Using 'collective intelligence' to detect the start of a recession period?

I try to read the Economist regularly, but it's hard to keep up because each issue is so good. I just came across an article in the Economist from Jan 10th 2008 issue talking about using the frequency of the word 'recession' in Washington Post and NYTimes to identify the start of an actual recession period. Interestingly, according the graph below and the Economist, "This simple formula pinpointed the start of recession in 1981 and 1990 and 2001." Seems somewhat believable to me.


However, since news articles are written by the 'elite' journalists at Washington Post and the NYTimes, so this isn't quite what people have in mind when they think of 'wisdom of the crowd'. So I tried Google Trends instead, to see if the way people searched for the keyword 'recession' also correspond to the start of the recession period predicted by the R-index method by the Economist. Sure enough, the results seem to agree:


I then checked the same trend on the access traffic numbers for the 'recession' article on Wikipedia, and found the same peak in January:


I guess we don't need any more evidence that 'recession' started in January, or at least everyone seems to be obsessed about it then.

I then thought to myself: Perhaps, 'wisdom of the searchers' can also be used to predict who will win the presidential race in November? Here I deliberately made sure that blue is the keyword 'Obama', while red is the keyword 'McCain'.

It sure looks like Obama has the upper hand right now.

Social Media is better than Broadcast media?

One of the distinguishing features of social media has been that it enables communications of one-to-one, as well as one-to-group and one-to-large-many. Twitter now reports that it is faster than broadcast media in covering the latest news. The latest earthquake in Southern California apparently broke into Twitter chatter 4 minutes sooner than first broadcast media. It also says that 'By then, "Earthquake" was trending on Twitter Search with thousands of updates and more on the way.'

The figure here from the Twitter Blog explains what happened:

This is somewhat interesting, because I think Twitter is a more efficient way to get the news out to the people who cares. By using social connections to spread the information, only topics that have been socially filtered gets to me. This means, if I don't know anyone in Southern California or I'm not interested in earthquakes, I'm not likely to have friends who are interested in such news, which means the twitter traffic is less likely to reach me. This filters the information for me, automatically, simply by participating in the social network.

On the other hand, if I'm interested in such news, my social filters will help me get at such information faster than broadcast media can. I heard about the earthquake from our intern Brynn, who is from UCSD, so naturally she thought it would be good news to relay to me. My brother, who lives in Alhambra, didn't send an email to the rest of the family until an hour later. He was surprised that I had heard already, since it was just starting to get onto various news websites.