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Does dirty data affect google scholar citations?
Author(s) -
Doğan Güleda,
Şencan İpek,
Tonta Yaşar
Publication year - 2016
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2016.14505301098
Subject(s) - affect (linguistics) , internet privacy , data science , computer science , psychology , communication
Google Scholar (GS) is a database that enables researchers to create their scholarly profiles and keeps track of, among others, their citation counts, and h‐ and i10‐index values. GS is now increasingly being used for research evaluation purposes. Although rich in bibliometric data, GS indexes some duplicate publications and citations, and therefore tends to inflate the citation counts to some extent. Based on a small sample of GS profiles of researchers, this paper aims to study the extent by which duplicates change the citation counts and metrics based thereupon. Findings show that duplicates in GS database somewhat inflates the citation metrics. The scale of the problem as well as the effect of dirty data on performance evaluations based on GS citations data need to be studied further using larger samples.