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Abstract

In this article, we explore how strongly author name disambiguation (AND) affects the results of an author-based citation analysis study, and identify conditions under which the traditional simplified approach of using surnames and first initials may suffice in practice. We compare author citation ranking and cocitation mapping results in the stem cell research field from 2004 to 2009 using two AND approaches: the traditional simplified approach of using author surname and first initial and a sophisticated algorithmic approach. We find that the traditional approach leads to extremely distorted rankings and substantially distorted mappings of authors in this field when based on first- or all-author citation counting, whereas last-author-based citation ranking and cocitation mapping both appear relatively immune to the author name ambiguity problem. This is largely because Romanized names of Chinese and Korean authors, who are very active in this field, are extremely ambiguous, but few of these researchers consistently publish as last authors in bylines. We conclude that a more earnest effort is required to deal with the author name ambiguity problem in both citation analysis and information retrieval, especially given the current trend toward globalization. In the stem cell research field, in which laboratory heads are traditionally listed as last authors in bylines, last-author-based citation ranking and cocitation mapping using the traditional approach to author name disambiguation may serve as a simple workaround, but likely at the price of largely filtering out Chinese and Korean contributions to the field as well as important contributions by young researchers.

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Abstract

This article explores the feasibility, benefits, and limitations of in-text author citation analysis and tests how well it works compared with traditional author citation analysis using citation databases. In-text author citation analysis refers to author-based citation analysis using in-text citation data from full-text articles rather than reference data from citation databases. It has the potential to help with the application of citation analysis to research fields such as the social sciences that are not covered well by citation databases and to support weighted citation and cocitation counting for improved citation analysis results. We found that in-text author citation analysis can work as well as traditional citation analysis using citation databases for both author ranking and mapping if author name disambiguation is performed properly. Using in-text citation data without any author name disambiguation, ranking authors by citations is useless, whereas cocitation analysis works well for identifying major specialties and their interrelationships with cautions required for the interpretation of small research areas and some authors' memberships in specialties.

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Abstract

How can citation analysis take into account the highly collaborative nature and unique research and publication culture of biomedical research fields? This study explores this question by introducing last-author citation counting and comparing it with traditional first-author counting and theoretically optimal all-author counting in the stem cell research field for the years 2004-2009. For citation ranking, last-author counting, which is directly supported by Scopus but not by ISI databases, appears to approximate all-author counting quite well in a field where heads of research labs are traditionally listed as last authors; however, first author counting does not. For field mapping, we find that author co-citation analyses based on different counting methods all produce similar overall intellectual structures of a research field, but detailed structures and minor specialties revealed differ to various degrees and thus require great caution to interpret. This is true especially when authors are selected into the analysis based on citedness, because author selection is found to have a greater effect on mapping results than does choice of co-citation counting method. Findings are based on a comprehensive, high-quality dataset extracted in several steps from PubMed and Scopus and subjected to automatic reference and author name disambiguation.