this post has been retitled from “it’s a twitter-happy go-go springsteen nation.”

Via @courtneybird who retweeted @nickbilton and an email message from my father (who refuses to Twitter but sends me all sorts of Twitter-related news articles), the New York Times has semantically and geographically represented on the continental United States an interactive tag cloud of popular terms Twittered during the Super Bowl:

I am fascinated by how information changes as we move it from one medium to the next. Here, the graphic artists have remediated words found in linear tweets composed across the nation (excluding Alaska and Hawaii) by locating them across several data points: term count, time, geography, size of font, color of font, and along 6 categories. This allows for an outstanding range of comparative data points. Adding to the graphic is the fact that time, scrolling over the terms, and the 6 categories can be controlled by users. As a result, if we move the time line to 8:13pm eastern time, we get the following semantic celebration of the halftime show:

Of course, as with all data we must ask what is missing from this representation, namely the tweets that referred to Springsteen as “Bruce” or “Bruuuuuuuuuuuuuuuuuuuuuuce!” or with any number of uuus in-between. For example, looking at my tweets about Springsteen during the Super Bowl reveals than none of my tweets (my location in orange) about him found their way into the semantic representation:

Similarly, we must also ask what is missing visually when all categories are combined. Here via the “All Tweets” option at 9:50pm, just after the Cardinals took a 23-20 lead:

This mapping suggests that the primary terms being tweeted at 9:50pm were Cardinals and Steelers. There are several problems with how this data has been represented. First, the designers have decided to use the same font color in the All Tweets option even though in the “Steelers vs. Cardinals” option the teams are represented by their team colors: Steelers in Black, Cardinals in red:

The change in font color brings forward the fact that the term Cardinals was being tweeted at a much higher rate than the term Steelers. The black on black font in the All Tweets option is not nearly as effective because the uniform font color reduces the comparative nature of the graphic.

Continuing with this line of thought, the All Tweets graphic for 9:50pm suggests that there were only small pockets in Kansas, Kentucky, western Texas, and Idaho where fans were tweeting the word “go.” However, selecting the “People Saying ‘Go'” option at 9:50pm reveals a nation shouting the word “go”:

According to my viewing of the flash animation, 9:50pm is when the word “go” reached its tweet apex and yet the words are hidden in the All Tweets option. This remediation begs the question: who were the fans saying go for? The answer lies in the comparison of data, and because that comparison has not been provided in the original I have merged the two above graphics using Fireworks:

The result is a nation of fans tweeting “go” and “cardinals.” Everyone loves an underdog.