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Source code authorship attribution is the task of identifying who develops the code based on learning based on the programmer style. It is one of the critical activities which used extensively in different aspects such as computer security, computer law, and plagiarism. This paper attempts to investigate source code authorship attribution by capturing natural language aspects of the code rather than only using minimal set of syntactic and stylistic code features as explored in the previous literature. It proposes an evolutionary feature selection model to improve the accuracy of authorship attribution by implementing two language models (uni-gram and bi-gram). The proposed approach uses K-Nearest Neighbor as a classifier and Genetic Algorithm as a feature selection technique. Two experiments have been demonstrated on a public Authorship Attribution dataset on GitHub, the experiments include various evolutionary feature selection models. Notably, the obtained results in both experiments were compared with the related studies, and show a significant improvement in terms of accuracy.

Authorship attribution (AA) is a field of natural language processing that aims to attribute text to its author. Although the literature includes several studies on Arabic AA in general, applying AA to classical Arabic texts has not gained similar attention. This study focuses on investigating recent Arabic pretrained transformer-based models in a rarely studied domain with limited research contributions: the domain of Islamic law. We adopt an experimental approach to investigate AA. Because no dataset has been designed specifically for this task, we design and build our own dataset using Islamic law digital resources. We conduct several experiments on fine-tuning four Arabic pretrained transformer-based models: AraBERT, AraELECTRA, ARBERT, and MARBERT. Results of the experiments indicate that for the task of attributing a given text to its author, ARBERT and AraELECTRA outperform the other models with an accuracy of 96%. We conclude that pretrained transformer models, specifically ARBERT and AraELECTRA, fine-tuned using the Islamic legal dataset, show significant results in applying AA to Islamic legal texts. [ABSTRACT FROM AUTHOR]

The paper presents a description of the research methodology dedicated to a two-step discretisation process applied to the input numeric data, with combining the characteristics of selected supervised and unsupervised algorithms, which leads to extended processing of some attributes in train and test sets. The methodology was illustrated with the investigations carried out in the domain of stylometric analysis of texts, for two datasets prepared for the task of binary authorship attribution. The several variants of transformed input data obtained were subjected to exploration using two selected machine learning methods capable of inducing knowledge from both continuous and categorical forms, namely the PART and J48 classifiers. The results from the experiments indicate that, as can be expected, supervised transformations of data work well enough, however, they do not always return the best outcome. The two-step processing of some attributes shows sufficient promise to warrant a closer study, as opposed to always unconditionally relying only on supervised algorithms as outperforming all other approaches. [ABSTRACT FROM AUTHOR]

Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and performance of different dissimilarity measures D using three different representations of texts—vocabularies, word frequency distributions, and vector embeddings—and three simple tasks—clustering texts by author, subject, and time period. Using the Project Gutenberg database, we found that the generalised Jensen–Shannon divergence applied to word frequencies performed strongly across all tasks, that D's based on vector embedding representations led to stronger performance for smaller texts, and that the optimal choice of approach was ultimately task-dependent. We also investigated, both analytically and numerically, the behaviour of the different D's when the two texts varied in length by a factor h. We demonstrated that the (natural) estimator of the Jaccard distance between vocabularies was inconsistent and computed explicitly the h-dependency of the bias of the estimator of the generalised Jensen–Shannon divergence applied to word frequencies. We also found numerically that the Jensen–Shannon divergence and embedding-based approaches were robust to changes in h, while the Jaccard distance was not. [ABSTRACT FROM AUTHOR]

The authenticity of the Additamentum Aldinum (Sil. Pun. 8.144–223) has long been a matter of debate. While many scholars have expressed doubts that it is by Silius and suggest rather that it is from the hands of a skilful humanist, it has not, up to this time, been possible to provide solid evidence to support their intuition. This paper not only re-examines the standard arguments for and against authenticity but brings the latest computational stylometric techniques to bear on the question. These analyses reveal that the style of the Additamentum differs in statistically significant terms from the rest of Silius' Punica. [ABSTRACT FROM AUTHOR]

The present paper revisits the discussion concerning the authenticity of a crucial part in Aeschylus' Seven Against Thebes : the highly controversial ending of the play. Much has been written on the subject by various scholars, and even though there is now a general consensus that at some point in antiquity the ending of the play was "touched" by an author other than Aeschylus, the problem still remains unresolved in its devilish details. The question is of critical importance for classicists and theatre practitioners but also for anyone interested in classical literature, since, if the ending in the manuscripts is in fact Aeschylean, then Aeschylus could have been the first dramatist—long before Sophocles—to put on stage a defiant Antigone, eager to bury her brother Polyneices despite the civic prohibition. If the ending is spurious, then this will decisively affect how the play in question is read, studied, and staged. To address the problem, we used various tried and tested computer authorship attribution methods: Common n-grams, Support Vector Machines , and n-gram tracing. Thus, this study sheds new, interdisciplinary light on an old and perplexing philological question. [ABSTRACT FROM AUTHOR]

The present article seeks to demonstrate the usefulness of artificial intelligence for the exploration of the sources of Roman law. In a small experiment, a so-called support vector machine was employed to determine to which Roman jurist a given source text can be attributed. While the results were not perfect, they are sufficient to show the potential of new technologies for future research in the area.

Authorship attribution for less-resourced languages like Romanian, characterized by the scarcity of large, annotated datasets and the limited number of available NLP tools, poses unique challenges. This study focuses on a hybrid Transformer combining handcrafted linguistic features, ranging from surface indices like word frequencies to syntax, semantics, and discourse markers, with contextualized embeddings from a Romanian BERT encoder. The methodology involves extracting contextualized representations from a pre-trained Romanian BERT model and concatenating them with linguistic features, selected using the Kruskal–Wallis mean rank, to create a hybrid input vector for a classification layer. We compare this approach with a baseline ensemble of seven machine learning classifiers for authorship attribution employing majority soft voting. We conduct studies on both long texts (full texts) and short texts (paragraphs), with 19 authors and a subset of 10. Our hybrid Transformer outperforms existing methods, achieving an F1 score of 0.87 on the full dataset of the 19-author set (an 11% enhancement) and an F1 score of 0.95 on the 10-author subset (an increase of 10% over previous research studies). We conduct linguistic analysis leveraging textual complexity indices and employ McNemar and Cochran’s Q statistical tests to evaluate the performance evolution across the best three models, while highlighting patterns in misclassifications. Our research contributes to diversifying methodologies for effective authorship attribution in resource-constrained linguistic environments. Furthermore, we publicly release the full dataset and the codebase associated with this study to encourage further exploration and development in this field.

Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work.

The task of trying to quantify a person’s intelligence has been a goal of psychologists for over a century. The area of estimating IQ using stylometry has been a developing area of research and the effectiveness of using machine learning in stylometry analysis for the estimation of IQ has been demonstrated in literature whose conclusions suggest that using a large dataset could improve the quality of estimation. The unavailability of large datasets in this area of research has led to very few publications in IQ estimation from written text. In this paper, we review studies that have been done in IQ estimation and also that have been done in author profiling using stylometry and we conclude that based on the success of IQ estimation and author profiling with stylometry, a study on IQ estimation from written text using stylometry will yield good results if the right dataset is used.

Nearly four decades after David Raynor attributed to David Hume an allegorical Scots militia pamphlet from the early 1760s popularly known as Sister Peg, there is still no scholarly consensus about whether the author was in fact Hume or his friend Adam Ferguson. Using new evidence that has emerged since the appearance of Raynor's edition in 1982 – including information about Sister Peg's publication history, Ferguson's handwritten corrections and revisions in the Abbotsford copy of the work, a 1767 newspaper article by James Boswell and a copy of a 1775 letter by Sir John Dalrymple that both named Ferguson as the author, two volumes of Ferguson's correspondence published in 1995, and a recently discovered letter by Ferguson from 1809 – this article seeks to resolve this controversy by establishing that Sister Peg was written by Ferguson, as his fellow militia agitator Alexander Carlyle asserted in his memoirs. In the process, the article refutes Raynor's arguments about Ferguson's supposed incapacity for writing a satirical pamphlet like Sister Peg and clarifies the nature of Hume's views and actions in regard to the Scots militia cause during the Seven Years' War. The article also throws light on several related issues affecting Hume and Ferguson's circle. [ABSTRACT FROM AUTHOR]

The issue of authorship attribution has long been considered and continues to be a popular topic. Because of advances in digital computers, this field has experienced rapid developments in the last decade. In this article, a survey of recent advances in authorship attribution in text mining is presented. This survey focuses on authorship attribution methods that are statistically or computationally supported as opposed to traditional literary approaches. The main aspects covered include the changes in research topics over time, basic feature metrics, machine learning techniques, and the advantages and disadvantages of each approach. Moreover, the corpus size, number of candidates, data imbalance, and result description, all of which pose challenges in authorship attribution, are discussed to inform future work. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining [ABSTRACT FROM AUTHOR]

Copyright of Revista Signos is the property of Pontificia Universidad Catolica de Valparaiso and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Authorship profiling is a subtask of authorship identification. This task can be regarded as an analysis of personal writing styles, which has been widely investigated. However, no previous studies have attempted to analyze the authorship of classical Chinese poetry. First, we provide an approach to evaluate the popularity of poets, and we also establish a public corpus containing the top 20 most popular poets in the Tang Dynasty for authorship profiling. Then, a novel poetry authorship profiling framework named multidimensional domain knowledge poet profiling (M-DKPP) is proposed, combining the knowledge of authorship attribution and the text's stylistic features with domain knowledge described by experts in traditional poetry studies. A case study for Li Bai is used to prove the validity and applicability of our framework. Finally, the performance of M-DKPP framework is evaluated with four poem datasets. On all datasets, the proposed framework outperforms several baseline approaches for authorship attribution. [ABSTRACT FROM AUTHOR]

This paper provides an up-to-date summary of how the US legal system treats evidence that uses the forensic stylistics method of authorship attribution analysis. Many scholars in both law and linguistics have written about some of the more notable cases on this issue, but none have attempted to trace the entire line of relevant case law since the advent of modern forensic stylistics, and relatively few summaries of any case law have appeared in the last ten years. It is hoped that a fresh look with updated legal research can add new insights for litigants, lawyers and linguists alike. [ABSTRACT FROM AUTHOR]

Copyright of Pragmalingüística is the property of Facultad de Filosofia y Letras, Universidad de Cadiz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Authorship attribution – the computational task of identifying the author of a given text document within a set of possible candidates – has been attracting interest in Natural Language Processing research for many years. At the same time, significant advances have also been observed in the related field of author profiling, that is, the computational task of learning author demographics from text such as gender, age and others. The close relation between the two topics – both of which focused on gaining knowledge about the individual who wrote a piece of text – suggests that research in these fields may benefit from each other. To illustrate this, this work addresses the issue of author identification with the aid of author profiling methods, adding demographics predictions to an authorship attribution architecture that may be particularly suitable to extensions of this kind, namely, a stack of classifiers devoted to different aspects of the input text (words, characters and text distortion patterns.) The enriched model is evaluated across a range of text domains, languages and author profiling estimators, and its results are shown to compare favourably to those obtained by a standard authorship attribution method that does not have access to author demographics predictions. [ABSTRACT FROM AUTHOR]

Copyright of Prace Filologiczne. Literaturoznawstwo is the property of University of Warsaw and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Given an anonymous text, automatically attributing a name from a group of known writers is called "Authorship Attribution" (AA). It is a classification problem, and feature extraction techniques are initially applied, followed by the training of a model using a collection of texts whose authors are known. Numerous features, such as lexical, semantic, structural, n-grams, etc., can be used to identify the stylistic characteristics of writers. The authors of this research propose a novel approach to this problem by using sequential pattern mining on part-of-speech (PoS) tags. This paper introduces and discusses the concept of a Part-of-Speech Skip-Gram (PoSSG) that is different from traditional n-gram. A sequential pattern mining algorithm is applied to obtain PoSSG patterns, which are then used for authorship attribution tasks. Experimental studies on two different datasets: novels extracted from Project Gutenberg and Stamatatos06 Author Identification: C10-Attribution confirms that this approach of mining PoSSG patterns facilitates author identification. [ABSTRACT FROM AUTHOR]

The study investigates the task of authorship attribution on short texts in Slovenian using the BERT language model. Authorship attribution is the task of attributing a written text to its author, frequently using stylometry or computational techniques. We create five custom datasets for different numbers of included text authors and fine-tune two BERT models, SloBERTa and BERT Multilingual (mBERT), to evaluate their performance in closed-class and open-class problems with varying numbers of authors. Our models achieved an F1 score of approximately 0.95 when using the dataset with the comments of the top five users by the number of written comments. Training on datasets that include comments written by an increasing number of people results in models with a gradually decreasing F1 score. Including out-of-class comments in the evaluation decreases the F1 score by approximately 0.05. The study demonstrates the feasibility of using BERT models for authorship attribution in short texts in the Slovenian language.

In this work, we use machine learning techniques to address a research question regarding the authorship of two famous essays in the nineteenth century. On Liberty (1859) and The Subjection of Women (1869) were published under John Stuart Mill’s name, a widely studied nineteenth-century British philosopher. Mill himself attributed them to collaboration with his wife and partner, Harriet Taylor Mill. More than 150 years later, the question remains whether the author of these two canonical texts in the history of political thought was solely John Stuart Mill. Experts are divided on taking John Stuart Mill’s attribution at face value, since Harriet Taylor Mill had died in 1858. Addressing this question, we use a dataset consisted in essays of both authors, to train three state-of-the-art classifiers that are able to learn and distinguish the writing style of each author. Then, we use the models built to attribute the two famous essays of disputed authorship to one of the two. From the results, we conclude that the classifiers are able to learn the two classes very well, and they return high accuracies on the validation set. Regarding the test set, most of the models attribute the two essays to John Stuart Mill, however, the contribution of Harriet Taylor Mill is shown for some chunks of text of both essays. These results, we conclude, explain why experts are divided on this particular research question.

Determining the author of a text is a difficult task. Here, we compare multiple Artificial Intelligence techniques for classifying literary texts written by multiple authors by taking into account a limited number of speech parts (prepositions, adverbs, and conjunctions). We also introduce a new dataset composed of texts written in the Romanian language on which we have run the algorithms. The compared methods are artificial neural networks, multi-expression programming, k-nearest neighbour, support vector machines, and decision trees with C5.0. Numerical experiments show, first of all, that the problem is difficult, but some algorithms are able to generate acceptable error rates on the test set. [ABSTRACT FROM AUTHOR]

This paper describes procedures of the use of the Z-score for text document classification purposes. The author tested the efficiency of this approach to the solution of authorship attribution and genre classification tasks, based on the analysis of distribution of stop words. The paper finds that the calculation of this score based on the raw counts of stop words produces a negative result, while its calculation based on the deviations of frequencies of stop words from the Zipfian score allows a higher classification efficiency. Matching against the previously developed Y-method demonstrated a higher Z-score efficiency for the solution of text classification purposes. [ABSTRACT FROM AUTHOR]