Researchers at the Palo Alto Research Center (PARC) are developing a new Twitter client application that aims to derive meaning from the next-ending influx of tweets. The application, called "Eddi," automatically groups tweets for you into topics mentioned either explicitly or, unlike most Twitter clients that also provide topic browsing, implicitly. The end result is a Twitter app you can use to quickly find the popular discussions within your own personal Twitter stream, either by search, tag cloud, timeline or category list. It even suggests tweets you might be interested in reading, helping you sort the signal from the noise.

Project "Eddi"

Ed Chi, area manager and principal scientist for the Augmented Social Cognition Research Group at PARC, told MIT's Technology Review that the way people use Twitter is that they "dip in" to the Twitter stream from time to time, but don't want to consume it all at once. The Eddi Project was created so that those brief dips into Twitter are more valuable to the end users.

The tool, Eddi, a Twitter client application named after the idea of eddies in a stream, has the barebones look of something built by data researchers as opposed to web designers. But its user interface isn't the most important aspect - it's the algorithms behind the facade that are its standout feature.

In order to filter Twitter's content, Eddi provides two tools: a topic browser that shows tweets broken down into categories and a recommendation engine.

Twitter Topics - And Not Just the Popular Ones

The idea of browsing Twitter by topic is not unique - plenty of Twitter apps do the same, as does Twitter's own search interface at search.twitter.com. But the problem with most of these systems is that they rely on keywords or hashtags - the latter being the annotations preceded by the pound sign (#) which users add to their tweets to make them searchable.

When there is a major event, such as the Icelandic volcano eruption, Michael Bernstein, a researcher at the Computer Science and Artificial Intelligence Lab at MIT who is involved with the project, explained, the sheer volume of tweets provides a lot of information for an algorithm to use. What's harder is to figure out are the topics attached to tweets that are more unique.

"The essence of the approach is to coerce a tweet to look more like a search query and then get a search engine to tell us more," says Bernstein. After cleaning up the tweet, the tool feeds them into Yahoo's Build your Own Search Service interface in an effort to surface web pages related to the tweet in question. This helps the system to appropriately categorize the tweets into topics.

Recommendation Engine

The second aspect to the system is a recommendation engine that ranks tweets by how interesting they are to you. To determine this, Eddi's algorithms look at your own tweets and interactions with other Twitter users.

The new system will go live on the web for public testing sometime this summer. In the meantime, you can sign up for another of PARC's experimental Twitter recommendation engines, this one called ZeroZero88. Information on sign up is here.