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dc.contributor.authorUmar, Hafsah
dc.contributor.authorArandelovic, Oggie
dc.date.accessioned2017-06-14T10:30:14Z
dc.date.available2017-06-14T10:30:14Z
dc.date.issued2017-04-03
dc.identifier.citationUmar , H & Arandelovic , O 2017 , Learning nuanced cross-disciplinary citation metric normalization using the hierarchical Dirichlet process on big scholarly data . in Proceedings of the Symposium on Applied Computing . Association for Computing Machinery, Inc , pp. 1842-1847 , 32nd ACM SIGAPP Symposium On Applied Computing , Marrakech , Morocco , 3/04/17 . https://doi.org/10.1145/3019612.3019842en
dc.identifier.citationconferenceen
dc.identifier.isbn9781450344869
dc.identifier.otherPURE: 250204359
dc.identifier.otherPURE UUID: 2f455e8f-b7af-4bd5-b16e-e17f8b969434
dc.identifier.othercrossref: 10.1145/3019612.3019842
dc.identifier.otherScopus: 85020844429
dc.identifier.urihttp://hdl.handle.net/10023/10990
dc.description.abstractCitation counts have long been used in academia as a way of measuring, inter alia, the importance of journals, quantifying the significance and the impact of a researcher's body of work, and allocating funding for individuals and departments. For example, the h-index proposed by Hirsch is one of the most popular metrics that utilizes citation analysis to determine an individual's research impact. Among many issues, one of the pitfalls of citation metrics is the unfairness which emerges when comparisons are made between researchers in different fields. The algorithm we described in the present paper learns evidence based, nuanced, and probabilistic representations of academic fields, and uses data collected by crawling Google Scholar to perform field of study based normalization of citation based impact metrics such as the h-index.
dc.language.isoeng
dc.publisherAssociation for Computing Machinery, Inc
dc.relation.ispartofProceedings of the Symposium on Applied Computingen
dc.rights© 2017 the Authors. This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1145/3019612.3019842en
dc.subjectAcademicen
dc.subjectPublicationen
dc.subjectPublishingen
dc.subjectQuantificationen
dc.subjectUniversityen
dc.subjectIndexen
dc.subjectScienceen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectNDASen
dc.subject.lccQA75en
dc.titleLearning nuanced cross-disciplinary citation metric normalization using the hierarchical Dirichlet process on big scholarly dataen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1145/3019612.3019842


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