The citation impact of information behavior theories in scholarly literature

Theories of information behavior have been developed and used in a variety of disciplines to describe the needs, seeking/search/sharing behaviors, and uses of information among individuals and groups in a variety of contexts. While the development of a theory is a complex, evolutionary process, often involving many thinkers over a long period of time, there is generally a single or small group of publications that can be pointed to as the genesis of a theory. The genesis publications for information behavior theories were examined to determine the information behavior theories receiving the most direct citations in scholarly literature. The findings of this study may be beneficial to library and information science researchers in identifying influential and growing theories of information behavior for incorporation as the foundation of their own research.     

On the relationships between bibliographic characteristics of scientific documents and citation and Mendeley readership counts: A large-scale analysis of Web of Science publications

In this paper we present a first large-scale analysis of the relationship between Mendeley readership and citation counts with particular documents’ bibliographic characteristics. A data set of 1.3 million publications from different fields published in journals covered by the Web of Science (WoS) has been analyzed. This work reveals that document types that are often excluded from citation analysis due to their lower citation values, like editorial materials, letters, news items, or meeting abstracts, are strongly covered and saved in Mendeley, suggesting that Mendeley readership can reliably inform the analysis of these document types. Findings show that collaborative papers are frequently saved in Mendeley, which is similar to what is observed for citations. The relationship between readership and the length of titles and number of pages, however, is weaker than for the same relationship observed for citations. The analysis of different disciplines also points to different patterns in the relationship between several document characteristics, readership, and citation counts. Overall, results highlight that although disciplinary differences exist, readership counts are related to similar bibliographic characteristics as those related to citation counts, reinforcing the idea that Mendeley readership and citations capture a similar concept of impact, although they cannot be considered as equivalent indicators.     

Beyond citation analysis: a model for assessment of research impact

Question:

Is there a means of assessing research impact beyond citation analysis?

Setting:

The case study took place at the Washington University School of Medicine Becker Medical Library.

Method:

This case study analyzed the research study process to identify indicators beyond citation count that demonstrate research impact.

Main Results:

The authors discovered a number of indicators that can be documented for assessment of research impact, as well as resources to locate evidence of impact. As a result of the project, the authors developed a model for assessment of research impact, the Becker Medical Library Model for Assessment of Research.

Conclusion:

Assessment of research impact using traditional citation analysis alone is not a sufficient tool for assessing the impact of research findings, and it is not predictive of subsequent clinical applications resulting in meaningful health outcomes. The Becker Model can be used by both researchers and librarians to document research impact to supplement citation analysis.     

Improving the reliability of short-term citation impact indicators by taking into account the correlation between short- and long-term citation impact

The normalized citation indicator may not be sufficiently reliable when a short citation time window is used, because the citation counts for recently published papers are not as reliable as those for papers published many years ago. In a limited time period, recent publications usually have insufficient time to accumulate citations and the citation counts of these publications are not sufficiently reliable to be used in the citation impact indicators. However, normalization methods themselves cannot solve this problem. To solve this problem, we introduce a weighting factor to the commonly used normalization indicator Category Normalized Citation Impact (CNCI) at the paper level. The weighting factor, which is calculated as the correlation coefficient between citation counts of papers in the given short citation window and those in the fixed long citation window, reflects the degree of reliability of the CNCI value of one paper. To verify the effect of the proposed weighted CNCI indicator, we compared the CNCI score and CNCI ranking of 500 universities before and after introducing the weighting factor. The results showed that although there was a strong positive correlation before and after the introduction of the weighting factor, some universities’ performance and rankings changed dramatically.     

A business citation analysis: A local follow-up study

While there is no official style for business writing, the most common citation style in business research has not been determined previously. The purpose of this study is to evaluate the top scholarly business journals to determine the most commonly used citation style in business academic research. The author used the list of 452 top business journals included in the Social Sciences Citation Index (SSCI) Journal Citation Report. The results show that 69% of business journals use a publisher-dictated style, whereas of the “traditional” citation styles, 11% use the American Psychological Association style, 10% use Harvard style, 8% use Chicago style, and none use Modern Language Association style.     

Statistical relationships between corresponding authorship,international co-authorship and citation impact of national research systems

This paper presents a statistical analysis of the relationship between three science indicators applied in earlier bibliometric studies, namely research leadership based on corresponding authorship, international collaboration using international co-authorship data, and field-normalized citation impact. Indicators at the level of countries are extracted from the SIR database created by SCImago Research Group from publication records indexed for Elsevier’s Scopus. The relationship between authorship and citation-based indicators is found to be complex, as it reflects a country’s phase of scientific development and the coverage policy of the database. Moreover, one should distinguish a genuine leadership effect from a purely statistical effect due to fractional counting. Further analyses at the level of institutions and qualitative validation studies are recommended.     

Dissemination and impact of U.S. department of education's library research and demonstration projects: A citation analysis

Between 1965 and 1980, the Library Research and Demonstration Branch within the Department of Education awarded over $25 million to 312 projects. By tracing the citations in Social Sciences Citation Index from a random sampling of 52% of these projects, this study has attempted to assess the dissemination and impact of the projects in the professional literature.Approximately half of the projects were not cited in SSCI. The citations tended to be clustered among a small number of library-related serials. A small number of funded projects accounted for a large number of the citations. The most cited projects cost only one-fifth as much as the most expensive studies, yet were cited nearly five times as often.     

Methods for examining small literatures: Explication,physical analysis,and citation patterns

Three methods—explication, physical analysis, and citation patterns—are used to dissect a small literature: the information overload research from library studies. Explication is an exercise in critical reading and the trilevel explication used here examines overt research structure, backward citation chaining, and within- text inquiry. Overt structure seeks standard research characteristics. Backward citation chaining follows the abstract cognitive train of thought and a within-text inquiry analyzes textual anatomy according to implicit cues: syntactic, semantic, and pragmatic. Physical examination considers the text as artifact and inventories physical properties: publishing entity, co-authorship, literature age, or the amount of explanatory materials. Citation patterns follow bibliometric tenets and identify core researchers, co-authorship, linking citations, overall citing behavior, and the degree of peer- and self-citing. Crossdiscipline comparisons arise from a similar analysis of the overload research from consumer science and psychology/psychiatry. Conclusions arise from the literature itself and result in simple evidentiary statements.     

A comparative study of cross-domain research output and citations: Research impact cubes and binary citation frequencies

Across the various scientific domains, significant differences occur with respect to research publishing formats, frequencies and citing practices, the nature and organisation of research and the number and impact of a given domain's academic journals. Consequently, differences occur in the citations and h-indices of the researchers. This paper attempts to identify cross-domain differences using quantitative and qualitative measures. The study focuses on the relationships among citations, most-cited papers and h-indices across domains and for research group sizes. The analysis is based on the research output of approximately 10,000 researchers in Slovenia, of which we focus on 6536 researchers working in 284 research group programmes in 2008–2012.As comparative measures of cross-domain research output, we propose the research impact cube (RIC) representation and the analysis of most-cited papers, highest impact factors and citation distribution graphs (Lorenz curves). The analysis of Lotka's model resulted in the proposal of a binary citation frequencies (BCF) distribution model that describes well publishing frequencies. The results may be used as a model to measure, compare and evaluate fields of science on the global, national and research community level to streamline research policies and evaluate progress over a definite time period.     

A systematic empirical comparison of different approaches for normalizing citation impact indicators

We address the question how citation-based bibliometric indicators can best be normalized to ensure fair comparisons between publications from different scientific fields and different years. In a systematic large-scale empirical analysis, we compare a traditional normalization approach based on a field classification system with three source normalization approaches. We pay special attention to the selection of the publications included in the analysis. Publications in national scientific journals, popular scientific magazines, and trade magazines are not included. Unlike earlier studies, we use algorithmically constructed classification systems to evaluate the different normalization approaches. Our analysis shows that a source normalization approach based on the recently introduced idea of fractional citation counting does not perform well. Two other source normalization approaches generally outperform the classification-system-based normalization approach that we study. Our analysis therefore offers considerable support for the use of source-normalized bibliometric indicators.     

Characterizing references from different disciplines: A perspective of citation content analysis

Multidisciplinary cooperation is now common in research since social issues inevitably involve multiple disciplines. In research articles, reference information, especially citation content, is an important representation of communication among different disciplines. Analyzing the distribution characteristics of references from different disciplines in research articles is basic to detecting the sources of referred information and identifying contributions of different disciplines. This work takes articles in PLoS as the data and characterizes the references from different disciplines based on Citation Content Analysis (CCA). First, we download 210,334 full-text articles from PLoS and collect the information of the in-text citations. Then, we identify the discipline of each reference in these academic articles. To characterize the distribution of these references, we analyze three characteristics, namely, the number of citations, the average cited intensity and the average citation length. Finally, we conclude that the distributions of references from different disciplines are significantly different. Although most references come from Natural Science, Humanities and Social Sciences play important roles in the Introduction and Background sections of the articles. Basic disciplines, such as Mathematics, mainly provide research methods in the articles in PLoS. Citations mentioned in the Results and Discussion sections of articles are mainly in-discipline citations, such as citations from Nursing and Medicine in PLoS.     

Journal topic citation potential and between-field comparisons: The topic normalized impact factor

The journal impact factor is not comparable among fields of science and social science because of systematic differences in publication and citation behavior across disciplines. In this work, a source normalization of the journal impact factor is proposed. We use the aggregate impact factor of the citing journals as a measure of the citation potential in the journal topic, and we employ this citation potential in the normalization of the journal impact factor to make it comparable between scientific fields. An empirical application comparing some impact indicators with our topic normalized impact factor in a set of 224 journals from four different fields shows that our normalization, using the citation potential in the journal topic, reduces the between-group variance with respect to the within-group variance in a higher proportion than the rest of indicators analyzed. The effect of journal self-citations over the normalization process is also studied.     

Do open access articles have greater citation impact?: A critical review of the literature

The last few years have seen the emergence of several open access options in scholarly communication which can broadly be grouped into two areas referred to as ‘gold’ and ‘green’ open access (OA). In this article we review the literature examining the relationship between OA status and citation counts of scholarly articles. Early studies showed a correlation between the free online availability or OA status of articles and higher citation counts, and implied causality without due consideration of potential confounding factors. More recent investigations have dissected the nature of the relationship between article OA status and citations. Three non-exclusive postulates have been proposed to account for the observed citation differences between OA and non-OA articles: an open access postulate, a selection bias postulate, and an early view postulate. The most rigorous study to date (in condensed matter physics) showed that, after controlling for the early view postulate, the remaining difference in citation counts between OA and non-OA articles is explained by the selection bias postulate. No evidence was found to support the OA postulate per se; i.e. article OA status alone has little or no effect on citations. Further studies using a similarly rigorous approach are required to determine the generality of this finding.     

The landmark citation method: analysis of a citation pattern as a collection assessment method.

The landmark citation method is a new collection assessment method based on the citation record of a single landmark article. This citation record is developed by identifying sources which cite the landmark article. A bibliography, extracted from the citation record, is then used to complete an assessment of the collection. This method was developed and used to assess the biotechnology collection of the National Library of Medicine. The information gained from this study, in addition to demonstrating the technique, also provided insight into the evolution of the biotechnology literature.     

Characterizing in-text citations in scientific articles: A large-scale analysis

We report characteristics of in-text citations in over five million full text articles from two large databases – the PubMed Central Open Access subset and Elsevier journals – as functions of time, textual progression, and scientific field. The purpose of this study is to understand the characteristics of in-text citations in a detailed way prior to pursuing other studies focused on answering more substantive research questions. As such, we have analyzed in-text citations in several ways and report many findings here. Perhaps most significantly, we find that there are large field-level differences that are reflected in position within the text, citation interval (or reference age), and citation counts of references. In general, the fields of Biomedical and Health Sciences, Life and Earth Sciences, and Physical Sciences and Engineering have similar reference distributions, although they vary in their specifics. The two remaining fields, Mathematics and Computer Science and Social Science and Humanities, have different reference distributions from the other three fields and between themselves. We also show that in all fields the numbers of sentences, references, and in-text mentions per article have increased over time, and that there are field-level and temporal differences in the numbers of in-text mentions per reference. A final finding is that references mentioned only once tend to be much more highly cited than those mentioned multiple times.     

Utilizing citation network structure to predict paper citation counts: A Deep learning approach

With the advancement of science and technology, the number of academic papers published each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the standard for evaluation and decision-making of them, such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very critical. The most common standard for measuring the quality of academic papers is the number of citation counts of them, as this indicator is widely used in the evaluation of scientific publications. It also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers. To improve the effective of citation counts prediction, we try to solve the citation counts prediction problem from the perspective of information cascade prediction and take advantage of deep learning techniques. Thus, we propose an end-to-end deep learning framework (DeepCCP), consisting of graph structure representation and recurrent neural network modules. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and outputs the citation counts of the corresponding paper after a period of time. It only exploits the structure and temporal information of the citation network, and does not require other additional information. According to experiments on two real academic citation datasets, DeepCCP is shown superior to the state-of-the-art methods in terms of the accuracy of citation count prediction.