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dc.contributor.authorTimilsina, Mohan
dc.contributor.authorDavis, Brian
dc.contributor.authorTaylor, Mike
dc.contributor.authorHayes, Conor
dc.identifier.citationMohan Timilsina, Brian Davis, Mike Taylor, and Conor Hayes. 2017. Predicting Citations from Mainstream News, Weblogs and Discussion Forums. In Proceedings of WI ’17, Leipzig, Germany, August 23-26, 2017, 8 pages.
dc.description.abstractThe 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 scienti!c articles found in mainstream news, weblogs and Stack Over"ow to the citation database of peer-reviewed literature called Scopus. We then explored how standard graph-based in"uence metrics can be used to measure the social impact of scienti!c articles. We also proposed the variant of Katz centrality metrics called EgoMet score to measure the local importance of scienti!c articles in its ego network. Later we evaluated these computed graph-based in"uence 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 Over"ow.en_IE
dc.description.sponsorshipWe would like to acknowledge Science Foundation of Ireland (SFI/12/RC/2289) and the targeted project Elsevier for funding this research.en_IE
dc.publisherACM (Association for Computing Machinery)en_IE
dc.relation.ispartofIEEE/ACM International Conference of Web Intelligenceen
dc.titlePredicting citations from mainstream news, weblogs and discussion forumen_IE
dc.typeConference Paperen_IE
dc.contributor.funderScience Foundation Irelanden_IE
dc.local.contactConor Hayes, Information Technology, School Of Engineering &, Informatics, Nui Galway. 5077 Email:
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/

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