Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact.
Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact.
复制标题
DOI：
10.2196/jmir.2012
复制
发表时间：
2011-12-19
影响因子：
7.4
通讯作者：
Eysenbach G
中科院分区：
文献类型：
--
作者：
Eysenbach G
同行评审文章中的引用和影响因子是普遍接受的科学影响的衡量标准。Twitter、博客或社会书签工具等Web 2.0工具提供了构建创新的文章级或期刊级指标来衡量影响力的可能性。然而，这些新指标与传统指标（如引文）的关系尚不清楚。(1)探讨通过分析社交媒体上的热议来衡量学术文章的社会影响力和公众对学术文章的关注度的可行性，（2）探索与学术文章发表相关的推文的动态、内容和时间，（3）探索这些指标是否足够敏感和具体，以预测高引用文章。在2008年7月至2011年11月期间，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1573条推文（约55篇文章）的子集，计算了不同的社交媒体影响指标，并与17至29个月后来自Scopus和Google Scholar的后续引文数据进行了比较。验证了一种通过推文指标预测每期最高被引文章的启发式方法。共有4208条推文引用了286篇不同的JMIR文章。文章发布后的前30天内，推文的分布遵循幂律（Zipf，Bradford或Pareto分布），大多数推文在文章发布当天（1458/3318，占60天内所有推文的43.94%）或第二天（528/3318，15.9%）发送，然后迅速衰减。推特和引文之间的Pearson相关性是中等的，具有统计学意义，对数转换的Google Scholar引文的相关系数范围为0.42至0.72，但Scopus引文和排名相关性不太清楚。以时间和推文作为显著预测因子的线性多变量模型（P <0.001）可以解释27%的引用变异。高推文文章被高引用的可能性是低推文文章的11倍（9/12或75%的高推文文章被高引用，而只有3/43或7%的低推文文章被高引用;率比0.75/0.07 = 10.75，95%置信区间，3.4-33.6）。最高引用的文章可以从最高推文文章预测，特异性为93%，灵敏度为75%。 推文可以预测文章发表后前3天内的高引用文章。社交媒体活动要么增加了引用，要么反映了文章的潜在质量，这些质量也预测了引用，但这些指标的真正用途是衡量社会影响的独特概念。基于推文的社会影响力的措施，提出了补充传统的引文指标。拟议的双影响因子可能是一个有用和及时的指标，以衡量研究成果的吸收和过滤研究成果与公众产生共鸣的真实的时间。
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. (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. 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. 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. 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.
登录
查看更多内容
影响因子：
3.7
作者：
Chew C;Eysenbach G
通讯作者：
Eysenbach G
DOI：
10.1002/asi.20803
发表时间：
2008-03-01
影响因子：
--
作者：
Thelwall, Mike;Kousha, Kayvan
通讯作者：
Kousha, Kayvan
影响因子：
4.5
作者：
Niederkrotenthaler T;Dorner TE;Maier M
通讯作者：
Maier M
影响因子游戏。现在是时候找到一种评估科学文献的更好方法了。
影响因子：
15.8
作者：
通讯作者：
--
影响因子：
3.7
作者：
Kousha, Kayvan;Thelwall, Mike;Rezaie, Somayeh
通讯作者：
Rezaie, Somayeh