The WSJ.com’s Jeremy Wagstaff (subscription required) on the folks attempting to make sense of your browsing habits:
Attention plays a complex role in this new world. Google quietly makes money from the data we unconsciously give out when we do anything online. But then there are the data we consciously put out when we post photos to Flickr, add a post to our blog, or send stream-of-consciousness messages to services like Twitter. Put all this stuff together and you have an “attention stream,” painting a picture of what we are paying attention to during our day. Indeed, a service like Jaiku (jaiku.com) allows us to do exactly that pretty simply: My Jaiku “stream,” for example, not only includes excerpts from my blog posts, photos and Twitterings, but also plots my geographical location via a service called Plazes (www.plazes.com) and what music I’m listening to via something called Last.fm (last.fm). Anyone who wants to, then, can “subscribe” to my Jaiku feed and know what I’m paying attention to.
A stream of my attention might be of little interest to you. But it might be to marketers. Such attention streams become most useful when we look for a way to use technology to help match our scarce attention with the glut of information. If people know what I’m paying attention to, then it should be easy to work out what information is worth interrupting me with. Which brings me to my tip: a Sydney-based service called Particls (recently renamed from Touchstone).
Particls (www.particls.com) looks simple enough: a downloadable ticker that runs across the top of your screen, pumping you information. Nothing new about this; the difference lies in what information it presents, and how it appears. Instead of shoveling data at you, Particls tries to figure out what you’re paying attention to. Enter a few keywords of things you’re interested in and Particls scours millions of blogs and news sources to find stuff that matches them. You can then tweak this flow by raising or lowering the relevance of any particular feed or keyword (from strongly like to strongly dislike).