Quantifying the structural and temporal characteristics of negative links in signed citation networks     

《Information processing & management》2022,59(4):102996

Although the citation relationships among papers can help in tracking and understanding the development of knowledge, few studies have noted that the content and sentiments of citations of a paper differ. Here, we use sentiment-labeled citation data to construct a directed signed citation network, in which an author may agree with or criticize the cited paper and these represent different ways of inheriting knowledge. The dataset we use consists of 9,038 papers in the field of Computational Linguistics, including 25,275 citations, with 20.8% positive citations, 8.6% negative citations and 70.6% neutral citations. We systematically quantify the structural patterns of negative citations, impact assortativity of involved papers, occurrence time distribution and consequences of receiving negative attention. Remarkably, we find that papers with different impacts have a similar probability of receiving negative citations, and highly cited papers tend to give negative citations to low-impact papers around but avoid giving negative citations to high-impact papers. Our research also reveals the random occurrence rules and colocation patterns of negative citation distribution. In addition, we show that, in the short term, around 60% of multiple negative citations is positively related to the impact of the cited paper while more than 80% are negatively related to the impact in the long run. Our findings explain the pattern by which negative citations occur and deepen the understanding of negative citations.  相似文献   

从心理学博士论文引文看我馆的期刊保障率   总被引：4，自引：0，他引：4  

祁思妍  谈琼  糜凯 《新世纪图书馆》2003,(3):14-16

我们选择了本校发展与教育心理学2001届的博士论文作为引文分析的对象。从引文的文献类型、引文出版年代和引文的语言类型的角度统计出使用率较高的期刊，获知本校的期刊文献保障率，为订购专业期刊提供参考依据。  相似文献   

国内引文数据库发展综述   总被引：4，自引：0，他引：4  

康延兴  李恩科 《情报科学》2004,22(6):765-768

本文主要从国内引文数据库发展的基本情况、主要应用情况、未来发展方向和建议三个方面叙述和分析了当前国内引文数据库的总体情况，并指出当前国内引文数据库应在注意深化和扩展应用的同时，力争在建设方面能有一个大的跨越式的发展。  相似文献   

2000年化学领域热点研究课题的引文分析   总被引：2，自引：0，他引：2  

赵英莉 《情报科学》2002,20(2):151-154

本文应用文献计量学的方法，对2000年《Chemistry Citation Index》引证的参考文献进行了统计分析，评价出了目前化学领域热点研究课题。  相似文献   

The effect of use and access on citations     

Michael J. Kurtz  Guenther Eichhorn  Alberto Accomazzi  Carolyn Grant  Markus Demleitner  Edwin Henneken  Stephen S. Murray 《Information processing & management》2005,41(6):1395-1402

It has been shown (Lawrence, S. (2001). Online or invisible? Nature, 411, 521) that journal articles which have been posted without charge on the internet are more heavily cited than those which have not been. Using data from the NASA Astrophysics Data System (ads.harvard.edu) and from the ArXiv e-print archive at Cornell University (arXiv.org) we examine the causes of this effect.  相似文献   

从期刊引文分析看经济学学科内部和学科间的知识交流   总被引：1，自引：0，他引：1  

杜奕才 《情报科学》2003,21(12):1252-1255

本文用通俗易懂的方法向一般读者介绍引文分析在经济学等方面的应用，读者可以从中了解引文的意义，认识经济学中两个重要领域在学科内和学科间知识交流中的作用。同时了解经济学与其相邻的商学学科和基本社会学科等九个学科之间知识交流的类型。  相似文献   

Ranking scientific articles based on bibliometric networks with a weighting scheme     

Yu Zhang  Min Wang  Florian Gottwalt  Morteza Saberi  Elizabeth Chang 《Journal of Informetrics》2019,13(2):616-634

As the volume of scientific articles has grown rapidly over the last decades, evaluating their impact becomes critical for tracing valuable and significant research output. Many studies have proposed various ranking methods to estimate the prestige of academic papers using bibliometric methods. However, the weight of the links in bibliometric networks has been rarely considered for article ranking in existing literature. Such incomplete investigation in bibliometric methods could lead to biased ranking results. Therefore, a novel scientific article ranking algorithm, W-Rank, is introduced in this study proposing a weighting scheme. The scheme assigns weight to the links of citation network and authorship network by measuring citation relevance and author contribution. Combining the weighted bibliometric networks and a propagation algorithm, W-Rank is able to obtain article ranking results that are more reasonable than existing PageRank-based methods. Experiments are conducted on both arXiv hep-th and Microsoft Academic Graph datasets to verify the W-Rank and compare it with three renowned article ranking algorithms. Experimental results prove that the proposed weighting scheme assists the W-Rank in obtaining ranking results of higher accuracy and, in certain perspectives, outperforming the other algorithms.  相似文献   

An empirical approach based on quantile regression for estimating citation ageing     

《Journal of Informetrics》2019,13(2):738-750

An aspect of citation behavior, which has received longstanding attention in research, is how articles’ received citations evolve as time passes since their publication (i.e., citation ageing). Citation ageing has been studied mainly by the formulation and fit of mathematical models of diverse complexity. Commonly, these models restrict the shape of citation ageing functions and explicitly take into account factors known to influence citation ageing. An alternative—and less studied—approach is to estimate citation ageing functions using data-driven strategies. However, research following the latter approach has not been consistent in taking into account those factors known to influence citation ageing. In this article, we propose a model-free approach for estimating citation ageing functions which combines quantile regression with a non-parametric specification able to capture citation inflation. The proposed strategy allows taking into account field of research effects, impact level effects, citation inflation effects and skewness in the distribution of cites effects. To test our methodology, we collected a large dataset consisting of more than five million citations to 59,707 research articles spanning 12 dissimilar fields of research and, with this data in hand, tested the proposed strategy.  相似文献   

Can Google Scholar and Mendeley help to assess the scholarly impacts of dissertations?     

Kayvan Kousha  Mike Thelwall 《Journal of Informetrics》2019,13(2):467-484

Dissertations can be the single most important scholarly outputs of junior researchers. Whilst sets of journal articles are often evaluated with the help of citation counts from the Web of Science or Scopus, these do not index dissertations and so their impact is hard to assess. In response, this article introduces a new multistage method to extract Google Scholar citation counts for large collections of dissertations from repositories indexed by Google. The method was used to extract Google Scholar citation counts for 77,884 American doctoral dissertations from 2013 to 2017 via ProQuest, with a precision of over 95%. Some ProQuest dissertations that were dual indexed with other repositories could not be retrieved with ProQuest-specific searches but could be found with Google Scholar searches of the other repositories. The Google Scholar citation counts were then compared with Mendeley reader counts, a known source of scholarly-like impact data. A fifth of the dissertations had at least one citation recorded in Google Scholar and slightly fewer had at least one Mendeley reader. Based on numerical comparisons, the Mendeley reader counts seem to be more useful for impact assessment purposes for dissertations that are less than two years old, whilst Google Scholar citations are more useful for older dissertations, especially in social sciences, arts and humanities. Google Scholar citation counts may reflect a more scholarly type of impact than that of Mendeley reader counts because dissertations attract a substantial minority of their citations from other dissertations. In summary, the new method now makes it possible for research funders, institutions and others to systematically evaluate the impact of dissertations, although additional Google Scholar queries for other online repositories are needed to ensure comprehensive coverage.  相似文献   

Predicting citation counts based on deep neural network learning techniques     

《Journal of Informetrics》2019,13(2):485-499

With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.  相似文献