I'm late to comment, but something which I'd like to point out is that this is done by the same team behind the Twitter trending topic prediction technique from

I'm late to comment, but something which I'd like to point out is that this is done by the same team behind the Twitter trending topic prediction technique from a few years back, as mentioned also in the article [1]. When their Twitter technique was released, I spend a few weeks reading through Nikolov's PhD thesis (the advisor gets most of the the fame in the press articles but Nikolov's thesis has all the details) and trying to implement it in R. My observations at the time: extremely simple algorithm which would be shot down by most peer reviewers for being not very novel (the affiliation helps a lot here). That said, I believe greatly in pragmatism, and the approach was actually working well. What I did find out however is that their was a great deal of data selection and pre-processing involved making the approach hard to implement in a real-life, real-time setup. I get similar feelings from this work. [1]: http://newsoffice.mit.edu/2012/predicting-twitter-trending-topics-1101 http://newsoffice.mit.edu/2012/predicting-twitter-trending-t...