Search results

Citations have been used as a common basis to measure the academic accomplishments of scientific books. However, traditional citation analysis ignored content mining and without consideration of citation equivalence, which may lead to the decline of evaluation reliability. Hence, this paper aims to integrate multi-level citation information to conduct multi-dimensional analysis.

In this paper, books’ academic impacts were measured by integrating multi-level citation resources, including books’ citation frequencies and citation-related contents. Specifically, firstly, books’ citation frequencies were counted as the frequency-level metric. Secondly, content-level metrics were detected from multi-dimensional citation contents based on finer-grained mining, including topic extraction on the metadata and citation classification on the citation contexts. Finally, differential metric weighting methods were compared with integrate the multi-level metrics and computing books’ academic impacts.

The experimental results indicate that the integration of multiple citation resources is necessary, as it can significantly improve the comprehensiveness of the evaluation results. Meanwhile, compared with the type differences of books, disciplinary differences need more attention when evaluating the academic impacts of books.

Academic impact assessment of books via integrating multi-level citation information can provide more detailed evaluation information and cover shortcomings of methods based on single citation data. Moreover, the method proposed in this paper is publication independent, which can be used to measure other publications besides books.

The study of the diffusion of innovations into libraries has become a cottage industry of sorts, as libraries have always provided a fascinating test-bed of nonprofit institutions attempting improvement through the use of new policies, practices, and assorted apparatus (Malinconico, 1997). For example, Paul Sturges (1996) has focused on the evolution of public library services over the course of 70 years across England, while Verna Pungitore (1995) presented the development of standardization of library planning policies in contemporary America. For the past several decades, however, the study of diffusion in libraries has tended to focus on the implementation of information technologies (e.g., Clayton, 1997; Tran, 2005; White, 2001) and their associated competencies (e.g., Marshall, 1990; Wildemuth, 1992), the improvements in performance associated with their use (e.g., Damanpour, 1985, 1988; Damanpour & Evan, 1984), and ways to manage resistance to technological changes within the library environment (e.g., Weiner, 2003).

The use of bibliographic citation is a crucial aspect in the creation and dissemination of information. Numerous studies have been done ranging from simple citation counts to more complex bibliographic coupling studies, and from citation classifications to citation motivation studies. This review focusses on the citation studies that have explored the complexities and the underlying norms of the citation process. The major emphasis is placed upon the studies which have dealt with citation functions, citation quality, citation concepts and citation motivation. The international perspectives of citation practice are also discussed.

The paper introduces bibliometrics to the research area of knowledge organization – more precisely in relation to construction and maintenance of thesauri. As such, the paper reviews related work that has been of inspiration for the assembly of a semi‐automatic, bibliometric‐based, approach for construction and maintenance. Similarly, the paper discusses the methodical considerations behind the approach. Eventually, the semi‐automatic approach is used to verify the applicability of bibliometric methods as a supplement to construction and maintenance of thesauri. In the context of knowledge organization, the paper outlines two fundamental approaches to knowledge organization, that is, the manual intellectual approach and the automatic algorithmic approach. Bibliometric methods belong to the automatic algorithmic approach, though bibliometrics do have special characteristics that are substantially different from other methods within this approach.

The purpose of this paper is to present a narrative review of studies on the citing behavior of scientists, covering mainly research published in the last 15 years. Based on the results of these studies, the paper seeks to answer the question of the extent to which scientists are motivated to cite a publication not only to acknowledge intellectual and cognitive influences of scientific peers, but also for other, possibly non‐scientific, reasons.

The review covers research published from the early 1960s up to mid‐2005 (approximately 30 studies on citing behavior‐reporting results in about 40 publications).

The general tendency of the results of the empirical studies makes it clear that citing behavior is not motivated solely by the wish to acknowledge intellectual and cognitive influences of colleague scientists, since the individual studies reveal also other, in part non‐scientific, factors that play a part in the decision to cite. However, the results of the studies must also be deemed scarcely reliable: the studies vary widely in design, and their results can hardly be replicated. Many of the studies have methodological weaknesses. Furthermore, there is evidence that the different motivations of citers are “not so different or ‘randomly given’ to such an extent that the phenomenon of citation would lose its role as a reliable measure of impact”.

Given the increasing importance of evaluative bibliometrics in the world of scholarship, the question “What do citation counts measure?” is a particularly relevant and topical issue.

Citation content in academic papers and academic monographs promotes the knowledge flow among different publications. However, existing citation content analysis (CCA) focuses on academic papers and monographs have not received much research attention. We want to know if monographs are appropriate objects of CCA and whether existing methods of analyzing citation in papers are suitable for citation in monographs. Therefore, this paper aims to learn more about features of cited references and citation content in monographs and compare the characteristic of citation pattern between monographs and papers.

The authors manually annotate the references and syntactic citation content in academic monographs published by Morgan & Claypool and automatically extracted the references and citation content from academic papers published by Public Library of Science. Five features in two types citation pattern, namely, pattern of cited reference (including year, source and mention frequency of reference) and pattern of citation content (including location, length of citation content) are used to examine similarities and differences between monographs and papers.

The results indicate that between monographs and papers, differences are shown in location, length of citation content and year, source of reference, whereas frequency of mention of reference is similar.

Previous studies have explored the patter of citation content in academic papers. However, none of the existing literature, as far as the authors know, has considered the citation content in academic monographs and the similarities or differences among academic documents when studying the citation pattern.

Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing.

This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeer^X data set and then evaluates the generated citation context lists by applying a user case study on 30 articles.

Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeer^X using a list sorted by citation counts.

This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization.

The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

Citation data needs to be recognised as a part of the Commons – those works that are freely and legally available for sharing – and placed in an open repository. The paper aims to discuss this issue.

The Open Citation Corpus is a new open repository of scholarly citation data, made available under a Creative Commons CC0 1.0 public domain dedication and encoded as Open Linked Data using the SPAR Ontologies.

The Open Citation Corpus presently provides open access (OA) to reference lists from 204,637 articles from the OA Subset of PubMed Central, containing 6,325,178 individual references to 3,373,961 unique papers.

Scholars, publishers and institutions may freely build upon, enhance and reuse the open citation data for any purpose, without restriction under copyright or database law.

This paper argues for precision in citing. It proposes an approach to citation analysis that would encourage that precision, and contrasts this approach with one based on inferred motivations for citing. It argues that it is misguided to classify citation‐signallers by the nature of the motivations inferred to underlie their use, the inference process being both philosophically and methodologically less satisfactory than the approach this paper proposes; and that, moreover, it is unnecessary, since certain formal characteristics of citation‐signallers of themselves provide ample means of classification.

The present study aims to investigate how textual features, depth of citation treatment, reasons for citation, and relationships between citers and citees predict author‐rated citation importance.

A total of 49 biology and 50 psychology authors assessed the importance, reason for citation, and relationship to the cited author for each cited reference in his or her own recently published empirical article. Participants performed their evaluations on individualized web‐based surveys.

The paper finds that certain textual features, such as citation frequency, citation length, and citation location, as well as author‐stated reasons for citation predicted ratings of importance, but the strength of the relationship often depended on citation features in the article as a whole. The relationship between objective citation features and author‐rated importance also tended to be weaker for self‐citations.

The study sample included authors of relatively long empirical articles with a minimum of 35 cited references. There were relatively few disciplinary differences, which suggests that citation behavior in psychology may be similar to that in natural science disciplines. Future studies should involve authors from other disciplines employing diverse referencing patterns in articles of varying lengths and types.

Findings of the study have enabled a comprehensive, profound level of understanding of citation behaviors of biology and psychology authors. It uncovered a number of unique characteristics in authors' citation evaluations, such as article‐level context effects and rule‐ versus affective‐based judgments. The paper suggests possible implications for developing retrieval algorithms based on automatically predicted importance of cited references.