Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

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Prédiction distillée sur la base complète

Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

Catégories candidates

Métarecherche, Charge utile insuffisante (le modèle a refusé de juger)

Catégories consensuelles

aucune

Domaine

Signal candidat: aucuneSignal consensuel: aucune

Devis d'étude

Signal candidat: ObservationnelSignal consensuel: Observationnel

Genre

Signal candidat: EmpiriqueSignal consensuel: Empirique

Score de désaccord entre enseignants

0,207

Score d'incertitude au seuil

0,999

Statut de validation

machine_predicted_unvalidated · codex-gemma-dda1882f352a

Scores Codex et Gemma par catégorie

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Écart entre enseignants

0,123 · la distance entre les deux têtes enseignantes sur ce seul travail

Statut de validation

score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Résumé

BACKGROUND: Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact. Web 2.0 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence. However, the relationship of the these new metrics to traditional metrics such as citations is not known. OBJECTIVE: (1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles. METHODS: Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined. For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later. A heuristic to predict the top-cited articles in each issue through tweet metrics was validated. RESULTS: A total of 4208 tweets cited 286 distinct JMIR articles. The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 43.94% of all tweets in a 60-day period) or on the following day (528/3318, 15.9%), followed by a rapid decay. The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from .42 to .72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations. A linear multivariate model with time and tweets as significant predictors (P < .001) could explain 27% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95% confidence interval, 3.4-33.6). Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity. CONCLUSIONS: Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time.

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La notice

Revue

Journal of Medical Internet Research

Thématique

Social Media in Health Education

Domaine

Social Sciences

Établissements canadiens

University Health NetworkJMIR PublicationsUniversity of Toronto

Organismes subventionnaires

non disponible

Mots-clés

AltmetricsSocial mediaMicrobloggingRanking (information retrieval)Impact factorCitationBibliometricsMarketing buzzComputer scienceCitation impactData scienceInformation retrievalWorld Wide WebPolitical science

Résumé présent dans OpenAlex

oui

API

/api/v1/works/W1965964177