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在神经外科研究中,社交媒体提及比5年影响因子更能有力地预测论文被引用情况。

Social Media Mentions are a Stronger Predictor of Citations than 5-Year Impact Factor in Neurosurgical Research.

作者信息

Behal Aditya, Gajjar Avi A, Srinivasan Aditya, Burkhardt Jan-Karl, Field Nicholas C, Paul Alexandra R

机构信息

Department of Neurosurgery, Albany Medical Center, Albany, New York, USA.

Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, New York, USA.

出版信息

Neurosurgery. 2025 Jan 16;97(2):501-507. doi: 10.1227/neu.0000000000003325.

Abstract

BACKGROUND AND OBJECTIVES

X (formerly known as Twitter) is a social media platform gaining popularity in neurosurgery. Other disciplines have demonstrated a positive correlation between Twitter activity and traditional citation metrics. This study aims to determine if X activity is a greater predictor of citation rates than a journal's 5-year impact factor (IF) among major neurosurgical journals.

METHODS

Using a mixed linear model, we compared the predictive value between alternative metrics (such as mentions on X and Altmetric attention score, a weighted aggregate of the attention an article receives on various platforms) and traditional citation metrics (5-year journal IF) on the number of citations an article received by analyzing 7592 articles published from January 2022 to December 2023 in 18 neurosurgical journals. It was necessary to also account for the confounding variable time since publication in the model to determine the true effect of altmetrics. The relative importance (RI) of each predictor variable was determined through permutation testing.

RESULTS

X mentions, time since publication, 5-year journal IF, and Altmetric attention score all significantly predict citation rates (P < .001). RI of X mentions on citation rates indicate that X (RI = 0.13) is approximately 8.7x times greater of a predictor of citations than 5-year IF (RI = 0.015) and 5.4x times greater of a predictor than the Altmetric attention score (RI = 0.024). Time of publication remains the strongest predictor (RI = 0.83).

CONCLUSION

Our study shows that in neurosurgical research, while social media mentions (X mentions) are significant, they are weaker predictors of citation rates than time since publication. Traditional journal IF and Altmetric attention scores have weaker predictive value. These findings indicate that altmetrics, especially X mentions, can complement traditional citation metrics.

摘要

背景与目的

X(前身为推特)是一个在神经外科领域日益流行的社交媒体平台。其他学科已证明推特活跃度与传统引用指标之间存在正相关。本研究旨在确定在主要神经外科期刊中,X平台的活跃度是否比期刊的5年影响因子(IF)更能预测引用率。

方法

我们使用混合线性模型,通过分析2022年1月至2023年12月在18种神经外科期刊上发表的7592篇文章,比较了替代指标(如X平台提及次数和Altmetric关注度得分,后者是文章在各种平台上获得的关注度的加权总和)与传统引用指标(5年期期刊IF)对文章引用次数的预测价值。为确定替代计量指标的真实效果,还需在模型中考虑自发表以来的混杂变量时间。通过置换检验确定每个预测变量的相对重要性(RI)。

结果

X平台提及次数、自发表以来的时间、5年期期刊IF和Altmetric关注度得分均能显著预测引用率(P <.001)。X平台提及次数对引用率的RI表明,X(RI = 0.13)对引用次数的预测能力约为5年IF(RI = 0.015)的8.7倍,是Altmetric关注度得分(RI = 0.024)的5.4倍。发表时间仍是最强的预测因素(RI = 0.83)。

结论

我们的研究表明,在神经外科研究中,虽然社交媒体提及次数(X平台提及次数)具有显著意义,但与自发表以来的时间相比,它们对引用率的预测能力较弱。传统期刊IF和Altmetric关注度得分的预测价值较低。这些发现表明,替代计量指标,尤其是X平台提及次数,可以补充传统引用指标。

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