EPJ Data Science Highlight - Estimating unemployment rates from Twitter user routines

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Published on Wednesday, 12 July 2017 17:00

The buzz of busy commuters, as well as the lack of it, leave behind digital footprints that are rich in information about all aspects of people's lives. In EPJ Data Science, Eszter Bokányi and team analyze 63 million tweets originating all over the US for a period of 10 months, and find links between unemployment rates and and the users' Twitter activity.

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EPJ Data Science Highlight - Social media trending: real or manufactured?

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Published on Tuesday, 11 July 2017 18:16

The era of "fake news" is upon us. Navigating social media is a constant exercise of judgement, but data science can be a helpful to distinguish real from fabricated trending topics. In EPJ Data Science, Emilio Ferrara and team set out to determine from very early on whether information is being organically or artificially disseminated on social media.

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EPJ Data Science Highlight - Are your tweets feeling well? Opinion and emotion in tweets change when you get sick

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Published on Sunday, 02 July 2017 14:57

Can we tell if a person is physically ill by the way they tweet? On a recently published article in the journal EPJ Data Science, researchers at the Pacific Northwest National Laboratory uncover links between the health of users and the emotional tone of their social media output.

Guest post by by Svitlana Volkova, originally published on SpringerOpen blog

Any doctor or nurse knows good public health begins with prevention. Whether it’s a severe strain of the flu or mental illness, identifying the need for help early can save lives. Social media could be the game-changing solution public health workers have been looking for. Whereas traditional data from clinics may take weeks to collect, social media streams in real time. In other words, public health workers could monitor social media like a heartbeat, and take action before people visit a doctor.

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