Finding high-impact interdisciplinary users based on friend discipline distribution in academic social networking sites

Abstract

Specialized academic social networking sites are gaining popularity in scientific communication. A huge volume of interdisciplinary information is generated when researchers from multiple disciplines participate in scientific communication, which makes it possible to discover interdisciplinary users from a range of disciplines. In this study we analyze ScienceNet, one of the most well-known academic social networking sites in China, to find high-impact interdisciplinary users. We focus on the discipline distribution of friends and adopt phylogenetic species evenness on discipline phylogenetic trees to find 128 high-impact interdisciplinary users. A questionnaire was then sent to these academics to test the accuracy of this method. The questionnaire results show that our approach can determine authority users who span specific disciplines. Thus our approach will be useful for finding interdisciplinary collaborators and academic social networking site-related international peer reviewers.

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    https://www.wjx.cn/jq/21238770.aspx

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Acknowledgements

This work was supported by National Social Science Fund Project (No. 17CTQ047).

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Correspondence to Chengzhi Zhang.

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Wu, X., Zhang, C. Finding high-impact interdisciplinary users based on friend discipline distribution in academic social networking sites. Scientometrics 119, 1017–1035 (2019). https://doi.org/10.1007/s11192-019-03067-2

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Keywords

  • Academic social network
  • Interdisciplinary users
  • Interdisciplinary distance
  • Phylogenetic species evenness

Mathematics Subject Classification

  • 68T30

JEL Classification

  • D830