Here's another way to approach the user's end of it: have them log in to their Google, Facebook, Twitter, et al, and shove all of it into a single stream. Let them filter by source, topic, relevance, time, and anything else you can implement. Record their preferences by watching what items they click through or spend time on or interact with, and your relevance sort gets better. Add other users' data, and you have a fantastic recommendation engine without ever making anyone rate anything.
Another problem to solve with current news reading is duplication. Even if you do want to read about Tiger Woods, there's a lot of duplication in the echo chamber of the internet.
Yep, that's the position google has to analyze data, it's incredibly powerful to have almost the whole activitystream on the internet.
An interesting sidenote, apparently google could activate face recognition for google goggles but have chosen not to do that at this moment for privacy issues.
Remember the end goal here is to deliver content to the user that they want to spend time consuming. Why use Twitter for anything but another way to find content?
I'm not sure I understand your comparison. What is the difference between finding content I want to spend time consuming, and finding content another way (via some content filtering process), which has every intention of limiting the content to what you "want" to consume.
I agree. The problem is definitely a matter of related vs. unrelated content. My suggestion (or babble) was more so just pointing out how easily we could implement a "source content filtering" logic into an already existing interface.. If we felt that was the best way to go about it.
Another problem to solve with current news reading is duplication. Even if you do want to read about Tiger Woods, there's a lot of duplication in the echo chamber of the internet.