# Post-Authorship Attribution Using Regularized Deep Neural Network
## 2 Related Works
The work achieved a similar purpose because text representations generated from n-gram features tend to be of higher dimension, and sparse [59]. However, measuring the choice of the vocabulary of an author is insufficient to adequately capture the extreme intertextual and intertextual variation in style-related tasks [60]. There are different subtasks of authorship attribution that heavily depend on the nature of an input text. They include text-authorship attribution (TAA), which generally involves a text document comprising several words [21; 22]. This subtask is commonly found in digital publications and data-mining applications [23]. The subtask of code-authorship attribution (CAA) is to identify the real author of a given computer-programming code. The application of CAA includes software forensics [61], detection of malicious code [61; 62], detection of code plagiarism [63; 64; 65], and learning software ownership [66]. The subtask of post-authorship attribution (PAA) generally involves short snippets with about 500 words that are commonly found on social networks and the internet [26]. Most of the past studies on authorship attribution have generally focused on long texts. However, because of the proliferation of web applications, widespread acceptance of social media networks, and advancement in internet technology, an increasing volume of studies are focused on developing models for improving the interpretability of online messages [18; 67]. The authors in [68] used the methods of common n-grams (CNG) [69], source code author profile (SCAP) [44; 45; 46], and recentered local profiles (RLP) [70] to weight the profiles of authors on a collection of character-level \(n\)-gram features. They achieved an accuracy of 54.93% with the character \(n\)-gram length, \(n\) using the RLP on an Internet relay chat (IRC) dataset. The character and word n-grams were used in [71] as a K-signature to interpret the unique writing style that appears in the least K% tweets posted by an author. They achieved an accuracy of 50.7% on a dataset containing 50 authors with 50 training tweets per author. Moreover, an accuracy of 71.2% was achieved with 10,000 training tweets per author. The accuracy of 20.25% was reported in [72] using the naive Bayes (NB) method on 2000 messages taken from an SMS corpus with 81 authors and a maximum of 50 messages per author. The authors in [73] achieved an accuracy of 53.2% in identifying an author from 10,000 scale microblog users using character n-gram frequency with cosine similarity to discover the most relevant stylometric features. The authors in [74] used lexical, syntactic, tweet-specific, and other useful features to extract stylometric information from a given tweet using a natural language toolkit (NLTK). The features corresponding to an anonymous author were identified with the help of the SVM method. The researchers achieved an accuracy of 64.54% with 300 most recent tweets of 20 authors. The researchers in [19] presented an exhaustive bibliography survey of the state-of-the-art (SOTA) techniques for PAA tasks. They employed shallow classifiers such as support vector machine (SVM) and random forest (RF) allied with a bag-of-words (BOW) feature representation. They explored the performance dependency of their proposed method with multiple authors and messages. However, their results are still far from satisfactory, despite advancing the SOTA for PAA tasks. The authors presented a classification accuracy of less than 65% in a new experimental dataset of 50 authors. This number plummets to less than 45% in the context of 1000 authors, showing that PAA is still an open research endeavor that requires further improvement. The authorship attribution has lately been built on deep neural networks by researchers [60; 75; 76; 77]. The sequence of words or a string of characters is mainly used as input to the neural networks. Most methods focus on lexical features, even though lexical-based language models are not scalable when dealing with authorship containing diverse topics [78; 79].