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Social-networking data analysis can help you understand content, connections, and opportunities for your personal and business associations. This article presents tools and code to extract key components of your social network using the Twitter API to chart, geolocate, and visualize your social-networking data.

This tool shows you how popular a Google search query is in each U.S. state, giving a ranking like the one you see in the left column. It then compares this ranking with other ways of ranking states, like average income or population density, using Spearman's rank correlation. The middle column shows the results of these comparisons, with the strongest correlations listed first. High numbers (close to 1.0) mean that the rankings "line up" closely, which may indicate a relationship between the search query and the ranking metric. For example, mittens tends to be searched by users who are in northerly states (high latitude) and states with a lot of frost. Low numbers (close to -1.0) indicate a negative relationship -- that is, the rankings are close to being opposites, as in "yoga" and "VotedForBush".

JUNG — the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries.

A StreamGraph is shown for the latest 200 tweets which contain the search word. The default search word is 'interesting' but a new one can be typed into the text box at the top of the application. You can also enter a Twitter ID preceded by the '@' symbol to see the latest tweets from that user. A parameter to the URL can be used to specify the initial search word. For example, use http://www.neoformix.com/Projects/TwitterStreamGraphs/view.php?q=coffee to see the latest tweets about coffee.