Mapping Online Publics

It’s difficult to believe that one year ago, significant parts of Brisbane were inundated by floodwaters; thankfully, there has been no repeat of the flood crisis this year. One of the few good news stories to emerge from the disaster was the – overall, very successful – way in which social media such as Twitter and Facebook were used during the event, both by key emergency authorities and by everyday users, from directly affected local residents to onlookers further afield.

Particular kudos in this must go to the Queensland Police Service Media Unit, which – not quite from a standing start, but certainly without much time to prepare a comprehensive strategy for its social media crisis communication approaches – delivered timely, informative, and level-headed updates on the flood crisis as it unfolded. Its Facebook followers grew, literally overnight, by a factor of ten, and @QPSMedia also became the single most visible account participating in the #qldfloods Twitter hashtag.

Read the rest of this entry →

OK, it’s taken a little while, but we’ve now finally put all the presentations from our panel on social media and crisis communication at the Association of Internet Researchers conference in Seattle in October online. Three of the four have audio as well – my apologies to our last presenter, Anders Larsson, but the batteries on my audio recorder ran out just as he got started!

Read the rest of this entry →

Hi! Let me introduce myself–my name is Tanya Nitins and I’m working with my QUT colleagues Axel and Jean, and researchers based at the University of Muenster on an ATN-DAAD project (see related blog post here). My particular research interests in relation to this project center around brand development and management in social media, with a particular emphasis on entertainment industries. The research collaboration between QUT and Muenster is focusing on deciphering the new dynamics of brand communication in the context of social media. In particular, we want to understand how businesses monitor and respond to negative publicity and/or criticism in such social media sites as Twitter.

Read the rest of this entry →

Jean and I are currently at the European Consortium for Political Research conference in Reykjavík, where we’ve presented a paper about hashtags today. Below is our presentation (with audio), which also includes some new hashtag metrics we cooked up during our week-long workshop with our ATN-DAAD project partners at the University of Münster last week. More on this soon! The full paper is also online.

The first part of this post examined some of the basic stats on Twitter use during the 29 April 2011 royal wedding. Here, we’ll try something a little different: in the tweets using the #royalwedding hashtag between 00:00 and 23:59 GMT that day, what other hashtags were also used?

Hashtags, of course, aren’t mutually exclusive, and are often used for emphasis (or comic relief) as much as to make a genuine contribution to an existing conversational hashtag feed – or indeed, both at the same time. So, beyond #royalwedding as the key hashtag to be used to refer to the actual event itself, an examination of these other hashtags provides us with some useful nuances on how Twitter users perceived and contextualised the wedding, and a correlation of what secondary hashtags were used by which groups of users helps group these perspectives to some extent.

Read the rest of this entry →

OK: I realise this may induce some cognitive dissonance in susceptible readers while those images of the London riots continue to flash across our TV screens (and we’re now also tracking some of the Twitter coverage of the riots and subsequent cleanup – more on that some other time, if anything interesting emerges). For some time, though, I’ve been meaning to post up some observations about that rather more glamorous event in recent British history: the royal wedding between Kate Middleton and Prince William on 29 April 2011.

We’re planning to explore this in detail in a paper some time down the track, so the main purpose of this blog post is to try on some approaches to analysing the event, and to test out some new approaches to crunching the data that I’ve played with recently – some of these ideas, in fact, resulted from our intensive research workshops with our visitors from the Universität Düsseldorf, Katrin Weller and Cornelius Puschmann, so they’re also a first outcome of that ATN-DAAD project.

Read the rest of this entry →

I’m following up a little further on my post of our first, very tentative and incomplete, map of the Australian Twittersphere, for another slightly more detailed look. First, though – also in response to some of the Twitter comments to the first posting, here’s another clarification of what you’re seeing.

In the first place, the total number of ‘Australian’ (by our criteria) Twitter users we’ve identified so far is about 550,000. Of these, at this point we have data on their follower/followee networks for about 450,000. If we exclude from this group all those users who have fewer than five followers, we’re left with roughly 150,000. So, if you see me use these numbers, that’s where they’re coming from.

The maps I’ve posted display these follower/followee networks based on affinity – those users who are closely interlinked through a range of connections with each other appear close to one another in the network, forming clusters which (we should assume) are determined by shared interests and other shared attributes. From a quick glance at the users involved in these clusters (or at least, at their usernames, which often indicate their interests or backgrounds), this seems to hold true, especially for the most connected users.

Read the rest of this entry →

We’ve been neglecting the blog a little – not because there hasn’t been anything worth writing about, but rather because there’s been too much going on. So, before our big trip to Europe in August and September (more on that soon), it’s time to clear the backlog of updates. And what better way to start than with an early map of Australia. No, we’re not talking about ancient seafarers’ maps here (though there are some similarities): part of our aim with the Mapping Online Publics project has always been to develop a better understanding of the Australian Twittersphere – to go beyond the observation of individual hashtag conversations, and to examine the overall network of Australian Twitter users (similar to what we’ve started, and are also continuing, with the Australian blogosphere).

So, over the past few months we’ve worked with our project partners at Sociomantic Labs in Berlin to identify as many Australian Twitter users as we could find, and to trace their networks of followers and followees. The core problem in this is to define what constitutes an Australian user, of course – here, we’ve been relying on a combination of the timezone they’ve set for themselves (e.g. ‘Brisbane, GMT+10’), the location they’ve started in their profile, and other characteristics. This isn’t without its drawbacks, of course – some users may never have set their profile information; some have even deliberately set their details ‘wrongly’ (following the disputed Iranian elections, some users set their timezone to Tehran time, for example, to show sympathy and/or confuse Iranian authorities trying to find the accounts of local dissidents); some use non-standard descriptions of their location (Brisvegas, Brisneyland) or are in Australian cities whose names also occur elsewhere (there’s a Toronto, Texas, and Bolivia here, and any number of suburbs called Paddington). And some users are simply very confused – quite a few users with timezones set to GMT-10 should have chosen GMT+10, and vice versa…

Read the rest of this entry →

Television programmes spruiking their associated Twitter hashtags is now a common spectacle; we’re seeing this for everything from political debate (#qanda) to reality TV (#masterchef). One particularly successful example of this viewer engagement strategy was SBS’s recent Go Back to Where You Came From mini-series, which aimed to raise the tone of Australia’s depressingly low-brow political debate about asylum seekers by taking six contestants with strong views on ‘illegal’ migration on a reverse journey from Australia back to the countries of origin of many asylum seekers – in a reality TV-style three-part TV series.

The hashtag associated with the series was #GoBackSBS, and generated a substantial amount of participation over the three nights of the show  (21-23 June 2011), as well as in the following week, when a panel discussion with the contestants was screened as well. Sadly, because I was stuck in Melbourne due to the volcanic ash cloud on 21 June and couldn’t start our hashtag capture in time, we weren’t able to capture the entire #GoBackSBS Twitter discussion about the show (for 21 June, we have data only from 22:04 onwards) – so it’s great to see that another group of Twitter researchers, Datalicious, have already published their analysis of some key content patterns in #GoBackSBS (some more precise detail on the dataset would be great, though, guys!).

Read the rest of this entry →

Working through our available data on Twitter use during crisis events ahead of the Eidos Institute symposium on Monday, I started thinking about some of the broader patterns we are seeing. Very obviously, a good bit of the #hashtag activity around acute events is taken up with retweeting information – both simply passing it along unedited, and adding further details or commentary in edited, manual retweets. Additionally, there is a certain amount of @replying between participants, though such follow-on conversations tend not to include the #hashtag much any more, unless the conversants deliberately seek to make their discussion visible to the wider #hashtag community (to perform it publicly, in other words).

So, the question becomes: how much of the #hashtag space around specific acute events is taken up by these forms, and how much consists of new tweets that are neither retweets nor @replies? That’s what I want to explore in this post, for the key examples of the Queensland floods (#qldfloods), the Christchurch earthquake (#eqnz), and the Japanese tsunami (#tsunami). I also need to note again that our data on retweets includes only manual, old-style retweets (‘RT @[user]’), not retweets made using the Twitter retweet button – but that’s actually a valuable limitation in our current context, since a manual retweet by definition requires users to make a more conscious effort than a mere press of the retweet button.

Read the rest of this entry →