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Social impact assessment of scientist from mainstream news and weblogs

  • Mohan Timilsina
  • Waqas Khawaja
  • Brian Davis
  • Mike Taylor
  • Conor Hayes
Original Article
  • 414 Downloads

Abstract

Research policy makers, funding agencies, universities and government organizations evaluate research output or impact based on the traditional citation count, peer review, h-index and journal impact factors. These impact measures also known as bibliometric indicators are limited to the academic community and cannot provide the broad perspective of research impact in public, government or business. The understanding that scholarly impact outside scientific and academic sphere has given rise to an area of scientometrics called alternative metrics or “altmetrics.” Moreover, researchers in this area incline to center around gauging scientific activity via social media, namely Twitter. However, these count-based measurements of impact are sensitive to gaming as they lack concrete references to the primary source. In this work, we expand a conventional citation graph to a heterogeneous graph of publications, scientists, venues, organizations based on more reliable social media sources such as mainstream news and weblogs. Our method is composed of two components: the first one is combining the bibliometric data with social media data like blogs and mainstream news. The second component investigates how standard graph-based metrics can be applied to a heterogeneous graph to predict the academic impact. Our result showed moderate correlations and positive associations between the computed graph-based metrics with academic impact and also reasonably predict the academic impact of researchers.

Keywords

Altmetrics Heterogeneous Graph Impact h-index Scientist Prediction 

Notes

Acknowledgements

We would like to acknowledge Science Foundation of Ireland (SFI/12/RC/2289) and the targeted project Elsevier for funding this research. We extend our gratitude to John Lonican for creating a citation graph from SCOPUS database and Erik Aumayr for insightful thoughts and constructive criticism. We would like to appreciate Prof. Jonice Oliveira from the Federal University of Rio de Janeiro for creative feedback and support.

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Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  1. 1.Insight Centre for Data AnalyticsNational University of Ireland GalwayGalwayIreland
  2. 2.Statistical Cybermetrics Research GroupUniversity of WolverhamptonWolverhamptonUK

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