# Digital Authorship Attribution in Russian-Language Fanfiction and Classical Literature
## 7 Discussion and Conclusions
 & **MU** & **SW** \\ \hline
2 + 1 & 86.3 \(\pm\) 1.3 & 89.7 \(\pm\) 2.1 & 88.1 \(\pm\) 3.5 & 90.0 \(\pm\) 3.0 & 87.2 \(\pm\) 3.1 \\
4 + 1 & 80.8 \(\pm\) 2.6 & 78.2 \(\pm\) 6.3 & 74.4 \(\pm\) 4.4 & 81.3 \(\pm\) 4.2 & 80.1 \(\pm\) 2.9 \\
9 + 1 & 72.3 \(\pm\) 3.9 inclusion/exclusion of features turned out to be the least effective for any number of features and classes, especially for 20 and 50 authors, where the loss in accuracy compared to the GA reaches 10%. The features selected using the SHAP and RbFS methods were also considered in the comparison. SHAP can outperform the results obtained using the genetic algorithm for a small number of classes, such as 2, 5, and 10 classes, and no more than 100 features. With a greater number of features, the accuracy is inferior to all considered methods. RbFS is recognized as the main selection method since its combination with SVM made it possible to improve accuracy rates for the maximum of the considered number of classes, i.e., 50 authors, up to 5%. Regarding the more complicated version of closed attribution, the introduction of artificial texts generated using RuGPT-2 and RuGPT-3 based on the original corpus of classics, the maximum accuracy loss for fastText was 9%, and for SVM it was 12%. The greatest loss of accuracy was recorded during experiments for 50 and 20 classes, while for 2 and 5 classes, despite the complication of the task, the losses did not exceed 3%. The average classification accuracy of the original samples was higher in the case of fastText, but the feature selection SVM outperforms fastText in the case of testing the method using the injection of generated samples, which indicates the effectiveness of the feature space. In the case of open attribution, when testing the model, authors who did not participate in the training were added to the test sample. Thus, in addition to classifying the initially specified authors, we included an additional task: to determine added anonymous texts. Also, the limitation was imposed by the specifics of the data: fanfiction is the result of a fanfiction writer's work. Such texts are based on the original plot, which is the reason why such texts contain features common to all texts of the category including the special names, words, phrases, and expressions inherent in the original characters. When developing the methods, the importance of the formed feature space containing the frequency distributions was taken into account, as well as the undoubted advantages of NNs that have proven themselves in text classification tasks. Thus, the method based on the joint use of One-Class SVM and fastText combines the above features. Another method based on the probability threshold of the Softmax function for fastText shows comparable results in classification for pairs of thematic categories but does not perform as well as the first method when working with complex cases. For example, One-Class SVM + fastText accuracy reaches 80% for 20 classes and 51% for 50 classes even in the complex case, while the results of the second method do not exceed 75% and 46% for the same cases. In addition, compared to statistical methods based on Euclidean distance and cosine similarity, One-Class SVM + fastText can improve the results by up to 15%. At the same time, the assessment of the One-Class SVM + fastText method in the case of classification within the thematic group allows us to declare independence from the specific subject of amateur texts, since the results vary by no more than 8% from group to group with the same number of classes. Thus, One-Class SVM + fastText was chosen as the recommended method for open attribution. When determining the authorship of amateur texts, the accuracy within categories reaches 98%, and for pairwise comparison of various thematic categories, the accuracy reaches 99%. It is necessary to highlight informative features when forming the author's style of writing, where the author is considered both a professional writer and a fanfiction writer when writing a text.