Yesterday was my research group’s first hackday. It’s a concept I borrowed from the software geeks, but which I thought we could use a bit of in psychological science. The plan was for the whole lab to get together and spend the day working on the same dataset, to see what we could come up with after a day of intense work.

Inspiration was provided by visiting data wizard Mike Dewar, who works with the link shortening service bit.ly. Mike was able to give us a slice of bit.ly data – all the shared links which the people of Sheffield had clicked on in a week. The leap from tech/internet business to psychology department isn’t so weird when you think about it. We’re both interested in taking high volume measurements of behaviour and trying to understand what is really going on (for us, inside the mind, for bit.ly, with the users behind the clicks).

We got together in one room and Mike guided us though some of the nuances of analysing the data. After a few busy hours, and along with those essential hackday accompaniments – takeaway food and cola (open source of course) – we had a snapshot of the kind of sites that people in Sheffield shared with each other.

This plot shows the trend of the weeks’ clicks for the top ten shared sites for Sheffield (with total click rate on the y-axis, and time on the x-axis). The scale is a bit small (click to expand), so here in a list is Sheffield’s top ten shared links for the analysed week:

1. Facebook (of course)
2. BBC (public service broadcasting FTW)
3. YouTube
4. GiveMeFootball
5. Celebuzz
6. Guardian
7. Google
8. Linksynergy
9. southyorkshire
10. swfc

Perhaps not a surprise, but we can see that people are sharing information on facebook, on news sites and about celebrities and football. And I note that the Owls win the Sheffield link-sharing derby! You can also see the daily peaks in click activity (at lunchtime? Or just after lunch perhaps!). With a bit more time we could delve into what times people preferred to click on different types of links (news vs business vs gossip would be an interesting comparison), and how the activity of a particular links changes over time, as it spreads out along social networks, passing from person to person, and a thousand other things. So think of this as a work in progress report. I’ll come back to you if we generate anything else.

Thanks to Mike and bit.ly for allowing us to play with their data, and to C, Maria, Donny, Tom, Martin and Stu for taking part.