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Authorship attribution in the wild.

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Title: Authorship attribution in the wild.
Authors: Koppel, Moshe1 moishk@gmail.com, Schler, Jonathan1 schler@gmail.com, Argamon, Shlomo2 argamon@iit.edu
Source: Language Resources & Evaluation. Feb2011, Vol. 45 Issue 1, p83-94. 12p. 2 Charts, 5 Graphs.
Subjects: Attribution of authorship, Authors, Polyglot texts, selections, quotations, etc., Resemblance (Philosophy), Anonyms & pseudonyms, Language & languages
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Language Resources & Evaluation is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Authorship attribution in the wild.
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  Data: <searchLink fieldCode="AR" term="%22Koppel%2C+Moshe%22">Koppel, Moshe</searchLink><relatesTo>1</relatesTo><i> moishk@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Schler%2C+Jonathan%22">Schler, Jonathan</searchLink><relatesTo>1</relatesTo><i> schler@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Argamon%2C+Shlomo%22">Argamon, Shlomo</searchLink><relatesTo>2</relatesTo><i> argamon@iit.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Language+Resources+%26+Evaluation%22">Language Resources & Evaluation</searchLink>. Feb2011, Vol. 45 Issue 1, p83-94. 12p. 2 Charts, 5 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Attribution+of+authorship%22">Attribution of authorship</searchLink><br /><searchLink fieldCode="DE" term="%22Authors%22">Authors</searchLink><br /><searchLink fieldCode="DE" term="%22Polyglot+texts%2C+selections%2C+quotations%2C+etc%2E%22">Polyglot texts, selections, quotations, etc.</searchLink><br /><searchLink fieldCode="DE" term="%22Resemblance+%28Philosophy%29%22">Resemblance (Philosophy)</searchLink><br /><searchLink fieldCode="DE" term="%22Anonyms+%26+pseudonyms%22">Anonyms & pseudonyms</searchLink><br /><searchLink fieldCode="DE" term="%22Language+%26+languages%22">Language & languages</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Language Resources & Evaluation is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Authors
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      – SubjectFull: Polyglot texts, selections, quotations, etc.
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              Text: Feb2011
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