Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/78407
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Automated authorship attribution using advanced signal classification techniques
Author: Ebrahimpour, M.
Putnins, T.
Berryman, M.
Allison, A.
Ng, B.
Abbott, D.
Citation: PLoS One, 2013; 8(2):1-12
Publisher: Public Library of Science
Issue Date: 2013
ISSN: 1932-6203
1932-6203
Statement of
Responsibility: 
Maryam Ebrahimpour, Tālis J. Putniņš, Matthew J. Berryman, Andrew Allison, Brian W.-H. Ng, Derek Abbott
Abstract: In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the limitations lie, motivating a number of open questions for future work. An open source implementation of our methodology is freely available for use at https://github.com/matthewberryman/autho​r-detection.
Keywords: Discriminant Analysis; Likelihood Functions; Language; Textbooks as Topic; Databases as Topic; Support Vector Machines; Automation; Authorship
Rights: © 2013 Ebrahimpour et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0020125799
DOI: 10.1371/journal.pone.0054998
Appears in Collections:Electrical and Electronic Engineering publications

Files in This Item:
File Description SizeFormat 
hdl_78407.pdfPublished version483.72 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.