Title |
Automated Authorship Attribution Using Advanced Signal Classification Techniques
|
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Published in |
PLOS ONE, February 2013
|
DOI | 10.1371/journal.pone.0054998 |
Pubmed ID | |
Authors |
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/author-detection. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Australia | 2 | 29% |
Brazil | 1 | 14% |
United Kingdom | 1 | 14% |
United States | 1 | 14% |
Unknown | 2 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 57% |
Scientists | 2 | 29% |
Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 2% |
Mexico | 1 | 2% |
United States | 1 | 2% |
Australia | 1 | 2% |
Unknown | 57 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 19 | 31% |
Student > Master | 9 | 15% |
Researcher | 7 | 11% |
Lecturer | 4 | 7% |
Other | 4 | 7% |
Other | 8 | 13% |
Unknown | 10 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 28 | 46% |
Arts and Humanities | 4 | 7% |
Engineering | 3 | 5% |
Linguistics | 2 | 3% |
Agricultural and Biological Sciences | 2 | 3% |
Other | 9 | 15% |
Unknown | 13 | 21% |