Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/5844
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dc.contributor.authorMiguel Oliveira Sandimen
dc.contributor.authorPaula Teixeira Fortunaen
dc.contributor.authorÁlvaro Figueiraen
dc.contributor.authorLuciana Gomes Oliveiraen
dc.date.accessioned2018-01-10T10:30:56Z-
dc.date.available2018-01-10T10:30:56Z-
dc.date.issued2017en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/5844-
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-50901-3_50en
dc.description.abstractSocial networks are becoming a wide repository of information, some of which may be of interest for general audiences. In this study we investigate which features may be extracted from single posts propagated throughout a social network, and that are indicative of its relevance, from a journalistic perspective. We then test these features with a set of supervised learning algorithms in order to evaluate our hypothesis. The main results indicate that if a text fragment is pointed out as being interesting, meaningful for the majority of people, reliable and with a wide scope, then it is more likely to be considered as relevant. This approach also presents promising results when validated with several well-known learning algorithms.en
dc.languageengen
dc.relation6548en
dc.relation5088en
dc.relation6655en
dc.relation6547en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleJournalistic Relevance Classification in Social Network Messages: an Exploratory Approachen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:CRACS - Articles in International Conferences
CSIG - Articles in International Conferences

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