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Abstract

Journal rankings, frequently determined by the journal impact factor or similar indices, are quantitative measures for evaluating a journal's performance in its discipline, which is presently a major research thrust in the bibliometrics field. Recently, text mining was adopted to augment journal ranking-based evaluation with the content analysis of a discipline taking a time-variant factor into consideration. However, previous studies focused mainly on a silo analysis of a discipline using either citation-or content-oriented approaches, and no attempt was made to analyze topical journal ranking and its change over time in a seamless and integrated manner. To address this issue, we propose a journal-time-topic model, an extension of Dirichlet multinomial regression, which we applied to the field of bioinformatics to understand journal contribution to topics in a field and the shift of topic trends. The journal-time-topic model allows us to identify which journals are the major leaders in what topics and the manner in which their topical focus. It also helps reveal an interesting distinct pattern in the journal impact factor of high- and low-ranked journals. The study results shed a new light for understanding topic specific journal rankings and shifts in journals' concentration on a subject.

Type

a

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Abstract

To understand the current state of a discipline and to discover new knowledge of a certain theme, one builds bibliometric content networks based on the present knowledge entities. However, such networks can vary according to the collection of data sets relevant to the theme by querying knowledge entities. In this study we classify three different bibliometric content networks. The primary bibliometric network is based on knowledge entities relevant to a keyword of the theme, the secondary network is based on entities associated with the lower concepts of the keyword, and the tertiary network is based on entities influenced by the theme. To explore the content and properties of these networks, we propose a tomographic content analysis that takes a slice-and-dice approach to analyzing the networks. Our findings indicate that the primary network is best suited to understanding the current knowledge on a certain topic, whereas the secondary network is good at discovering new knowledge across fields associated with the topic, and the tertiary network is appropriate for outlining the current knowledge of the topic and relevant studies.

Type

a