Making Sense of TV Tweeting: The Case of #qanda

Next up at ASMC14 is Philip Pond, whose focus is on tweets during televised political debates in Australia. He takes a particularly temporal perspective to his research, and highlights the impact of electronic media on our experience of time and space; there is a kind of hyper-fast network time which is qualitatively different from its predecessor, the time of the clock.

Philip's focus is on the Australian political talk show Q&A and it's associated hashtag #qanda, which has a weekly audience of around 900,000 viewers. It invites journalists, politicians, and other panellists to its conversations (centred around largely pre-scripted questions), also streams live online. Its hashtag attracts some 20,000 tweets per week, and some 50-100 tweets from this are superimposed onto the live broadcast as the show airs.

The temporally of the hashtag as a forum extends and strengthens the idea of the public sphere, but does Twitter sustain the conversation that is necessary under such normative models? Philip points to one episode ahead of the last election, billed as the national economic debate and involving the then Treasurer and Shadow Treasurer; unsurprisingly, the vast majority of #qanda tweets occurred once the show had begun.

Within this hashtag, different subjects were discussed; these mirror the preset questions which had been chosen for the show. The temporarily of such topical discussions can differ, though – some discussions produce only a brief spike in activity, while others continue for longer if at lower levels. Some other topics persist throughout the show (for example, meta-discussions about the political patties themselves).

But to measure these is not without its problems, since textual features of tweets themselves also affect what can be reliably measured. Further, tweet volume itself, though an indication of activity, might also undermine engagement as it becomes much harder for users to engage meaningfully where there are many new tweets being posted every second.

Further qualitative analysis of such content must include content coding, of course. One coding approach is to code for off-topic / emotional / generally productive / and externally referenced productive tweets, for example – and this coding approach showed largely productive responses.

Another approach to understanding what happens in the hashtag is to examine retweet rates, which shows engagement with and/or endorsement of other users' tweets; here again, the volume of tweets at any given moment may affect how much users are able to engage with their peers' comments through retweeting.