Isaac Asimov's 1951 Science Fiction classic Foundation imagined a world where Statistical Physics has been applied to Sociology. The result is a society capable of predicting future events. At the time, the sheer impossibility of collecting and analyzing the opinions of billions of individuals ensured Foundation was one of Asimov's more fanciful creations. Who could have predicted the rise of Online Social Networks (OSNs), which collect and make public exactly the required information!
Inspired by Asimov's Foundation, I will discuss my first steps at using OSNs to measure public opinion. The largest measurement hurdle is creating an unbiased sample of individuals. I will cover the many sources of bias present when mining OSNs and approximate methods for creating unbiased samples. This leads to a novel algorithm, Conditional Independence Coupling, that can create a perfect sample from OSNs. Once I've established the measurement methodology, I will present several examples of what can be measured from OSNs. Finally, I will explore the issues encountered when computing global properties on OSNs and end with a new measurement framework inspired by similar approaches from Condensed Matter physics.