# Digital Authorship Attribution in Russian-Language Fanfiction and Classical Literature
## 2 Related Works
### Related Works Aimed at Authorship Attribution of a Russian-Language Text
To improve the quality of comparison of texts in natural language, as well as to reduce the dimension of the vector representation of texts, they are preprocessed, which includes the removal of stop-words and stemming. The similarity was calculated using the word2 vec tool. A module was added to the system for clustering articles that were not recognized at the stage of comparison with publications from eLIBRARY, which made it possible to improve the result of identifying the authorship of articles up to 92%. The approaches used in this system are applicable for disambiguating the authorship of publications from various bibliographic databases. Forensic experts note the need to set the parameters for establishing the author's style for the attribution of Internet communication products [25]. In correspondence, users actively use non-verbal communication (stickers, emoticons, etc. ), explaining or introducing a completely different meaning of the text. Therefore, the semantics of the message depends on the graphic level, while the importance of spelling and punctuation features for identifying authorship is reduced. The reason is the simplification of interaction in the digital space-even authors with a high level of spelling and punctuation skills do not always follow the rules of spelling and punctuation. Criminologists also note the importance of such features as deliberate errors. The main point stated in the work of [PERSON] aimed at the modification of the key features of the text towards semantic ones. This will allow not only to distinguish between different authors by quantitative components (frequency distributions and aggregated statistical values) but also to determine the main intentions of the creator of the text, which will make it possible to optimize the production of forensic author's examinations. There can be a large number of parameters describing the author's style: preferred words, local speech features, sentence length, use of turns of speech, and vocabulary. However, changing these parameters leads to a change in the frequency characteristics of the text, i.e., these changes affect the frequency of occurrence of the characters with which the text is written. The study of character frequencies separately allows for solving many problems related to text attribution, but the question arises of determining the set of the most informative frequencies, as well as eliminating redundant ones. There are at least three reasons for the negative impact of a large number of non-informative features. First, with an increase in the number of features, the statistical reliability of the algorithm decreases. Second, the more features, the more learning objects are needed for reliable classification. Third, the more features, the longer the algorithm runs. Feature selection is applied to find an optimal set of features and decrease the number of uninformative features and dimensionality while, at the same time, increasing the accuracy of the classification. The selected set of features reduces the computational cost, in particular for time and memory, but at the same time, the efficiency of the algorithm will increase. Feature selection can be divided into three steps:  
1. Generation of a subset of features;
2. Estimation of the generated subset;
3. If the generated subset is relevant or a break condition has occurred, then the process is terminated. Otherwise, return to step 1. In the estimation step, the following methods are used:  
1. Filter [26]. This method is based on identifying the generalizing properties of data and is not focused on specific algorithms and statistics. They are fast and less computationally expensive, so these methods are more often applied to datasets with a large number of features. However, there is a significant disadvantage of these methods: it considers each feature in isolation, not taking into account their mutual influence on each other.