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Concept Drift Awareness in Twitter Streams

2014-12-03Comunicação em conferência Acesso restrito
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datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorCosta, Joana
dc.contributor.authorSilva, Catarina
dc.contributor.authorAntunes, Mario
dc.contributor.authorRibeiro, Bernardete
dc.date.accessioned2026-05-25T10:49:51Z
dc.date.available2026-05-25T10:49:51Z
dc.date.issued2014-12-03
dc.description.abstractLearning in non-stationary environments is not an easy task and requires a distinctive approach. The learning model must not only have the ability to continuously learn, but also the ability to acquired new concepts and forget the old ones. Additionally, given the significant importance that social networks gained as information networks, there is an evergrowing interest in the extraction of complex information used for trend detection, promoting services or market sensing. This dynamic nature tends to limit the performance of traditional static learning models and dynamic learning strategies must be put forward. In this paper we present a learning strategy to learn with drift in the occurrence of concepts in Twitter. We propose three different models: a time-window model, an ensemble-based model and an incremental model. Since little is known about the types of drift that can occur in Twitter, we simulate different types of drift by artificially timestamping real Twitter messages in order to evaluate and validate our strategy. Results are so far encouraging regarding learning in the presence of drift, along with classifying messages in Twitter streams.eng
dc.description.sponsorshipWe gratefully acknowledge iCIS project (CENTRO-07-ST24-FEDER-002003).
dc.identifier.citationJ. Costa, C. Silva, M. Antunes and B. Ribeiro, "Concept Drift Awareness in Twitter Streams," 2014 13th International Conference on Machine Learning and Applications, Detroit, MI, USA, 2014, pp. 294-299, doi: 10.1109/ICMLA.2014.53
dc.identifier.doi10.1109/icmla.2014.53
dc.identifier.urihttp://hdl.handle.net/10400.8/16345
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/7033130
dc.relation.ispartof2014 13th International Conference on Machine Learning and Applications
dc.rights.uriN/A
dc.subjectTwitter
dc.subjectAdaptation models
dc.subjectTime-frequency analysis
dc.subjectEvent detection
dc.subjectContext
dc.subjectVectors
dc.titleConcept Drift Awareness in Twitter Streamseng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2014-12-03
oaire.citation.conferencePlaceDetroit, MI, USA
oaire.citation.title2014 13th International Conference on Machine Learning and Applications
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCosta
person.familyNameSilva
person.givenNameJoana
person.givenNameCatarina
person.identifier.ciencia-id1B19-3DDC-BE75
person.identifier.orcid0000-0002-4053-5718
person.identifier.orcid0000-0002-5656-0061
relation.isAuthorOfPublication23d200dc-1a81-4bd9-9a4a-0efc28af6ce4
relation.isAuthorOfPublicationee28e079-5ca7-4842-9094-372c40f75c38
relation.isAuthorOfPublication.latestForDiscovery23d200dc-1a81-4bd9-9a4a-0efc28af6ce4

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