In this work we present a tool that generates real world political networks from user provided lists of politicians and news sites. The tool downloads articles in which the politicians appear amongst the different news sites, processes them, enriches them with data obtained from various open sources and then generates various network visualizations, tools and maps that allow the user to explore and better understand those politicians and their surrounding environments. To demonstrate the capabilities of the tool for use in studying political and media landscapes, we construct a comprehensive list of current Texas politicians, select six news sites that convey a spectrum of representative political viewpoints publishing articles on the state, and examine the results produced by running the system with them as input. We propose a “Combined” co-occurrence distance metric to better assess the strength of the relationship between two actors in a graph and additionally provide automated summarization tools that utilize text-mining techniques to extract topics and issues characterizing the individual politicians. A similar topic modeling technique is also proposed as a novel way of labeling communities that exist within a politician’s “extended” network. Finally, we present media centric results of our case study that show who the different news sources publish articles about both from a geographic and individual perspective.