Master of Science (MS)
Many small social aid organizations could benefit from collaborating with other organizations on common causes, but may not have the necessary social relationships. We present a framework for a recommender system for the Louisiana Poverty Initiative that identifies member organizations with common causes and aims to forge connections between these organizations. Our framework employs a combination of graph and text analyses of the organizations' Facebook pages. We use NodeXL, a plugin to Microsoft Excel, to download the Facebook graph and to interface with SNAP, the Stanford Network Analysis Platform, for calculating network measurements. Our framework extends NodeXL with algorithms that analyze the text found on the Facebook pages as well as the connections between organizations and individuals posting on those pages. As a substitute for more complex text data mining, we use a simple keyword analysis for identifying the goals and initiatives of organizations. We present algorithms that combine this keyword analysis with graph analyses that compute connectivity measurements for both organizations and individuals. The results of these analyses can then be used to form a recommender system that suggests new network links between organizations and individuals to let them explore collaboration possibilities. Our experiments on Facebook data from the Louisiana Poverty Initiative show that our framework will be able to collect the information necessary for building such a user-to-user recommender system.
Document Availability at the Time of Submission
Release the entire work immediately for access worldwide.
Jose, Neha Clare, "Social Media Network Data Mining and Optimization" (2016). LSU Master's Theses. 3024.