Academic Citation Research Articles

From bench to bedside: determining what drives academic citations in clinical trials

From bench to bedside: determining what drives academic citations in clinical trials

The Influence of Open Access on Institutional Scientific Research Output in MENA Countries: A Comparative Analysis

The release of scientific research has substantial consequences for both the research and the institution in which it was carried out. The selection of publishing methods for scientific articles, such as journal type, quartile, indexed databases, and publishing models, plays a crucial role. Various publishing models exist, including platinum, gold, and green open access. In this study, we examine the implications of open access publishing on reputable journals for Middle East and North Africa (MENA) countries. Additionally, we explore the correlation between open access publishing and MENA country classification according to the World Bank. Bibliometric data from Scopus database are used, focusing on researchers from each MENA country. The World Bank determines if a country is low, middle, or high income. The total number of published documents and open access publications are also considered. The study investigates how open access publishing affects university and research center rankings within MENA countries, utilizing Scopus database information. This encompasses the number of open access documents indexed in Scopus and citations from MENA countries. Multiple ranking systems, such as 2024-QS World University Ranking (QSWUR) and 2023-Scimago Institutions Ranking, are assessed. Findings reveal that open access articles contribute to 72% to 95% of citations received by institutions in MENA countries, highlighting the influence of this publishing model. Additionally, the percentage of open access publishing ranges from 32% in low-income countries to 56% in other MENA countries. The study also demonstrates that MENA countries with higher numbers of open access publications are more likely to have institutions ranked in prestigious systems such as QS World University Ranking and Scimago Institutions. It is discovered that the average percentage of citations received from open access publications indexed in Scopus for MENA countries is 82%, indicating the significant impact of open access publishing on academic citations in these nations. We believe that the procedures done in this study for MENA countries can be generalized further for global countries.

Normalization of Web of Science Institution Names Based on Deep Learning

Academic evaluation is a process of assessing and measuring researchers, institutions, or disciplinary fields. Its goal is to evaluate their contributions and impact in the academic community, as well as to determine their reputation and status within specific disciplinary domains. Web of Science (WOS), being the most renowned global academic citation database, provides crucial data for academic evaluation. However, due to factors such as institutional changes, translation discrepancies, transcription errors in databases, and authors’ individual writing habits, there exist ambiguities in the institution names recorded in the WOS literature, which in turn affect the scientific evaluation of researchers and institutions. To address the issue of data reliability in academic evaluation, this paper proposes a WOS institution name synonym recognition framework that integrates multi-granular embeddings and multi-contextual information.

Scientific text citation analysis using CNN features and ensemble learning model.

Citation illustrates the link between citing and cited documents. Different aspects of achievements like the journal's impact factor, author's ranking, and peers' judgment are analyzed using citations. However, citations are given the same weight for determining these important metrics. However academics contend that not all citations can ever have equal weight. Predominantly, such rankings are based on quantitative measures and the qualitative aspect is completely ignored. For a fair evaluation, qualitative evaluation of citations is needed in addition to quantitative ones. Many existing works that use qualitative evaluation consider binary class and categorize citations as important or unimportant. This study considers multi-class tasks for citation sentiments on imbalanced data and presents a novel framework for sentiment analysis in in-text citations of research articles. In the proposed technique, features are retrieved using a convolutional neural network (CNN), and classification is performed using a voting classifier that combines Logistic Regression (LR) and Stochastic Gradient Descent (SGD). The class imbalance problem is handled by the synthetic minority oversampling technique (SMOTE). Extensive experiments are performed in comparison with the proposed approach using SMOTE-generated data and machine learning models by term frequency (TF), and term frequency-inverse document frequency (TF-IDF) to evaluate the efficacy of the proposed approach for citation analysis. It is found that the proposed voting classifier using CNN features achieves an accuracy, precision, recall, and F1 score of 0.99 for all. This work not only advances the field of sentiment analysis in academic citations but also underscores the importance of incorporating qualitative aspects in evaluating the impact and sentiments conveyed through citations.

Citation as representation: gendered academic citation politics persist in environmental studies publications

Citation as representation: gendered academic citation politics persist in environmental studies publications

Economic Fraud and Associated Risks: An Integrated Bibliometric Analysis Approach

This study offers a comprehensive insight into the realms of economic fraud and risk management, underscoring the necessity of adaptability to evolving technologies and shifts in financial market dynamics. Through the application of bibliometric methodologies, this study meticulously maps the relevant literature, delineating influential works, notable authors, collaborative networks, and emerging trends. It reviews key research contributions within the field, alongside reputable journals and institutions engaged in academic research. The examination highlights the logical, conceptual, and social interconnections that define the landscape of economic fraud and associated risks, elucidating how these findings inform the understanding, mitigating, and combating of the risk of fraud. Our bibliometric analysis methodology is grounded in the utilization of the Scopus database, employing rigorous filtering and extraction processes to obtain a substantial corpus of pertinent articles. Through a fusion of performance analysis and science mapping, our investigation elucidates central themes and visually represents the interrelationships between studies. Our research outcomes underscore the frequency of paper publications across diverse regions, with particular emphasis on the predominant scientific output from the US and China. Additionally, trends in academic citations are identified, indicative of the significant impact of papers on academic research and the formulation of public policies. By means of bibliometric analysis, this study not only consolidates existing knowledge but also catalyzes the exploration of future research trajectories, emphasizing the imperative of addressing these issues with heightened scientific rigor.

Societal and scientific impact of policy research: A large-scale empirical study of some explanatory factors using Altmetric and Overton

This study investigates how scientific research influences policymaking by analyzing citations of research articles in policy documents (policy impact) for nearly 125,000 articles across 434 public policy journals. We reveal distinct citation patterns between policymakers and other stakeholders like researchers, journalists, and the public. News and blog mentions, social media engagement, and open access publications (excluding fully open access) significantly increase the likelihood of a research article being cited in policy documents. Conversely, articles locked behind paywalls and those published under the full open access model (based on Altmetric data) have a lower chance of being policy-cited. Publication year and policy type show no significant influence. Our findings emphasize the crucial role of science communication channels like news media and social media in bridging the gap between research and policy. Interestingly, academic citations hold a weaker influence on policy citations compared to news mentions, suggesting a potential disconnect between how researchers reference research and how policymakers utilize it. This highlights the need for improved communication strategies to ensure research informs policy decisions more effectively. This study provides valuable insights for researchers, policymakers, and science communicators. Researchers can tailor their dissemination efforts to reach policymakers through media channels. Policymakers can leverage these findings to identify research with higher policy relevance. Science communicators can play a critical role in translating research for policymakers and fostering dialogue between the scientific and policymaking communities.

Spotting Suspicious Academic Citations Using Self-Learning Graph Transformers

The study introduces a novel approach to identify potential citation manipulation within academic papers. This method utilizes perturbations of a deep embedding model, integrating Graph-Masked Autoencoders to merge textual information with evidence of graph connectivity. Consequently, it yields a more intricate model of citation distribution. By training a deep network with partial data and reconstructing masked connections, the approach capitalizes on the inherent characteristics of central connections amidst network perturbations. It demonstrates its ability to pinpoint trustworthy citations within the analyzed dataset through comprehensive quantitative evaluations. Additionally, it raises concerns regarding the reliability of specific references, which may be subject to manipulation.

From hype to insight: Exploring ChatGPT's early footprint in education via altmetrics and bibliometrics

AbstractBackgroundThe emergence of ChatGPT in the education literature represents a transformative phase in educational technology research, marked by a surge in publications driven by initial research interest in new topics and media hype. While these publications highlight ChatGPT's potential in education, concerns arise regarding their quality, methodology, and uniqueness.ObjectiveOur study employs unconventional methods by combining altmetrics and bibliometrics to explore ChatGPT in education comprehensively.MethodsTwo scholarly databases, Web of Science and Altmetric, were adopted to retrieve publications with citations and those mentioned on social media, respectively. We used a search query, “ChatGPT,” and set the publication date between November 30th, 2022, and August 31st, 2023. Both datasets were within the education‐related domains. Through a filtering process, we identified three publication categories: 49 papers with both altmetrics and citations, 60 with altmetrics only, and 66 with citations only. Descriptive statistical analysis was conducted on all three lists of papers, further dividing the entire collection into three distinct periods. All the selected papers underwent detailed coding regarding open access, paper types, subject domains, and learner levels. Furthermore, we analysed the keywords occurring and visualized clusters of the co‐occurring keywords.Results and ConclusionsAn intriguing finding is the significant correlation between media/social media mentions and academic citations in ChatGPT in education papers, underscoring the transformative potential of ChatGPT and the urgency of its incorporation into practice. Our keyword analysis also reveals distinctions between the themes of the papers that received both mentions and citations and those that received only citations but no mentions. Additionally, we noticed a limitation that authors' choice of keywords might be influenced by individual subjective judgements, potentially skewing results in thematic analysis based solely on author‐assigned keywords such as keyword co‐occurrence analysis. Henceforth, we advocate for developing a standardized keyword taxonomy in the educational technology field and integrating Large Language Models to enhance keyword analysis in altmetric and bibliometric tools. This study reveals that ChatGPT in education literature is evolving from rapid publication to rigorous research.

A Correlation Study Between Altmetrics and Citations Using the Parameters of Title Rendering, Open Access, and Funding Status

A correlation study between professional academic citations and social media (SM)-dominated Altmetrics measures is of interest to library professionals. Three parameters namely, title wording, open access (OA) facility, and research funding credentials impact the citation of an article. The study investigates how these parameters relate to the literature related to COVID-19, SM, and misinformation. The citations were obtained from the Dimension- Altmetric citation database. The database was searched using the search terms “Covid-19 AND Social Media AND Misinformation”. The Mann-Whitney-U test, pearson correlation and point- biserial correlation analysis were used to analyse the 1,489 citations. A moderate positive pearson correlation (r= 0.429, p= 0.00) between the altmetric score and dimension-based citations was found. A significant association between the altmetric score and OA compared to those of non-OA articles was observed. No significant association was found between the altmetric score and the use of specific words in the title of the article except for the length of the title. Articles that received financial support had significantly higher altmetric scores but, in the case of citations, this was not significant.

Trends of Creative Thinking Skill Research in Chemistry Education Journal Across Indonesia

This study focuses on analyzing how trend research about creative thinking ability in chemistry. This research contains an analysis study with the number of articles published in chemistry education with the research in Indonesia from 2018-2022. Studies are carried out on 23 journal articles, including from different journals with the scope in Indonesia search engine journal uses publish or perish with determined inclusive criteria. Publish or Perish is a software that retrieves and analyze academic citations and uses a variety data of sources. Step analysis started with reading the abstract and data reduction also data inference based on the research instrument used. Topic research involves publication number, types of research, chemistry material studied, research subject, and data analysis technique. The main research is quantitative research on hydrocarbon material with an analysist technique by using mean. Based on the result, some recommendations can be guidance for further study so that the research result is more useful and has an impact on Indonesian life especially in teaching and learning terms.

Twitter and citations

Social media, especially Twitter, plays an increasingly important role among researchers in showcasing and promoting their research. Does Twitter affect academic citations? Making use of Twitter activity about columns published on VoxEU, a renowned online platform for economists, we develop an instrumental variable strategy to show that Twitter activity about a research paper has a causal effect on the number of citations that this paper will receive. We find that the existence of at least one tweet, as opposed to none, increases citations by 16–25%. Doubling overall Twitter engagement boosts citations by up to 16%.

The relation between social media mentions and academic citations in orthodontic journals: A preliminary study.

To study the relation between social media mentions and academic citations for articles published in peer-reviewed orthodontic journals. Articles published in early 2018 in seven peer-reviewed orthodontic journals were retrospectively analyzed in September 2022. Citation counts of the articles were evaluated using two databases: Google Scholar (GS) and Web of Science (WoS). The Altmetric Attention Score, Twitter, Facebook mentions, and Mendeley reads were tracked using the Altmetric Bookmarklet. The citation counts and social media mentions were correlated using Spearman rho. A total of 84 articles were identified during the initial search; 64 (76%) were original studies and systematic review articles and included in the analysis. A total of 38% of the articles had at least one mention on social media. Over the study period, the average number of citations of the articles mentioned on social media was higher than the non-mentioned articles for GS and WoS, respectively. Moreover, significant positive correlations existed between the Altmetric Attention Score and the number of citations in GS and WoS (rs=0.31, P=0.001 and rs=0.26, P=0.04). Social media mentions and citations of articles published in peer-reviewed orthodontic journals are correlated, with a clear difference in the number of citations in articles mentioned on social media versus those not mentioned, indicating possible increased reach of articles disseminated on social media.

The Academy and the Courts

In a recent article, Chief Justice of the High Court of Australia Susan Kiefel’s vision explores the symbiotic relationships between the courts and the academy, suggesting that academic writing can be a valuable resource for the judiciary. This suggestion raises the question, propounded in this article, that there is value for both the judiciary and academy to identify what judges cite and where academies should aim to publish. The literature review reveals two different approaches to academic citations, namely, that the High Court of Australia regularly cites the academy, but state supreme courts rarely cite academic material. This article aims to fill a gap in the existing literature to examine citation practices in two Australian territories: the Northern Territory and the Australian Capital Territory. Using the law-as-data and citation counting method, the article examines data published from 2010 to 2019 by the supreme courts of the two territories. It compares this data to the existing research of the state supreme courts and finds that the citation patterns of the supreme courts in the territories are consistent with those in the existing literature: in brief, the judiciary cite few publications, predominantly books. The data shows that there is a trend of decline in citing academic work by the judiciary in both territories. This trend is alarmingly problematic for the academy writing for the judiciary, and the potential of a flow-on effect of an ongoing and detrimental diminution of the symbiotic relationship envisioned by Kiefel CJ.

Twitter use by ophthalmology journals and its impact on citations

PurposeTo compare the use of Twitter and other social media among leading ophthalmology journals and its impact on academic citations. DesignCross-sectional study. MethodsThe tweets, accumulated citations, and Altmetric Attention Score (AAS) of all original manuscripts published in November 2017 from ten top ophthalmology journals were used to examine differences in tweeting habits among journals in ophthalmology. Multivariable linear regression analyses were performed to explore associations between tweeting, AAS, and the number of academic citations. ResultsA total of 221 journal articles and 1602 tweets were included in this study. Each original tweet (a tweet that is not a retweet) correlated with an additional 2.566 citations (95% CI=1.555 to 3.577, P<0.001). Additionally, randomized control trial study design (β=40.122, 95% CI=8.082 to 72.141, P=0.015), the journal impact factor (β=3.376, 95% CI=0.731 to 6.022, P=0.012), and the last author h-index (β=0.233, 95% CI=0.048 to 0.418, P=0.02) were also positively correlated with more academic citations. AAS was found to be weakly associated with having more academic citations (β=0.058, 95% CI=0.005 to 0.111, P=0.022). ConclusionsOriginal tweets and higher AAS for the articles of interest were positively associated with more academic citations for the specialty journals studied. These findings suggest a relationship between social media activity and traditional metrics of scientific influence within ophthalmology.

Translating Scientific Discovery Into Health Policy Impact: Innovative Bibliometrics Bridge Translational Research Publications to Policy Literature.

To understand how translational science efforts lead to outcomes, it is common to examine publications as a key step in the translational process. The National Institutes of Health's Clinical and Translational Science Awards (CTSA) program aims to accelerate that process by providing support to investigators. Although it is challenging to measure the impact of such support on translational outcomes, CTSA-supported research that arises in research publications can advance translation through use of these publications in public policy and guideline documents from government health agencies, intergovernmental organizations, and other outlets. Using cutting-edge bibliometric tools, the authors evaluated how CTSA-supported research has extended its impact beyond academic silos to influence public policy literature. The authors identified approximately 118,490 publications that acknowledged receiving support from a CTSA hub, from the inception of the program in 2006 through 2021. Articles were queried in the Overton policy database, which indexes references to publications in global policy literature. The search revealed 13% of CTSA-supported articles were referenced in policy documents, significantly more than the expected proportion (10%) calculated by Overton. References came from 576 policy source outlets across 87 countries, predominantly the United States and Europe. The most frequent sources included guidelines in PubMed Central, the World Health Organization, and the Centers for Disease Control and Prevention. The authors illustrate the bridge from translational research to public policy with case studies of 6 articles based on CTSA-supported research and having notable policy impact. They found articles with greater clinical relevance, altmetric attention (i.e., nonacademic community/public attention), and academic citation influence were more likely to be referenced in policy literature. Study findings help to characterize the kinds of research that have influenced and may be expected to influence health policy in the future.

A Preliminary Investigation of Fake Peer-Reviewed Citations and References Generated by ChatGPT

An analysis of academic citations and references generated by the ChatGPT artificial intelligence (AI) chatbot reveals the citations and references are in fact, fake. They are clearly generated by a predictive process rather than known facts. This suggests that early optimism regarding this technology for assisting in research could be misplaced, and that student misuse of the chatbot can be detected by the identification of fake citations and references. Despite these problems, the technology could have application in the writing of course materials for lower level undergraduate courses that do not necessarily require references. Subject matter expertise is required, however, to identify and remove incorrect information. The need to identify incorrect information provided by an AI chatbot is a skill that students will also increasingly need.

Global Publication Trends in Augmented Reality and Virtual Reality for Learning: The Last Twenty-One Years

Augmented reality (AR) and virtual reality (VR) provide new experiences in the digital and simulation worlds. While they are intended to stimulate users’ perception and senses in general, the two differ significantly. This study aimed to seek and analyze international publications on the trends, similarities, differences, advantages, and disadvantages of AR and VR for learning over the period 2001-2021. In this study, bibliometric analysis using VOSViewer with the help of MS Excel was utilized to visualize metadata obtained from Scopus. Publish or Perish software supported the data collection of this study, which included retrieving and analyzing academic citations. The findings show that the AR/VR has trend continued to rise over the years. The results showed that Denmark and India (based on country bibliographic pairs), Journal of Physics Conference Series (based on journal bibliographic pairs), Y. Zhou publications (2018) (based on bibliographic pairs of publications), and Archana M. (India) with Lars K. (Denmark) (based on author bibliographic pairs) have become the most influential in the field of augmented reality and virtual reality for learning. AR and VR are two technologies changing how people use screens, thus creating new and exciting interactive experiences. In the future, it is expected that further research related to AR/VR and even Mixed Reality (MR) will continue to develop along with technological advancement. However, pedagogical competence remains an essential key to learning.

Citation impact and reach of the IOC sport and exercise medicine consensus statements

BackgroundResearch evidence is commonly compiled into expert-agreed consensus statements or guidelines, with an increasing trend towards their publication in peer-reviewed journals. Prominent among these has been the publication of several...

A study of the correlation between publication delays and measurement indicators of journal articles in the social network environment-based on online data in PLOS.

The development of network technique and open access has made numerous research results freely obtained online, thereby facilitating the growth of the emerging evaluation methods of Altmetrics. However, it is unknown whether the time interval from reception to publication has an impact on the evaluation indicators of articles in the social network environment. We construct a range of time-series indexes that represent the features of the evaluation indicators and then explore the correlation of acceptance delay, technical delay, and overall delay with the relevant indicators of citations, usage, sharing and discussions, and collections that are obtained from the open access journal platform PLOS. Moreover, this research also explores the differences in the correlations of the delays for the literature in six subject areas with the corresponding indicators and the discrepancies of the correlations of delays and indexes in various metric quartiles. The results of the Mann-Whitney U test reveal that the length of delays affects the performance of the literature on some indicators. This study indicates that reducing the acceptance time and final publication time of articles can improve the efficiency of knowledge diffusion through the formal academic citation channel, but in the context of social networking communication, an appropriate interval at a particular stage in the publishing process can enhance the heat of sharing, discussion, and collection of articles to a small extent, hence boosting the influence and attention received by the literature on the internet.