## 2 Related Works  
An excellent overview of articles up to 2010 is presented in [1]. However, since then, methods based on deep neural networks (NN) have become more and more popular, replacing classical methods of machine learning. For example, the topic of author identification is considered annually at the PAN conference [6]. As part of the conference, researchers were offered two datasets of different sizes, containing texts by well-known authors. The authors of [7] emphasize that they have proposed an approach that takes into account only the topic-independent features of a writing style. Guided by this idea, the authors chose several features such as the frequency of punctuation marks, highlighting the last word in a sentence, consideration of all existing categories of functional words, abbreviations and contractions, verb tenses, and adverbs of time and place. An ensemble of classifiers was used in the work. Each of them accepts or rejects the supposed authorship. Research is distinguished by the application of an approach that, in general, is aimed at recognizing a person based on his behavior. Here, Equal Error Rate (EER) has been applied as the thresholding mechanism. Essentially, the EER corresponds to the point on the curve where the false acceptance rate is equal to the false rejection rate. The results are 80% and 78% accuracy for the large and small datasets, respectively. The results of the approach allowed the authors to take third place among all the submitted works. In [8], stylometric features were extracted for each pair of documents. The absolute difference between the feature vectors was used as input data for the classifier. Logistic regression was used for a small dataset, and a NN was used for a large one. These models achieved 86% and 90% accuracy for small and large datasets, respectively. As a result, the authors of the study took second place. The work that achieved the best result in the competition [9] presents the combination of NN with statistical modeling. Research is aimed at studying pseudo metrics that represent a variable-length text in the form of a fixed-size feature vector. To estimate the Bayesian factor in the studied metric space, a probability layer was added. The ADHOMINEM system [10] was designed to transmit the association of selected tokens into a two-level bi-directional long short-term memory (LSTM) network with an attention mechanism. Using additional attention levels made it possible to visualize words and sentences that were marked by the system as \"very significant\". It was also found that using the sliding window method instead of dividing a text into sentences significantly improves results. The proposed method showed excellent overall performance, surpassing all other systems in the PAN 2020 competition on both datasets. The accuracy was 94% for the large dataset and 90% for the small one. The authors of [11] took into account the syntactic structure of a sentence when determining the author of a text, highlighting two components of the self-supervisednetwork: lexical and syntactic sub-network, which took a sequence of words and their corresponding structural labels as input data. The lexical sub-network was used to code a sequence of words in a sentence, while the syntactic subnetwork was used to code selected labels, e.g., parts of speech. The proposed model was trained on the publicly available LAMBADA dataset, which contains 2662 texts of 16 different genres in English. The consideration of the syntactic structure made it possible to eliminate the need for semantic analysis. The resulting accuracy was 92.4%. The work in [12] provides an overview of the methods for establishing authorship with the possibility of their subsequent application in the field of forensic research on social networks. According to the authors, in forensic sciences, there is a significant need for new attribution algorithms that can take context into account when processing multimodal data.