Citation Behavior Research Articles

A modified LSTM network to predict the citation counts of papers

Quantifiable predictability in the citation counts of articles is significant in scientometrics and informetrics. Many metrics based on the citation counts can evaluate the scientific impact of research articles and journals. Utilising time series models, an article’s citation counts up to the yth year after publication can be predicted by those up to the previous years. However, the typically used models cannot predict the fat tail of the actual citation distributions. Thus, based on cumulative advantage of the citation behaviour, we propose a method to predict the accumulated citation counts, by using a random number sampled from a power-law distribution to modify the results given by a recurrent neural network (RNN), long short-term memory. Extensive experiments on the data set including 17 journals in information science verified the effectiveness of our method by the good fittings on distributions and evolutionary trends of the citation counts of articles. Our method has the potential to be extended to predict other popular assessment measures such as impact factor and h-index for journals.

La producción científica de Uruguay disponible en acceso abierto a partir de Web of Science (1980-2019)

Within the framework of open science, this study focuses in open access to uruguayan scientific production. Its Objectives are to analyze the evolution of open access (OA), the distribution by routes, by areas of knowledge, its citation behavior, international collaboration and payment of Article Processing Charges (APC). A quantitative methodology based in metric studies of the information is used. Analyzes uruguayan researchers´s scientific production indexed in Web of Science (WOS) in the last 40 years. Results show that 28% of articles are available in AA for the analyzed period and half of them (14%) are published in journals charging APC, according to the DOAJ directory. Green Open Access publications receive the highest average citations number and articles in international collaboration are more frequently OA. Concludes that information about the state of Open Access to academic publications is a fundamental input for policy making and promoting open access practices by uruguayan scientific community.

Factors influencing the citation behavior of Pakistani novice LIS researchers

This study aims to understand the citation behavior of LIS research graduates in terms of their citation practices, citation motivation with the perceived quality and trustworthiness of cited sources. The paper endeavors to gain an insight into their citation behavior to gender, age, qualification, and publications. An online questionnaire survey was conducted in July 2020 to collect data from researchers who have been studying or completed their research degrees from the Institute of Information Management, University of the Punjab, Lahore-Pakistan. Data showed that almost half of the researchers published articles in journals. They preferred to cite: the most recent sources, articles written by reputable authors, highly cited sources, and articles which contain high-quality references. Researchers were motivated to cite open access and impact factor journals. There was no significant relationship between gender and any of the variables of citation behavior. However, only qualification affected the citation motivation of novice researchers. The findings may guide the policymakers about fund allocation to research projects.

Credit Lost: Two Decades of Software Citation in Astronomy

Software has been a crucial contributor to scientific progress in astronomy for decades, but practices that enable machine-actionable citations have not been consistently applied to software itself. Instead, software citation behaviors developed independently from standard publication mechanisms and policies, resulting in human-readable software citations that cannot effectively represent the influence software has had in the field. These historical software citation behaviors need to be understood in order to improve software citation guidance and develop relevant publishing practices that fully support the astronomy community. To this end, a twenty-three year retrospective analysis of software citation practices in astronomy was developed. Astronomy publications were mined for 410 aliases associated with nine software packages and analyzed to identify practices and trends that negatively impact software citation implementation.

Cited Reference Analysis in a National Laboratory to Assess Journal Usage*

Librarians at a large, federally funded, multidisciplinary research organization sought to assess the value provided by the library’s collections. One method for capturing this value includes determining how well their library’s collections meet researcher needs, as expressed by the materials those researchers choose to cite in the production of new scholarly communications. A cited reference analysis was performed on the publications originating from the institution to provide the necessary data for the assessment, as well as to aid in the creation of a core list of journals which will inform future collection development efforts. This paper fills a gap in the library and information science literature by addressing the citation behaviors of researchers at a large multidisciplinary government research facility, a setting that has not been examined previously.

The effect of citation behaviour on knowledge diffusion and intellectual structure

Citation behaviour is the source driver of scientific dynamics, and it is essential to understand its effect on knowledge diffusion and intellectual structure. This study explores the effect of citation behaviour on disciplinary knowledge diffusion and intellectual structure by comparing three types of citation behaviour trends, namely the high citation trend, medium citation trend, and low citation trend. The diffusion power, diffusion speed, and diffusion breadth were calculated to quantify knowledge diffusion. The properties of the global and local citation network structure were used to reflect the particular influences of citation behaviour on the scientific intellectual structure. The primary empirical results show that (a) the high citation behaviour trend could improve the knowledge diffusion speed for papers with a short citation history span. Additionally, the medium citation trend has the broadest diffusion breadth whereas the low citation behaviour trend might make the citation counts take off for papers with a long citation history span; (b) the high citation trend has a stronger influence and greater control over the intellectual structure, but this relationship is true only for papers with a short or normal citation history span. These findings could play important roles in scientific research evaluation and impact prediction.

Evidence and consequences of academic drift in the field of dental research: A bibliometric analysis 2000\u20132015

The mission of academic excellence has resulted in a science system that incentivises publications within high impact, often basic science journals, and less in application-oriented journals. For the dental research field this so-called academic drift can result in a research portfolio that moves away from research that serves dental healthcare. Therefore, we examined if and how academic drift has changed the dental research field. Web of Science data were used to develop a network map for dental research containing journal clusters that show similar citation behavior. From the year 2000 up to 2015, we explored the intensity of knowledge exchange between the different clusters through citation relations. Next, we analyzed changes in research focus of dental research institutes in seven countries, in dental research, clinical medicine research, basic science, public health research and other fields. Within the citation network, 85.5% of all references in dental journals concern references to other dental journals. The knowledge contribution of non-dental research fields to dental research was limited during the studied period. At the same time, the share of output of dental research institutes in dental research has declined. The research activity of the dental research institutes increased mainly in basic science while the knowledge input from basic science into dental research did not increase. Our findings suggest that the dental research portfolio is influenced by academic drift. This academic drift has increased the disbalance towards basic science, and presents a challenge for the scientific progress in dental healthcare services.

A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.

Knowledge Spillovers in ICT Industry of India: Evidence from the Firm’s Patent Citation Behavior

This study examines the knowledge spillovers in Indian Information and Communication Technology (ICT) industry by foreign multinational corporations using patent citation data from 2013 to 2017. Patents granted by the US patent office to applicants located in India are collected along with their citation counts. In order to examine the subject, this study applies negative binomial regression analysis. The outcome of this study shows that foreign ICT firms cited more patents with higher technology scope as compared to patents with smaller technology scope. Results also confirm the positive relationship between knowledge spillovers and geographical localization. This implies that the first inventor of both cited and citing patents share the same geographical region. However, the result of technological similarities is negative. Further, US firms citing both US and non-US patents, and non-US firms are citing more non-US patents and lesser US patents. This reveals that the knowledge flow patterns of US and non-US firms are significantly different.

An analysis of top author citations in software engineering and a comparison with other fields

Ioannidis et al. provided a science-wide database of author citations. The data offers an opportunity to researchers in a field to compare the citation behavior of their field with others. In this paper, we conduct a systematic analysis of citations describing the situation in software engineering and compare it with the fields included in the data provided by Ioannidis et al. For comparison, we take the measures used by Ioannidis into consideration. We also report the top-scientists and investigate software engineering researchers’ activities in other fields. The data was obtained and provided by Ioannidis et al. based on the Scopus database. Our method for analysis focuses on descriptive statistics. We compared software engineering with other fields and reported demographic information for the top authors. The analysis was done without any modifications to the ranking. In the later analysis, we observed that 37% of researchers listed as software engineers were not in the software engineering field. On the other hand, the database included a large portion of top authors (ca. 60% to 80%) identified in other software engineering rankings. Other fields using the database are advised to review the author lists for their fields. Our research’s main risk was that researchers are listed that do not belong to our studied field.

Detecting anomalous citation groups in journal networks

The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a “citation cartel” of journals. “Citation cartel” behavior has become increasingly common in recent years, with several instances being reported. Here, we propose an algorithm—named CIDRE—to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended from Journal Citation Reports due to anomalous citation behavior in the year of suspension or in advance. Furthermore, CIDRE detects many new anomalous groups, where the impact factors of the member journals are lifted substantially higher by the citations from other member journals. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate.

The citation behaviours and the academic performance of first-year Political Science students

This research will utilise citation analysis to explore the information behaviours of a cohort of first-year Political Science students at a university in the UK. Using a dataset of the citation behaviour of 262 students, we find that students who locate and cite particular sources of information receive better grades than those that do not. These findings suggest that students who know how to locate and subsequently cite these sources—which tend to be those regarded as more reliable and of higher quality—will achieve higher grades on their course work. This might sound obvious, but such assumptions are rarely checked; furthermore, such findings might convince doubtful students—and staff—to take information literacy more seriously.

What happens when a journal converts to open access? A bibliometric analysis

In recent years, increased stakeholder pressure to transition research to Open Access has led to many journals converting, or ‘flipping’, from a closed access (CA) to an open access (OA) publishing model. Changing the publishing model can influence the decision of authors to submit their papers to a journal, and increased article accessibility may influence citation behaviour. In this paper we aimed to understand how flipping a journal to an OA model influences the journal’s future publication volumes and citation impact. We analysed two independent sets of journals that had flipped to an OA model, one from the Directory of Open Access Journals (DOAJ) and one from the Open Access Directory (OAD), and compared their development with two respective control groups of similar journals. For bibliometric analyses, journals were matched to the Scopus database. We assessed changes in the number of articles published over time, as well as two citation metrics at the journal and article level: the normalised impact factor (IF) and the average relative citations (ARC), respectively. Our results show that overall, journals that flipped to an OA model increased their publication output compared to journals that remained closed. Mean normalised IF and ARC also generally increased following the flip to an OA model, at a greater rate than was observed in the control groups. However, the changes appear to vary largely by scientific discipline. Overall, these results indicate that flipping to an OA publishing model can bring positive changes to a journal.

Applying text similarity algorithm to analyze the triangular citation behavior of scientists

Triangular citation is a particular citation structure with research value and application significance, and the fundamental mechanism of its formation lies in the indirect citation between Literature A and Literature C. It is necessary to dig deeply into the citation context and citation motivation of indirect citation behavior to understand the internal mechanism of triangular citation. In this paper, the text-similarity algorithm was used to calculate the similarity of citation contents between B→A and C→A in 22,536 triangular citation data, and the followers C with high citation similarity were identified. Secondly, from the perspectives of language, literature type, interdisciplinary citation, and author self-citation, combined literature characteristics and followers’ identification results, the context and motivation of indirect citation behaviors imposed by the followers C were analyzed. The results show that followers C appear more frequently in triangular citations. At the same time, due to the influence of language differences, literature types, discipline differences or author self-citation, follower C will have lazy citation motivation when citing Literature A, partially or out-and-out requote Literature A from the citation content of Literature B, resulting in a high similarity between the citation content of C→A and B→A.

The aging effect in evolving scientific citation networks

The study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Traditional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.

Paper self-citation rates of leading soil science journals

Self-citation is a common practice in soil science publications, but can we find a common behaviour in soil science papers, and are there outliers? Here we investigate self-referencing (referencing earlier papers on which a researcher is an author or co-author) for papers published in nine leading soil science journals (Soil Biology and Biochemistry, Geoderma, Soil & Tillage Research, Catena, European Journal of Soil Science, Soil Science Society of America Journal, Soil Use and Management, Geoderma Regional, and SOIL) in 2018 to understand the self-referencing distribution of soil science papers. From 1753 research and review papers, a typical soil science paper (based on median values) had 5 authors, 56 references, and 10 pages. Interestingly, these numbers were very similar across all nine journals. We found that the median rate of self-referencing was 10%, and an interquartile range from 5 to 17%. The values were also very similar across the nine journals. The self-referencing values were reasonable and comparable to other disciplines and a past study of pedometrics papers. Self-referencing rates increase with the number of references and authors. However, its distribution was quite variable but tended to be less than 25% for papers with less than 10 authors with less than 60 references. We further conducted a graphical procedure on the self-referencing distribution to establish outlier thresholds related to the inter-quartile range. We found thresholds of self-referencing between 35% and 53%, which would suggest high values. There were only 61 papers (3.5% of total) above the lower threshold. We also established median and 80-percentile models based on the number of authors and the number of references. Based on the data, we can present an acceptable range of self-references, followed by an expert interpretation of the citation behaviour. This way can help authors, editors and reviewers to understand whether exceptional citation is at work.

The Protocol Gap.

Although peer review is considered one of the main pillars of modern science, experimental methods and protocols seem to be not a rigorous subject of this process in many papers. Commercial equipment, test kits, labeling kits, previously published concepts, and standard protocols are often considered to be not worth a detailed description or validation. Even more disturbing is the extremely biased citation behavior in this context, which sometimes leads to surrogate citations to avoid low-impact journals, preprints, or to indicate traditional practices. This article describes some of these surprising habits and suggests some measures to avoid the most unpleasant effects, which in the long term may undermine the credibility of science as a whole.

Applied artificial intelligence : A bibliometric study of an International Journal

Purpose: The study aims to conduct a bibliometric analysis of an international journal “Applied Artificial Intelligence (AAI)” to analyze publication trends, authorship patterns, collaborative networks, citation behaviors, and research hotspots of authors, organizations, and countries. Research Design/Methodology: “Applied Artificial Intelligence” is a peer-reviewed international journal, published by Taylor & Francis. The journal has published more than 1100 articles in 34 volumes so far. The idea was conceived to conduct the bibliometric study of the AAI journal. The data were collected from the Web of Science (WOS) database, owned by the Clarivate Analytics. A total of 1109 articles were retrieved and their metadata was collected for further analysis and interpretation. Additionally, the VOS viewer software was used for mapping and visualization of the bibliographic information. Findings: The journal has experienced positive growth in research productivity and negative growth in citations. Authors of 74 prominent countries have contributed to the journal and the USA has occupied the first position in publication count followed by Italy, India and England respectively. All the countries work in close collaboration and created a collaborative network and sub-networks. The USA is the pivot of the collaborative network, mostly collaborating with England, Japan, Italy, China and Germany. The keywords like the classification, optimization, algorithms and neural networks are the most common and hence the hot topics of research in the journal. Originality/Value: The main advantage of this study is that it provides profound knowledge of the content structure and developmental process of the journal to date. It is also valuable for researchers in the field of artificial intelligence to identify the research hotspots in this field.

Citation Indexing and Indexes

A citation index is a bibliographic database that provides citation links between documents. The first modern citation index was suggested by the researcher Eugene Garfield in 1955 and created by him in 1964, and it represents an important innovation to knowledge organization and information retrieval. This article describes citation indexes in general, considering the modern citation indexes, including Web of Science, Scopus, Google Scholar, Microsoft Academic, Crossref, Dimensions and some special citation indexes and predecessors to the modern citation index like Shepard’s Citations. We present comparative studies of the major ones and survey theoretical problems related to the role of citation indexes as subject access points (SAP), recognizing the implications to knowledge organization and information retrieval. Finally, studies on citation behavior are presented and the influence of citation indexes on knowledge organization, information retrieval and the scientific information ecosystem is recognized.

Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature.

On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data. It is imperative and only ethical for modern scientists to be vigilant in inspecting and be prepared to mitigate the potential biases when working with any datasets. This article describes a framework to examine biases in scientific document collections like CORD-19 by comparing their properties with those derived from the citation behaviors of the entire scientific community. In total, three expanded sets are created for the analyses: 1) the enclosure set CORD-19E composed of CORD-19 articles and their references and citations, mirroring the methodology used in the renowned “A Century of Physics” analysis; 2) the full closure graph CORD-19C that recursively includes references starting with CORD-19; and 3) the inflection closure CORD-19I, that is, a much smaller subset of CORD-19C but already appropriate for statistical analysis based on the theory of the scale-free nature of the citation network. Taken together, all these expanded datasets show much smoother trends when used to analyze global COVID-19 research. The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary. A question-answering system with such expanded scope of knowledge may perform better in understanding the literature and answering related questions. However, while CORD-19 appears to have topical coverage biases compared to the expanded sets, the collaboration patterns, especially in terms of team sizes and geographical distributions, are captured very well already in CORD-19 as the raw statistics and trends agree with those from larger datasets.