A two level learning model for authorship authentication

Nowadays, forensic authorship authentication plays a vital role in identifying the number of unknown authors as a result of the world’s rapidly rising internet use. This paper presents two-level learning techniques for authorship authentication. The learning technique is supplied with linguistic knowledge, statistical features, and vocabulary features to enhance its efficiency instead of learning only. The linguistic knowledge is represented through lexical analysis features such as part of speech. In this study, a two-level classifier has been presented to capture the best predictive performance for identifying authorship. The first classifier is based on vocabulary features that detect the frequency with which each author uses certain words. This classifier’s results are fed to the second one which is based on a learning technique. It depends on lexical, statistical and linguistic features. All of the three sets of features describe the author’s writing styles in numerical forms. Through this work, many new features are proposed for identifying the author’s writing style. Although, the proposed new methodology is tested for Arabic writings, it is general and can be applied to any language. According to the used machine learning models, the experiment carried out shows that the trained two-level classifier achieves an accuracy ranging from 94% to 96.16%.

Detection of Online Contract Cheating Through Stylometry: A Pilot Study

Contract cheating, instances in which a student enlists someone other than themselves to produce coursework, has been identified as a growing problem within academic integrity literature and in news headlines. The percentage of students who have utilized this type of cheating has been reported to range between 6% and 15.7%. Generational sentiments about cheating and the prevalent accessibility of contract cheating providers online seems to only have exacerbated the issue. The problem is that there is currently no simple means identified and verified to detect contract cheating, as available plagiarism detection software has been shown to be ineffective in these cases. One method that is commonly used for authorship authentication in nonacademic settings, stylometry, has been suggested as a potential means for detection. Stylometry uses various attributes of documents to determine if they were written by the same individual. This pilot study sought to assess the utility of three easy to use and readily available stylometry software systems to detect simulated cases of contract cheating on academic documents. Average accuracy ranged from 33% to 88.9%. While more research is necessary to further investigate the reliability of the best performing software packages, stylometry software appears to show significant promise for the potential detection of contract cheating.

An extensive study of authorship authentication of Arabic articles

Purpose The authorship authentication (AA) problem is concerned with correctly attributing a text document to its corresponding author. Historically, this problem has been the focus of various studies focusing on the intuitive idea that each author has a unique style that can be captured using stylometric features (SF). Another approach to this problem, known as the bag-of-words (BOW) approach, uses keywords occurrences/frequencies in each document to identify its author. Unlike the first one, this approach is more language-independent. This paper aims to study and compare both approaches focusing on the Arabic language which is still largely understudied despite its importance. Design/methodology/approach Being a supervised learning problem, the authors start by collecting a very large data set of Arabic documents to be used for training and testing purposes. For the SF approach, they compute hundreds of SF, whereas, for the BOW approach, the popular term frequency-inverse document frequency technique is used. Both approaches are compared under various settings. Findings The results show that the SF approach, which is much cheaper to train, can generate more accurate results under most settings. Practical implications Numerous advantages of efficiently solving the AA problem are obtained in different fields of academia as well as the industry including literature, security, forensics, electronic markets and trading, etc. Another practical implication of this work is the public release of its sources. Specifically, some of the SF can be very useful for other problems such as sentiment analysis. Originality/value This is the first study of its kind to compare the SF and BOW approaches for authorship analysis of Arabic articles. Moreover, many of the computed SF are novel, while other features are inspired by the literature. As SF are language-dependent and most existing papers focus on English, extra effort must be invested to adapt such features to Arabic text.