Timilsina, Mohan and Davis, Brian and Taylor, Mike and Hayes, Conor (2017) Predicting citations from mainstream news, weblogs and discussion forums. In: Proceedings 2017 IEEE/WIC/ACM International Conference on Web Intelligence WI 2017. Association for Computing Machinery, New York, pp. 237-244. ISBN 978-1-4503-4951-2

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Abstract

The growth in the alternative digital publishing is widening the breadth of scholarly impact beyond the conventional bibliometric community. Thus, research is becoming more reachable both inside and outside of academic institutions and are found to be shared, downloaded and discussed in social media. In this study, we linked the scientific articles found in mainstream news, weblogs and Stack Overflow to the citation database of peer-reviewed literature called Scopus. We then explored how standard graph-based influence metrics can be used to measure the social impact of scientific articles. We also proposed the variant of Katz centrality metrics called EgoMet score to measure the local importance of scientific articles in its ego network. Later we evaluated these computed graph-based influence metrics by predicting absolute citations. Our results of the prediction model describe 34% variance to predict citations from blogs and mainstream news and 44% variance to predict citations from Stack Overflow.

Item Type:	Book Section
Additional Information:	The 2017 IEEE/WIC/ACM International Conference on Web Intelligence was held in Leipzig, Germany from 23 to 26 August 2017
Keywords:	Graphs; Centrality; Impact; Prediction; Altmetrics;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	11840
Depositing User:	IR Editor
Date Deposited:	28 Nov 2019 10:15
Publisher:	Association for Computing Machinery
Refereed:	Yes
URI:	Publisher Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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