Ensemble analysis of topical journal ranking in bioinformatics

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.

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