Behavior of symptoms on twitter

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dc.contributor.authorSalcedo, Dennis
dc.contributor.authorLeon, Alejandro
dc.date.accessioned2019-08-02T21:56:29Z
dc.date.available2019-08-02T21:56:29Z
dc.date.issued2015
dc.description.abstractenglishWith the amount of data available on social networks, new methodologies for the analysis of information are needed. Some methods allow the users to combine different types of data in order to extract relevant information. In this context, the present paper shows the application of a model via a platform in order to group together information generated by Twitter users, thus facilitating the detection of trends and data related to particular symptoms. In order to implement the model, an analyzing tool that uses the Levenshtein distance was developed, to determine exactly what is required to convert a text into the following texts: ’gripa’-”flu”, ”dolor de cabeza”-”headache”, ’dolor de estomago’- ”stomachache”, ’fiebre’-”fever” and ’tos’- ”cough” in the area of Bogota. Among the ´ information collected, identifiable patterns emerged for each one of the texts.eng
dc.format.mimetypeapplication/pdf
dc.identifier.instnameinstname:Universidad El Bosquespa
dc.identifier.issn1613-0073
dc.identifier.reponamereponame:Repositorio Institucional Universidad El Bosquespa
dc.identifier.repourlrepourl:https://repositorio.unbosque.edu.co
dc.identifier.urihttps://hdl.handle.net/20.500.12495/1588
dc.language.isoeng
dc.publisherSIMBigspa
dc.publisher.journalCEUR Workshop Proceedingsspa
dc.relation.ispartofseriesCEUR Workshop Proceedings, 1613-0073, Vol.1478, 2nd Annual International Symposium on Information Management and Big Data, 2015 p. 83-84spa
dc.relation.urihttps://www.semanticscholar.org/paper/Behavior-of-Symptoms-on-Twitter-Salcedo-Le%C3%B3n/62f2305b9f261cd4f6f62c71f4963091dc3cf9ac
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.accessrightshttps://purl.org/coar/access_right/c_abf308
dc.rights.creativecommons2015
dc.rights.localAcceso cerradospa
dc.subject.armarcRedes sociales en líneaspa
dc.subject.armarcBig dataspa
dc.subject.armarcProcesamiento de la informaciónspa
dc.subject.keywordsInformation managementspa
dc.subject.keywordsSocial networking (online)spa
dc.subject.keywordsLevenshtein distancespa
dc.titleBehavior of symptoms on twitterspa
dc.typearticlespa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.localartículospa

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