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.
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Notes
For purposes of clarity, we note the following: Recall, as discussed in Sect. 6, is simply H*P, the product of coverage and precision. Furthermore, results shown in Sect. 6 refer to those at score σ or above, while results shown in this section refer to those at a given score or in a given score interval.
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Koppel, M., Schler, J. & Argamon, S. Authorship attribution in the wild. Lang Resources & Evaluation 45, 83–94 (2011). https://doi.org/10.1007/s10579-009-9111-2
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DOI: https://doi.org/10.1007/s10579-009-9111-2