In response, and as an addition, to Wilbert Baans’ storytelling with public databases, I made a little twitter scraper. It indexed all twits for the query http://search.twitter.com/search?q=%22i+voted%22+%2B+McCain+OR+Obama+-twitvote, up till a month ago. From November the 4th till November the 5th the script scrapes new twits for this query every minute.

A point goes to Obama if the regular expression /vote.*?obama/i succeeds, it goes to McCain if the regular expression /vote.*mccain/i succeeds, else it is undecided / unrecognized.

The result can be found at Twitter Poll. So far Obama is winning.

Update: Wilbert Baan has made theses stats into a beautiful visualization:

There is an other twitter poll on Mashable.

Update 2: I tried making a similar exit poll based on MySpace, but that query does not yield results sorted on time. Also, just like Google, MySpace only returns a maximum of 1000 results per query. Unfortunately I’m thus stuck with the same numbers, no matter how many times I will scrape it.

Since 1998, and on paper since 1989 (Stemwijzer 2008), general elections in the Netherlands have spawned a variety of so called voting recommendation machines. These systems typically ask the user to answer some questions after which they offer the user a voting recommendation, based on the compatibility between his or her answers and the political parties. The questions are either based on the political party’s programs (Kieskompas 2008), or on its actual voting behavior in Parliament over the past few years (Politix 2008).

Since the birth of online social networking sites lots of people have, quite unconsciously, put their likes and dislikes on public display. They not only show with whom they affiliate, but also what kind of music, movies, food, or even brands they prefer.

We have developed a post-demographic recommendation tool derived from digital life software systems, while at the same time addressing them – based on the aggregated profiles of pals of political party leaders as they appear on the biggest Dutch online social network, Hyves. By providing appropriate visualizations we show both the demographics and the relations of a group of pals, and replicate the existing, arguably anti-participatory democratic, voting recommendation machines. Ultimately the goal is to raise awareness of one’s digital public self – one’s data body (Well.com 1995) – to create conscience of simple yet powerful profiling techniques, and the tools of the surveillance and control society. (Bogard 1996, Deleuze 1992). We intentionally chose to highlight the entertaining quality and lightness of peer-based behavior this society is so immersed in. In addition, this paper introduces and explains the term post-demographics (first coined by Richard Rogers in August 2007) in the context of control society.
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