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Maps

What Twitter Can Tell Us About Unemployment

Where, when, and how people tweet reveals information about the socioeconomics of a region.

Mobility patterns between municipalities mapped over a year. (Esteban Moro and colleagues)

The Twitterverse is not easy to navigate. It's noisy. It's full of trolls and Twitterbots. Whether you're trolling or tweeting for good, one thing is clear—every time you tweet, you leave a little trace of yourself.

Scientists can piece these digital clues together and generate big pictures about who we are and where we live. Last week, my colleague Laura Bliss explained how researchers used Twitter to demonstrate how the 'six degrees of separation' theory breaks down within cities. In a new study, mathematicians in Spain have isolated patterns in geo-mapped tweets and examined how these patterns relate to unemployment rates.

"It was [started as] a coffee shop conversation—what are the kind of things you do when you’re unemployed?” explains Esteban Moro, one of the authors of the study and a professor at Universidad Carlos III de Madrid. Where do you travel? When do you tweet about where you are traveling to? What do you tweet? Moro and his colleagues looked for the answers to these questions in 145 million geo-located tweets in Spain—a country with the national unemployment rate of 24.4 percent.

The first thing they found surprised them: The more Twitter users in a region, the higher the rate of unemployment there. This high correlation between what Moro calls "Twitter penetration" and unemployment rate is perhaps why data from the micro-blogging site is good way to predict unemployment in real time, Moro says.

There were other revelations. In tweets across 340 different regions, they found patterns about how people were moving around and communicating.

If the same people tweet from and to several economically distinct areas, the authors say the regions they are from exhibit "high diversity in mobility.” In the analysis, they found that that these regions have a lower unemployment rate.

They also noticed that in areas where unemployment rate is low, the number of tweets surges between 8 a.m. to 10 a.m. Moro guesses this is because people with jobs probably get into work around that time, sit at their desk and start tweeting. True, jobs in construction and agriculture generally don't have desks, but jobs in these industries are the ones worst affected by the economic crisis, so they've decreased in number, Moro says.

Tweets from places with low unemployment rates surged from 8 a.m. to 10 a.m. (Esteban Moro and colleagues)

A pattern in the tweets' content also stuck out. Tweets from regions of high unemployment tended to have significant spelling mishaps—not just typos or internet abbreviations—but real misspellings in Spanish, says Moro. The tweeters' level of education might have something to do with that, he thinks.

These were the variables that correlated heavily with unemployment rates in Moro's analysis. "Entropy" measures the mobility and communication patterns in the tweets. (Esteban Moro and colleagues)

But it could also be that some super-educated people who live in areas with high unemployment are terrible at spelling. The research only shows correlations, not causations, as of now. But it makes a case for further analysis and demographic mapping, says Moro. The tweets they analyzed were only two percent of all the tweets in Spain at the time; they would love to go bigger.  

"The larger the area you study the better the model,” he says.  

About the Author

  • Tanvi Misra
    Tanvi Misra is a staff writer for CityLab covering demographics, inequality, and urban culture. She previously contributed to NPR's Code Switch blog and BBC's online news magazine.