Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

The analysis of authorial style, termed stylometry, assumes that style is quantifiably
measurable for evaluation of distinctive qualities. Stylometry research has yielded several …

Authorship attribution supported by statistical or computational methods has a long history
starting from the 19th century and is marked by the seminal study of Mosteller and Wallace …

Statistical authorship attribution has a long history, culminating in the use of modern
machine learning classification methods. Nevertheless, most of this work suffers from the …

Logistic regression analysis of high-dimensional data, such as natural language text, poses
computational and statistical challenges. Maximum likelihood estimation often fails in these …

We study techniques for identifying an anonymous author via linguistic stylometry, ie,
comparing the writing style against a corpus of texts of known authorship. We experimentally …

Almost any conceivable authorship attribution problem can be reduced to one fundamental
problem: whether a pair of (possibly short) documents were written by the same author. In …

Character n-grams have been identified as the most successful feature in both singledomain
and cross-domain Authorship Attribution (AA), but the reasons for their discriminative value …

Most previous work on authorship attribution has focused on the case in which we need to
attribute an anonymous document to one of a small set of candidate authors. In this paper …

Automatic authorship identification offers a valuable tool for supporting crime investigation
and security. It can be seen as a multi-class, single-label text categorization task. Character …