The bailout package might prevent total economic collapse. But many Greeks are worried about the changes that the financial rescue will bring -- and they've been talking about their fears on Twitter.
Mashable partnered with social media analysis company Crimson Hexagon to analyze more than 13,000 tweets about the Greek bailout. We looked only at tweets about the financial negotiations sent over the last month from within Greece.
Overwhelmingly, Greek Twitter users were concerned with the austerity measures and pay cuts that a deal would bring. Sixteen percent of tweets were worried about the cuts would mean for the Greek economy and society as a whole. Fourteen percent of tweeters expressed concern that their pay might be cut, that they might lose benefits or that they would lose their job entirely. Ten percent doubted if the planned cuts would actually lessen the Greek deficit.
Other Greeks used Twitter to blame their government or society as a whole for the problem. Seven percent of bailout tweets said that tax evasion was the major reason for Greece's economic woes, 10 percent accused the government of being corrupt and 13 percent of tweets suggested that democracy, born and nurtured in Greece, was now "lost."
And as austerity riots broke out in the Greek capital of Athens, locals took to Twitter to send pictures and reports from the scene. Sixteen percent of tweets over the past month that discussed the bailout mentioned those protests, workers' strikes or police brutality.
Crimson Hexagon’s software was developed at Harvard University’s Institute for Quantitative Social Science and is used by such organizations as the Pew Research Center’s Project for Excellence in Journalism.
In order to generate a report, Crimson Hexagon’s research team enters a keyword – such as “Austerity” – into the software. The program pulls more than one million random online posts with the keyword. Then a human researcher starts sorting some of those posts into different categories, like the ones mentioned above. The software starts to find patterns in what the researcher is doing and repeats it for the rest of the hundreds of thousands of posts.