Some people look for controversy in the strangest places. As hundreds of computer-science researchers congregated for TechFest on Tuesday, one Web site interpreted a cross-disciplinary but topical demo from a team of Microsoft Research Redmond folks as evidence of a forthcoming news site to "rate media bias."

Michael Gamon, here's your chance to set the record straight about BLEWS.

"What we're trying to do is to give you a way to prioritize your news reading a little bit better," says Gamon, a computational linguist within the Natural Language Processing group.

"The problem is that there's way too much news for anybody to consume. You've got topic aggregators, news aggregators--all wonderful tools. But you still end up with more than you can read."

BLEWS, a research project based on a platform provided by Microsoft's Live Labs, is designed to address this information glut in one specific area of current interest: U.S. politics.

"Specifically," Gamon says, "in the political domain, what you can do is use the blogosphere as an annotation on the news. You can look at the number of conservative bloggers who link to a news article, the number of liberal bloggers who link to a news article, and you can just surface that information. The user, themselves, can then do with it what they want."

If the project ever received real-world implementation, users might be able to sort the resultant information by articles most linked to by those of a particular political persuasion. In addition, they could read news that has a preponderance or a paucity of heated verbiage.

"What we also do is try to look at the language around the news links in the blog and detect whether the language is more neutral or more emotionally charged," Gamon says. "That's not positive or negative--that's a different thing--it's just about emotionally charge. It could be enthusiastic, it could be very antagonistic. We don't make that distinction."

For example, words such as "failure," "progress," "strong," "unbelievable," and "better" would indicate a level of emotion in a blog posting's text, while the absence of such words would indicate a more measured approach.

BLEWS, though, doesn't make those determinations itself.

"It's not words that we pre-identified," Gamon continues. "We take a random sample of blog posts, recategorize it as neutral or emotionally charged, and then we have a machine learn the weights for individual terms and words. They are not manually identified. It's a machine that actually does that.

"We aggregate it all in a UI where you see, at one glance, the number of liberal links to a news article, the number of conservative links, and you have little boxes to the side that indicate the level of emotional charge. You can sort in these dimensions, and the UI is also fully navigational, so you can click on the news article, go read it, form your own opinion, and see what conservatives or liberals are thinking about that article."

In this U.S. political season, with emotions running at full throttle, such a tool could serve to help news fanatics take the partisanship down a notce. Such filters also could prove instructive,

"Oftentimes," Gamon notes, "it's actually more interesting to see what the other side is thinking about an article. Those are the arguments that people might find more challenging."

The BLEWS technology, though, is not limited to political discourse.

"Political stuff is really just one point to illustrate it," Gamon says. "Every piece of news, whether you're talking gardening, entertinment, sports--it works in this context. The context is provided by the blogosphere, and the minimun you can do is look at the link counts from the blogosphere and try to do something intelligent around the links to provide additional information."

Something intelligent to provide additional information: These days, that seems entirely reasonable--unless you're just itching to use the words "media bias" in a headline.