Automatically Predicting the Arab Spring

By Deane Barker on November 10, 2011

Supercomputer Predicts Civil Unrest:  I find the concept of sentiment analysis fascinating, and I think it’s the Next Big Thing.  With so much data, the next frontier is automated ways of deriving concepts and trends involving emotion, actions, and other human factors that can’t be easily quantified.

A researcher at the University of Tennessee applied this to news articles to find hot spots of world unrest.  It worked quite well, apparently.

Leetaru used a database of 100 million news articles spanning the period from 1979 to early 2011. The data is from the Open Source Center and Summary of World Broadcasts, both set up by the U.S. and British intelligence agencies to monitor what amounts to nearly every news source in the world and translate them into nuanced English. By analyzing the text in the news stories and the tone — whether they were largely positive or negative — Leetaru found patterns emerging that seemed to line up with major periods of unrest. For example, in Egypt, the tone of news articles about Mubarak grew increasingly negative as the protests grew, until eventually Mubarak resigned.

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