# Post-Authorship Attribution Using Regularized Deep Neural Network
## 2 Related Works  
Research on the authorship-attribution process has generally been exhilarating for a long time. Manifold studies have presented different methods of developing automated systems for identifying the genuine author of a text. In most cases, authorship attribution can be effectively accomplished in two stages. The first stage is related to the extraction of stylometric features from the original post to characterize the unique writing style of an author [21]. Stylometric features can be compactly organized into three main categories of lexical features, syntactic features, and structural features [7, 21, 22, 23, 24]. Lexical features are statistical measures of text characters and word-based lexical text variations such as vocabulary richness and word length distribution [21]. Syntactic features capture the universal part of speech (POS) tags from the text structure [42]. Structural features are associated with the organization of texts, such as the average number of words in a sentence, and the use of indentations or paragraphs [43]. The second stage of the authorship-attribution process is allied with the training of machine-learning methods to learn the extracted stylistic features for finding the boundaries between the predicted author, and an anonymous writer through the minimization of a loss function [23]. The connotation of feature extraction is paramount to the task of authorship attribution. Past studies have found that character-level n-grams are among the most compelling features for effectively identifying authors [32, 44, 45, 46, 47]. The authors in [44, 45] used the common n-gram model to capture a representation of the writing style of a writer who translated text between the English language and Greek language. The byte-level n-gram profile was applied in [46] to identify the author of a source code. The writing style of an author was captured in [47] using a set of local histograms (LH) over character n-grams. The researchers achieved an accuracy of 86.4% from Reuters corpus volume 1 (RCV1) [48] comprising English journalism documents authored by 10 and 50 writers, refer to as CCAT10 and CCAT50, respectively. The researchers in [49] used 10,000 blogs for the task of authorship detection, in which 500-word snippets, one for each author, were considered as test examples. The 20% to 34% of the texts were classified with an average accuracy of 80% using the linear support-vector machine (SVM) while the remaining texts were considered unknown. In a separate study using the same dataset, the authors in [50] used character n-grams to effectively capture the stylistic and morphological information of the authors. They employed the similarity-based method to attribute 500-word snippets to 1000 authors to achieve a high precision rate of 93.2%. The survey of a variety of feature types and categorization methods proposed in the past for authorship attribution based on literature corpus, email and blog corpora was reported in [22]. The SVM and Bayesian regression methods were experimented to achieve 80% on the 1000 most frequent words and to 86% on 1000-character n-grams with the highest information gain. The advantage of using character-level n-gram features was attributed to the high priority of sub-word features for authorship attribution [51]. The authors in [52] used tensors of second order to represent the stylistic features of texts with 2500 most frequent 3-g. The importance of punctuation marks for cross-domain authorship attribution of the PAN 2019 competition that comprised the identification of the authors of fan fiction was explicated in [53]. The application of an n-gram model with everything except for punctuation marks masked by an asterisk symbol was shown in [54]. The authors in [55] claimed that digits and named entities are important identifiers of the writing styles of authors. The application of disjoint author-document topic (DADT) [56] over the author topic (AT) [57] and latent Dirichlet allocation (LDA) [58] was used to extract domain-indicative lexical items from a text.