  Authorship verification (AV) is a research subject in the field of digital
text forensics that concerns itself with the question, whether two documents
have been written by the same person. During the past two decades, an
increasing number of proposed AV approaches can be observed. However, a closer
look at the respective studies reveals that the underlying characteristics of
these methods are rarely addressed, which raises doubts regarding their
applicability in real forensic settings. The objective of this paper is to fill
this gap by proposing clear criteria and properties that aim to improve the
characterization of existing and future AV approaches. Based on these
properties, we conduct three experiments using 12 existing AV approaches,
including the current state of the art. The examined methods were trained,
optimized and evaluated on three self-compiled corpora, where each corpus
focuses on a different aspect of applicability. Our results indicate that part
of the methods are able to cope with very challenging verification cases such
as 250 characters long informal chat conversations (72.7% accuracy) or cases in
which two scientific documents were written at different times with an average
difference of 15.6 years (> 75% accuracy). However, we also identified that all
involved methods are prone to cross-topic verification cases.
