# Digital Authorship Attribution in Russian-Language Fanfiction and Classical Literature
## 2 Related Works
### Related Works Aimed at Author Profiling  
Author profiling is understood as the establishment of the personality of the writer using the characteristics of the author. Among these characteristics, gender, age, education, profession, and personal qualities are distinguished. Gender and age are considered primary features because their concretization will allow the establishment of the remaining features (secondary) and reduce the set of candidates when determining the author. The feature set forms a unique linguistic personality. This concept is widely used today in forensic examinations [28], classification, psychological and marketing research, sociolinguistics [29], and diagnosing a person by text, as well as in other applied areas. As part of the world-famous PAN conference, participants were offered tasks both for profiling the author [30] and for determining gender and age separately [31]. The study [32] is devoted to gender profiling the author of a substandard text. By a substandard text, the authors considered a limited scope of use, for example, professional texts. The study was divided into three stages: feature selection; reducing the set of features in order to obtain only those suitable for determining a gender; and selection of features applicable to substandard texts. In the first stage, various groups of features were selected, including frequency distributions of characters, punctuation marks, parts of speech, numbers, average values of word lengths, and sentences. To determine the gender of the author, the final feature set included: the average values of word lengths, indexes of lexical diversity, the particle composition of the texts under study, and the type of sentences. Frequency distributions and lexical diversity indices are particularly applicable to substandard texts. Despite the many conclusions and the specific listing of the selected features, the authors did not provide information about the dataset, classification methods, or results of the study. The article [33] is devoted to the methods used to analyze Russian-language fiction from the end of the XX century (1960-2000), namely, the determination of the gender of the authors of these materials. To solve the problem, it is necessary to determine the features of the text that the classification methods can use to distinguish between female and male texts. A list of frequently used punctuation marks and parts of speech was compiled for the analysis. To solve the problem of classification, a dataset of 87 works by 9 authors was collected. These authors were chosen based on differences in style and subject matter of the texts. Regarding the determination of the gender of the author, it was found that the frequency of using different parts of speech in the female and male texts of the given period were as follows: nouns, verbs, prepositions, pronouns, conjunctions, and adjectives, which reflects the specificity of the artistic style. The authors concluded that, in the literature of the 20 th-century, women more actively used punctuation as an expressive means: the proportion of the use of exclamation marks, question marks, and commas by women writers significantly exceeded the value obtained through the analysis of men's texts. Four classifiers were used in the experiment to determine the gender of the author of the text: SVM naive Bayesian classifier, random forest, and \(k\)-nearest neighbor method. The classification results were as follows: SVM (75% male and 68% female), Bayesian classifier (65% male and 55% female), \(k\)-nearest neighbor method (75% male texts and 55% female), and random forest (70% male and 64% female). Determining an author's gender using Twitter posts was discussed in [34]. The data included 4126 Twitter users and 6500 entries. When selecting features, the authors focused on semantic ones, and they extracted semantic categories from the tweets. The semantic categories were combined with tags of parts of speech, emoticons, and mentions of other users.