Short Message Analysis

Nov 29 22:23

Mining the Wikileaks 9/11 Pager Data, Another Take

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.

Nov 28 00:26

Mining the Wikileaks 9/11 Pager Data

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.

Jul 01 08:28

Raw Data From First 48 Hours of #iranelection

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.)

Jun 15 11:18

Twitter Graph Analysis Results for Iranian Elections

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.

Jun 14 16:58

Twitter Analysis for GLS09

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.

Apr 29 10:27

Twitter Graph Analysis Results for #mit6

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.

Apr 28 13:57

Twitter Graph Analysis Results for #mw2009

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.

Apr 28 07:26

Early Notes on Conference Tweeting

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.

Apr 26 17:57

Twenty Four Hours of #swineflu

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.)

Apr 15 12:16

Who Failed With #amazonfail?

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.

Copyright Mike Edwards 2006-2009. All content available under the Creative Commons Attribution ShareAlike license, unless otherwise noted.