  We establish concrete mathematical criteria to distinguish between different
kinds of written storytelling, fictional and non-fictional. Specifically, we
constructed a semantic network from both novels and news stories, with $N$
independent words as vertices or nodes, and edges or links allotted to words
occurring within $m$ places of a given vertex; we call $m$ the word distance.
We then used measures from complex network theory to distinguish between news
and fiction, studying the minimal text length needed as well as the optimized
word distance $m$. The literature samples were found to be most effectively
represented by their corresponding power laws over degree distribution $P(k)$
and clustering coefficient $C(k)$; we also studied the mean geodesic distance,
and found all our texts were small-world networks. We observed a natural
break-point at $k=\sqrt{N}$ where the power law in the degree distribution
changed, leading to separate power law fit for the bulk and the tail of $P(k)$.
Our linear discriminant analysis yielded a $73.8 \pm 5.15%$ accuracy for the
correct classification of novels and $69.1 \pm 1.22%$ for news stories. We
found an optimal word distance of $m=4$ and a minimum text length of 100 to 200
words $N$.
