“Meme-tracking and the Dynamics of the News Cycle” is an excellent paper put out by Leskovec, Backstrom and Kleinberg.
In it, they track meme’s against the news cycle. The empirical findings of their study, which focuses on Palin, is really cool. How they chose to vizualize what was going on that’s quite new.
Traditionally, we tend to graph social networks using graph theory: each person is a node, and you draw links. Sometimes we color in the nodes and represent the strength between nodes by thickness of the lines. This kind of social network vizualization is something very cool, but the type of math that’s required to derive a real business strategy out of it is not. People have a hard enough time deciding what to do to reduce “bounce rate”, little though “eigenvector centricity”. But, there’s competitive advantage is making something hard to do – easy.
All social networks can be expressed as a mathematical matrix – where if two people know each other, we populate a ‘1’ at the intersection. You can also populate it with an interger or a float, variously to represent the strength or the nature of the relationship between two people.
What I like about what was done here is that the authors populated each node with a pointer to more information. There’s something about this transmission of text throughout a social network – how it evolves, twists, mutates and spins – that really is something really quite special. Hopefully the graph below is clickable and you see it better.
Your message, even if you do a good job of seeding it to the right people, will not be spread in quite the same way that you want it to be spread. Expect it to shift and to evolve. Sometimes you’ll like the evolution – because it’ll drive more people to your site and sales. Sometimes you won’t – because people won’t like you or what they hear.
For certain companies, at certain levels of social media spend – it’s a worthy part of their social analytics programme. It’s a lot nicer to see the evolution of what people are saying as a message is getting transmitted in conversation than to look at a tag cloud that doesn’t really tell you all that much. There’s something more human, and something vastly more conversational about vizualizing conversations over long periods of time in this way.
I applaud Leskovec, Backstrom and Kleinberg. Great work. Thank you.