Authorship Attribution Research Articles

A Bibliometric Analysis of Articles on Pit and Fissure Sealants During 2012 - 2023

Background: Pit and fissure sealants have become an essential preventive approach for occlusal caries, mainly in the young population. In the last decade, tremendous research has been done in sealant materials, effectiveness, application procedures, and clinical results; therefore, the trends, collaboration networks, and influential contributions need to be summarized in a structured manner. To conduct a deep bibliometric analysis of the published articles on pit and fissure sealants between 2012 and 2023, to track research trends, key contributors, collaborative networks, and gaps in the literature.Methods: Articles were obtained from Scopus, Web of Science, and PubMed databases with a bibliometric analysis of articles published from 2012 to 2023. The metrics were calculated using the package Biblioshiny in R while VOSviewer was used in the analysis of authorship, citation, and thematic network.Results: A total of 1,215 articles with 4,059 authors was analyzed, averaging 4.56 authors per paper, attesting to the high collaboration level. The United States, Brazil, and India are known to lead the sealant research activities. Sealant research tends to indicate the extent to which the world is going in the preventive dentistry field. Trends indicated a maximum output in 2015 and 2019 followed by a decline in subsequent years, indicating an overall annual decline in the rate of publication of -2.16%. Thematic analysis found that the most common themes of research were sealant efficacy, material innovation, and application techniques.Conclusion: Analysis Dramatic progresses in pit and fissure sealant research are highlighted due to an increasing global network. However, areas include improved long-term clinical follow up and optimizing retention when these sealants are put on under different conditions. Gaps should be addressed on how to innovate materials, form, and techniques and strategies for sealants that aim for evidence-based preventive recommendations and guidelines in public health dental practice.

Authorship Analysis, Social Networks and Catalan

This paper presents data that can help determine whether factors such as gender or age can become significant in authorship analysis through X in Catalan. Considering the principles of forensic linguistics (in particular, authorship analysis and idiolectal style), 500 publications have been analyzed from a stylistic point of view, focusing on three discursive aspects: specific features of X, pragmatic variables and stylistic variables. Contrary to what some authors have found for English X users (Cicres, 2015), the paper shows that, in Catalan, emoticons, exclamations or letter multiplication are not distinctive features for gender or age. However, elements such as the concatenation of hashtags, the use of links, the intensification of first-person subject pronouns, the use of capital letters or the use suspension points can be meaningful for age ¾but not for gender. This paper thus constitutes a first step towards finding truly distinctive elements in the use of X in Catalan, even though more studies, with larger corpora, need to be done to confirm these tendencies.

Si Maqian's Sense of Revenge from the Biography of Wu Zixu

Abstract: In literary history, Si Maqian often appears as the image of a loyal and patriotic character who endures humiliation. The positive tendencies in his personal emotions are infinitely magnified under human control, but the negatively inclined emotions are always shunned because they do not conform to the Confucian laws used by the king for ruling. If we look at Si Maqian as a complete human being, it is impossible for him not to resent the fact that he was alienated by the king and tortured in the palace. This paper mainly discusses Si Maqian's sense of revenge as revealed in the Biography of Wu Zixu, and this study has made the character of Si Maqian more vivid and concrete, taking him away from the saintly state of melancholic concern for his country. It provides a more choice and reference for the future analysis of Si Maqian's authorship; it provides more possibilities for the future analysis of the cultural environment of the Han Dynasty.

Visual analysis of patient safety research hotspots and trends in the context of telemedicine based on Web of Science.

To analyze the research status, hotspots, and trends of patient safety in the context of international telemedicine, and to provide reference for future research in various countries. The literature pertaining to patient safety within the realm of telemedicine was systematically retrieved from the Web of Science core collection database, encompassing the period from January 2010 to December 2023. Visual analysis of publication quantity, primary authorship, and keyword trends was conducted using CiteSpace (6.2R6) software. The geographical distribution of research focus was visualized through VOSviewer software and SCImago Graphica software, while research institutions were depicted using VOSviewer software and Highcharts software. Data organization was facilitated by Excel 2019 software. A total of 5356 related articles were included, and the number of published papers showed an overall upward trend, and the countries and institutions with the largest number of papers were the United States and Harvard University, respectively, and a stable core author research population had not yet been formed in this research field. Through keyword analysis, it can be seen that the research hotspots mainly focus on the research on the influencing factors of patient safety in the context of telemedicine, the research on the application value of telemedicine, and the research on coping strategies that affect patient safety. The research on patient safety in the context of telemedicine in foreign countries has a certain depth and breadth, which has important reference significance for improving the medical quality and patient safety of Internet hospitals in various countries.

The Beauty and Importance of Scientific Journals

For more than 300 years, academic journals have been the main means of communication in all disciplines to disseminate scientific discoveries and advances, both theoretical and methodological, through scientific article, since they have covered all the functions of knowledge generation. Writing articles is the highlight of the research process and through it we not only share ideas and foster the spirit of free and cooperative exchange of information, but also fuel the process of self-correction that makes science powerful. To be shared with the community, articles must go through a process of attribution of authorship, certification of validity, dissemination, distribution and archiving of knowledge. Throughout history, scientific journals have gone through different stages and through different problems inherent to the editorial process. In this essay on scientific publications, we will reflect on the nature of publications, their importance and usefulness both social and economic, their less luminous side as well as their role, as a systemic and almost irreplaceable mechanism, in the generation and transmission of scientific culture, of our knowledge and understanding about the world and about ourselves.

Long-term trends of publications in journal of neuro-oncology: a bibliographic study of a core journal in the field of neuro-oncology.

The Journal of Neuro-Oncology (JNO), established in 1983, plays a key role in publishing research on brain and spinal cord tumors. This study examines JNO's publication trends, focusing on country and gender representation to highlight its global impact. Statistical analyses were conducted using R. Gender of the first authors was predicted using a gender-guesser, and author affiliations were used to determine publication countries. We introduced a novel Country-Related Diversity (CRD) index to assess the JNO's representativeness, comparing a country's JNO publications to its overall neurosurgical output. An index value of 1 indicates proportional representation. The JNO corpus, spanning from 1983 to 2024, comprises 8,154 documents with an average document age of 14.4 years. The average citation count per document is 28.71, with a rate of 2.16 citations per document per year. JNO's scientific output has grown significantly, peaking at 397 articles in 2011, with a long-term annual growth rate of 3.7%. The keyword analysis highlights "glioblastoma" as the most frequent term, reflecting the journal's neuro-oncological focus. Geographically, the U.S. led with 2,535 articles (40.1%), followed by China and Germany. International collaboration rose steadily, with multi-country publications increasing from 4.76% in 1983 to 20.98% in 2024. Analyzing contributions from different countries showed a converging CRD index toward 1 (P < 0.01), with U.S. and non-U.S. countries trending similarly. Upper-middle-income countries displayed fluctuating CRD patterns, whereas lower-middle-income countries lagged behind. Authorship analysis showed an increasing trend in co-authorship (P < 0.01), with the average number of authors per paper reaching 10.4 by 2024. Gender representation revealed a growing number of female first and senior authors, although males still dominate. By 2024, 32.9% of first authors and 21.6% of senior authors were female, signaling a gradual trend toward gender parity (P < 0.01). The CRD index offers a standardized measure of country-specific research representation in the JNO. The convergence towards 1 reflects balanced international representation. JNO publication also reflects a trend toward gender equity, with a notable rise in female first authors, enhancing global research inclusivity.

Equity and representation in cardiology research: a comprehensive analysis of authorship from low and low-middle income countries in leading cardiology journals

Abstract Background Cardiovascular disease is the leading global cause of mortality, with low-income and low-middle-income countries (LICs/LMICs) bearing a disproportionate burden. Despite this, research output is skewed towards affluent regions. This study aims to assess the authorship trends from LICs/LMICs in cardiology research in 5 of the leading cardiology journals. Methods PubMed authorship affiliation was obtained from 2000-2022 in (1) Journal of the American College of Cardiology (JACC), (2) Circulation, (3) Journal of the American Medical Association (JAMA) Cardiology, (4) Nature Reviews Cardiology, (NRC) and (5) European Heart Journal (EHJ). Countries were classified according to the World Bank. Results 62873 original research, clinical cases, editorials, and reviews were published in all five journals (2000-2022). The percentage of articles with at least one author affiliated with an institution from a LIC or LMIC was 1.03% (N=652): 0.96% (N=190) in JACC, 0.82% (N=198) in Circulation, 2.42% (N=51) in JAMA Cardiology, 0.84% (N=22) in NRC, and 1.33% (N=191) in EHJ. There has been an increase in the representation of authorship from LICs and LMICs in the last decade. However, the distribution of authorship between LMICs and LICs is uneven, represented mainly -by Mozambique, Uganda, Sudan, and Ethiopia for LICs, and India, Iran, Egypt, Pakistan and Lebanon for LMICs. Discussion: Our findings reveal an underrepresentation of authors from institutions from LICs and LMICs in these 5 leading cardiology journals. Efforts to enhance funding, access, and inclusion of authors from LICs and LMICs are imperative to advance global cardiovascular outcomes and promote equity in medical research.

Authorship Improprieties: Analyzing the Practice of Including Advisers as Co-Authors in Students’ Research

Purpose. This research aims to investigate the prevalent practice of including advisers as co-authors in students' research works, with a specific focus on understanding the attitudes of the academic community, particularly students and educators. The study seeks to explore. Students' and educators' perceptions, beliefs, and/or opinions regarding the practice raise concerns in higher education institutions worldwide. Methodology. Through a descriptive-correlational research design following a quantitative research approach, the researcher gathered data on the attitudes of 200 students and 200 educators of 4 Higher Education Institutions in Region IV-A, Philippines, towards the inclusion of advisers as co-authors in students' theses, particularly when presented in conferences and published in journals. The collected data is analyzed to identify prevailing opinions and perspectives within the academic community. This analysis takes into account factors such as mentorship, intellectual contribution, and ethical considerations related to authorship attribution. By weighing these factors, the researcher forms a stance on the practice and offers recommendations for its ethical implementation. Results revealed differing opinions between students and educators. Moreover, significant differences in the attitudes of faculty members towards the practice when grouped based on their highest educational attainment, academic rank, and tenure were established, as well as the significant relationship between the research engagement and productivity of faculty members, and their attitude towards the matter. Although the inclusion of advisers in students’ theses was found acceptable within the academic community, the authorship guidelines set by the Committee on Publication Ethics, also known as COPE, suggest otherwise. Conclusion. These findings contribute to the existing literature on academic authorship improprieties and provide insights into the attitudes and opinions of the academic community. They are expected to stimulate discussions, fostering a better understanding of the implications of including advisers as co-authors in students' research works. The paper also offers practical recommendations to promote transparency, fairness, and integrity in authorship practices, ensuring that credit is appropriately attributed to all contributors in academic publications.

Automatic authorship attribution in Albanian texts.

Automatic authorship identification is a challenging task that has been the focus of extensive research in natural language processing. Regardless of the progress made in attributing authorship, the need for corpora in under-resourced languages impedes advancing and examining present methods. To address this gap, we investigate the problem of authorship attribution in Albanian. We introduce a newly compiled corpus of Albanian newsroom columns and literary works and analyze machine-learning methods for detecting authorship. We create a set of hand-crafted features targeting various categories (lexical, morphological, and structural) relevant to Albanian and experiment with multiple classifiers using two different multiclass classification strategies. Furthermore, we compare our results to those obtained using deep learning models. Our investigation focuses on identifying the best combination of features and classification methods. The results reveal that lexical features are the most effective set of linguistic features, significantly improving the performance of various algorithms in the authorship attribution task. Among the machine learning algorithms evaluated, XGBoost demonstrated the best overall performance, achieving an F1 score of 0.982 on literary works and 0.905 on newsroom columns. Additionally, deep learning models such as fastText and BERT-multilingual showed promising results, highlighting their potential applicability in specific scenarios in Albanian writings. These findings contribute to the understanding of effective methods for authorship attribution in low-resource languages and provide a robust framework for future research in this area. The careful analysis of the different scenarios and the conclusions drawn from the results provide valuable insights into the potential and limitations of the methods and highlight the challenges in detecting authorship in Albanian. Promising results are reported, with implications for improving the methods used in Albanian authorship attribution. This study provides a valuable resource for future research and a reference for researchers in this domain.

Give Credit Where It’s Due: The Ethical Imperatives of Authorship Attribution in Collaborative Research

This article proposes practical steps for those new to collaborative research or those seeking to improve the nature of collaborative research partnerships. Questioning academia’s exaltation of publications and authorship as coveted assets, the authors critically analyze the often complex, contentious, and ambiguous environment of collaborative research and negotiating authorship. With respect to academic publishing and ethics of authorship, this article grapples with two critical questions: (1) What factors create ethical dilemmas in authorship in academia? (2) How can academics and researchers better navigate the complexities of collaborative research to sustain ethical authorship practices? A seven-phase process model is proposed to guide the collaborative research process to mitigate ambiguities that reside in the black box. The seven phases encompass problem settings and pre-negotiations, direction setting: action planning, social capital, re-negotiations, equity and inclusivity, implementation- the submission-revision cycle, and reflexivity. This theoretical analysis aims to offer a useful resource to promote enhanced collaborative practices, and the authors take an explicit advocacy and visionary perspective to promote transparency, accountability, and reciprocal trust, to shatter overarching issues such as oppressive, deceitful, and exploitative norms in academic authorship practices.

Detection of news written by the ChatGPT through authorship attribution performed by a bidirectional LSTM model

The large language based-model chatbot ChatGPT gained a lot of popularity since its launch and has been used in a wide range of situations. This research centers around a particular situation, when the ChatGPT is used to produce news that will be consumed by the population, causing the facilitation in the production of fake news, spread of misinformation and lack of trust in news sources. Aware of these problems, this research aims to build an artificial intelligence model capable of performing authorship attribution on news articles, identifying the ones written by the ChatGPT. To achieve this goal, a dataset containing equal amounts of human and ChatGPT written news was assembled and different natural processing language techniques were used to extract features from it that were used to train, validate and test three models built with different techniques. The best performance was produced by the Bidirectional Long Short Term Memory (LSTM) Neural Network model, achieving 91.57% accuracy when tested against the data from the testing set.

A Quantitative Style Analysis of Four Turkish Authors: Changes Over Time, and Differences

ABSTRACT We present a stylometric analysis of the writings of four famous Turkish authors: Abdülhak Şinasi Hisar, Refik Halid Karay, Ahmet Hamdi Tanpınar, and Halit Ziya Uşaklıgil. Our aim is to internally analyse the shifts in their writing styles and examine the differences between them. First, we evaluate the changes in word lengths in the writers’ novels over time and observe that they do not necessarily follow the pattern of writing with longer words as time passes, which was common for 20th-century Turkish literature. We then employ a sliding text window approach to capture the shift in writing styles in novels, by focusing changes in word lengths throughout the entire text. Based on this analysis, we hypothesize a relationship between changes in word lengths and meaning shift within a novel. Next, we investigate the stylochronometry and authorship attribution problems for these four authors and show that their styles change with time and that their works are distinguishable from each other. Finally, we analyse differences in their vocabulary richness within close contexts and demonstrate a strong relationship between poetic writing and lower vocabulary richness in the running text.

Authorship Attribution for English Short Texts

Authorship Attribution for English Short Texts

Deep Learning for Stylometry and Authorship Attribution: a Review of Literature

The application of deep learning techniques to stylometry and authorship attribution has emerged as a promising frontier in computational linguistics, offering new possibilities for understanding literary style and authorship in both historical and contemporary contexts. This review paper synthesizes recent advances in the use of deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer architectures, for identifying and attributing authorship based on stylistic analysis. We examine the effectiveness of these models in comparison to traditional statistical methods, highlighting their ability to capture complex linguistic patterns and nuances that are often overlooked by conventional approaches. Furthermore, we explore how deep learning models handle challenges such as multilingual texts, limited data, and variations across genres and periods. This review also addresses the interpretability of neural networks in the context of stylometry and discusses the implications of these methods for fields ranging from literary studies to digital forensics. By providing a comprehensive overview of the current state of research, this paper identifies key trends, challenges, and future directions for the application of deep learning to stylometry and authorship attribution.

Investigating the Linguistic Fingerprint of GPT-4o in Arabic-to-English Translation Using Stylometry

This study explores the linguistic and stylistic characteristics of machine-generated texts, focusing on the output of GPT-4o. Using various natural language processing (NLP) techniques, including word frequency and stopword count analysis, readability and sentence structure metrics, lexical diversity measures, syntactic frequency analysis, and named entity recognition (NER), the research aims to uncover the stylometric fingerprints present in machine-generated content. The results reveal that GPT-4ogenerated texts exhibit moderate lexical diversity and syntactic complexity, with certain chapters reflecting higher readability and more varied sentence structures, while others lean toward simpler linguistic patterns. The findings also highlight thematic variation across chapters, as observed in the distribution of named entities, which contributes to understanding the model’s handling of different contextual content. The research suggests that while GPT-4o maintains a consistent style in its generated text, there are distinguishable characteristics that may serve as indicators of machine authorship. This provides valuable insights for stylometric analysis, authorship attribution, and the identification of machine-generated texts in various contexts. Future research could extend this work by exploring deeper stylometric features, conducting cross-model comparisons, and developing advanced authorship detection algorithms tailored for AI-generated content. Moreover, the ethical implications of stylometric analysis in the context of AI-generated texts warrant further investigation, particularly as machine-generated content becomes increasingly prevalent across different domains.

An Empirical Comparison and Ensemble Learning Methods of BERT Models on Authorship Attribution

An Empirical Comparison and Ensemble Learning Methods of BERT Models on Authorship Attribution

Malware authorship attribution: Unmasking the culprits behind malicious software

With the digital age ushering in an unprecedented proliferation of malware, accurately attributing these malicious software variants to their original authors or affiliated groups has emerged as a crucial endeavor in cybersecurity. This study delves into the intricacies of malware authorship attribution by combining traditional analytical techniques with advanced machine learning methodologies. An integrated approach, encompassing static and dynamic analyses, yielded promising results in the challenging realm of malware attribution. Despite the encouraging outcomes, the research highlighted the multifaceted complexities involved, especially considering the sophisticated obfuscation techniques frequently employed by attackers. This paper emphasizes the merits of a holistic attribution model and underscores the importance of continuous innovation in the face of an ever-evolving threat landscape.

Attributions Of Authorship in the Gentleman’s Magazine, 1841–1845: A Supplement to the Union List

Attributions Of Authorship in the <i>Gentleman’s Magazine</i>, 1841–1845: A Supplement to the <i>Union List</i>

The Most Influential Publications Regarding Hair Transplantation: A Bibliometric Review.

This bibliometric review aims to assess the impact of significant publications within the field of hair transplantation. Citation counts will serve as a primary influence indicator. An exhaustive search was conducted using Clarivate's Web of Science database, yielding 260 publications related to hair transplantation. These were evaluated and sorted based on citations, narrowing down to the 50 most highly cited works for analysis. Parameters including citation density, authorship, institutional affiliations, country of origin, year of publication, article topic, and the level of evidence for each publication were obtained. Analyzed publications were cited a total of 1341 times. Authorship analysis revealed that the most significant contributors regarding hair transplantation were Bernstein and Rassman. We also identified the leading institutions affiliated with these works, highlighting the primary academic and research centers driving the field. Geographical analysis exhibited the US' dominance in producing impactful publications. Most publications were also classified within Level IV and Level V according to the Oxford Levels of Evidence system. This review provides a comprehensive snapshot of the pivotal publications shaping hair transplantation. Our findings underscore significant contributions within this field and may assist researchers and clinicians in understanding the evolution and the current state of the hair transplantation literature. This bibliometric analysis can serve as a roadmap for those seeking to delve into this rapidly evolving field, facilitating the identification of research gaps and formulating future research directions. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Inference through innovation processes tested in the authorship attribution task

Urn models for innovation capture fundamental empirical laws shared by several real-world processes. The so-called urn model with triggering includes, as particular cases, the urn representation of the two-parameter Poisson-Dirichlet process and the Dirichlet process, seminal in Bayesian non-parametric inference. In this work, we leverage this connection to introduce a general approach for quantifying closeness between symbolic sequences and test it within the framework of the authorship attribution problem. The method demonstrates high accuracy when compared to other related methods in different scenarios, featuring a substantial gain in computational efficiency and theoretical transparency. Beyond the practical convenience, this work demonstrates how the recently established connection between urn models and non-parametric Bayesian inference can pave the way for designing more efficient inference methods. In particular, the hybrid approach that we propose allows us to relax the exchangeability hypothesis, which can be particularly relevant for systems exhibiting complex correlation patterns and non-stationary dynamics.