  The authorship attribution is a problem of considerable practical and
technical interest. Several methods have been designed to infer the authorship
of disputed documents in multiple contexts. While traditional statistical
methods based solely on word counts and related measurements have provided a
simple, yet effective solution in particular cases; they are prone to
manipulation. Recently, texts have been successfully modeled as networks, where
words are represented by nodes linked according to textual similarity
measurements. Such models are useful to identify informative topological
patterns for the authorship recognition task. However, there is no consensus on
which measurements should be used. Thus, we proposed a novel method to
characterize text networks, by considering both topological and dynamical
aspects of networks. Using concepts and methods from cellular automata theory,
we devised a strategy to grasp informative spatio-temporal patterns from this
model. Our experiments revealed an outperformance over traditional analysis
relying only on topological measurements. Remarkably, we have found a
dependence of pre-processing steps (such as the lemmatization) on the obtained
results, a feature that has mostly been disregarded in related works. The
optimized results obtained here pave the way for a better characterization of
textual networks.
