Mapping with R and other free and open-source programs feels clunky and hacked-together at times. Tomislav Hengl provides a free e-book, A Practical Guide to Geostatistical Mapping, that can hopefully help you with such tools.

A recent study [pdf] by Sitaram Asur and Bernardo A. Huberman at HP Labs found that it's possible to use Twitter chatter to predict first-weekend box office revenues simply based on volume of tweets. The predictions were even more accurate when they introduced sentiment analysis (i.e. classified tweets as positive or negative).

The above chart shows predicted revenue on the first weekend versus actual. The blue line is the tweet predictor, and the green line is predictions from Hollywood Stock Exchange, a site where people can put down fake money in predicting box office revenues.

What you see are predictions that are more or less within in couple hundred thousand of the actual openings.

Ok, so what?
Asur and Huberman note that their tweet-based model outperformed HSX, which is interesting because HSX is switching to real money soon. That's like having a slight edge over the dealer in black jack.

This also has implications in predicting other stuff that involves the opinion of the masses, for example, who is going to win an election or how well a product will sell. With that type of information, candidates could design their campaign strategies, and manufacturers could prevent over-production.

Music to Twitter's ears, I am sure.

It's no wonder companies are so hungry to find out what people are talking about on Twitter. Put together a high-quality analysis and visualization package or application and you'll be rolling in it.

[The Technium via kottke]

Facebook has released a “happiness index” based on the status updates people make on their site. They have an algorithm that looks for words connected with positive and negative feelings, and categorize the status updates accordingly.

The GNH as it’s called, the Gross National Happiness index, currently only looks at status updates from US Facebook users, which makes sense since it’s a language-based study tool. (Hopefully Facebook will soon add similar indices for other countries as well.)

When you study the graphs that Facebook generates, a weekly pattern quickly becomes obvious. Unsurprisingly we’re at our happiest during public holidays and on Fridays and weekends, but a closer look at the graphs reveals what we’ve suspected for a long time:

People hate Mondays with a vengeance. (Garfield was right!)

Just look at these graphs.

Happiness level:

As you can see, the overall level of happiness bottoms out every Monday.

The low level of happiness on Mondays isn’t just caused by a lack of positivity. If we look at the negativity alone, it becomes clear that people are in a really negative mood on Mondays.

Negativity level:

People really do pick themselves up during the weekends, though, and we’re a lot happier on Fridays as well (anticipating the weekend, we presume?).

Positivity level:

We bet you can guess on which weekdays those drops in positivity take place…

This “happiness index” clearly shows Facebook’s data mining potential. Considering its huge user base and active users, there are tons upon tons of data available, and the question is how Facebook will be using it. We’d love to see more of these “indices”. As long as the data is of a general nature there shouldn’t be any privacy concerns, so we’re all for this kind of information being made available.

Additional information about the GNH index can be found over at the Facebook blog. (Check it out, it’s an interesting read.)

Data source: All graphs are from Facebook’s United States Gross National Happiness page, with those elegant arrows added by us.

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analysis and visualizations of search engines