Do citations and readership identify seminal publications?

This work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as se...

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Bibliographic Details
Published in:Scientometrics 2018-04, Vol.115 (1), p.239-262
Main Authors: Herrmannova, Drahomira, Patton, Robert M., Knoth, Petr, Stahl, Christopher G.
Format: Article
Language:English
Subjects:
Academic publications
Altmetrics
Bibliometrics
Citation analysis
Computer Science
Data mining
Datasets
Documents
GENERAL AND MISCELLANEOUS
Information retrieval
Information Storage and Retrieval
Library Science
Literature reviews
Motivation
Publication datasets
Research evaluation
Reviews
Scholarly communication
Scientific papers
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Summary:This work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline.
ISSN:0138-9130
1588-2861
DOI:10.1007/s11192-018-2669-y