Over at my own blog, I have been running an almost-daily series of posts looking at the current US reality series “Big Brother”, now in its 15th incarnation in the US (the 10th Australian series starts in a week or so). While I’ll direct you there for all of the details, here is a brief summary of …
Continue reading “Big Brother 15, West Coast Viewers & Guilt by Association”
Currently, at QUT Social Media HQ, we’re in the process of developing the new version of our Twitter capture software, led by CCI Data Scientist Troy Sadkowsky. During development, we’ve extracted a few interesting datasets, and this blog post is going to examine one of those; a set of one million Twitter IDs. This set …
Continue reading “Who’s Joining Twitter? A look at 1 million recent IDs”
The emerging maps of the Australian Twittersphere which I presented at the Digital Humanities Australasia conference in Canberra in March have received quite a bit of press coverage over the past week or so, following our press release about this work. Here are some of the highlights: The Australian ran a big page three article …
OK, so this is the second part of my post on turning Twitter data from Twapperkeeper into a dynamic network visualisation in Gephi. Last night’s post did the groundwork, generating a GEXF file from our #spill hashtag dataset (covering Twitter discussion of an Australian Labor Party leadership spill between 7 p.m. and midnight (AEST) on …
Continue reading “Visualising Twitter Dynamics in Gephi, Part 2”
In the following posts I’m finally keeping my promise to explore in earnest the use of Gephi‘s dynamic timeline feature for visualising Twitter-based discussions as they unfolded in real time. A few months ago, Jean posted a first glimpse of our then still very experimental data on Twitter dynamics, with a string of caveats attached …
Continue reading “Visualising Twitter Dynamics in Gephi, Part 1”
This is a quick demo of how the new timeline feature works in Gephi 0.7 beta. We’ve used 5 hours worth of @reply data from the Twapperkeeper archives for the #spill hashtag. This period corresponds to the ‘acute event’ in Australian politics that kicked off the election that sidetracked our research (in all kinds of …
Continue reading “Fun with Gephi’s new dynamic visualisation feature”
My previous post outlined a few more steps I’ve taken in cleaning up our emerging dataset of links in the Australian blogosphere (current limitations of our data are also listed there). It’s time to take those cleaner data for a spin, then. Beyond mapping the interlinkages between our known blogs during the period of 17 …
Continue reading “Mapping the Australian Blogosphere Some More”
In future analysis we’ll be interested in doing some form of comparison between the #ausvotes data we’ve been looking at (and that Axel has already blogged about earlier this week), and other topics of shared interest among Australian Twitter users. As an exceptionally high-rating Australian prime-time TV show that was also a trending topic on Twitter, Masterchef is a particularly interesting example of such a topic drawn from popular culture. The patterns of Twitter use around this highly popular, nationally-based show (perhaps even more so than around the pre-election debate) can hopefully help us to understand something about the practices of the networked television audience as a public.
Continuing my series of posts on methods for doing quantitative research using Twitter data, this will be a fairly tentative post. I’m currently looking into ways to examine the terms and concepts used by tweeters as they discuss specific issues; we’ve done similar work looking at the content of blog-based debates in the past, using …
Continue reading “Twitter Concept Mapping with Wordstat and Gephi: First Steps”
