Explainability of machine learning approaches in forensic linguistics: a case study in geolinguistic authorship profiling
This work addresses the problem of interpretability for forensic linguists, though it is incremental as it applies existing methods to a specific domain.
The study tackled the lack of transparency in machine learning for forensic linguistics by exploring explainability in geolinguistic authorship profiling, finding that extracted lexical features and place names are representative for classifying German dialect varieties from social media data.
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.