# Post-Authorship Attribution Using Regularized Deep Neural Network
## 1 Introduction  
The ubiquity of portable computing devices, coupled with the globalization of the internet and social networking sites, have fundamentally transformed how humans generate and use the information for profitable ventures [1]. Myriad pieces of online text snippets and documents, such as blogs, tweets, and news are continuously generated from varying sources to deliver valuable and entertaining content to people anywhere [1]. The information is frequently posted on social networks every millisecond in the form of ideas, stories, opinions, sentiments, and other forms of expression [2]. Twitter, for instance, is a popularmicroblogging service launched in the United States of America (USA) in 2006 [3] that has experienced significant growth in 2020, probably because of the coronavirus pandemic, with more than 330 million monthly active users at the beginning of 2019, as reported by Wikipedia [4]. Twitter is a famous platform with a huge volume of active users and a high revenue base, and its posts are widely called \"tweets\". A blog is a type of web page that a blogger usually maintains to transform user communication and overcome the unidirectionality of standard online communication [5]. There were approximately 600 million online blogs produced and read by about 77% of users as of July 2021 [6]. The present world is the era of \"infobesity\", where people have free access to post any kind of text snippets to convey their sentiments to the targeted audience [1]. However, posts are distally misused to promote neocuous activities such as pirated software, terrorist communications, child pornography, and grossly belligent, harassing, abusive, obscene, menacing, corrosive, and antediluvian messages [7; 8; 9; 10]. Twitter was recently reported to be increasingly used for misinformation related to the pandemic and promised to start terming misleading tweets [11]. Text is a common material for posting news with a unique anonymity feature [12]. People hide their identities when posting sensitive information [13; 14; 15]. The delinquents usually masked their identities across multiple networks to circumvent the prowess of text-mining methods [16; 17; 18]. In addition, the use of proxy internet addresses, hashtags, mentioning of people in texts, and reposting of texts written by others is a common phenomenon [7; 12; 16]. The address of a genuine sender can be forged through an anonymous server to dispense offensive text messages across diverse pseudonymous channels [7; 12; 16]. The anonymity of social media posts, for example, imposes unique challenges and risks of tracing the identity of an offender without compromising privacy in cyberspaces [18; 19; 20]. The advancement of authorship attribution is supported by stylometric methods. The methods can be applied in a forensic process to identify the original author of a given anonymous post by analyzing the specific writing features of the writer [21; 22; 23]. Post-authorship attribution (PAA) is one method to turn the burden of a vast amount of online text snippets into a piece of useful knowledge. It is the study of unveiling the identity and sociolinguistic characteristics of an underlying author. The quantitative representation of the underlying features can be viewed as a fingerprint to quantify the writing style of an author [24]. The authorship attribution process has multifarious applications in the disciplines of computational linguistics [25], information retrieval [26], data mining [27; 28], natural language processing [21], and personal electronic mail [29; 30; 31]. In addition, it is applicable for detecting plagiarism in an article. This is a scenario where, for instance, an editor of a journal wishes to identify whether a manuscript submitted for publication was written by someone else or has been published elsewhere [32; 33; 34; 35].