Search

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

• We compare the performance of global metrics and their local variants in the identification of expert-selected milestone papers on two distinct citation datasets. • We obtain a family of local variants of PageRank (LeaderRank) with a tunable degree of locality and find they can generate highly correlated scores with the scores by the original metrics. • The local variants of PageRank (LeaderRank) perform similarly well as the original global PageRank (LeaderRank) algorithms in the identification of seminal papers. • The local variants of PageRank (LeaderRank) perform significantly better than local metrics such as citation count, h-index and semi-local centrality. • Compared to network-based global metrics, the proposed local estimates provide a better trade-off between performance and computational efficiency. Recent works aimed to understand how to identify "milestone" scientific papers of great significance from large-scale citation networks. To this end, previous results found that global ranking metrics that take into account the whole network structure (such as Google's PageRank) outperform local metrics such as the citation count. Here, we show that by leveraging the recursive equation that defines the PageRank algorithm, we can propose a family of local impact metrics. Our results reveal that the obtained PageRank-based local metrics outperform the citation count and other local metrics in identifying the seminal papers. Compared with global metrics, these local metrics can reach similar performance in the identification of seminal papers in shorter computational time, without requiring the whole network topology. Our findings could help to better understand the nature of groundbreaking research from citation network analysis and find practical applications in large-scale data. [ABSTRACT FROM AUTHOR]

With series on conventional television and pay TV platforms now a key element of media consumption, they have gained increasing academic attention in the last decade, both as a main object of study and in combination with other social phenomena. However, the boundaries of this line of research, which draws together researchers from different fields, have become increasingly blurred. This paper undertook a bibliometric investigation to understand how this line of research has come about, what its characteristics are, the main streams within it, and the extent to which the rise in publications reflects a mature and consolidated field of research in its own right. This analysis focused on the development of scientific production on television series indexed by database Scopus between 2010 and 2019 (n = 1,679 documents). More specifically, this study analyzed authorship, journals, national output and international collaboration, co-citation of keywords to ascertain the main intellectual trends in the area and the co-occurrence of references to find out if there is a theoretical body of works that serve as a foundation for this research. The results show symptoms of immaturity, such as a lack of continuity in authority, little concurrence between specialized journals and the most cited authors and works, vagueness in both the keyword clusters and the papers that are often cited together. In addition, a large number of the most cited works come from fields outside Communication that consider television series an accessory aspect of their main theme, such as their impact on the influx of tourists as a result due to the consumption of cultural works. On the other hand, this points to a strong, versatile line of research capable of hosting research on new and old media and related to various topics. [ABSTRACT FROM AUTHOR]

Education quality has undoubtedly become an important local and international benchmark for education, and an institute’s ranking is assessed based on the quality of education, research projects, theses, and dissertations, which has always been controversial. Hence, this research paper is influenced by the institutes ranking all over the world. The data of institutes are obtained through Google Scholar (GS), as input to investigate the United Kingdom’s Research Excellence Framework (UK-REF) process. For this purpose, the current research used a Bespoke Program to evaluate the institutes’ ranking based on their source. The bespoke program requires changes to improve the results by addressing these methodological issues: Firstly, Redundant profiles, which increased their citation and rank to produce false results. Secondly, the exclusion of theses and dissertation documents to retrieve the actual publications to count for citations. Thirdly, the elimination of falsely owned articles from scholars’ profiles. To accomplish this task, the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new evaluation. The data extracted from GS is processed into structured data, and afterward, it is utilized to generate statistical computations of citations’ analysis that contribute to the ranking based on their citations. The research promoted the predictive approach of correlational research. Furthermore, experimental evaluation reported encouraging results in comparison to the previous modification made by the proposed taxonomy. This paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

This article investigates subject-related research metrics from the most popular scientometrics platforms with the capability of citation count. Quantitative analyses have been carried out to determine the most- to the least-cited subject areas to indicate the most active and dynamic fields in research. Subject areas are classified based on statistical analyses and the number of citations received in the period of search. Numerous articles were analyzed from top-ranked journals in all research fields covered by SCImago journal classification. A comprehensive data set was generated in 27 research subject areas, including 313 subject categories and 81 high-ranked journals. Results showed that the highest number of citations were related to the sciences and science-related subject areas. The subject areas of computer science and mathematics had the lowest research metrics and the smallest number of highly cited publications. Web of Science performed better than Scopus for subject-related evaluations. Analysis of frequency data showed that Google Scholar cited fewer papers for low-cited publications than did Scopus or Web of Science. For highly cited publications, however, Google Scholar was found to have better performance. Web of Science showed the best consistency in citation coverage in most of the investigated subject areas. [ABSTRACT FROM AUTHOR]

(1) Introduction: Psoriasis is a chronic, immune-mediated disease that negatively impacts patients' quality of life and predisposes them to cardiovascular or metabolic diseases. This paper aims to summarize the knowledge structure and future directions in psoriasis research by means of bibliometrics. (2) Material and methods: The Thomson Reuters Web of Science database was interrogated using preestablished keywords. A list of the top 100 most cited articles focusing solely on psoriasis was compiled and analyzed. VOSviewer software was used to assess and visualize collaboration networks, citation, co-citation and co-wording analysis, and bibliographic coupling. (3) Results: The articles were written by 902 authors from 20 countries and were published in 31 journals. The United States was at the forefront of this field. Griffiths, CEM had the most citations, while the most prolific institution was Rockefeller University, New York City. Pathogenesis, especially key-pathogenic factors, immune pathways, and epidemiology were the most discussed topics. Work published in the last decade focused on the use of biologics. Keywords such as "quality of life", "efficacy", and "necrosis-factor alpha" have been widely used. (4) Conclusion: Research interest regarding psoriasis is high, leading to the rapid development of this field. Treatment modalities, especially novel-targeted therapies, immune pathways, and an integrative approach to such cases are receiving great interest and represent research hotspots in the future. [ABSTRACT FROM AUTHOR]

Existing research combines acupuncture theory with network science and proposes a new paradigm for the study of acupoint selection patterns—a key acupoint mining algorithm based on acupoint networks. However, the basic idea of this study for finding key acupoints is based on binary acupoint synergy relationships, which ignores the higher-order synergy among multiple acupoints and does not truly reflect the implicit patterns of each acupoint among meridian systems. Moreover, the mining results assessment method, which this new paradigm involves, does not have wide applicability and universality. In this paper, with the introduction of higher-order interactions between multiple acupoints, a high-specificity key acupoint mining algorithm based on 3-node motif is proposed in the acupoint-disease network (ADN). In response to the narrow applicability of the new research paradigm involving the evaluation of algorithms' measures, new and widely applicable and universal evaluation criteria are introduced in terms of resolution, network loss, and accuracy, respectively. Based on the principles of acupoint selection involved in acupuncture clinics in Chinese medicine, the acupoints involved in the data were divided into a total of 19 regions according to their distribution characteristics. From these 19 regions, we selected the key acupoints that have a large impact on the global network. Finally, we compared this algorithm with five other acupoint importance assessment algorithms in terms of resolution, network loss, and accuracy, respectively. The comprehensive results show that the algorithm identifies key acupoints with an accuracy of 63%, which is 14% to 21% higher than other existing methods. The key acupoints identified by the algorithm have a significant disruptive effect on the connectivity of the network, indicating that the key acupoints are at the core of the acupoint-disease network topology. They have a significant propagation influence on other acupoints, which means that the key acupoints have high-synergistic cooperation with other acupoints. Meanwhile, the stability and specificity of the algorithm ensure the reliability of the key acupoints. We believe that the key acupoints identified by the algorithm can be used as core acupoints from the perspective of network topology and high synergy of other acupoints, respectively, and help researchers explore targeted and high-impact combinations of acupoints to optimize existing acupuncture prescriptions under condition constraints. [ABSTRACT FROM AUTHOR]

The quality of scientific publications can be measured by quantitative indices such as the h‐index, Source Normalized Impact per Paper, or g‐index. However, these measures lack to explain the function or reasons for citations and the context of citations from citing publication to cited publication. We argue that citation context may be considered while calculating the impact of research work. However, mining citation context from unstructured full‐text publications is a challenging task. In this paper, we compiled a data set comprising 9,518 citations context. We developed a deep learning‐based architecture for citation context classification. Unlike feature‐based state‐of‐the‐art models, our proposed focal‐loss and class‐weight‐aware BiLSTM model with pretrained GloVe embedding vectors use citation context as input to outperform them in multiclass citation context classification tasks. Our model improves on the baseline state‐of‐the‐art by achieving an F1 score of 0.80 with an accuracy of 0.81 for citation context classification. Moreover, we delve into the effects of using different word embeddings on the performance of the classification model and draw a comparison between fastText, GloVe, and spaCy pretrained word embeddings. [ABSTRACT FROM AUTHOR]

This study investigates scholars' citation behaviors from a fine‐grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision‐making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest‐based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision‐making, followed by citation authority and citation inertia. However, big‐name scholars whose h‐indexes rank among the top 1% exhibit a unique pattern of citation behaviors—their citation decision‐making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia. [ABSTRACT FROM AUTHOR]

Despite overwhelming evidence to the contrary, the concept of 'race' as a biological unit continues to persist in various scientific disciplines, notably in the field of medicine. This paper explores the persistence of 'race as biology' in medical research via examining select citational practices that have perpetuated this problematic concept. Citations serve as a cornerstone in scientific literature, signifying reliability and expert affirmation. By analyzing citation threads and historical patterns, we aim to shed light on the creation and perpetuation of false scientific truths and their impact on medical research, training, and practice. We focus on two prominent examples, eGFR and Spirometry, and trace key articles' citational histories, highlighting the flawed evidence in support of racial corrections in medical assessments. The eGFR equation incorporates 'race' as a factor based on the erroneous belief that Black individuals have higher muscle mass than white individuals. Our analysis reveals that key cited sources for this belief lack robust and well-developed datasets. Similarly, Spirometry measurements incorporate racial correction factors, relying on questionable evidence dating back to the Civil War era. Citations serve as a cornerstone in scientific literature, signifying reliability and expert affirmation. They play a crucial role in shaping theoretical positions and validating data and assumed knowledge. Evaluating citation threads and key articles consistently referenced over time can reveal how falsehoods and erroneous assertions are constructed and maintained in scientific fields. This study underscores the need for critical examination of citational practices in medical research and urges a shift toward a more cautious approach when citing sources that support 'race as biology.' The paper calls for a reevaluation of pedagogical approaches and assigned readings in medical education to prioritize an anti-racist perspective in future research endeavors. • Algorithms in contemporary medicine often involve "race" as a biological variable. • We offer a review of the citation threads in eGFR and Spirometry algorithms. • Support for race as variable relies on faulty, weak, and unsupportive citations. • Medical training must attend to and use caution around "truths" about racialized bodies. [ABSTRACT FROM AUTHOR]

Purpose: This study identified the top 100 most impactful global contributors to social work journal scholarship. Methods: To conduct this descriptive study, we used a publicly available database of the world's leading scientists. After extracting all scholars in the social work category, we rank ordered them according to a composite measure of scholarly impact that controls for self-citations and author order. Results: All identified contributors to the profession's journals ranked highly relative to the larger global population of published scientists. Furthermore, 23 individuals were in the top 100,000 scientists globally. Scholars were based in seven different nations and most had solid social work credentials according to three measures: current affiliation in a social work program and Master of Social Work/doctoral degree status. Conclusions: The results reveal that social work is home to some of the world's leading scientists. Leveraging their skills and knowledge can help advance the profession's collective knowledge development and dissemination. [ABSTRACT FROM AUTHOR]

Bibliometric‐enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data‐driven approach, this article describes the development of a bibliometric‐enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric‐enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known‐item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions. [ABSTRACT FROM AUTHOR]

In this article, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS‐AND, two large, gold‐standard sub‐datasets for author name disambiguation (AND), of which LAGOS‐AND‐BLOCK is created for clustering‐based AND research and LAGOS‐AND‐PAIRWISE is created for classification‐based AND research. Our LAGOS‐AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021) include 7.5 M citations authored by 798 K unique authors (LAGOS‐AND‐BLOCK) and close to 1 M instances (LAGOS‐AND‐PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available. [ABSTRACT FROM AUTHOR]