## 2 Related Works
### Related Works on Identification the Author of a Russian-Language Text  
However, not all methods showing excellent results for English and other languages can demonstrate high accuracy for Russian-language texts due to some peculiarities of the Russian: part-of-speech ambiguity, a large number of idioms and speech turns, a variety of synonyms, the flectivness, and complex morphology. These and other language constructions should be considered by a researcher analyzing the Russian-language text.  
[PERSON] et al. [30] evaluated the effectiveness of using different types of character \(n\)-grams in the authorship identification task. Russian-language messages from the KavkazChat forum were used as the dataset. KavkazChat is a Russian-language forum with a focus on jihad in the North Caucasus. KavkazChat contains 699,981 posts written by 7125 members in 2003-2012. Neutral and extremism topics, written by 10 users, communicating on these topics, were selected for the study. The final dataset consisted of 32-374 messages per user. As features were calculated frequency distribution for affix, word, and punctuation trigrams. The classification was carried out using LinearSVC and 10-fold cross-validation. The results showed that character \(n\)-grams and affix \(n\)-grams are especially successful in attribution. The best results were obtained using all of the mentioned features: 41.9% for both topics, 48.8% for neutral topics, and 46.2% for extremist topics.  
The study [31] was devoted to authorship identification on the database of 30 authors of 1506 Russian-language texts written in the XVIII-XXI centuries. To solve the problem several approaches were applied: RF, LR, SVM. The authors examined texts at three linguistic levels: lexical, morphological, and syntactic. The best model was doc2 vec with LR--98% accuracy. Accuracy using only syntax features and LR was 89%. The simple morphology model was, on average, 10% less accurate than the model based on syntax features. The same trend was observed using morphology--syntax was 14% better on average.  
The authors of [32] proposed their own specially designed datasets--RusIdioStyle. The peculiarity of the dataset was using mostly short texts on different topics written by 125 authors. For authorship identification, a scheme of pairwise classification was used in the case that this method is close to the real forensic examination. In cross-topic tasks, semantic coherence features were introduced to supplement well-established \(n\)-gram features. Distance-based measures were compared with ML algorithms (SVM, KNN). The authors identified 17 out of 300 features as the most significant. Among them were frequency distributions of six punctuation marks, five words, four symbols, and two parts of speech. The results of the experiment confirmed the authors' hypothesis that for short texts distance-based measures work better than ML methods. Moreover, the results of the pairwise classification show that in complex cross-thematic scenarios the best results were obtained for features that did not depend on the topic of the text. In the pairwise comparison, the obtained accuracy was from 60.1% to 87.5%.  
The Russian language has many unique characteristics that distinguish Russian from others: specific accents, complex word formation, borrowings from ancient Slavic languages. As a result, it is incorrect to use the set of informative features which provide high accuracy for other languages. In addition to the language, the size in characters and the theme of the text impose some limitations on the method.