  Authorship verification is the task of analyzing the linguistic patterns of
two or more texts to determine whether they were written by the same author or
not. The analysis is traditionally performed by experts who consider linguistic
features, which include spelling mistakes, grammatical inconsistencies, and
stylistics for example. Machine learning algorithms, on the other hand, can be
trained to accomplish the same, but have traditionally relied on so-called
stylometric features. The disadvantage of such features is that their
reliability is greatly diminished for short and topically varied social media
texts. In this interdisciplinary work, we propose a substantial extension of a
recently published hierarchical Siamese neural network approach, with which it
is feasible to learn neural features and to visualize the decision-making
process. For this purpose, a new large-scale corpus of short Amazon reviews for
text comparison research is compiled and we show that the Siamese network
topologies outperform state-of-the-art approaches that were built up on
stylometric features. Our linguistic analysis of the internal attention weights
of the network shows that the proposed method is indeed able to latch on to
some traditional linguistic categories.
