## 2 Related Works
For each tweet were calculated the values of the following: unigram frequencies, average word length, sentence length, letter case, and punctuation frequencies. Then 16 features were randomly selected and used for classification using SVM. The accuracy of classification was calculated individually for each author. The average result for the five authors was 54%, the maximum was 75%, and the minimum--30%. The authors of [19] conducted research of language-independent open set authorship verification in several representative Indo-European languages. A specialty of this work was a selection of one set of features for four different languages: 513 English texts, 280 Greek texts, 400 Spanish texts, and 158 Dutch texts. Text examples of Greek, Spanish, and Dutch were taken from the PAN2015 corpus, for English--from the PAN2014. A 90% accuracy was achieved using the classical ML methods: KNN, and SVM-SMO with the feature selection method SVM-RFE. A final set consisted of 26 stylometric features. Performances of all classification models were measured by 10-fold cross-validation using standard ML metrics: accuracy, precision, recall, and AUC-ROC. The results were improved from 90% to 94% by the MultiboostAB ensemble method. In [20] identification of the author was provided using CNN. As a part of the study, the authors developed their own dataset. The dataset included 400 scientific publications written by 20 authors in the field of ML. The model proposed by the authors, at the top level, included a multi-layer CNN, which either calculated the probability distribution for the entire text (authorship verification task), or the average probability distribution for individual sentences (multiple candidate authors). At the lower level, texts were reduced to a length of 128 characters. Then Word2 Vec and Glove were used. The authors' model was compared with SVM, KNN, and Multilayer Perceptron. For these models, \(n\)-grams with \(n\) ranging from 1 to 5 words and from 1 to 8 characters were used. The results showed that the multi-label CNN (word2 vec+glove) achieved the highest accuracy of 65.3%. SVM(with parameters sgd, squared hinge loss, l2, binary 1-g)--45.3%, MLP (doc2 vec)--40%, KNN (3-g)--52%. The topic of authorship identification is one of the most popular tasks at the PAN conference [21]. In 2021, the researchers were offered two datasets of different sizes, containing texts of authentically known authors. All texts were obtained from fanfiction.net. A special feature of PAN-2021 was open attribution. The datasets consisted of pairs of fragments from two different fanfics. Each pair was assigned a unique identifier, distinguishing between pairs with the same author and pairs with different authors. Participants were also provided information about the random (i.e., thematic category) for each text in the pair. The best result for the larger dataset was obtained by team [PERSON] [22], with an accuracy of 95.45%. For the smaller dataset, team [PERSON] [23] achieved an accuracy of 92.8%. The boenninghoff21 team proposed a hybrid neural-probabilistic framework. The authors finalized their own system presented at PAN-2020 for the 2021 competition. The updates were aimed to reduce sensitivities to topical variations and to improve the system calibration using the uncertainty adaptation layer. In addition, a significant addition to the system was the introduction of an out-of-distribution detector (O2D2) for defining non-responses. The [PERSON] team used POS tagging and POS tag chunking to prepare the text for feature extraction. TF-IDF values for character \(n\)-grams, POS-Tag trigrams, 31 special characters, frequencies of 851 common English words, the average number of characters per token, and distribution of word-lengths were used as text features. A Stochastic Gradient Descent training algorithm was used to store the complete feature matrix in memory. LR was used as a classifier. Most modern works on authorship attribution are devoted to an analysis of English texts.