Difference between revisions of "Chance News 78"

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Submitted by Steve Simon
  
 
===Questions===
 
===Questions===

Revision as of 04:08, 3 October 2011

Quotations

"Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom." -- Clifford Stoll

Submitted by Steve Simon

Forsooth

Cheerful tweets in the morning

Twitter Study Tracks When We Are :) by Benedict Carey, The New York Times, September 29, 2011

If you read the mood of people on Twitter, they are happy in the morning, but then things go downhill.

However grumpy people are when they wake up, and whether they stumble to their feet in Madrid, Mexico City or Minnetonka, Minn., they tend to brighten by breakfast time and feel their moods taper gradually to a low in the late afternoon, before rallying again near bedtime, a large-scale study of posts on the social media site Twitter found.

How can you measure this? The researchers

analyzed the text of each message, using a standard computer program that associates certain words, like “awesome” and “agree,” with positive moods and others, like “annoy” and “afraid,” with negative ones. They included so-called emoticons, the face symbols like “:)” that punctuate digital missives.

It's not an accurate sample, though, as the researchers admit.

For starters, Twitter users are computer-savvy, skew young and affluent, and post for a variety of reasons.

You might think that going to work causes a decline in mood, but the same burst of enthusiasm occurs on Saturday and Sunday mornings, but two hours later than on weekdays. There is, however, no evidence for Seasonal Affective Disorder, the tendency of people to become depressed near Christmas when the days are the shortest. Here's a graph showing the daily trends.

http://graphics8.nytimes.com/images/2011/09/30/science/30twitter_graphic/30twitter_graphic-popup-v2.gif

Submitted by Steve Simon

Questions

1. What aspects of Twitter make you nervous about these findings?

2. Can sentiments like happiness be discovered adequately by text analysis? Does the 140 character limit in Twitter make this more difficult or easier?

Item 2