Author identification attempts to reveal the authors behind texts. It is an emerging area of
research associated with applications in literary research, cyber-security, forensics, and …

The goal of style change detection is to identify text positions within a multi-author document
at which the author switches. Detecting these positions is a crucial part of processing multi …

The analysis of the writing style of multi-authored texts aims at identifying those places
where authorship changes in a document. This task is an important step in reliably …

Style change detection is an important area of research with many applications, including
plagiarism detection, digital forensics, and authorship attribution. This paper proposes a …

This article describes an unsupervised language model (LM) adaptation approach that can
be used to enhance the performance of language identification methods. The approach is …

The task of multi-author writing style detection aims at finding any positions of writing style
change in a given text document. We formulate the task as a natural language inference …

This paper describes a proposed system design for Style Change Detection (SCD) tasks for
PAN at CLEF 2022. We propose a unified architecture of ensemble neural networks to solve …

The majority of previous authorship attribution studies mainly focus on a dataset of
documents (or parts of documents) with labeled authorship. This scenario, however, is not …

Style change detection aims at detecting writing style breaches: the positions at which the
writing style changes and authors switch within a multi-authored document. This task has a …

In this work, we attempt to differentiate authors of fake news and real news as part of the
Profiling Fake News Spreaders on Twitter task at PAN. We propose a set of eight different …