The noise reduction system

David Risley this morning wrote about all the noise in all these systems like Twitter and FriendFeed. Of course that kicked off a whole discussion over on FriendFeed.

Oh, the glorious noise! Everyone loves beating me up for causing the noise. No, I am not the cause. I pass it along. You should see my inbound streams. Every second or two a new Twitter is aimed at me. Every few seconds, a new blog post comes into Google Reader. Every few seconds, a new thing on FriendFeed.

24 hours a day of noise. And we’re not even counting the professional noise over on TechMeme and Google News.

Buried by noise.

So, how do we get out?

Well, we have a couple of choices.

1. We can choose to remain ignorant. Billions of people choose this route every day. Pop open a beer and pretend nothing interesting is happening in the world. That explains why American Media would rather talk about Britney Spears than about anything really important (like what Barack Obama’s new policies are).

2. We can try to swim in all the noise and soak it in. That’s what I do, but only a small number of people are going to have time or willingness to do that.

3. We can build noise reduction systems. Techmeme is one such system. It shows you only what the bloggers think is important. Google News is another. That shows you only what professional journalists think is important (or, at least their algorithms are designed to show you that and, while the algorithms don’t always match real-world behavior, they do get close enough to have high value).

4. We can use search to only present high value items. For instance, let’s say you work for my sponsor, Seagate, wouldn’t you be very interested in only items that mention Seagate? Like this search on TweetScan? Yes, you would. There’s still SOME noise there, but a lot less for someone interested in stuff about Seagate than there is coming through, say, TwitterVision, which shows a random selection of all Tweets being posted in the last few minutes.

The problem? Twitter and FriendFeed have brought new noise into our lives (at least for the early adopter types) and there aren’t good ways to reduce the noise.

But FriendFeed shows us a way out. How about seeing only posts that have at least two “likes?” Isn’t that a way to reduce the noise? Yes! In fact, my eyes are already doing that. I scan the page of FriendFeed looking for things that stick out of the noise and I’ve noticed that items with lots of votes and lots of comments stand out.

Tonight I’ll be attending a FriendFeed party and I’ll ask them just what their plans are in terms of giving us new views into their streams of info: one with noise, one with noise removed. Yes, of course I’ll post videos to my Qik feed and they get forwarded to my FriendFeed account too (which shows up on my blog’s sidebar too). More noise ahead! :-)

What kinds of noise reduction systems are you seeing? What kinds do we need?

Oh, and here’s a FriendFeed search for all items that include the word “noise” in them. That’s one reason I wrote this post. The noise has our attention and we need to damp it back down.

UPDATE: In just half an hour we’ve gotten tons of more comments on this blog post over on FriendFeed.

UPDATE 2: another way to remove noise is to just watch the things I’m commenting on or liking. That ensures that my noise isn’t there, and that I’ve hand filtered the noise for you. Another way? Don’t subscribe to many people, just to people you know will provide you interesting stuff and little noise.

70 thoughts on “The noise reduction system

  1. Two of your approaches which filters based on what either the most prominant bloggers say or by votes means that my prioritised reading gets scewed by other people rather than what I consider of most interest. This could be relatively easily addressed through key word analysis of the twitters or blog entries that I read as opposed to just see in my RSS feed. So if read blog entries which mentioned ‘scoble’ then other blog entries about you or referencing your articles would get bumped up my reading list.

    This is something that the likes of google could probably provide today given that key word analysis is all part of ad placement in search engines.

  2. Two of your approaches which filters based on what either the most prominant bloggers say or by votes means that my prioritised reading gets scewed by other people rather than what I consider of most interest. This could be relatively easily addressed through key word analysis of the twitters or blog entries that I read as opposed to just see in my RSS feed. So if read blog entries which mentioned ‘scoble’ then other blog entries about you or referencing your articles would get bumped up my reading list.

    This is something that the likes of google could probably provide today given that key word analysis is all part of ad placement in search engines.

  3. Until tools like Twitter and FriendFeed install features that let you prioritize followers, you can use feeds and Google Reader to help filter the glut of info.

  4. Until tools like Twitter and FriendFeed install features that let you prioritize followers, you can use feeds and Google Reader to help filter the glut of info.

  5. Robert – I live somewhere in between two and three. Like you, I love the noise (I personally find it very useful). But I do wish there was a better filter. I am starting to use FriendFeed and Social Thing more and more but haven’t figured out the best way to optimize my usage. Maybe we can do a follow up podcast to discuss these tools! ;)

    I look forward to reading more and giving FF/ST add’l attention over the coming weeks.

    Best,
    Aaron | @astrout

  6. Robert – I live somewhere in between two and three. Like you, I love the noise (I personally find it very useful). But I do wish there was a better filter. I am starting to use FriendFeed and Social Thing more and more but haven’t figured out the best way to optimize my usage. Maybe we can do a follow up podcast to discuss these tools! ;)

    I look forward to reading more and giving FF/ST add’l attention over the coming weeks.

    Best,
    Aaron | @astrout

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