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Record W2888923337 · doi:10.1002/pra2.2017.14505401040

On the citation advantage of tweeted papers at the journal level

2017· article· en· W2888923337 on OpenAlexafffund
Fei Shu, Stefanie Haustein

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2017
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité de MontréalMcGill University
FundersFonds de Recherche du Québec-Société et CultureAndrew W. Mellon Foundation
KeywordsCitationWeb of scienceLibrary sciencePolitical scienceInformation retrievalComputer scienceMEDLINELaw

Abstract

fetched live from OpenAlex

ABSTRACT This study investigates the effect of diffusing scientific articles on Twitter on their citation impact. For a set of 1.3 million papers covered by the Web of Science and published in 2012, normalized citation rates are compared between tweeted and non‐tweeted articles published in the same journal. The results indicate that tweeted papers, published in the same year and in the same journal received around 30% higher citation rates than papers which were not diffused on Twitter. This type of citation advantage appeared across disciplines and countries of authors, but varied in extent.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.215
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.215
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.023
Science and technology studies0.0030.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.276
GPT teacher head0.478
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2017
Admission routes2
Has abstractyes

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