Do altmetrics correlate with the quality of papers? A large-scale empirical study based on F1000Prime data

In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first s...

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Veröffentlicht in:PloS one Jg. 13; H. 5; S. e0197133
Hauptverfasser: Bornmann, Lutz, Haunschild, Robin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 23.05.2018
Public Library of Science (PLoS)
Schlagworte:
Assessments
Authorship
Bibliometrics
Citation analysis
Citations
Computer and Information Sciences
Empirical analysis
Empirical Research
Evaluation
Factor analysis
Humans
Informetrics
Journal Impact Factor
Library associations
Multivariate analysis
Peer Group
Peer Review, Research - standards
Physical Sciences
Principal Component Analysis
Principal components analysis
Quality
Regression analysis
Research and Analysis Methods
Science journals
Scientometrics
Social Media
Social networks
Social Sciences
Technical writing
United States > US
Germany
ISSN:1932-6203, 1932-6203
Online-Zugang:Volltext
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Zusammenfassung:In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first step, we analyse the underlying dimensions of measurement for traditional metrics (citation counts) and altmetrics-by using principal component analysis (PCA) and factor analysis (FA). In the second step, we test the relationship between the dimensions and quality of papers (as measured by the post-publication peer-review system of F1000Prime assessments)-using regression analysis. The results of the PCA and FA show that altmetrics operate along different dimensions, whereas Mendeley counts are related to citation counts, and tweets form a separate dimension. The results of the regression analysis indicate that citation-based metrics and readership counts are significantly more related to quality, than tweets. This result on the one hand questions the use of Twitter counts for research evaluation purposes and on the other hand indicates potential use of Mendeley reader counts.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0197133