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科学与脸书:相同的流行规律!

Science and Facebook: The same popularity law!

作者信息

Néda Zoltán, Varga Levente, Biró Tamás S

机构信息

Babeș-Bolyai University, Department of Physics, Cluj-Napoca, Romania.

HIRG, HAS Wigner Research Centre for Physics, Budapest, Hungary.

出版信息

PLoS One. 2017 Jul 5;12(7):e0179656. doi: 10.1371/journal.pone.0179656. eCollection 2017.


DOI:10.1371/journal.pone.0179656
PMID:28678796
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5497968/
Abstract

The distribution of scientific citations for publications selected with different rules (author, topic, institution, country, journal, etc…) collapse on a single curve if one plots the citations relative to their mean value. We find that the distribution of "shares" for the Facebook posts rescale in the same manner to the very same curve with scientific citations. This finding suggests that citations are subjected to the same growth mechanism with Facebook popularity measures, being influenced by a statistically similar social environment and selection mechanism. In a simple master-equation approach the exponential growth of the number of publications and a preferential selection mechanism leads to a Tsallis-Pareto distribution offering an excellent description for the observed statistics. Based on our model and on the data derived from PubMed we predict that according to the present trend the average citations per scientific publications exponentially relaxes to about 4.

摘要

如果将不同规则(作者、主题、机构、国家、期刊等)选择的出版物的科学引用分布相对于其平均值进行绘制,这些分布会汇聚在一条曲线上。我们发现,脸书帖子的“份额”分布以与科学引用完全相同的方式重新缩放至同一条曲线。这一发现表明,引用与脸书人气指标受相同的增长机制影响,受到统计上相似的社会环境和选择机制的作用。在一个简单的主方程方法中,出版物数量的指数增长和优先选择机制导致了一个Tsallis-帕累托分布,它对观测到的统计数据提供了出色的描述。基于我们的模型和从PubMed获得的数据,我们预测,按照目前的趋势,每篇科学出版物的平均引用将呈指数级弛豫至约4次。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2aaa/5497968/66a14ddb7ff7/pone.0179656.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2aaa/5497968/47233b9f9f8f/pone.0179656.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2aaa/5497968/7c70ae271bdb/pone.0179656.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2aaa/5497968/66a14ddb7ff7/pone.0179656.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2aaa/5497968/47233b9f9f8f/pone.0179656.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2aaa/5497968/7c70ae271bdb/pone.0179656.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2aaa/5497968/66a14ddb7ff7/pone.0179656.g003.jpg

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[2]
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本文引用的文献

[1]
Quantifying the evolution of individual scientific impact.

Science. 2016-11-4

[2]
Model-based evaluation of scientific impact indicators.

Phys Rev E. 2016-9

[3]
Universality of Citation Distributions for Academic Institutions and Journals.

PLoS One. 2016-1-11

[4]
Towards a simple mathematical theory of citation distributions.

Springerplus. 2015-11-5

[5]
Bibliometrics: The Leiden Manifesto for research metrics.

Nature. 2015-4-23

[6]
Power laws in citation distributions: evidence from Scopus.

Scientometrics. 2015

[7]
On a heuristic point of view concerning the citation distribution: introducing the Wakeby distribution.

Springerplus. 2015-2-26

[8]
The Matthew effect in empirical data.

J R Soc Interface. 2014-9-6

[9]
Quantifying long-term scientific impact.

Science. 2013-10-4

[10]
Research efficiency: Turn the scientific method on ourselves.

Nature. 2012-4-4

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