Same or different? Diff-vectors for authorship analysis

Corbara S., Moreo A., Sebastiani F.

Authorship analysis 

In this paper we investigate the efects on authorship identiication tasks (including authorship veriication, closed-set authorship attribution, and closed-set and open-set same-author veriication) of a fundamental shift in how to conceive the vectorial representations of documents that are given as input to a supervised learner. In ?classic? authorship analysis a feature vector represents a document, the value of a feature represents (an increasing function of) the relative frequency of the feature in the document, and the class label represents the author of the document. We instead investigate the situation in which a feature vector represents an unordered pair of documents, the value of a feature represents the absolute diference in the relative frequencies (or increasing functions thereof) of the feature in the two documents, and the class label indicates whether the two documents are from the same author or not. This latter (learner-independent) type of representation has been occasionally used before, but has never been studied systematically. We argue that it is advantageous, and that in some cases (e.g., authorship veriication) it provides a much larger quantity of information to the training process than the standard representation. The experiments that we carry out on several publicly available datasets (among which one that we here make available for the irst time) show that feature vectors representing pairs of documents (that we here call Dif-Vectors) bring about systematic improvements in the efectiveness of authorship identiication tasks, and especially so when training data are scarce (as it is often the case in real-life authorship identiication scenarios). Our experiments tackle same-author veriication, authorship veriication, and closed-set authorship attribution; while DVs are naturally geared for solving the 1st, we also provide two novel methods for solving the 2nd and 3rd that use a solver for the 1st as a building block. The code to reproduce our experiments is open-source and available online.

Source: ACM transactions on knowledge discovery from data (Online) (2023). doi:10.1145/3609226

Publisher: Association for Computing Machinery, New York, NY , Stati Uniti d'America

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