Predicting the future success of scientific publications through social network and semantic analysis
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11192_2020_3479_ReferencePDF.pdf
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Author(s)
Fronzetti Colladon, Andrea
D’Angelo, Ciriaco A
Gloor, Peter A
Date Issued
April 29, 2020
Publisher
Springer International Publishing
Version
Author's final manuscript
Abstract
Abstract
Citations acknowledge the impact a scientific publication has on subsequent work. At the same time, deciding how and when to cite a paper, is also heavily influenced by social factors. In this work, we conduct an empirical analysis based on a dataset of 2010–2012 global publications in chemical engineering. We use social network analysis and text mining to measure publication attributes and understand which variables can better help predicting their future success. Controlling for intrinsic quality of a publication and for the number of authors in the byline, we are able to predict scholarly impact of a paper in terms of citations received 6 years after publication with almost 80% accuracy. Results suggest that, all other things being equal, it is better to co-publish with rotating co-authors and write the papers’ abstract using more positive words, and a more complex, thus more informative, language. Publications that result from the collaboration of different social groups also attract more citations.
MIT Department
Massachusetts Institute of Technology. Center for Collective Intelligence
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s11192-020-03479-5
