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dc.contributor.authorTimilsina, Mohan
dc.contributor.authorDavis, Brian
dc.contributor.authorTaylor, Mike
dc.contributor.authorHayes, Conor
dc.date.accessioned2018-05-28T08:57:42Z
dc.date.available2018-05-28T08:57:42Z
dc.date.issued2016-11-24
dc.identifier.citationTimilsina, M., Davis, B., Taylor, M., & Hayes, C. (2016). Towards predicting academic impact from mainstream news and weblogs: A heterogeneous graph based approach. Paper presented at the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 18-21 August, San Fransciso.en_IE
dc.identifier.urihttp://hdl.handle.net/10379/7379
dc.description.abstractThe realization that scholarly publications are discussed and have influence on discourse outside scientific and academic domains has given rise to area of scientometrics called alternative metrics or “altmetrics”. Furthermore, researchers in this field tend to focus primarily on measuring scientific activity on social media platforms such as Twitter, however these count-based metrics are vulnerable to gaming because they tend to lack concrete justification or reference to the primary source. In this collaboration with Elsevier, we extend the conventional citation graph to a heterogeneous graph of publications, scientists, venues, organizations and more authoritative media sources such as mainstream news and weblogs. Our approach consists of two parts: one is integrating the bibliometric data with the social data such as blogs, mainstream news. The other involves understanding how standard graph-based metrics can be used to predict the academic impact. Our result showed the computed graph-based metrics can reasonably predict the academic impact of early stage researchers.en_IE
dc.description.sponsorshipThis work has been funded by Scientific Foundation of Ireland (SFI/12/RC/2289) and the targeted project Elsevier. We appreciated Dr. Jonice Oliveira from Federal University of Rio de Jeneiro for creative feedback and supporten_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherIEEEen_IE
dc.relation.ispartof2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)en
dc.subjectAcademic impacten_IE
dc.subjectMainstream newsen_IE
dc.subjectWeblogsen_IE
dc.subjectPredictionen_IE
dc.subjectGraph based approachen_IE
dc.titleTowards predicting academic impact from mainstream news and weblogs: A heterogeneous graph based approachen_IE
dc.typeConference Paperen_IE
dc.date.updated2018-05-28T08:31:59Z
dc.identifier.doi10.1109/ASONAM.2016.7752425
dc.local.publishedsourcehttp://dx.doi.org/10.1109/ASONAM.2016.7752425en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.internal.rssid12631433
dc.local.contactConor Hayes, Information Technology, School Of Engineering &, Informatics, Nui Galway. 5077 Email: conor.hayes@nuigalway.ie
dc.local.copyrightcheckedYes
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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