Language Resources and Evaluation

, Volume 45, Issue 1, pp 83–94 | Cite as

Authorship attribution in the wild

  • Moshe KoppelEmail author
  • Jonathan Schler
  • Shlomo Argamon


Most previous work on authorship attribution has focused on the case in which we need to attribute an anonymous document to one of a small set of candidate authors. In this paper, we consider authorship attribution as found in the wild: the set of known candidates is extremely large (possibly many thousands) and might not even include the actual author. Moreover, the known texts and the anonymous texts might be of limited length. We show that even in these difficult cases, we can use similarity-based methods along with multiple randomized feature sets to achieve high precision. Moreover, we show the precise relationship between attribution precision and four parameters: the size of the candidate set, the quantity of known-text by the candidates, the length of the anonymous text and a certain robustness score associated with a attribution.


Authorship attribution Open candidate set Randomized feature set 


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Bar-Ilan UniversityRamat-GanIsrael
  2. 2.Illinois Institute of TechnologyChicagoUSA

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