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 …

Authorship attribution, the science of inferring characteristics of the author from the
characteristics of documents written by that author, is a problem with a long history and a …

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 …

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 …

With the recent progress made in network and computing technology, the ubiquity of data,
and textual repositories freely available, the scientific practice evolves towards a more data …

Research in automatic text plagiarism detection focuses on algorithms that compare
suspicious documents against a collection of reference documents. Recent approaches …

The task of intrinsic plagiarism detection deals with cases where no reference corpus is
available and it is exclusively based on stylistic changes or inconsistencies within a given …

We present a method for authorship discrimination that is based on the frequency of bigrams
of syntactic labels that arise from partial parsing of the text. We show that this method, alone …

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 …

Unlike previous fraud detection research, a vast majority of which has focused primarily on
the use of quantitative financial information to predict fraud, in this study we examine …