Slashdot Mirror
Tracking the Congressional Attention Span
Turismo writes "Ars Technica covers a new research project that uses computers to look at 70 million words from the Congressional Record. The project's goal was to track what our representatives were talking about at any given time, and researchers were able to do it without human training or intervention. From the article: '...researchers found, for instance, that "judicial nominations" have consumed steadily more Congressional attention between 1997 and 2004. In fact, the topic produced the most number of words published in a single "day" of the Congressional Record: 230,000 on November 12, 2003.' It looks like automated topic analysis has truly arrived."
"It looks like automated topic analysis has truly arrived."
+ topic+analysis%22&btnG=Google+Search
Not according to my in-depth research. Looks like "automated topic analysis" isn't arriving at all.
http://www.google.com/search?hl=en&q=%22automated
Vincent J. Murphy
Spandex Justice
Think about it: "Who thinks we should elect Joe Six-Pack"
Lots of talk, chit-chat, chatter, etc...
"Okay, now who would want to oppose the True American, Patriot, Love, Peace Act*"
Cricket! Cricket!
*And of course this Act happens to have about thirty-thousand ridders attached to it...
The conclusion. Congress has ADD, just like me.
I see the glass as full with a FoS of 2.
The congressional record is a false document of what happened in congress. Watch C-Span one day and hear each person request "Unamious support to change or extend". This allows 30 second comment say to begainst the bill to become a 2 hr speech to supporting the bill WITHOUT editing marks.
This program may count time on paper but can not count time that congress is actually spending.
30 years ago, I learned in my high school civics class that any Senator or Representative can insert anything he or she wants into it at any time. Examples that were pointed out to us were speeches on the floor of the Senate that were never made, modifications to committee meetings, etc. The CR is by no means an accurate measure of anything. Except maybe the size of their combined egos.
You might also be interested in another topic model that not only automatically discovers topics, but also automatically discovers topic-specific groupings of the senators by their votes. http://www.cs.umass.edu/~mccallum/papers/grouptopi c_linkkdd05.pdf "Group and Topic Discovery from Relations and Text."
It uses not only word data (from the text of 16 years worth of bills voted on in the U.S. Senate), but also the senator's voting records.
For example, you can see that Sen. Chafee (R-RI) (who was mentioned on this morning's NPR as a "liberal Republican") actually does fall into a cluster of Democrats, not fellow Republicans. When automatically discovering topics using word data alone (without the votes, as does the wustl.edu paper above) the topics on this Senate data are reasonably coherent, but the topics created by this "Group-Topic" new model are even more interesting because their discovery is driven by the need to predict the votes as well as the words. For example, "Social Security" doesn't appear in the old model, but pops out clearly in the new model because it has such a distinct voting pattern.
Some of the other results are also pretty interesting---on Education and Domestic policy the Republicans are more split than the Democrats (forming 3 groups, to the Democrats 1 group). On other topics, the split is the other way around.
Using the same technique, there is also an analysis of 60 years worth of voting records from the U.N. On the topic of "human rights", Nicaragua, Papua, Rwanda, Swaziland and Fiji all get clustered together---ouch!