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Automated authorship attribution using advanced signal classification techniques

Journal Article


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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.

Authors


  •   Ebrahimpour, Maryam (external author)
  •   Putnins, Talis J. (external author)
  •   Berryman, Matthew J.
  •   Allison, Andrew (external author)
  •   Ng, Brian W-H. (external author)
  •   Abbott, Derek (external author)

Publication Date


  • 2013

Citation


  • Ebrahimpour, M., Putnins, T. J., Berryman, M. J., Allison, A., Ng, B. W-H. & Abbott, D. (2013). Automated authorship attribution using advanced signal classification techniques. PLoS One, 8 (2), e54998-1-e54998-12.

Scopus Eid


  • 2-s2.0-84874249358

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1318&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/313

Start Page


  • e54998-1

End Page


  • e54998-12

Volume


  • 8

Issue


  • 2

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.

Authors


  •   Ebrahimpour, Maryam (external author)
  •   Putnins, Talis J. (external author)
  •   Berryman, Matthew J.
  •   Allison, Andrew (external author)
  •   Ng, Brian W-H. (external author)
  •   Abbott, Derek (external author)

Publication Date


  • 2013

Citation


  • Ebrahimpour, M., Putnins, T. J., Berryman, M. J., Allison, A., Ng, B. W-H. & Abbott, D. (2013). Automated authorship attribution using advanced signal classification techniques. PLoS One, 8 (2), e54998-1-e54998-12.

Scopus Eid


  • 2-s2.0-84874249358

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1318&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/313

Start Page


  • e54998-1

End Page


  • e54998-12

Volume


  • 8

Issue


  • 2