By using a supercomputer to analyze news stories, a University of Illinois researcher believes that he can predict major upcoming global events, according to various news and wire reports.
Writing in the journal First Monday on September 5, Kalev Leetaru, the Assistant Director for Text and Digital Media Analytics at the school’s Institute for Computing in the Humanities, Arts, and Social Science, writes, “Pooling together the global tone of all news mentions of a country over time appears to accurately forecast its near–term stability, including predicting the revolutions in Egypt, Tunisia, and Libya, conflict in Serbia, and the stability of Saudi Arabia.”
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.