Spending during this year's presidential election was projected by the Center for Responsive Politics to top $5.8B. Where did the money come from?
The Federal Election Commission provides us with detailed contribution information showing the flow of money from individuals, political action committees, and candidates so that we can start to answer that question.
We'll see how to extract a network from the FEC data, load the nodes and edges into Neo4j, then use the Groovy and Java implementations of Gremlin to navigate the graph.
We'll also discuss the possibility of linking up election data with other sources such as Wikipedia, congressional voting records, and corporate board memberships. This link analysis could expose interesting social ties that lead to a predictive model about contribution behavior within particular industries, locations, or interest groups.
This work will be open-source to encourage other data scientists and hackers to join the search for interesting connections.
About the presenter: Bobby Norton is a programmer and the technical lead of a stealth-mode education startup in Chicago. He has built software for over ten years at places like Lockheed Martin, NASA, ThoughtWorks, and DRW Trading Group. His tools of choice include Java, Clojure, Ruby, Bash, and R. Bobby holds a M.S. in Computer Science from FSU.