The Future of Social Networking: Fixing low SNR

overloadOne of the things I ponder about is how the world of social networking is going to evolve and change in the future. It is clear that current modes of communication and interaction are going to change to something else. That will be the next Facebook/Twitter/LinkedIn/Flickr/Ning.

Obviously, it is hard to imagine what those modes of communication will be, but it is somewhat easier to look at the problems with the current social networks and see if the future will fix those shortcomings.

One such problem is the Signal-to-Noise Ratio (SNR). In non-tech speak, it is the amount of useful stuff found in the useless stuff. Applied to social networks, it is the amount of relevant useful information you can use when there is a ton of useless information you don’t care about. Currently the problem is that SNR is real low in many social networks. In Twitter for example, following several people will drown you in a sea of tweets.

The same issue in Facebook.

A poster on Joel on Software forums said it best, which I quote below:

I’m on Facebook, linked up to a few hundred “friends,” most of whom I barely know.  With few exceptions, the most prolific posters/updaters are also the least interesting.  One of the entertaining few is a guy who mostly posts silly cartoons and pictures of dogs.

The signal-to-noise ratio in “social media” is incredibly low.  Jerry Seinfeld, of all people, stated the problem back in the ’90s when cell phones and pagers were becoming commonplace:
“[W]e all have absolutely nothing to say, and we’ve got to talk to someone about it right now.”

The difference is that now, with something like Twitter, we can talk to hundreds (or thousands) of people about it all at once, and without ever having to deal with immediate feedback.

How true.

Is it really humanly possible to keep up with following a few hundred people in Twitter or the dumb “Which kind of vegetable are you?” updates in Facebook? You tell me.

One Response

  1. pealmasa says:

    Perhaps with Text Mining and a Reputation System.

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