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dc.contributor.authorCristiano Prati, Ronaldo
dc.contributor.authorSaid-Hung, Elías Manuel
dc.date2019-09-01
dc.date.accessioned2019-10-01T14:18:59Z
dc.date.available2019-10-01T14:18:59Z
dc.identifier.issn09515666
dc.identifier.urihttps://reunir.unir.net/handle/123456789/9378
dc.description.abstractThrough the application of machine learning techniques, this paper aims to estimate the importance of messages with ideological load during the elections held in Spain on May 24th, 2015 posted by Twitter’s users, as well as other variables associated with the publication of these types of messages. Our study collected and analysed 24,900 tweets associated to two of the main trending topics’ hashtags (#24M and #Elections2015) used in the election day and build a predictive model to infer the ideological orientation for the messages which made use of these hashtags during Election Day. This approach allows us to classify the ideological orientation of all collected tweets, instead of only tweets that explicitly express their ideological or partisan preferences in the messages. Using the ideological orientation for all tweets predicted by our model, it was possible to identify how messages with a defined ideological load were pushed forward by users with leftist tendencies. We also observed a relationship between these messages and the partisan orientation of those who published them.es_ES
dc.language.isoenges_ES
dc.publisherAI and Societyes_ES
dc.relation.ispartofseries;vol. 34, nº 3
dc.relation.urihttps://link.springer.com/article/10.1007%2Fs00146-017-0761-0es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectsocial mediaes_ES
dc.subjectpolitical participationes_ES
dc.subjectelectionses_ES
dc.subjectSpaines_ES
dc.subjectideologyes_ES
dc.subjectmachine learninges_ES
dc.subjectScopuses_ES
dc.subjectEmerginges_ES
dc.titlePredicting the ideological orientation during the Spanish 24M elections in Twitter using machine learninges_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1007/s00146-017-0761-0


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