Cited by 908scientometrics and bibliometrics researchWeb visibility and informetricsAcademic Publishing and Open Access

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Do Altmetrics Work? Twitter and Ten Other Social Web Services

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Thelwall, Mike, Haustein, Stefanie, Larivière, Vincent, Sugimoto, Cassidy R.·2013·PLoS ONE

DOI 10.1371/journal.pone.0064841PMC PMC3665624

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Abstract

Altmetric measurements derived from the social web are increasingly advocated and used as early indicators of article impact and usefulness. Nevertheless, there is a lack of systematic scientific evidence that altmetrics are valid proxies of either impact or utility although a few case studies have reported medium correlations between specific altmetrics and citation rates for individual journals or fields. To fill this gap, this study compares 11 altmetrics with Web of Science citations for 76 to 208,739 PubMed articles with at least one altmetric mention in each case and up to 1,891 journals per metric. It also introduces a simple sign test to overcome biases caused by different citation and usage windows. Statistically significant associations were found between higher metric scores and higher citations for articles with positive altmetric scores in all cases with sufficient evidence (Twitter, Facebook wall posts, research highlights, blogs, mainstream media and forums) except perhaps for Google+ posts. Evidence was insufficient for LinkedIn, Pinterest, question and answer sites, and Reddit, and no conclusions should be drawn about articles with zero altmetric scores or the strength of any correlation between altmetrics and citations. Nevertheless, comparisons between citations and metric values for articles published at different times, even within the same year, can remove or reverse this association and so publishers and scientometricians should consider the effect of time when using altmetrics to rank articles. Finally, the coverage of all the altmetrics except for Twitter seems to be low and so it is not clear if they are prevalent enough to be useful in practice.

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Authors: Thelwall, M., Haustein, S., Larivière, V., & Sugimoto, C. R.
Affiliations: University of Wolverhampton (GB)
Science-Metrix (Canada) (CA)
Université du Québec à Montréal (CA)
Indiana University Bloomington (US)
Corresponding author: Thelwall, Mike
ORCID: Thelwall, Mike
Haustein, Stefanie
Larivière, Vincent
Sugimoto, Cassidy R.
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2013-05-28
Volume: 8
Issue: 5
Pages: e64841
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0064841
PMC: PMC3665624
OpenAlex: W2073023209
Cited by: 907
References: 48
Citations by year: 2026: 29 · 2025: 36 · 2024: 48 · 2023: 42 · 2022: 61 · 2021: 82 · 2020: 89 · 2019: 75 · 2018: 103 · 2017: 101 · 2016: 73 · 2015: 85 · 2014: 68 · 2013: 15
FWCI: 88.64
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS ONE (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Papyrus : Institutional Repository (Université de Montréal) (submittedVersion, cc-by)
Funders: National Science Foundation (1208804)
Joint Information Systems Committee
Arts and Humanities Research Council
Economic and Social Research Council
Topics: Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
Keywords: altmetrics, Twitter, social web services, article impact, citation analysis, PubMed articles, sign test, research highlights, mainstream media coverage
MeSH terms: Humans, Publishing, Bibliometrics, PubMed, Social Media

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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