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Authorship attribution using author profiling classifiers

Published online by Cambridge University Press:  19 January 2022

Caio Deutsch Open the ORCID record for Caio Deutsch [Opens in a new window]  and
Ivandré Paraboni Open the ORCID record for Ivandré Paraboni [Opens in a new window]
Caio Deutsch
Affiliation:
School of Arts, Sciences and Humanities, University of São Paulo, Av. Arlindo Bettio 1000, São Paulo, Brazil
Ivandré Paraboni*
Affiliation:
School of Arts, Sciences and Humanities, University of São Paulo, Av. Arlindo Bettio 1000, São Paulo, Brazil
*
*Corresponding author. E-mail: ivandre@usp.br
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Abstract

Authorship attribution – the computational task of identifying the author of a given text document within a set of possible candidates – has been attracting interest in Natural Language Processing research for many years. At the same time, significant advances have also been observed in the related field of author profiling, that is, the computational task of learning author demographics from text such as gender, age and others. The close relation between the two topics – both of which focused on gaining knowledge about the individual who wrote a piece of text – suggests that research in these fields may benefit from each other. To illustrate this, this work addresses the issue of author identification with the aid of author profiling methods, adding demographics predictions to an authorship attribution architecture that may be particularly suitable to extensions of this kind, namely, a stack of classifiers devoted to different aspects of the input text (words, characters and text distortion patterns.) The enriched model is evaluated across a range of text domains, languages and author profiling estimators, and its results are shown to compare favourably to those obtained by a standard authorship attribution method that does not have access to author demographics predictions.

Keywords

Authorship attributionAuthor profilingText classification
Type
Article
Information
Natural Language Engineering , Volume 29 , Issue 1 , January 2023 , pp. 110 - 137
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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