Twitter can be used to track unemployment numbers and patterns


Governments aren’t usually quick to react to changes in demographics. They frequently have to take surveys that are not only slow, but don’t always paint a complete picture of what’s going on. Researchers at the Autonomous University of Madrid have discovered a far more effective way of keeping tabs on the population, however: tracking Twitter updates. They’ve found that the content, frequency and timing of tweets across Spain correlate well with joblessness levels in their respective regions. People in high unemployment areas tend to not only mention jobs more often in their posts, but tweet more in the morning and make a larger number of spelling mistakes.

Human behaviour is closely linked to social and economic status. For example, the way an individual travels round a city is influenced by their job, their income and their lifestyle. So it shouldn’t come as a surprise that economic status might also be reflected in patterns of social media behaviour. Indeed, that’s exactly what, say Alejandro Llorente at the Autonomous University of Madrid in Spain and a few pals. Today, these guys show that the broad pattern of tweets across cities and counties in Spain reveals fascinating detail about unemployment rates in these areas. These guys began with a database of 19.6 million geolocated tweets in Spain published between November 2012 and June 2013. Llorente and co wanted to correlate these tweets with regions of economic activity but these are not easy to determine. That’s because they do not correspond well to the administrative boundaries in Spain, which reflect historical and political boundaries rather than economic ones. So the team analysed the rate at which messages were exchanged between regions using a standard community detection algorithm. This revealed 340 independent areas of economic activity, which largely coincide with other measures of geographic and economic distribution. “This result shows that the mobility detected from geolocated tweets and the communities obtained are a good description of economical areas,” they say.

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