Authorship Attribution

Download or read book Authorship Attribution written by Patrick Juola and published by Now Publishers Inc. This book was released on 2008 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Authorship Attribution surveys the history and present state of the discipline, presenting some comparative results where available. It also provides a theoretical and empirically-tested basis for further work. Many modern techniques are described and evaluated, along with some insights for application for novices and experts alike.

Download or read book Versification and Authorship Attribution written by Petr Plecháč and published by Charles University in Prague, Karolinum Press. This book was released on 2021-07-01 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: The technique known as contemporary stylometry uses different methods, including machine learning, to discover a poem’s author based on features like the frequencies of words and character n-grams. However, there is one potential textual fingerprint stylometry tends to ignore: versification, or the very making of language into verse. Using poetic texts in three different languages (Czech, German, and Spanish), Petr Plecháč asks whether versification features like rhythm patterns and types of rhyme can help determine authorship. He then tests its findings on two unsolved literary mysteries. In the first, Plecháč distinguishes the parts of the Elizabethan verse play The Two Noble Kinsmen written by William Shakespeare from those written by his coauthor, John Fletcher. In the second, he seeks to solve a case of suspected forgery: how authentic was a group of poems first published as the work of the nineteenth-century Russian author Gavriil Stepanovich Batenkov? This book of poetic investigation should appeal to literary sleuths the world over.

Download or read book Machine Learning Methods for Stylometry written by Jacques Savoy and published by Springer Nature. This book was released on 2020-09-28 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents methods and approaches used to identify the true author of a doubtful document or text excerpt. It provides a broad introduction to all text categorization problems (like authorship attribution, psychological traits of the author, detecting fake news, etc.) grounded in stylistic features. Specifically, machine learning models as valuable tools for verifying hypotheses or revealing significant patterns hidden in datasets are presented in detail. Stylometry is a multi-disciplinary field combining linguistics with both statistics and computer science. The content is divided into three parts. The first, which consists of the first three chapters, offers a general introduction to stylometry, its potential applications and limitations. Further, it introduces the ongoing example used to illustrate the concepts discussed throughout the remainder of the book. The four chapters of the second part are more devoted to computer science with a focus on machine learning models. Their main aim is to explain machine learning models for solving stylometric problems. Several general strategies used to identify, extract, select, and represent stylistic markers are explained. As deep learning represents an active field of research, information on neural network models and word embeddings applied to stylometry is provided, as well as a general introduction to the deep learning approach to solving stylometric questions. In turn, the third part illustrates the application of the previously discussed approaches in real cases: an authorship attribution problem, seeking to discover the secret hand behind the nom de plume Elena Ferrante, an Italian writer known worldwide for her My Brilliant Friend’s saga; author profiling in order to identify whether a set of tweets were generated by a bot or a human being and in this second case, whether it is a man or a woman; and an exploration of stylistic variations over time using US political speeches covering a period of ca. 230 years. A solutions-based approach is adopted throughout the book, and explanations are supported by examples written in R. To complement the main content and discussions on stylometric models and techniques, examples and datasets are freely available at the author’s Github website.

Download or read book Social Network Analytics for Contemporary Business Organizations written by Bansal, Himani and published by IGI Global. This book was released on 2018-03-23 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social technology is quickly becoming a vital tool in our personal, educational, and professional lives. Its use must be further examined in order to determine the role of social media technology in organizational settings to promote business development and growth. Social Network Analytics for Contemporary Business Organizations is a critical scholarly resource that analyzes the application of social media in business applications. Featuring coverage on a broad range of topics, such as business management, dynamic networks, and online interaction, this book is geared towards professionals, researchers, academics, students, managers, and practitioners actively involved in the business industry.

Download or read book Scalability Issues in Authorship Attribution written by Kim Luyckx and published by ASP / VUBPRESS / UPA. This book was released on 2011-08 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an in-depth and systematic study of the so-called scalability issues in authorship attribution -- the task that aims to identify the author of a text, given a model of authorial style based on texts of known authorship. Computational authorship attribution does not rely on in-depth reading, but rather automates the process. This book investigates the behavior of a text categorization approach to the task when confronted with scalability issues. By addressing the issues of experimental design, data size, and author set size, the dissertation demonstrates whether the approach taken is valid in experiments with limited or sufficient data, and with small or large sets of authors.

Download or read book Machine Learning for Authorship Attribution and Cyber Forensics written by Farkhund Iqbal and published by Springer Nature. This book was released on 2020-12-04 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book first explores the cybersecurity’s landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes. Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potential suspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals. Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law.

Download or read book Building Machine Learning Systems with Python written by Willi Richert and published by Packt Publishing Ltd. This book was released on 2013-01-01 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.