Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection

Anastasia Fedotova, Aleksandr Romanov, Anna Kurtukova and Alexander Shelupanov
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Anastasia Fedotova: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
Aleksandr Romanov: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
Anna Kurtukova: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
Alexander Shelupanov: Department of Security, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia

Future Internet, 2021, vol. 14, issue 1, 1-24

Abstract: Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in particular, researches of social networks. The article is a continuation of the series of studies aimed at the identification of the Russian-language text’s author and reducing the required text volume. The focus of the study was aimed at the attribution of textual data created as a product of human online activity. The effectiveness of the models was evaluated on the two Russian-language datasets: literary texts and short comments from users of social networks. Classical machine learning (ML) algorithms, popular neural networks (NN) architectures, and their hybrids, including convolutional neural network (CNN), networks with long short-term memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and fastText, that have not been used in previous studies, were applied to solve the problem. A particular experiment was devoted to the selection of informative features using genetic algorithms (GA) and evaluation of the classifier trained on the optimal feature space. Using fastText or a combination of support vector machine (SVM) with GA reduced the time costs by half in comparison with deep NNs with comparable accuracy. The average accuracy for literary texts was 80.4% using SVM combined with GA, 82.3% using deep NNs, and 82.1% using fastText. For social media comments, results were 66.3%, 73.2%, and 68.1%, respectively.

Keywords: authorship identification; natural language processing; machine learning; deep neural networks; fastText; support vector machine; genetic algorithms (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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