Supercomputer predicts revolution

Feeding a supercomputer with news stories could help predict major world events, according to US research, the BBC News reported.

A study, based on millions of articles, charted deteriorating national sentiment ahead of the recent revolutions in Libya and Egypt.

While the analysis was carried out retrospectively, scientists say the same processes could be used to anticipate upcoming conflict.

The system also picked up early clues about Osama Bin Laden's location.

Kalev Leetaru, from the University of Illinois' Institute for Computing in the Humanities, Arts and Social Science, presented his findings in the journal First Monday.

The study's information was taken from a range of sources including the US government-run Open Source Centre and BBC Monitoring, both of which monitor local media output around the world.

News outlets which published online versions were also analysed, as was the New York Times' archive, going back to 1945.
In total, Leetaru gathered more than 100 million articles.

Reports were analysed for two main types of information: mood - whether the article represented good news or bad news, and location - where events were happening and the location of other participants in the story.

Mood detection, or "automated sentiment mining" searched for words such as "terrible", "horrific" or "nice".

Location, or "geocoding" took mentions of specific places, such as "Cairo" and converted them in to coordinates that could be plotted on a map.

Analysis of story elements was used to create an interconnected web of 100 trillion relationships.

Data was fed into an SGI Altix supercomputer, known as Nautilus, based at the University of Tennessee.

The machine's 1024 Intel Nehalem cores have a total processing power of 8.2 teraflops (trillion floating point operations per second).

Based on specific queries, Nautilus generated graphs for different countries which experienced the "Arab Spring".

In each case, the aggregated results of thousands of news stories showed a notable dip in sentiment ahead of time - both inside the country, and as reported from outside.