Predicting the future success of scientific publications through social network and semantic analysis

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.

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Fig. 1

Notes

  1. 1.

    See https://service.elsevier.com/app/answers/detail/a_id/15181/supporthub/scopus/ for details. Last accessed on March 19, 2020.

  2. 2.

    https://www.scopus.com/freelookup/form/author.uri. Last accessed on March 19, 2020.

  3. 3.

    - This prevent the need for normalizing citation count, since all publication used for prediction are of the same year and subject field.

  4. 4.

    https://arxiv.org/stats/monthly_submissions.

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Correspondence to Ciriaco Andrea D’Angelo.

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Fronzetti Colladon, A., D’Angelo, C.A. & Gloor, P.A. Predicting the future success of scientific publications through social network and semantic analysis. Scientometrics 124, 357–377 (2020). https://doi.org/10.1007/s11192-020-03479-5

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Keywords

  • Social network analysis
  • Text mining
  • Social capital
  • Abstract
  • Citability
  • Scholarly impact