I spent Thanskgiving and the day after relaxing in my own peculiar way--by mining the Wikileaks 9/11 pager data.

Here are some early results:

I started by pulling out all the email addresses from the pages and storing them in their own table, with keys to their original page. I also pulled the unique pager numbers from the pages. What I got was a bipartite directed graph with one side being emails and the other being pagers, with messages functioning as edges. Using Django, Graphviz, Cinelerra and a bunch of other tools, I was able to make a video of the graph as it lights up on each relevant page.

If you've read my swineflu analysis, some of this should make sense. I ran a search on '#iranelection OR Tehran OR Ahmadinejad OR Mousavi' in Twitter for the period between Friday and Sunday evening. From the 79,957 results I got back, below is some graph analysis of what came out.

Because I've recently been... let's just come out and say obsessed with looking at the social relationships that seem to emerge from examining sociograms of Twitter users within the "channel" of a particular hashtag, here's another one I thought was interesting: Media in Transition 6, a.k.a. #mit6.

I just did a run on the first two days of the 2009 Nonprofit Technology Conference using the tools I've been working on (see my post on #swineflu earlier this week.) Using the hashtag #09ntc, I parsed 3834 tweets, and I looked up the hubs and authorities, plus generated the graph of the largest strongly connected component within the larger directed graph created from all the "@" replies and retweets.