I had an interesting conversation with Hayes Davis of Appozite about influence calculations and their importance. I have to admit I hadn’t really spend much time thinking about the big picture of the real time web and how influence fits into it, I found Klout and the like interesting but mostly in terms of adding context.
However, during the course of our conversation last night it occurred to me that an influence metric could prove to be a very fundamental part of being able to extract any useful information from the deluge of data the web is now producing.
If we go back to the 90′s and the introduction of Google’s Page Rank algorithm, that metric provided us (via Larry and Sergey!) with the ability to extract useful and relevant information from the billion or so web pages on the web. The number of pages has of course continued to increase but not by an order of magnitude. In the meantime the likes of Twitter and Facebook have appeared with the news feed and tweets changing the landscape (again) of our primary information sources. Now we have a billion (for the sake of argument, certainly a lot) little pieces of information being published every week, with no domain to attach them to, no link patterns to aggregate and organize so how can we turn all that noise into information…….
Influence! It’s basically the same as page rank, but now based on the individual producing the content and not the domain.
But, what is influence really, number of followers, followees, friends, posts count, tweets count, networks joined, blog subscribers, retweets – probably some combination of those and many other variables (maybe with a heavy time component to give recent activity higher priority – basically I have no idea currently!). I do suspect that due to the sheer volume of data a global metric might become impossible and we will need to use the likes of DataSift to apply initial cuts (context) to the incoming data and then apply an influence metric within that restricted data set to actually extract the most valuable content.
Just some thoughts……
So what do you think of PeerIndex and Klout ? Are they solving this problem?
Klout and PeerIndex are certainly doing some interesting things around the space. Having the ability to create a score is one thing though, being able to integrate that in a useful way into search results is quite another! I suspect a lot of information is lost in arriving at a single number too. The actual network of nodes and interactions they are parsing contains far more data, in fact thinking about it it is really relative influence that matters.
Also, sorry for the tardy reply. Have to figure out why I didn’t get emailed now…..