Friday, November 12, 2010

Another Day of Hope (mostly), and some Fear and Worry in the US

Today I checked on the mood of the US Population through Twitter, using Twitter’s Geotagging feature. Alan Mislove from Northeastern had already found that the mood of the nation changes over the course of the day, with people having a low over lunch, and getting collectively happier in the evening, when work is over. Using our Twitter-collector-tool built into Condor, I was able to easily replicate this result.

I counted the number of retweets about “hope”, “fear”, and “worry” in the major population centers of the US, by collecting the tweets at four 2000 kilometers circles with centers at Pittburgh (North East), Atlanta (South East) Las Vegas (South West), and Boise (North West). (see picture below) I then constructed the social network between the retweeters as described in a previous blog post. The way it is calculated, it also factors in the importance of the retweeters, where a link is drawn between two people if a person retweets a post from the other person.

The picture above shows the areas I covered, as well as the fraction of retweets on hope (green), fear (blue), and worry (red) around noon. As we can see, people are more hopeful in the West around noon EST (which is still in the morning in the West) than they are on the east coast.
The picture changes four hours later. The graph below shows hopefulness (fraction of retweets on hope/fractions of retweets on fear and worry) around noon EST and around 6pm EST. Hopfulness shoots up sharply in the Northeast (NE), Southeast (SE), and Southwest (SW). It also goes up in the Northwest (NW), although much less.

Note that there are always more retweets on hope than there are on fear or worry, showing that people are basically hopeful, particularly when work is over. Let’s hope that hopefulness will also go up in the evening in the Northwest!


  1. How do you figure out if a tweet is about hope or worrying etc?

    How do you detect sarcasm, e.g. I just got a speeding ticket, oh i'm so happy now"?

  2. I don't detect sarcasm - my experience is that while there are indeed sarcastic or cynic comments, the majority is not, so sarcasm drowns in the sea of straightforward tweets

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