J. Med. Internet Res. - Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground citat peerreview articl impact factor general accept measur scientif impact web tool twitter blog social bookmark tool provid possibl construct innov articlelevel journallevel metric gaug impact influenc howev relationship new metric tradit metric citat knownobject explor feasibl measur social impact public attent scholar articl analyz buzz social media explor dynam content time tweet relat public scholar articl explor whether metric sensit specif enough predict high cite articlesmethod juli novemb tweet contain link articl journal medic internet research jmir mine subset tweet articl publish issu differ metric social media impact calcul compar subsequ citat data scopus googl scholar month later heurist predict topcit articl issu tweet metric validatedresult total tweet cite distinct jmir articl distribut tweet first day articl public follow power law zipf bradford pareto distribut tweet sent day articl publish tweet day period follow day follow rapid decay pearson correl tweetat citat moder statist signific correl coeffici rang logtransform googl scholar citat less clear scopus citat rank correl linear multivari model time tweet signific predictor p explain variat citat high tweet articl time like high cite lesstweet articl high tweet articl high cite lesstweet articl high cite rate ratio confid interv topcit articl can predict toptweet articl specif sensitivityconclus tweet can predict high cite articl within first day articl public social media activ either increas citat reflect under qualiti articl also predict citat true use metric measur distinct concept social impact social impact measur base tweet propos complement tradit citat metric propos twimpact factor may use time metric measur uptak research find filter research find reson public real time

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