Yesterday Jeff Miller posted some interesting data on the Twitter users most followed by readers of Hacker News.
I just took those top 100 Tweeters and added Jeff’s data (their rank and the fraction of HN readers who follow them) to FluidDB. The tags I used in FluidDB are ycombinator.com/top-100 and ycombinator.com/follow-percent. The top-100 tag has values that are the Twitter user’s rank (from 1 to 100), and the follower-percent tag holds the (floating point) percentage of Hacker News readers that follow that Twitter user, as found by Jeff.
What does this all mean?
It means you can now query on Jeff’s data using FluidDB. And because FluidDB contains various other pieces of information about Twitter users, you can combine his data with other data in searches – including searches that Jeff probably never anticipated (and, because of FluidDB, never had to anticipated).
It also mean you can add to the data too. All you need is a FluidDB account (sign up) and then you can take the FluidDB API for a spin (docs).
To see the kinds of things that are possible, you can also do some queries using the advanced tab of Tickery.
For example, Who are more than 20.0 percent of HN readers following that have a TunkRank score of at least 60?
Or, Who is in the HN top 100 that I have met?
Or, Who of the top 100 do I follow?
The possibilities are endless. The main point of FluidDB is that you can play too. You can add your own data (any data) to the exact same objects that I’ve put Jeff’s data onto and which Tickery and TunkRank and We Met At are all using – and you don’t have to ask permission.
We’ve written plenty more on this subject. See also Tickery, for programmers, TunkRank scores added to FluidDB, Putting metadata onto tweets with FluidDB and FluidDB as a universal metadata engine.
You can get all the code I used to put the data into FluidDB from our hackernews repo on GitHub. It was about 90 minutes of work from start to finish.
Have fun, and please comment below!
