Valdis Krebs argues that the next big thing in network analysis will focus on the contents of what we read (and not just the titles as entities) in order to draw connections between people. It’s a natural evolution of network analysis. "It is not just the also-bought data that matters (which books bought by same customer), it is what we specifically find interesting and useful in those books that reveals deep similarities between people — the hi-lites, bookmarks and the notes will be the connectors. Our choices reveal who we are, and who we are like!"
I’m going to argue that what would be even more valuable than a better way to determine who is most similar to us would be what important ideas do we not know, and perhaps we should. We have this crazy amount of data on what people read, what videos they watch, and who they chat with, which should in theory be able to help determine what "important" things they have not yet read, and even some of what they do not know. We should be able to use network analysis to direct learners into new learning experiences.
This is similar to the Netflix (and Amazon, etc…) recommendation engines, which suggest similar titles to what one has experienced already. Unfortunately this approach leads us isolated in a bubble. Instead, what if these recommendation engines looked at what was in our circles, and found something important to know that is perhaps an opposing point of view, or a different perspective on what we know. I’d happily sign up for a service that culled the best of the opposing ideas to my own perspectives and shared those new perspectives with me.
Instead of finding better ways of leading people to re-inforce existing knowledge, can we find better ways to direct them at new ideas?