive and suicidal thoughts, based off of studied characteristics of depressed and suicidal teens. . The use of semantic role labeling (SRL)  and latent Dirichlet allocation (LDA) , a predictive model that extracts trigger words from a document or tweet, have effectively been used for crime prediction by analyzing tweets . With the subset of tweets I establish, I will be able to apply SRL and LDA modeling techniques to predict hotspots for depressed and suicidal teens. This model will then be applied to a large populous, such as Chicago. Chicago is an ideal city because of its large population of nearly 2.7 million, the variety of social classes, and racial diversity within the population . These factors will allow analysis of teens socioeconomic and demographic background, in relation to their symptoms. I will be able to use R statistical programming language, S-Plus, and SAS to analyze the collected data . These software packages can help determine a generalized additive model, which can discover underlying factors of depression and help predict future incidents . For geographic data management and visual modeling, I will use a toolkit programmed in Visual C# and PostGIS .
This spring semester I will begin studying the symptoms of depression among teens, in order to create tweet subsets that are indicative of at risk teens. Also, I will be collecting and analyzing the spatial, demographic, and socioeconomic features of specific locations throughout Chicago to build an accurate model to target depressed and suicidal teens. There are many papers available through the Systems Engineering Department and at Brown Library published by Professor Gerber, Professor Brown, and Phd. candidate Matt Huddleston, which I will have access to in Charlottesville this summer. With the data I accumulate, I will apply a mixture of the modeling techniques from my previous research to build an accurate model of depressed teens within Chicago. After this summer, depending on the progress made with my research, this project can cascade into a capstone project within the Systems Engineering Department where further progress can be made.