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.

Pretty excited to be in Madison--it's a really beautiful town. I'm at GLS 09 for the week to check out the latest and greatest in my little field. I'll be presenting with Dave Birchfield and Katie tonight for the SMALLab poster session, and I'm running a game with Colleen, Eric Zimmerman, and John Sharp using twitter called BACKCHATTER which should be a lot of fun to play.

If you're around, I'm the tall scruffy one in the lime green shirt!

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.

I've been doing more research on Twitter recently, mostly looking at back channels from conferences (more on that to come). I wanted to post up a quick analysis, though, on a recent story that blew up big--the Swine Flu outbreak (found in twitter, in part, via the #swineflu hashtag.)

I just read two very interesting articles from two commentators I respect immensely: Clay Shirky's The Failure of #amazonfail and Mary Hodder's Why Amazon Didn’t Just Have a Glitch. I won't do their arguments justice here, but I'll try to summarize as best I can.