Twitter

The final presentation for AoIR 2015 is by Dayna Chatman and Kevin Driscoll, whose focus is on the communities and modes of social TV engagement with specific television texts. Their focus here is especially also on "black Twitter", a particular subset of the US Twitter population that has emerged in recent years: black American users on Twitter have been identified as a distinct group.

But black Twitter is actually a discursive phenomenon that is driven predominantly but not exclusively by black users in the US. The existence of this black Twitter community was detected especially through Twitter's trending topics, whose underlying algorithms were by accident especially well suited to detect the themes emerging from black Twitter, while "white Twitter" topics were not as prominently features.

The next speaker in our AoIR 2015 panel is Jonathon Hutchinson, who zooms in to a specific transmedia programme screened by the Australian Broadcasting Corporation, #7DaysLater. The premise of the show is to create comedy programming within seven days, and to incorporate social media engagement practices into the show.

Such viewing is more than just subsequent watercooler discussions – it's about viewer co-creation practices. The challenge is to break through the noise barrier on social media, and to find the techniques for encouraging audience participation, especially in the context of a public service broadcaster.

I was the next presenter at AoIR 2015, exploring network-based methods for understanding television audiences on Twitter. Below are our slides:

The final AoIR 2015 session is our panel on social television, and starts with a co-authored paper presented by Darryl Woodford (slides to follow soon below).

Darryl begins by noting that raw social media engagement numbers for television are useful only to an extent: they are usually not normalised to account for specific factors, and simply offer raw quantities.

Nielsen SocialGuide's Twitter engagement statistics for social media follow that pattern, for example, and obviously shows on major TV channels do better than those on niche cable channels. Beamly's social media rankings are skewed by the Twitter terms they track: any tweet containing the letters 'yr' is counted as engagement with The Young and the Restless, for example, which is obviously wrong.

This final morning at AoIR 2015 opens with my paper with Darryl Woodford and Troy Sadkowsky which explores the global Twitter userbase. Our slides are below:

The next speaker at AoIR 2015 is Mathieu O'Neil, whose focus is on the use of social network analysis in exploring online publics. Social network analysis treats the diffusion of information online as a form of contagion. It draws distinctions between leaders and followers in the social network, and the network properties of these accounts affect how information is disseminated across the network; there are certain threshold levels for information diffusion and the emergence of information cascades.

What different categories of actors exist in social networks, then? What is the impact of social status or structural positions, and what cultural dynamics may apply? One way to approach such questions is through the concept of fields, which have their own structural features and dynamics; indeed, the recent concept of the strategic action field suggests that collective actors are attuned to and interact with each other based on their shared understandings of the field itself.

The final speaker in this AoIR 2015 session is Veronika Karnowski, whose focus is on news diffusion across social media platforms. The Internet has become a main source of news, of course, and social media platforms play an increasingly important role in this. Social media are now not just a source of news content, but also act as a multiplier promoting the sharing of news; and of course they also enable follow-up communication about the news.

The various social media platforms available also enable a kind of cross-pollination between different platforms, and this needs to be researched in greater detail. How do sharing patterns on Facebook and Twitter interact with one another, for example?

The next speaker in this morning session at AoIR 2015 is Jakob Linaa Jensen, whose focus is on the social sharing of news. The landscape of news is changing, of course, and the question is whether the nature of news itself is changing. To some extent, our practices remain, and only our devices have changed – but at the same time, recent changes have also afforded us greater access to the production of news, as well as to new modes and a greater diversity in consuming news.

Perhaps the most important change is to how the news is being shared, as the borderline between production and consumption. Several studies have explored the processes of sharing the news through social media, in particular; Jakob is calling Facebook a meta-medium which incorporates multiple other media within its content, for example.

The first presenters on this second day at AoIR 2015 are Bernhard Rieder and Carolin Gerlitz, whose interest is in using data from Twitter's 'spritzer' firehose! which delivers a random 1% or all current tweets. How can this be used to identify individual types of activity in relation of the wider platform ecology? In particular, for the purposes of this paper, what light does it shed on the use of different devices for tweeting?

The project collected some 32 million tweets from the spritzer firehose over the course of one week, and key tools for tweeting were especially iPhone and Android devices. This may also be combined with the tweet contents themselves, to see which devices contribute especially strongly to specific hashtags, for example.

The next AoIR 2015 speaker is Fabio Giglietto, whose interest is in social television practices in Italy, around political talkshow Servizio Pubblico and reality TV contest X-Factor. What moments of these broadcasts trigger audience engagement? What similarities or differences are there in communication styles?

Fabio's project gathered relevant tweets from the shows' hashtags, and generated a number of key volumetric statistics on user activity; X-Factor audiences were considerably more active. They then algorithmically identified the major peaks in each of the shows' episodes, and explored the key dynamics for each peak.