Reproducibility and Robustness of Authorship Identification Approaches


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Year of publication 2023
Type Article in Proceedings
Conference Proceedings of the Seventeenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2023
MU Faculty or unit

Faculty of Informatics

Keywords authorship identification; evaluation; reproducibility
Description Authorship identification, framed as a classification task, assigns a digital text to a known author. State-of-the-art algorithms for this task often lack evaluation across diverse datasets. This paper reimplements and evaluates three approaches on three different datasets, exploring the robustness of algorithms on various text types (e.g., emails, articles, instant messages). Not all the published methods are fully reproducible. However, reasonable parameters were selected if they were not part of the original paper. The evaluation of the ensemble model shows it is somewhat robust on different texts and different numbers of potential authors.
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