UN tackles socio-economic crises with big data
Four of this year's Computerworld Honors Laureates use big data to battle the effects of unemployment and climate change, raise social awareness and help at-risk students.
Computerworld - United Nations researchers had a sobering realization in 2010. For all of the official data and reports collected by the group's member nations and various UN programs and agencies, precious little of the data that supports the organization's operations was truly up to date.
That left officials worldwide trying to respond to socio-economic crises, such as the financial meltdown and staggering unemployment, with data that was months and sometimes years old.
Global Pulse "came out of the very pointed recognition that there's a need for real-time information, especially in an age of hyperconnectivity when something that happens one place in the world can immediately impact somewhere else," says Anoush Tatevossian, a UN strategic communications officer and partnership manager.
The UN isn't the only organization working to harness the power of big data and analytics for social good. Several other 2013 Computerworld Honors Laureates gather and analyze information to do things like minimize the impact of climate change, identify at-risk students and improve financial services to underserved populations.
Celebrating its 25th year, the Computerworld Honors program recognizes organizations that create and use IT systems to promote and advance public welfare. On June 3, this year's 267 honorees will gather in Washington to celebrate their achievements. Here's a look at projects undertaken by four laureates.
Monitoring Mood Shifts
One of the first projects United Nations Global Pulse took up was analyzing social media chatter and sentiment to identify trends related to unemployment increases, and then inform policymakers of likely effects. Analyzing 500,000 blogs, forums and news sites, the team used text mining and social media analytics tools from SAS to examine two years of social media data from the U.S. and Ireland. They scanned for all references to unemployment and coping mechanisms. The team then compared and analyzed so-called mood scores, which were based on the tone and themes of various references to unemployment.
In the U.S., a rise in "hostile" or "depressed" mood scores occurred four months before the unemployment spike. Increases in "anxious" chatter in Ireland correlated with a spike five months later. Increased "confused" chatter preceded a spike by three months, while "confident" chatter decreased significantly two months out. A dashboard displayed trends such as mood change over time, and leading and lagging indicators of unemployment shocks.
Another analysis revealed that increased chatter about cutting back on groceries, increasing use of public transportation and downgrading one's automobile could predict an unemployment spike. After a spike, surges in conversations about canceled vacations, reduced healthcare spending, and foreclosures or evictions shed light on lagging economic effects. This kind of information is invaluable for policymakers trying to mitigate the negative effects of increased unemployment, Tatevossian says.
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