Microsoft Academic automatic document searches: accuracy for journal articles and suitability for citation analysis

2.50
Hdl Handle:
http://hdl.handle.net/2436/620842
Title:
Microsoft Academic automatic document searches: accuracy for journal articles and suitability for citation analysis
Authors:
Thelwall, Mike ( 0000-0001-6065-205X )
Abstract:
Microsoft Academic is a free academic search engine and citation index that is similar to Google Scholar but can be automatically queried. Its data is potentially useful for bibliometric analysis if it is possible to search effectively for individual journal articles. This article compares different methods to find journal articles in its index by searching for a combination of title, authors, publication year and journal name and uses the results for the widest published correlation analysis of Microsoft Academic citation counts for journal articles so far. Based on 126,312 articles from 323 Scopus subfields in 2012, the optimal strategy to find articles with DOIs is to search for them by title and filter out those with incorrect DOIs. This finds 90% of journal articles. For articles without DOIs, the optimal strategy is to search for them by title and then filter out matches with dissimilar metadata. This finds 89% of journal articles, with an additional 1% incorrect matches. The remaining articles seem to be mainly not indexed by Microsoft Academic or indexed with a different language version of their title. From the matches, Scopus citation counts and Microsoft Academic counts have an average Spearman correlation of 0.95, with the lowest for any single field being 0.63. Thus, Microsoft Academic citation counts are almost universally equivalent to Scopus citation counts for articles that are not recent but there are national biases in the results.
Publisher:
Elsevier
Journal:
Journal of Informetrics
Issue Date:
Feb-2018
URI:
http://hdl.handle.net/2436/620842
Additional Links:
https://www.sciencedirect.com/science/journal/17511577?sdc=2
Type:
Article
Language:
en
ISSN:
1751-1577
Appears in Collections:
Statistical Cybermetrics Research Group

Full metadata record

DC FieldValue Language
dc.contributor.authorThelwall, Mikeen
dc.date.accessioned2017-11-10T12:04:10Z-
dc.date.available2017-11-10T12:04:10Z-
dc.date.issued2018-02-
dc.identifier.issn1751-1577en
dc.identifier.urihttp://hdl.handle.net/2436/620842-
dc.description.abstractMicrosoft Academic is a free academic search engine and citation index that is similar to Google Scholar but can be automatically queried. Its data is potentially useful for bibliometric analysis if it is possible to search effectively for individual journal articles. This article compares different methods to find journal articles in its index by searching for a combination of title, authors, publication year and journal name and uses the results for the widest published correlation analysis of Microsoft Academic citation counts for journal articles so far. Based on 126,312 articles from 323 Scopus subfields in 2012, the optimal strategy to find articles with DOIs is to search for them by title and filter out those with incorrect DOIs. This finds 90% of journal articles. For articles without DOIs, the optimal strategy is to search for them by title and then filter out matches with dissimilar metadata. This finds 89% of journal articles, with an additional 1% incorrect matches. The remaining articles seem to be mainly not indexed by Microsoft Academic or indexed with a different language version of their title. From the matches, Scopus citation counts and Microsoft Academic counts have an average Spearman correlation of 0.95, with the lowest for any single field being 0.63. Thus, Microsoft Academic citation counts are almost universally equivalent to Scopus citation counts for articles that are not recent but there are national biases in the results.en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttps://www.sciencedirect.com/science/journal/17511577?sdc=2en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMicrosoft Academicen
dc.subjectCitation Analysisen
dc.subjectScientometricsen
dc.titleMicrosoft Academic automatic document searches: accuracy for journal articles and suitability for citation analysisen
dc.typeArticleen
dc.identifier.journalJournal of Informetricsen
dc.date.accepted2017-11-
rioxxterms.funderInternalen
rioxxterms.identifier.projectUoW101117MTen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0en
rioxxterms.licenseref.startdate2018-02-01en
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