I find it interesting that, in contrast to the other things that the article mentions that techniques like this can be used to predict ("We can try this on traffic data to predict the duration of a bus ride, on movie ticket sales, on stock prices, or any other time-varying measurements."), Twitter trends are artificial phenomena, with a very precise definition that was created by Twitter, not some natural emerging thing. The actual tweets are of course a natural phenomenon, but how topics are selected from them as 'trending' is not.<p>Of course, that's not to say this is not impressive work - predicting what Twitter's proprietary algorithm will select as trending without direct knowledge of the algorithm, before it selects them, and before all the tweets that make them be selected are made is impressive, and no doubt not any easier than predicting more natural phenomena or emergent behaviours.
Thanks for the excellent explanation, and many congratulations on your thesis!:)<p>Could you point to any resources on time series analysis? While i am well familiar with supervised/unsupervised learning methods for tasks like classification, anomaly detection etc, analyzing time series is a different beast. And most machine learning literature (eosl?) doesn't seem to address time series data either.
There's something I don't understand about this. It depends on twitter supplying its picks for trending topics. How do you use something like this in general if you're just given the stream of tweets but nothing else?
For me, the most striking thing about the article is looking at the chart of #Barclays and seeing <i>just how early</i> the trend was detected.<p>I would never guess that the pattern, when cut off just after 12, is indicative of a topic that's about to trend.