  Forensic authorship profiling uses linguistic markers to infer
characteristics about an author of a text. This task is paralleled in dialect
classification, where a prediction is made about the linguistic variety of a
text based on the text itself. While there have been significant advances in
recent years in variety classification, forensic linguistics rarely relies on
these approaches due to their lack of transparency, among other reasons. In
this paper we therefore explore the explainability of machine learning
approaches considering the forensic context. We focus on variety classification
as a means of geolinguistic profiling of unknown texts based on social media
data from the German-speaking area. For this, we identify the lexical items
that are the most impactful for the variety classification. We find that the
extracted lexical features are indeed representative of their respective
varieties and note that the trained models also rely on place names for
classifications.
