Here's another pass at some more of the 9/11 Pager data that was released by Wikileaks this week. I did some work earlier that explains most of what I'm doing. This time, I tried to improve the visualization. I'm also using a directed graph of domain names that appear in the messages as they relate to the unique pager numbers.
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.
I've put together a .csv (comma-separated file) with the results I pulled off of Twitter for the first 48 hours of the Iranian election events. Be aware--it's about 20MB. Hopefully, many of you will find this useful in your own research. The columns are tweet id, date and time, text, profile image path, twitter username, twitter user id, and twitter user id of the immediate "reply to" (note that, in my graph analysis, I keep track of all @'s in the message, not just the first one as Twitter does. Only that first id is listed in the data file.)
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.
Here is the latest in my continuing series on analyzing Twitter conference backchannels by their hashtags and replies/retweets. This one, though, is a bit different and special... because I was actually at the conference! Below is my breakdown of Games + Learning + Society 2009 via the #gls and #gls09 hashtags.
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.
Just a quick post about another conference's Twitter backchannel I analyzed recently. Take a look at my posts on #swineflu and #09ntc to get a full picture of what I'm up to here. Basically, I'm looking at the network formed by replies and retweets in Twitter inside of a particular hashtag. Here, I'll go over the results of Museums and the Web 2009, a.k.a. #mw2009.
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.
Copyright Mike Edwards 2006-2009. All content available under the Creative Commons Attribution ShareAlike license, unless otherwise noted.