Open Set Authorship Attribution Toward Demystifying Victorian Periodicals

Badirli, Sarkhan; Ton, Mary Borgo; Gungor, Abdulmecit; Dündar, Murat

doi:10.1007/978-3-030-86337-1_15

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…This, however, is not the case for open-set attribution, as limited research has been conducted thereon. Badirli, Ton, Gungor, and Dundar (2019) discussed the limitations of using standard machine learning approaches for such attribution tasks. Their experiments suggest that linear classifiers can achieve near-perfect attribution accuracy under closed-set assumptions; however, a more robust approach is required once a large list of potential authors is considered.…”

Section: Authorship Attributionmentioning

confidence: 99%

Cross-linguistic Authorship Attribution and Author Profiling. Is Machine Translation the Solution?

Mikros

Boumparis

2022

Preprint

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This paper aims to explore whether cross-linguistic authorship attribution and author's gender identification are feasible using Machine Translation (MT) as a method to bridge the language gap. We designed a series of computational stylistics experiments to explore whether the stylometric signal survives through the MT process. We compiled an extensive blog corpus in Greek containing 100 authors, balanced in gender with 50 texts from each author. Then, we used Google's Neural Machine Translation to automatically translate each text into English. We ran several classification experiments using the Random Forest algorithm in authorship attribution and gender profiling tasks employing different feature groups in both the source language (Greek) and the machine-translated (English) corpora. Moreover, we trained models in the source language and used the texts in the target language as the unseen test set, to simulate a cross-linguistic prediction. The results showed that cross-linguistic gender identification could be achieved reliably using n-gram features. Authorship attribution, however, was not feasible in our cross-linguistic experimental setting, although features of lexical diversity exhibited promising results. Finally, when performed using training and testing set from the same language, both authorship attribution and gender identification exhibit similar accuracy across the full range of the feature groups used.

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Section: Authorship Attributionmentioning

confidence: 99%

Cross-linguistic Authorship Attribution and Author Profiling. Is Machine Translation the Solution?

Mikros

Boumparis

2022

Preprint

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“…Nevertheless, limited research has been conducted on open‐set attribution. Badirli et al (2019) discussed the limitations of using standard machine learning techniques for open‐set authorship attribution problems. Their experiments suggest that linear classifiers can achieve near‐perfect attribution accuracy under closed‐set assumptions; however, a more robust approach is required once a large candidate pool is considered as in open‐set classification.…”

Section: Introductionmentioning

confidence: 99%

A review on authorship attribution in text mining

Zheng

Jin

2022

WIREs Computational Stats

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The issue of authorship attribution has long been considered and continues to be a popular topic. Because of advances in digital computers, this field has experienced rapid developments in the last decade. In this article, a survey of recent advances in authorship attribution in text mining is presented. This survey focuses on authorship attribution methods that are statistically or computationally supported as opposed to traditional literary approaches. The main aspects covered include the changes in research topics over time, basic feature metrics, machine learning techniques, and the advantages and disadvantages of each approach. Moreover, the corpus size, number of candidates, data imbalance, and result description, all of which pose challenges in authorship attribution, are discussed to inform future work.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining

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