For a long time, I just didn't get Twitter, thinking that nothing of substance could fit in 140 characters. The way I've come to use it is like scanning the headlines on a newspaper, following links to read more about interesting items. I follow folks that are interested in the same nerdy topics I am, in effect like a personal slashdot or hacker news with the riff-raff filtered out, or rather the interesting people filtered in.
Still, my cluster of interests is eclectic and my interests tend to only partially overlap with the people I follow. So, what I want is a feature that will help automatically weed out the stuff I don't care about.
I mostly read tweets on my phone using the Android version of TweetDeck. What if I could swipe right on a tweet for something I like, and swipe left to shit-can a lame tweet? The app could keep track of what I've liked or not and use that as an ever-growing training set to classify new tweets as interesting or dreck. The same data could also be used to find people I should follow or interesting tweets from people I don't necessarily want to follow, etc.
In my case, the app would soon learn to terminate sports-related tweets with deadly efficiency. Also, for you foursquare users, any tweet that starts with "I'm at..." would be ruthlessly eliminated. It would work great for facebook feeds as well, deleting all those "Your sister-in-law just unlocked the Cattywampus badge on whatever-game".
This must already exist. Is this what prismatic is? Maybe, I'll find out if I get my invite!
Looks like I'm not the only one wanted to waste time more efficiently. Seattle hacker Joel Grus built a classifier for Hacker News stories using naive Bayes. Joel gave a nice lightning talk at the latest Seattle Data/Analytics/Machine-Learning MeetUp.