## 2 Related Works
The authors of [11] proposed two algorithms: General Impostors (GI) and Ranking-Based Impostors (RBI). The study used all datasets released in PAN-2014 and PAN-2015 shared tasks. As a result, the dataset contained cross-topic (similar and mixed), cross-genre (essay, reviews, novels, articles) and cross-language (Dutch, English, German, Greek, Spanish languages) texts (1661 texts in total). To extract features, the authors used clustering based on the expectation-maximization algorithm [12]. The aggregation function used in GI and RBI was selected for each dataset. In almost all cases RBI was more effective than GI. AUC scores for PBI were 70.9-97.5%, for GI were 66.1-78.5%. It is also noted that RBI reached its maximum performance at 2700 words (45% of the average length), while BI performed best with a maximum length (about 6000 words). RBI demonstrated better results than BI for relatively short texts, while PBI was better for long texts. The Authorship Verification task was solved by the authors of [13] using an improved method based on implicitly defined features. The essence of the method lied in the fact that any sequence of characters in the text could be a potential feature of an author's style. The disadvantage was a strong dependence on a topic of a text, which lead to a low-quality classification. To solve this problem, the authors used the POSNoise (POS-Tag-based Noise smoothing) preprocessing method, which effectively masks thematic content in the given text. Data for research consisted of 6320 texts written by 3097 authors from seven well-known datasets: Gutenberg Corpus, ACL Anthology Corpus, Perverted Justice Corpus,The Telegraph Corpus, The Apricity Corpus, and Reddit Corpus. Empirical estimation of the TextDistortion method [14] showed that integration with POSNoise led to better results in 34 of 42 cases with an increase in accuracy to 10%. In [15] the authors proposed the supervised machine learning framework incorporating stylometric features--an approach based on the study of changes in writing styles between 50 authors. The accuracy improves significantly due to the common use of certain linguistic stylometric features with text. The final corpus consisted of 50 authors, each having 100 texts/news from the RCV1 (Reuters Corpus Volume 1) dataset. Accuracy of 81.6% and Kappa statistic of 0.81 were achieved on the holdout test set using LibLINEAR SVM. Based on obtaining results, the most effective combination of features was: clubbing stylometric meta-features with textual features such as bigrams, part of speech (POS) bigrams, and word/POS pairs. The authors of the article [16] applied authorship identification to prevent the spread of malicious messages on social media. The proposed model for extracting informative features used XGBoost as a preprocessor. XGBoost ranked texts using Multi-Criteria Decision-Making methods to build a classification model. Dataset consisted of 16,124 tweets of 280 characters from 20 Twitter users. The authors noted that data from any social network could be used instead of Twitter. According to the proposed method, _F_-measure was 94.3%. The dataset for the study [17] consisted of 500 tweets for each of the 34 authors who met certain criteria. Raw data were collected using Nvivo software. The collected data were preprocessed to extract frequencies of 200 features. Jordan Recurrent Neural Network was used in this work. For \(N\) authors, an \(N\times N\) network was trained for pairwise classification. These \(N\times N\) experts were then organized into \(N\) special groups to combine the decisions of the \(N\times N\) experts. Finally, it was observed that a large number of authors did not lead to a significant decrease of accuracy, and for any number of authors from 2 to 34, an accuracy of 66 to 88% was achieved. SVM was used to determine authorship in [18]. Only the latest and popular tweets without advertisements from personal accounts were used. Finally, 20 tweets for each of the five authors were selected for the experiments.