Modeling the co-citation dependence on semantic layers of co-cited documents

PurposeCo-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige of the co-cited articles. Despite its quantitative nature, it is found effective in retrieving and evaluating documents, signifying its linkage with the related documents' contents. To better understand the dynamism of the citation network, the present study aims to investigate various content features giving rise to the measure.Design/methodology/approachThe present study examined the interaction of different co-citation features in explaining the co-citation frequency. The features include the co-cited works' similarities in their full-texts, Medical Subject Headings (MeSH) terms, co-citation proximity, opinions and co-citances. A test collection is built using the CITREC dataset. The data were analyzed using natural language processing (NLP) and opinion mining techniques. A linear model was developed to regress the objective and subjective content-based co-citation measures against the natural log of the co-citation frequency.FindingsThe dimensions of co-citation similarity, either subjective or objective, play significant roles in predicting co-citation frequency. The model can predict about half of the co-citation variance. The interaction of co-opinionatedness and non-co-opinionatedness is the strongest factor in the model.Originality/valueIt is the first study in revealing that both the objective and subjective similarities could significantly predict the co-citation frequency. The findings re-confirm the citation analysis assumption claiming the connection between the cognitive layers of cited documents and citation measures in general and the co-citation frequency in particular.Peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.

Citation Distance: Measuring Knowledge Translation, Integration, Diffusion & Scope

We propose citation measures that weight the relationships between publications based on their semantic similarity. Measuring the semantic similarity between over 5 million patents and over 52 million citations, we define and demonstrate four distinct metrics that measure: knowledge translation, knowledge integration, knowledge diffusion, and knowledge scope. Applying these measures provides novel empirical demonstrations of how the research environment has changed in recent decades, showing that researchers have drawn from increasingly distant knowledge sources, and that knowledge diffusion has occurred at an ever-accelerating pace. These citation distance measures show substantial promise in furthering our understanding of the research process and improving our assessment of scientific impact. Nous proposons des mesures de citation capables de mesurer les relations entre les publications en fonction de leur similarité sémantique. En mesurant la similarité sémantique entre plus de 5 millions de brevets et plus de 52 millions de citations, nous définissons et démontrons quatre métriques qui permettent de mesurer le transfert des connaissances, l'intégration de connaissances, la diffusion des connaissances, et la portée des connaissances. L'application de ces mesures démontre de façon empirique la façon dont l'environnement de recherche a changé au cours des dernières décennies, montrant que les chercheurs utilisent des sources de connaissances de plus en plus éloignées sémantiquement, et que la diffusion des connaissances se produit à un rythme toujours plus rapide. Ces mesures de distance de citation contribuent à meilleure compréhension du processus de recherche et à l'amélioration de notre évaluation de l'impact scientifique.

How variable are the journal impact measures?

Purpose – The purpose of this paper is to examine the variability of the impact factor (IF) and additional metrics in biomedical journals to provide some clues to the reliability of journal citation indicators. Design/methodology/approach – Having used ISI Journal Citation Reports, from 2005 to 2011, the authors extracted 62 subject categories related to biomedical sciences. The category lists and citation profile for each journal were then downloaded and extracted. Coefficient of variation was applied to estimate the overall variability of the journal citation indicators. Findings – Total citation indicators for 3,411 journals were extracted and examined. The overall variability of IFs and other journal citation measures in basic, clinical or translational, open access or subscription journals decreased while the quality and prestige of those journals developed. Interestingly, journal citation measures produced dissimilar variability trends and thus highlighted the importance of using multiple instead of just one measure in evaluating the performance and influence of biomedical journals. Eigenfactor™, Article's Influence and Cited Half Life proposed as more reliable indicators. Originality/value – The relative variability of the journal citation measures in biomedical journals would decrease with a development in the impact and quality of journals. Eigenfactor™ and Cited Half Life are suggested as more reliable measures indicating few changes during the study period and across different impact level journals. These findings will be useful for librarians, researchers and decision makers who need to use citation measures as evaluative tools.