Authorship Attribution Research Articles

Attribution of Authorship: ‘Some Modern Tendencies in English Art’ in the Apollinaire Collection

Attribution of Authorship: ‘Some Modern Tendencies in English Art’ in the Apollinaire Collection

Did Pushkin write a review of Baumeister’s <i>Metaphysics</i>?

The article deals with the problem of the authorship attribution of the anonymous review published in Literaturnaya Gazeta on 2 March 1830 in response to a revised edition of the Russian translation of F. C. Baumeister’s Metaphysics [Institutiones Metaphysicae]. The hypothesis that names the author as A. Delvig (V. Vinogradov, S. Kibalnik) is criticized for its lack of proof. The scholar argues in favour of Pushkin’s authorship. The review is considered in the context of Pushkin’s ‘indirect polemic’ with the book’s translator Y. Tolmachev, a prominent figure of the Russian national education system in the 1810s–1820s. Pushkin’s possible authorship is revealed in the stylistic features of the review as well as parallels between the poet’s own works (A Scene from ‘Faust’ [Stsena iz Fausta], Mozart and Salieri [Motsart i Salieri], and the so-called ‘Objections to Küchelbecker’s articles published in Mnemozina’ [‘Vozrazheniya na statyi Kyukhelbekera v Mnemozine’]) and selected paragraphs from Baumeister’s Metaphysics. Nearly all those texts relate to the period of Pushkin’s exile to his Mikhaylovskoe estate, when the poet had the opportunity to familiarize himself with Baumeister’s book.

Cross-linguistic authorship attribution and gender profiling. Machine translation as a method for bridging the language gap

Abstract This study explores the feasibility of cross-linguistic authorship attribution and the author’s gender identification using Machine Translation (MT). Computational stylistics experiments were conducted on a Greek blog corpus translated into English using Google’s Neural MT. A Random Forest algorithm was employed for authorship and gender profiling, using different feature groups [Author’s Multilevel N-gram Profiles, quantitative linguistics (QL), and cross-lingual word embeddings (CLWE)] in both original and translated texts. Results indicate that MT is a viable method for converting a multilingual corpus into one language for authorship attribution and gender profiling research, with considerable accuracy when training and testing datasets use identical language. In the pure cross-linguistic scenario, higher accuracies than the baselines were obtained using CLWE and QL features.

Neuroleptic malignant syndrome and serotonin syndrome: a comparative bibliometric analysis

ObjectiveThis study aimed to analyze and map scientific literature on Neuroleptic Malignant Syndrome (NMS) and Serotonin Syndrome (SS) from prestigious, internationally indexed journals. The objective was to identify key topics, impactful articles, prominent journals, research output, growth patterns, hotspots, and leading countries in the field, providing valuable insights for scholars, medical students, and international funding agencies.MethodsA systematic search strategy was implemented in the PubMed MeSH database using specific keywords for NMS and SS. The search was conducted in the Scopus database, renowned for its extensive coverage of scholarly publications. Inclusion criteria comprised articles published from 1950 to December 31st, 2022, restricted to journal research and review articles written in English. Data were analyzed using Microsoft Excel for descriptive analysis, and VOSviewer was employed for bibliometric mapping.ResultsThe search yielded 1150 articles on NMS and 587 on SS, with the majority being case reports. Growth patterns revealed a surge in NMS research between 1981 and 1991, while SS research increased notably between 1993 and 1997. Active countries and journals differed between NMS and SS, with psychiatry journals predominating for NMS and pharmacology/toxicology journals for SS. Authorship analysis indicated higher multi-authored articles for NMS. Top impactful articles focused on review articles and pathogenic mechanisms. Research hotspots included antipsychotics and catatonia for NMS, while SS highlighted drug interactions and specific medications like linezolid and tramadol.ConclusionsNMS and SS represent rare but life-threatening conditions, requiring detailed clinical and scientific understanding. Differential diagnosis and management necessitate caution in prescribing medications affecting central serotonin or dopamine systems, with awareness of potential drug interactions. International diagnostic tools and genetic screening tests may aid in safe diagnosis and prevention. Reporting rare cases and utilizing bibliometric analysis enhance knowledge dissemination and research exploration in the field of rare drug-induced medical conditions.

Unveiling ChatGPT text using writing style

Extensive use of AI-generated texts culminated recently after the advent of large language models. Although the use of AI text generators, such as ChatGPT, is beneficial, it also threatens the academic level as students may resort to it. In this work, we propose a technique leveraging the intrinsic stylometric features of documents to detect ChatGPT-based plagiarism. The stylometric features were normalized and fed to classical classifiers, such as k-Nearest Neighbors, Decision Tree, and Naïve Bayes, as well as ensemble classifiers, such as XGBoost and Stacking. A thorough examination of the classifier was conducted by using Cross-Fold validation, hyperparameters tuning, and multiple training iterations. The results show the efficacy of both classical and ensemble learning classifiers in distinguishing between human and ChatGPT writing styles with a noteworthy performance of XGBoost where 100 % was achieved for accuracy, recall, and precision metrics. Moreover, the proposed XGBoost classifier outperformed the state-of-the-art result on the same dataset and same classifier highlighting the superiority of the proposed feature style extraction method over TF-IDF techniques. The ensemble learning classifiers were also applied to the generated dataset with mixed texts, where paragraphs are written by ChatGPT and humans. The results show that 98 % of the documents were classified correctly as either mixed or human. The last contribution consists in the authorship attribution of the paragraphs of a single document where the accuracy reached 92.3 %.

Beyond authorship: Analyzing contributions in PLOS ONE and the challenges of appropriate attribution

Abstract Purpose This study aims to evaluate the accuracy of authorship attributions in scientific publications, focusing on the fairness and precision of individual contributions within academic works. Design/methodology/approach The study analyzes 81,823 publications from the journal PLOS ONE, covering the period from January 2018 to June 2023. It examines the authorship attributions within these publications to try and determine the prevalence of inappropriate authorship. It also investigates the demographic and professional profiles of affected authors, exploring trends and potential factors contributing to inaccuracies in authorship. Findings Surprisingly, 9.14% of articles feature at least one author with inappropriate authorship, affecting over 14,000 individuals (2.56% of the sample). Inappropriate authorship is more concentrated in Asia, Africa, and specific European countries like Italy. Established researchers with significant publication records and those affiliated with companies or nonprofits show higher instances of potential monetary authorship. Research limitations Our findings are based on contributions as declared by the authors, which implies a degree of trust in their transparency. However, this reliance on self-reporting may introduce biases or inaccuracies into the dataset. Further research could employ additional verification methods to enhance the reliability of the findings. Practical implications These findings have significant implications for journal publishers, highlighting the necessity for robust control mechanisms to ensure the integrity of authorship attributions. Moreover, researchers must exercise discernment in determining when to acknowledge a contributor and when to include them in the author list. Addressing these issues is crucial for maintaining the credibility and fairness of academic publications. Originality/value This study contributes to an understanding of critical issues within academic authorship, shedding light on the prevalence and impact of inappropriate authorship attributions. By calling for a nuanced approach to ensure accurate credit is given where it is due, the study underscores the importance of upholding ethical standards in scholarly publishing.

Who wrote the first Constitutions of Freemasonry?

Abstract This article addresses the problematic authorship of The Constitutions of the Free-Masons (1723). Traditionally associated with James Anderson, using stylometry, we examine whether and, if so, where John T. Desaguliers, the prime mover of early English institutionalized Freemasonry, contributed to this publication. Our corpus includes writings by Anderson, Desaguliers, and two contemporary Freemasons used as distractors. The transcribed works contain texts from different genres and of varying lengths. In our methodology, we employ a wide range of robust, multivariate, unsupervised, and cross-validated supervised tests, verified through significance testing, which can hopefully contribute to the establishment of standards for historical authorship attribution. Our results suggest, in line with historical evidence, that the legendary history of the Constitutions was most likely primarily authored by Anderson. However, several of the Charges including the first one ‘Concerning God and religion’, one of the most disputed texts in the history of Freemasonry, are closer to the style of Desaguliers. The General Regulations concerning the organization of the lodges, hitherto attributed to George Payne, played a fundamental role in spreading Freemasonry worldwide. Our analyses show that the stylistic affinity of fifteen of the thirty-nine regulations has a pronounced closeness to Anderson’s style, five align more closely with Desaguliers’ style. The authorship of the rest remains inconclusive partly due to the insufficient length of texts by Payne. These novel findings are also supported by a close reading of the Constitutions and other contemporary primary sources.

Banking without limits: a bibliometric analysis of scholarly works on electronic banking

PurposeThis paper reviews scholarly literature on electronic banking (EB) research over a three-decade period. The focus of this review is to evaluate the intellectual structure and trends and unearth new areas in the research domain of EB.Design/methodology/approachDrawing data from the Scopus database, a bibliometric analysis was performed on 2,391 research articles published in the EB domain. The articles were examined and synthesized through volume analysis, citation analysis, authorship analysis and the level of collaboration, co-citation analysis and keyword co-occurrence analysis. A well-known bibliometric tool, VOSviewer, was used to create and analyse various bibliometric maps, networks and tables.FindingsThis study uncovers the temporal and spatial trends of publications on EB, the most influential documents, authors, productive publication outlets, countries in the domain, the status of collaboration and the major themes that have been studied in the literature. The publication trail over the years has been gauged using overlay visualization.Practical implicationsThis paper highlights new concepts that have emerged in EB over the past three decades and suggests future avenues for research in this area.Originality/valueResearch in the domain of EB is empirical, with only a handful of studies being literature reviews. This is the first-of-its-kind study that aims to trace the development of electronic banking over the past 29 years, from 1994 to 2022, through bibliometric analysis and network visualization.

Authorship Attribution of English Poetry using Sentiment Analysis

We present a basic methodology and share some interesting results from experiments on using sentiment analysis for authorship attribution of poetry. We demonstrate that sentiment analysis can be effectively used to determine the authorship of poetic works given a sufficiently large training corpus. We also share some promising preliminary results from sentiment-analysis-based attribution of non-poetry works. Most results compare well with traditional authorship attribution approaches. Moreover, adding sentiment analysis to a traditional-feature-based ensemble classifier improved the accuracy of attribution. The strengths and limitations of our methodology and directions for further research are outlined at the end of the paper.

Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation

Supervised discretisation is widely considered as far more advantageous than unsupervised transformation of attributes, because it helps to preserve the informative content of a variable, which is useful in classification. After discretisation, based on employed criteria, some attributes can be found irrelevant, and all their values can be represented in a discrete domain by a single interval. In consequence, such attributes are removed from considerations, and no knowledge is mined from them. The paper presents research focused on extended transformations of attribute values, thus combining supervised with unsupervised discretisation strategies. For all variables with single intervals returned from supervised algorithms, the ranges of values were transformed by unsupervised methods with varying numbers of bins. Resulting variants of the data were subjected to selected data mining techniques, and the performance of a group of classifiers was evaluated and compared. The experiments were performed on a stylometric task of authorship attribution.

Crossing Linguistic Barriers: Authorship Attribution in Sinhala Texts

Authorship attribution involves determining the original author of an anonymous text from a pool of potential authors. The author attribution task has applications in several domains, such as plagiarism detection, digital text forensics, and information retrieval. While these applications extend beyond any single language, existing research has predominantly centered on English, posing challenges for application in languages such as Sinhala due to linguistic disparities and a lack of language processing tools. We present the first comprehensive study on cross-topic authorship attribution for Sinhala texts and propose a solution that can effectively perform the authorship attribution task even if the topics within the test and training samples differ. Our solution consists of three main parts: (i) extraction of topic-independent stylometric features, (ii) generation of a small candidate author set with the help of similarity search, and (iii) identification of the true author. Several experimental studies were carried out to demonstrate that the proposed solution can effectively handle real-world scenarios involving a large number of candidate authors and a limited number of text samples for each candidate author.

Importance of Characteristic Features and Their Form for Data Exploration.

The nature of the input features is one of the key factors indicating what kind of tools, methods, or approaches can be used in a knowledge discovery process. Depending on the characteristics of the available attributes, some techniques could lead to unsatisfactory performance or even may not proceed at all without additional preprocessing steps. The types of variables and their domains affect performance. Any changes to their form can influence it as well, or even enable some learners. On the other hand, the relevance of features for a task constitutes another element with a noticeable impact on data exploration. The importance of attributes can be estimated through the application of mechanisms belonging to the feature selection and reduction area, such as rankings. In the described research framework, the data form was conditioned on relevance by the proposed procedure of gradual discretisation controlled by a ranking of attributes. Supervised and unsupervised discretisation methods were employed to the datasets from the stylometric domain and the task of binary authorship attribution. For the selected classifiers, extensive tests were performed and they indicated many cases of enhanced prediction for partially discretised datasets.

The increasing authorship trend in neuroscience: A scientometric analysis across 11 countries

Previous studies have demonstrated an increasing trend of the number of authors across various fields over the years. This trend has been attributed to the necessity for larger collaborations and, at times, to ethical issues regarding authorship attribution. Our study focuses on the evolution of authorship trends in the field of Neuroscience. We conducted our analysis based on a dataset containing 580,782 neuroscience publications produced from 2000 to 2022, focusing on the publications within the Group of ten (G10) countries. Using a matrix-based methodology, we extracted and analyzed the average number of authors per country. Our findings reveal a consistent rise in authorship across all G10 countries over the past two decades. Italy emerged with the highest average number of authors, while France stood out for experiencing the most significant increase, particularly in the last decade. The countries with the lowest number of authors per publication were the USA, UK and Canada. Differences between countries could result from variations in the size of collaboration between researchers in different countries. Additionally, these differences may depend on utilitarian considerations aimed at receiving higher scores in the individual evaluation of their own work. We propose that a normalization procedure for the number of authors should be implemented to ensure a fair evaluation of researchers.

Bibliometric analysis of the gut microbiota and stroke from 2002 to 2022

Stroke is the fifth leading cause of death worldwide, and the functional status of the gut plays a key role in patients' prognosis. Recent publications have explored the gut association with stroke, but few articles have been published that specifically address a comprehensive bibliometric analysis of the gut microbiota and its association with stroke. To address this gap, we used bibliometric methods to examine the landscape of research concerning the gut and stroke over approximately two decades, utilizing the Web of Science Core Collection (WoSCC). On November 1, 2022, a search was conducted for English-language articles published between 2002 and 2022, with only including original articles. Visual and statistical analyses were performed using CiteSpace, VOSviewer, and Bibliometrix 4.1.0 Package. After screening relevant articles, the results revealed that the number of articles published in this field has progressively increased during the last two decades. In particular, the total number of publications rapidly increased year by year from 2014. Among them, China ranked first in the world with a total of 227 publications. Authorship analysis highlighted Wang Z as the most prolific author, with 18 publications and an H-index of 14, highlighting significant contributions to this field. Meanwhile, the Southern Medical University of China was identified as the most productive institution. Moreover, analysis of keywords revealed that ‘cerebral ischemia’, ‘intestinal microbiota’, ‘gut microbiota’, and ‘trimethylamine N-oxide’ were popular topics searched, and research on the relationship between stroke and the gut continues to be a research hotspot. In summary, this study presents an overview of the progress and emerging trends in research on the relationship between stroke and gut health over the past two decades, providing a valuable resource for researchers aiming to understand the current state of the field and identify potential directions for future studies.

Identifying Authorship in Malicious Binaries: Features, Challenges & Datasets

Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to disassemble binaries to obtain authorship-related features. We perform a systematic analysis of works in the area of malware authorship attribution. We identify key findings and some shortcomings of current approaches and explore the open research challenges. To mitigate the lack of ground-truth datasets in this domain, we publish alongside this survey the largest and most diverse meta-information dataset of 17,513 malware labeled to 275 threat actor groups.

RETRACTION: Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics

RETRACTION: Z. Guo, L. Xu, Y. Si, and N. Razmjooy, “Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics,” International Journal of Imaging Systems and Technology 31, no. 4 (2021): 1954–1969. https://doi.org/10.1002/ima.22608The above article, published online on 5 June 2021 in Wiley Online Library, has been retracted by agreement between the authors, the journal's Editor in Chief, Mohamed Seghier, and Wiley Periodicals, LLC. Following the article's publication, the journal was contacted by co‐author Yujuan Si, who stated that she had not participated in any way in the work presented in the article or its preparation, and that the email address used to correspond with the journal in her name had been fabricated. In the following investigation, the authors stated that Yujuan Si's inclusion in the author list was due to her supervisory role over co‐author Lina Xu as a mentor for her PhD studies, and that authorship attribution occurred without Yujuan Si's knowledge nor consent due to inadequate communication among the co‐authors. However, the ownership of the email address attributed to Yujuan Si for journal correspondence (bio_sp@163.com) remains uncertain. The retraction has been agreed due to concerns about the fabricated email address and author impersonation, which calls all aspects of the work presented into question. Following the investigation's findings, the corresponding author Navid Razmjooy has agreed on the decision to retract the article on behalf of all authors.

Bon-APT: Detection, attribution, and explainability of APT malware using temporal segmentation of API calls

Advanced Persistent Threats (APTs) are highly sophisticated cyberattacks that are aimed at achieving strategic goals and are usually backed by a well-funded entity. In this paper, we tackle the challenges of detecting and attributing APTs by proposing Bon-APT, a temporal learning method that analyzes and segment the occurrences of API calls invoked during the dynamic analysis of the examined PE. Those segments can be used to profile the temporal behavior of an APT, provide insights into its modus operandi, and induce an accurate machine-learning based model for the detection and attribution of APTs. Moreover, Bon-APT provides a human comprehensible explainability regarding the relations among segments as well as the behavior of the APT in each of them. This not only improves transparency and reliability from a human expert perspective, but it can also enrich the security experts with new knowledge regarding APTs' behavior. To evaluate Bon-APT, we built a unique collection of 12,655 APTs, belonging to 188 different cyber-groups and 17 different nations, which, to the best of our knowledge, is the largest collection of its kind. We conducted four experiments to evaluate the proposed method and compared its performance to the performance of state-of-the-art methods on the tasks of APT detection and authorship attribution (for both group and nation). Bon-APT achieved promising results in each of the tasks while outperforming the state-of-the-art methods. Bon-APT also provides a simple and concise explanation regarding its decisions and the APT behavior, as well as an easy, straightforward visual and quantitative behavioral comparison.

Attributions of Authorship in the Gentleman’s Magazine, 1836–1840: A Supplement to the Union List

Attributions of Authorship in the <i>Gentleman’s Magazine</i>, 1836–1840: A Supplement to the <i>Union List</i>

Authorship Attribution in Arabic Poetry using Text mining Techniques

يهدف هذا البحث إلى تقديم طريقة لمعرفة الإسناد التلقائي لنص شعري مجهول في الشعر العربي إلى شاعره الحقيقي، وأتمَتَة هذه العملية باستخدام تقنيات تنقيب النصوص Text Mining، الجدير بالذكر أن هذا البحث تطوير لمشروع متكامل في عملية إسناد التأليف في الشعر العربي وهي عملية مهمة جدا في تنقيب النصوص العربية. في هذا البحث تمّ إدخال مجموعة من الدواوين الشعرية لعدد ستين شاعرًا من مختلف العصور في الشعر العربي الكلاسيكي بوصفهم مجتمعاً للتدريب وإدخال العدد نفسه لنصوص مجهولة المؤلف من نصوص مختلفة بوصفها عينة اختبار؛ ثم طُبقت خوارزميات Naïve Bayes (NB ),Support Vector Machine (SVM) على تلك النصوص مع بارامترات ومتغيرات هي: القافية، الحرف، طول الكلمة، وطول الجملة الشعرية، الكلمة الأولى والبحر الشعري. وقد خرج البحث بنتيجة دقة وصلت إلى 96,667 %.

Top 100 highly cited sustainability researchers

The announcement of the UN Sustainable Development Goals (SDGs) provided a fresh direction to sustainability research that spans different disciplines. Consequently, scholarly databases made available the mapping of research publications to different SDGs, unleashing many opportunities for analysis. In this work, the top 100 Highly Cited Sustainability Researchers (HCSRs) and information related to them, such as the institutions they belong to, the type of these institutions, the geographical diversity of these researchers, and gender representation patterns, are analyzed. Also, from their publications, their publication pattern, including (i) the least and most researched SDGs, (ii) their Open Access publishing pattern, (iii) their collaboration pattern (iv) the pattern of their research impact, are analyzed. The most sought thematic areas of their research, top journals in which they publish, important research categories handled by these journals, etc., are also investigated. The most significant contribution of these researchers and their recent contributions are also discussed. The data indicates a significant disparity in research focus among the top 100 HCSRs, with most concentrating on “Good Health and Well Being,” “Zero Hunger,” and “Quality Education,” while notably fewer researchers focus on “Decent Work and Economic Growth” and “No Poverty,” underscoring the need for a more balanced research agenda across all SDGs. The study reveals that the United States, China, and the United Kingdom are the leading contributors to the top 100 HCSRs, suggesting that these countries are predominant in global sustainability research output, while nations like Iran and Saudi Arabia also make notable, albeit smaller, contributions. The institutional affiliations of HCSRs show a significant imbalance, with only 16 from private institutions compared to 84 from public ones. Specifically, it shows that out of the top 100 researchers, 93 are men, while only 7 are women. The analysis of authorship in publications by HCSRs reveals a tendency towards middle and last author positions, underscoring their collaborative and leadership roles within the research community. All these analyses can inform academia, industry, and policymakers about the most significant developments in research regarding SDGs.