Some areas of the country are happier than others, according to a team of researchers at the University of Vermont. Researchers came to this conclusion after collecting more than 10 million geo-tagged, or location-based, tweets and scanning them for their use of “happy” and “sad” words.

They found that people living on the West Coast or in the New England area used positive words more frequently than those in the Southeast. Hawaii was ranked as the happiest state in the country, while Louisiana was the saddest, according to the study.

The study also ranked individual cities. Napa, Calif., had the highest number of positive tweets. Beaumont, Texas, was the most depressed, based on average word choice, according to the study.

“We created a list of the 50,000 most commonly used words online, and then asked users of Amazon’s Mechanical Turk service to score the top 10,000 of those words on a scale from 1 (sad) to 9 (happy),” said Lewis Mitchell, the study’s lead author, in an email.

They then compiled these scores to come up with an average happiness scale called The Language Assessment, Mitchell said. The study described this scale as a “hedonometer.”

The words classified as happy ranged from descriptive adjectives like “nice” and “beautiful,” to objects such as “rainbow,” “wine” and “beach.” Words that were deemed negative included “hate,” “smoke,” and “traffic,” as well as an array of swear words, according to the study.

“It’s interesting that both wine and family popped up in the study,” said Clay Gregory, CEO of Visit Napa Valley, the city with highest number of happy tweets, in an email. “Over 95% of our wineries are family-owned, so I think that sense of community and quality and dedication is really woven into the fabric of who we are.”

Using Twitter’s “garden hose” feed, a service that allows access to 10 percent of all public statuses, Mitchell and his team sifted through tweets in search of the words listed on the scale. Each tweet was sent from a cell phone where the user had elected to share their location. Researchers created an algorithm to determine which locations had the most positive and negative tweets.

“I think that, in general, tools like this and the ‘big data’ approach of looking at unusual data sources such as Twitter can give us unique and real-time insights into cities and geographies,” Mitchell said.

The team of researchers compared its results with existing measures of factors such as socioeconomic status and obesity. It discovered that wealthier areas generally produced happier tweets than places with more poverty. Also, they found obesity rates were higher in unhappier cities, according to the study.

Some critics have suggested that the context of the tweeted words could cause inaccurate results. For example, if someone had sarcastically tweeted, “I love getting parking tickets,” that tweet would contain the positive word “love.”

This was a common criticism, Mitchell said. To provide the best results, the study avoided particularly short tweets.

“We deliberately want to remove ourselves from the experiment and not make any judgments about the context or ‘meaning’ of any of these sentences,” he said.

But they did occasionally remove the names of states and cities to ensure they did not affect results. One example listed in the study said Hawaii’s state initials, HI, could be mistaken for the word “Hi,” which is listed on the happiness scale as a positive word, Mitchell said.

“We had to cut out a few words from each collection of tweets, such as the names of states and cities, as these were biasing the results somewhat,” Mitchell said.

He said because the study was conducted on such a large scale, any problems caused by out-of-context words were minor, and did not affect results as a whole. He said the team compared its results with those from earlier experiments and found they were in agreement.

Said Mitchell: “They certainly tell a consistent story, and relate well to underlying social, economic and health factors such as those above.”

Published on February 28, 2013 at 12:55 am

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