All this talk about metadata reminds me of a concept I remembered from my youth.
Psychohistory is a concept in Isaac Asimov's science fiction novels which combines history, sociology, psychology, and mathematics to make predictions about the future behavior of complex societies, such as a Galactic Empire.
See the Foundation novels
The premise of the series is that mathematician Hari Seldon spent his life developing a branch of mathematics known as psychohistory, a concept of mathematical sociology (analogous to mathematical physics). Using the laws of mass action, it can predict the future, but only on a large scale; it is error-prone on a small scale. It works on the principle that the behaviour of a mass of people is predictable if the quantity of this mass is very large (equal to the population of the galaxy, which has a population of quadrillions of humans, inhabiting millions of star systems). The larger the number, the more predictable is the future.
Using these techniques, Seldon foresees the imminent fall of the Galactic Empire, which encompasses the entire Milky Way, and a dark age lasting thirty thousand years before a second great empire arises. Seldon's psychohistory also foresees an alternative where the intermittent period will last only one thousand years. To ensure his vision of a second great Empire comes to fruition, Seldon creates two Foundations—small, secluded havens of all human knowledge—at "opposite ends of the galaxy".
The focus of the series is on the First Foundation and its attempts to overcome various obstacles during the formation and installation of the Second Empire, all the while being silently guided by the unknown specifics of The Seldon Plan.
Basically, collect enough data points and you can create a scientific model to predict future events just like weather forecasting.
The Internet and cyberspace, in general, can offer those data points and with enough computation, you'll get predictive power.
Yesterday three economists, (Tobias Preis of Warwick Business School in the U.K., Helen Susannah Moat of University College London, and H. Eugene Stanley of Boston University) published an eye-opening paper that said Google Trends data was useful in predicting daily price moves in the Dow Jones industrial average, which consists of 30 stocks.
Asimov was right.