All you and I have to do to tell a fiction from a non-fiction is to read a piece of text - but how can a computer tell the difference? It's tricky, but doable:
Joseph Stevanak and Lincoln Carr at the Colorado School of Mines in Golden have come up with a way to do it. They say that the key is to look at the networks that form when you examine how often words appear close together in each type of text.
The type of network they examined creates a graph in which each word in the text forms a vertex. A line connects two vertices if these words appear next to each other in the text. It is possible to explore longer range links by connecting vertices when they appear two or three or four words apart and so on.
Stevanak and Carr say that just two properties of this kind of network can help distinguish fiction from nonfiction stories. The first is the power law that describes the number of links to each vertex in the network. The second is the cluster coefficient which describes how well the vertices are connected to the rest of the network.
Measuring these two quantities alone can identify the type of story with remarkable accuracy. "Our analysis yielded a 73.8±5.15% accuracy for the correct classification of novels and 69.1 ± 1.22% for news stories," say Stevenak and Carr.
What's remarkable is them calling 3 out of 4 remarkable accuracy.