Social networking sites like Facebook and Twitter have resulted in the emergence of a type of data that is under-explored in the field of public opinion and survey research. Social network data is comprised of objects (typically people or groups) and the ties between the objects (e.g., relationships or transactions). Previously, obtaining these data to conduct thorough social network analysis was often impractically time consuming and costly. But increases in the ability to efficiently access such data have raised the potential for investigating new methods of analyses that may supplement current survey data, or otherwise fill holes in extant research where traditional analysis is limited. Addressing questions such as how objective measures of one’s network differ from self-reported measures of such relationships, or how information flows and one’s social context influence individual perception, thus survey results, are just a few examples of how survey and public opinion researchers might find value in social media and other Web 2.0 concepts. This paper demonstrates 1) how Facebook user data can be obtained through an application and utilized to reconstruct social networks, 2) how similar data scaled to a user’s entire network can be analyzed to understand the formation of opinions, attitudes, and behaviors, and 3) how social network analysis of data native to social networking sites (e.g., a Facebook friendship) can enhance the interpretation and precision of such data when used to supplement survey data. Specifically, I describe an approach to developing a Facebook application that obtains friendship data from users, processes for obtaining a user’s entire Facebook friendship network, and how I analyzed my personal social network on Facebook to produce measures. I then discuss how social network analysis techniques, such as cluster analysis and clique identification, can be used to supplement and provide precision to survey data.