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It’s sad. Several generations of college students learned their grammar from the uninformed bossiness of Strunk and White, and the result is a nation of educated people who know they feel vaguely anxious and insecure whenever they write “however” or “than me” or “was” or “which,” but can’t tell you why. The land of the free in the grip of The Elements of Style.

I know that insecurity and guilt. I know it well. What seems at first like blasphemy may be a relieved burden.

One that I didn’t get a chance to mention is one I’m working on for my day job at New Media Strategies: Freshly Squeezed Election Tweets

Here’s a rundown of some of its features:

What is it?

It’s an abstract visualization of the aggregate conversation on the popular social media platform, Twitter, showing frequency and context of election-related words.

How does it work?

We take a sample from Twitter every 30 seconds and analyze them in 50-result batches for associations and term matches. They accumulate for 5 minutes and then we flush sample aggregates to the database. As new terms trend, they begin to populate on the X-axis.

Wait, say that again?

The site will pull a continuous stream of tweets mentioning Obama and McCain, representing the most-used terms as a series of bubbles. The bigger the “bubble” the more frequently the term is being used.

So what am I looking for?

Each bubble is colored red and blue: the more red, the more the term correlates with McCain, the more blue, the more Obama. Keyword bubbles are themselves connected by half-circles (solid colors only), showing relationships between them. The stronger the connection, the thicker the line. And a bit later today, we’ll add a feature so you can sort by time periods.

So as I mentioned previously I’m starting my new job as an online analyst at New Media Strategies on Sept. 10. The downside of this is the fact that I’m missing out on all the kick ass stuff they’re doing at the two party conventions. As I write this, a few of my coworkers are at the Pepsi Center with credentialed blogger passes.

I’m also having to miss out on the work they’re doing with C-SPAN. They’ve created an entire convention hub for both Democrats and Republicans. Over the next two weeks my future coworkers are providing around-the-clock coverage of the blogosphere, particularly focusing in on those blogs that are covering the conventions. The Hub also includes Twitter feeds and NMS employees are on the ground at the conventions themselves in order to facilitate the coverage.

William Beutler has a good summary of what they’re doing and the hub has also been picked up by TechCrunch and several other major sites.

But I still have to wait two more weeks before I can join in on the action. By then, the conventions will be long over with.

Guess whose predictions were more accurate?

“Critics scoffed. Most of the public polls pointed to a close race in North Carolina,” writes the National Journal. “Looking back at Poblano’s efforts in Pennsylvania, pollster Dick Bennett decried the models as ‘stepwise regression run amok.’ Slate’s Mickey Kaus predicted failure for ‘a sophisticated model that ignores… what’s been happening in the campaign. Like Rev. [Jeremiah] Wright.’”

But in the end, they were wrong and Poblano was right.

What’s perhaps scary is that he was able to make these predictions based on demographic sub-groups. Are they really that predictable? Is this evidence that no matter what happens in a campaign, these subgroups will always vote the same?

The National Journal article has a nifty chart that gives a clear picture of how accurate the blogger’s results were compared to all the professional pollsters.