TY - JOUR AU - Eysenbach, Gunther PY - 2011 DA - 2011/12/16 TI -推文可以预测引用吗?指标与传统的基于Twitter和社会影响的相关指标的科学影响乔- J地中海互联网Res SP - e123六世- 13 - 4 KW -文献计量学KW -博客KW -期刊主题KW -同行评议KW -出版KW -社会媒体分析KW -科学计量学KW - infodemiology KW - infometrics KW -千瓦的结果的再现性医学2.0 KW -幂律KW - Twitter AB -背景:同行评议文章中的引用量和影响因子是普遍接受的科学影响的衡量标准。Web 2.0工具(如Twitter、博客或社交书签工具)提供了构建创新文章级或期刊级指标的可能性，以衡量影响和影响力。然而，这些新指标与传统指标(如引用量)之间的关系尚不清楚。目的:(1)通过分析社交媒体上的热议来探索衡量学术文章的社会影响和公众关注的可行性，(2)探索与学术文章发表相关的动态、内容和推文的时间，以及(3)探索这些指标是否足够敏感和具体，以预测高被引文章。方法:从2008年7月到2011年11月，所有包含医学互联网研究杂志(JMIR)文章链接的推文都被挖掘出来。对于2009年3月至2010年2月期间发表的55篇文章的1573条推文，计算了不同的社交媒体影响指标，并与17至29个月后Scopus和谷歌Scholar的后续引用数据进行了比较。验证了通过推文指标预测每期被引用最多的文章的启发式方法。结果:共有4208条推文引用了286篇不同的JMIR文章。 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. SN - 1438-8871 UR - //www.mybigtv.com/2011/4/e123/ UR - https://doi.org/10.2196/jmir.2012 UR - http://www.ncbi.nlm.nih.gov/pubmed/22173204 DO - 10.2196/jmir.2012 ID - info:doi/10.2196/jmir.2012 ER -