Visualising topic-based conversation networks: the #masterchef edition

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.

Continue reading “Visualising topic-based conversation networks: the #masterchef edition”

Twitter Concept Mapping with Wordstat and Gephi: First Steps

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 the (commercial) concept mapping software Leximancer, but I’ve never been fully satisfied with the information generated by Leximancer, and especially with its data visualisation functionality, so it’s time to look at the alternatives.

Ideally, I’d like to leave the visualisation aspects to the open source software Gephi, which I’ve already used for some useful network visualisations (more on that in another post), so what I’m really after is a software that produces word and concept co-occurrence data for my source texts (in this case, a database of tweets on a specific subject), and pushes this out in a format that Gephi can understand (e.g. UCINet or Pajek, or even Gephi‘s own network data format). At the ICA conference in Singapore last month, I came across a (commercial, sadly) quantitative text analysis software called WordStat – part of a larger software package available from Provalis Research that includes various other statistical tools which are less relevant for me here -, so that’s where I’ll start.

Continue reading “Twitter Concept Mapping with Wordstat and Gephi: First Steps”

More on Twitter during the Australian Election Campaign

Over on Fairfax’s National Times opinion site, I’ve now posted a first article examining the use of Twitter during the early election campaign – for the first week of campaigning, excluding the debate last Sunday (which I’ve examined here and here).

As with the debate, nothing much to see here yet, but there it is… I’ll also post up the full original draft of the article at our journalism group Gatewatching.org.

Tweeting the Debate: Some Content Patterns

Following on from our look at Twitter activity during the Australian leaders’ debate and Masterchef broadcasts, here’s an overview of the patterns we can see in the content of the tweets themselves. For this, I’ve grabbed all tweets containing the ‘#debate’ hashtag during 5 p.m. and midnight on Sunday (during which time, as we have seen, ‘#debate’ as more active than the general ‘#ausvotes’ tag for the election’).

In the first place, I’ve now selected all 2553 tweets containing either ‘Julia’ or ‘Gillard’, and created a simple word cloud using Wordle – manually removing ‘Julia’ and ‘Gillard’ (and variations thereof) as terms, as well as ‘debate’, ‘ausvotes’, and ‘RT’. Here’s the result:

Continue reading “Tweeting the Debate: Some Content Patterns”

Politics vs. Masterchef: The View from Twitter

Sunday night’s leaders’ debate is unlikely to be remembered for the policy positions it revealed – indeed, perhaps the most memorable aspect of the night was how federal politics was nearly upstaged by the finale of Masterchef (some kind of cooking show, I believe :-).

So, how did the night unfold? Following the methodology I’ve outlined in my previous post on using Twapperkeeper archives to track tweeting patterns, we’ve had a look at Twitter activity across the three key hashtags ‘#ausvotes’, ‘#debate’, and ‘#masterchef’.

Continue reading “Politics vs. Masterchef: The View from Twitter”

Creating Twitter Timelines from Twapperkeeper Data

This is the first in what will be an irregular series of methods posts outlining some of our approaches to working with datasets from various sources. Part of our work over the next few weeks will be to examine what happens in the Australian Twittersphere around the upcoming federal election, so I figured it would be a good idea to start with some of the basics of working with Twapperkeeper data. (Note that what I’ll outline here is a working solution, but not necessarily an elegant one – if anybody has a better suggestion, we’d love to hear it.)

Twapperkeeper is an online tool for capturing (public) tweets that contain specific #hashtags, keywords, or @usernames. The datasets it creates are delivered in a standard comma-separated value (CSV) format – including fields such as the tweet itself, the username of the poster, and a timestamp in various formats, as well as a few other bits of backend information.

One of the most immediate points of interest in working with a Twapperkeeper dataset is often to get a sense of the tweet timeline: how does the volume of tweets change over time, for example in response to events occurring in the world? The datasets provide that information – but to create an accurate visualisation of the timeline needs some doing. In this post, I’m going to work through an example, using Twapperkeeper data collected by my colleague Jean Burgess during the recent Australian Labor Party leadership spill (centred around the #spill hashtag and a few related ones).

Continue reading “Creating Twitter Timelines from Twapperkeeper Data”

Welcome

This blog is the online home of a research project underway at Queensland University of Technology called New Media and Public Communication: Mapping Australian User-Created Content in Online Social Networks. This is a place to share ideas, resources, and early findings, as well as discuss all kinds of related issues; and, upon occasion, funny YouTube videos.

There’s more at the about page.