If you ever wondered whether someone cares about how you were feeling when you posted your social media status update, be assured that a group of applied mathematicians from the University of Vermont do care.
They charted our words on Twitter to find the happiest and saddest states in the United States. Their findings are published in a report titled “The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place.”
“One of our main philosophies here is that happiness is a fundamental sort of metric about society,” said Lewis Mitchell, a postdoctoral researcher with the Computational Story Lab at the University of Vermont—the group that conducted the research and authored the report.
Happiness or unhappiness, in the eyes of the research group, is important to a society. Happiness levels are just as important as a society’s economic indicators, such as gross domestic product (GDP) or unemployment rates, according to Mitchell.
The group used geotagged tweets to figure out what people are talking about—and where they are talking about it. They then surveyed the characteristics of every state and close to 400 urban populations.
With the two sets of data combined—what and where—they figured out how people might be feeling across different locations.
“Southern states tend to produce sadder words than those in northern New England or out west,” Mitchell wrote on the group’s blog.
In the study, the 10 happiest states—based on words used on Twitter—were Hawaii, Maine, Nevada, Utah, Vermont, Colorado, Idaho, New Hampshire, Washington, and Wyoming, in that order.
People in Hawaii tweeted words like “hi,” “beach,” “thanks,” “pearl,” “coffee,” “resort,” “shopping,” “island,” and “happy.”
The 10 not-so-happy states, starting with the unhappiest, were Louisiana, Mississippi, Maryland, Delaware, Georgia, Alabama, Michigan, D.C., Arkansas, and Ohio.
People in Louisiana tweeted words like “lol,” “pressure,” “ain’t,” “gone,” “me,” and profane words.
A Closer Look at NYC
According to the authors, they created a happiness text-based hedonometer that they describe as “tunable, real-time, remote-sensing, and non-invasive.” It essentially measures happiness.
Putting their hedonometer to the test on their blog, they set out to find the happiest street corner in New York.
“The happiest ‘corner’ is actually just inside the western edge of Central Park, where the intersection of 7th and 77th would be (this is just north of the lake and east of the Hayden Planetarium),” Mitchell reported on the blog.
Positive words like “love” and “sky” were used more on the corner’s tweets, while negative words like “not,” “fear,” and “no,” were used less on the corner’s tweets.
Overall, he reported that the west side is slightly happier than the east side, and that happiness actually declines as one moves further uptown.
“Many of the happiest locations actually fall within Central Park!” he wrote.
The research group keeps a disclaimer on their blog about the project being “a fun and lighthearted exploration.”
In 2011, the mathematicians published another report titled “Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter.”
In the 2011 report, researchers found that happiness during the week peaks over the weekend and declines on Tuesday.
“While people collectively have strong opinions about the word ‘Monday’, the reality, at least in terms of tweets, is that Tuesday is the week’s low point,” states the report.
Mitchell said they found that people are happiest in the morning and that the feeling decreased over the course of the day.
They also measured happiness around national events and holidays. In 2008–2010, Christmas Day, followed by Christmas Eve, displayed the highest levels of happiness.
New Year’s Eve and Day, Valentine’s Day, Thanksgiving, Fourth of July, Easter Sunday, Mother’s Day, and Father’s Day showed high levels of happiness, too.
“The only singular, non-annual event to stand out as a positive day was that of the Royal Wedding of Prince William and Catherine Middleton, April 29, 2011,” the report states.
Happiness went down into a “multi-week depression” on a few occasions: in 2008 when the U.S. government bailed out banks, during the 2009 swine flu (H1N1) pandemic, and at Michael Jackson’s and Patrick Swayze’s deaths.
During the bailout, words like “bill,” “down,” “no,” “not,” “fail,” “blame,” and “panic” were highly tweeted. During the royal wedding, words like “wedding,” “beautiful,” “kiss,” “prince,” “princesses,” “dress,” and “gorgeous” were highly used.
Other sad times were during natural disasters, including “the February 2010 Chilean earthquake, the October 2010 record-size storm complex across the U.S., and the March 2011 earthquake and tsunami which devastated Japan,” the report states.
A Daily Measure of Happiness
The hedonometer is going to track our happiness every day, according to Mitchell. In the coming weeks, the group will launch a website called “hedonometer.org.”
It will be “a daily updating chart, or time series,” according to Mitchell, on the happiness within the Twitter-sphere at the global level.
It will collect all the tweets, process them, and spit out a point in a time series of happiness. Mitchell said that it will be interesting to see what happens to the hedonometer as global events crop up.
It goes “back to the idea of happiness is a GDP-type metric,” Mitchell said.
The information could be useful in looking at how different words relate to the happiness and different underlying social economic demographics of a place, according to Mitchell.
In their most recent study, the research group analyzed obesity rates using a Gallup 2011 survey on obesity that was done for different cities in the United States. They tied the survey data on obesity to their Twitter data set of words, and they “correlated obesity with the frequency of different words, and we correlated obesity with happiness,” Mitchell said.
Words like “McDonald’s” were tied to obesity, for example, and words like “café” were tied to lack of obesity.
“Happiness goes down as obesity goes up,” he said.
In the future, the group hopes to see how people might be feeling when it analyzes more complex language structures, like content, sentences, and phrases.