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The impact of longstanding messages in micro-blogging classification

2015-10Conference paper Restricted access
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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorCosta, Joana
dc.contributor.authorSilva, Catarina
dc.contributor.authorAntunes, Mário
dc.contributor.authorBernardete Ribeiro
dc.date.accessioned2025-06-17T12:07:16Z
dc.date.available2025-06-17T12:07:16Z
dc.date.issued2015-10
dc.descriptionArticle number - 7280731
dc.descriptionConference name - International Joint Conference on Neural Networks, IJCNN 2015
dc.description.abstractSocial networks are making part of the daily routine of millions of users. Twitter is among Facebook and Instagram one of the most used, and can be seen as a relevant source of information as users share not only daily status, but rapidly propagate news and events that occur worldwide. Considering the dynamic nature of social networks, and their potential in information spread, it is imperative to find learning strategies able to learn in these environments and cope with their dynamic nature. Time plays an important role by easily out-dating information, being crucial to understand how informative can past events be to current learning models and for how long it is relevant to store previously seen information, to avoid the computation burden associated with the amount of data produced. In this paper we study the impact of longstanding messages in micro-blogging classification by using different training timewindow sizes in the learning process. Since there are few studies dealing with drift in Twitter and thus little is known about the types of drift that may occur, we simulate different types of drift in an artificial dataset to evaluate and validate our strategy. Results shed light on the relevance of previously seen examples according to different types of drift.eng
dc.description.sponsorshipThis work is supported by CISUC, via national funding by the FCT - Fundação para a Ciência e a Tecnologia. The iCIS project (CENTRO-07-ST24-FEDER-002003) is co-financed by QREN, in the scope of the Mais Centro Program and European Union's FEDER. This work is financed by the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project UIDIEEAl5001412013.
dc.identifier.citationJ. Costa, C. Silva, M. Antunes and B. Ribeiro, "The impact of longstanding messages in micro-blogging classification," 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 2015, pp. 1-8, doi: 10.1109/IJCNN.2015.7280731.
dc.identifier.doi10.1109/ijcnn.2015.7280731
dc.identifier.isbn978-1-4799-1960-4
dc.identifier.issn2161-4407
dc.identifier.urihttp://hdl.handle.net/10400.8/13289
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationCENTRO-07-ST24-FEDER-002003
dc.relationUIDIEEAl5001412013
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/7280731
dc.relation.ispartof2015 International Joint Conference on Neural Networks (IJCNN)
dc.rights.uriN/A
dc.subjectTagging
dc.subjectTwitter
dc.titleThe impact of longstanding messages in micro-blogging classificationeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2015-07
oaire.citation.conferencePlaceKillarney, Ireland
oaire.citation.title2015 International Joint Conference on Neural Networks (IJCNN)
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCosta
person.familyNameSilva
person.familyNameAntunes
person.givenNameJoana
person.givenNameCatarina
person.givenNameMário
person.identifierR-000-NX4
person.identifier.ciencia-idAF10-7EDD-5153
person.identifier.orcid0000-0002-4053-5718
person.identifier.orcid0000-0002-5656-0061
person.identifier.orcid0000-0003-3448-6726
person.identifier.scopus-author-id25930820200
relation.isAuthorOfPublication23d200dc-1a81-4bd9-9a4a-0efc28af6ce4
relation.isAuthorOfPublicationee28e079-5ca7-4842-9094-372c40f75c38
relation.isAuthorOfPublicatione3e87fb0-d1d6-44c3-985d-920a5560f8c1
relation.isAuthorOfPublication.latestForDiscovery23d200dc-1a81-4bd9-9a4a-0efc28af6ce4

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