  A central problem that has been researched for many years in the field of
digital text forensics is the question whether two documents were written by
the same author. Authorship verification (AV) is a research branch in this
field that deals with this question. Over the years, research activities in the
context of AV have steadily increased, which has led to a variety of approaches
trying to solve this problem. Many of these approaches, however, make use of
features that are related to or influenced by the topic of the documents.
Therefore, it may accidentally happen that their verification results are based
not on the writing style (the actual focus of AV), but on the topic of the
documents. To address this problem, we propose an alternative AV approach that
considers only topic-agnostic features in its classification decision. In
addition, we present a post-hoc interpretation method that allows to understand
which particular features have contributed to the prediction of the proposed AV
method. To evaluate the performance of our AV method, we compared it with ten
competing baselines (including the current state of the art) on four
challenging data sets. The results show that our approach outperforms all
baselines in two cases (with a maximum accuracy of 84%), while in the other two
cases it performs as well as the strongest baseline.
