  Poetry and prose are written artistic expressions that help us to appreciate
the reality we live. Each of these styles has its own set of subjective
properties, such as rhyme and rhythm, which are easily caught by a human
reader's eye and ear. With the recent advances in artificial intelligence, the
gap between humans and machines may have decreased, and today we observe
algorithms mastering tasks that were once exclusively performed by humans. In
this paper, we propose an automated method to distinguish between poetry and
prose based solely on aural and rhythmic properties. In other to compare prose
and poetry rhythms, we represent the rhymes and phones as temporal sequences
and thus we propose a procedure for extracting rhythmic features from these
sequences. The classification of the considered texts using the set of features
extracted resulted in a best accuracy of 0.78, obtained with a neural network.
Interestingly, by using an approach based on complex networks to visualize the
similarities between the different texts considered, we found that the patterns
of poetry vary much more than prose. Consequently, a much richer and complex
set of rhythmic possibilities tends to be found in that modality.
