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I think one way to break down the problem is to start with what your objective is in terms of the information you’re consuming and what “signal” you’re trying to extract from the noise.

Conceptually and as it’s often been said, there are two high level types of media consumption: grazing and hunting/ gathering. When grazing, most people probably don’t necessarily want signal. Rather, they want serendipity or diversity. Put simply, they want entertainment. They’re killing time and what might interest one person from the next might be very different. Heck, what might interest the same person from one moment to the next might be totally different. In this case, the Twitter Product Manager’s metaphor of dipping your toe into a stream is probably pretty apropos. On the other hand, when you’re hunting, you’re on a mission. In this case, you do want to extract as much signal from the noise. There are certainly lots of variables – semantic, contextual and other — which could help present more meaning from the cacophony which is the Twitter stream, even when only following a few people who are active tweeters. No doubt, semantic analysis and other algorithms can help thread and make the information more relevant and meaningful. But as you suggest, that’s a pretty daunting challenge. And, it’s especially hard to do it in a vacuum and without knowing what you’re hunting for. It’s sort of like trying to answer every question before it’s been asked and then predict what question you’re interested in asking and present you with the answer. I’d posit that’s an unattainable objective.

In order for Twitter to “figure out how to present that stream in a way that adds value to [your] life” as you say, you need to be part of the hunt. And, you probably need to be an active part of it, not just passive. I’d propose two simple heuristics which might actually help: first, help people pick targets and provide input. Most everyone does this all the time on more traditional media outlets. We set up categories, topic alerts, keywords, favorites, etc. In a word, we search. Second, make it easier to filter the people you’re following. As they say, if you’re hunting for bison, go to the plains; if you’re looking to gather berries, look in the brush.

We’re trying to take a simple step forward on this last point with Poptuit (if you’re interested, you can check out some coverage here http://t.co/BS34wJ9). Of those 1,000 people you’re following, do you really care about what all of them are saying … all of the time … and on every subject? I’d guess probably not. When a specific topic comes to mind, the first filter most people apply is who — Who can/will help me? Who’s an expert? Who do I trust? After the who, we usually then go to the what/when/why questions. When I wanted to find a good bistro to take a client to on my recent trip to NY, I knew exactly which friends to ask — a few live in NYC and one is an incredible foodie that always knows good places for all occasions anywhere and everywhere. But, when I got stuck helping my son fix the clutch on his dirt bike this weekend, I asked someone entirely different. Not rocket science and maybe it wasn’t perfect, but both queries did the trick. Making it easier, faster and more intuitive to filter our ever-growing social networks with our personal networks could be a first, simple step towards getting more signal from the noise in Twitter.