Twitter Audience Profiling – OU/BBC Feynman Challenger Co-Pro

Another strong piece of TV commissioning via the Open University Open Media Unit (OMU) aired this week in the guide of The Challenger, a drama documentary telling the tale of Richard Feynman’s role in the accident enquiry around the space shuttle Challenger disaster. (OMU also produced an ethical game if you want to try you own hand out at leading an ethics investigation.)

Running a quick search for tweets containing the terms feynman challenger to generate a list of names of Twitter users commenting around the programme, I grabbed a sample of their friends (max 197 per person) and then plotted the commonly followed accounts within that sample.

If you treat this image as a map, you can see regions where the accounts are (broadly) related by topic or interest category. What regions can you see?! (For more on this technique, see Communities and Connections: Social Interest Mapping.)

I also ran a search for tweets containing bbc2 challenger:

Let’s peek in to some of the regions…”Space” related twitter accounts for example:

Or news media:

(from which we might conclude that the audience was also a Radio 4 audience?!;-)

How about a search on bbc2 feynman?

Again, we see distinct regions. As with the other maps, the programme audience also seems to have an interest in following popular science writers:

Interesting? Possibly – the maps provide a quick profile of the audience, and maybe confirm its the sort of audience we might have expected. Notable perhaps are the prominence of Brian Cox and Dara O’Briain, who’ve also featured heavily in BBC science programming. Around the edges, we also see what sorts of comedy or entertainment talent appear to the audience – no surprises to see David Mitchell, Charlton Brooker and Aianucci in there, though I wouldn’t necessarily have factored in Eddie Izzard (though we’d need to look at “proper” baseline interest levels of general audiences to see whether any of these comedians are over-represented in these samples compared to commonly followed folk in a “random” sample of UK TV watchers on Twitter. The patterns of following may be “generally true” rather than highlighting folk atypically followed by this audience.)

Useful? Who knows…?!

(I have PDF versions of the full plots if anyone wants copies…)