Behavior of symptoms on twitter

With 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 plat...

Full description

Autores:
Salcedo, Dennis
Leon, Alejandro
Tipo de recurso:
Fecha de publicación:
2015
Institución:
Universidad El Bosque
Repositorio:
Repositorio U. El Bosque
Idioma:
eng
OAI Identifier:
oai:repositorio.unbosque.edu.co:20.500.12495/1588
Acceso en línea:
http://hdl.handle.net/20.500.12495/1588
Palabra clave:
Information management
Social networking (online)
Levenshtein distance
Redes sociales en línea
Big data
Procesamiento de la información
Rights
License
Acceso cerrado
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repository_id_str
dc.title.spa.fl_str_mv Behavior of symptoms on twitter
title Behavior of symptoms on twitter
spellingShingle Behavior of symptoms on twitter
Information management
Social networking (online)
Levenshtein distance
Redes sociales en línea
Big data
Procesamiento de la información
title_short Behavior of symptoms on twitter
title_full Behavior of symptoms on twitter
title_fullStr Behavior of symptoms on twitter
title_full_unstemmed Behavior of symptoms on twitter
title_sort Behavior of symptoms on twitter
dc.creator.fl_str_mv Salcedo, Dennis
Leon, Alejandro
dc.contributor.author.none.fl_str_mv Salcedo, Dennis
Leon, Alejandro
dc.subject.keywords.spa.fl_str_mv Information management
Social networking (online)
Levenshtein distance
topic Information management
Social networking (online)
Levenshtein distance
Redes sociales en línea
Big data
Procesamiento de la información
dc.subject.armarc.spa.fl_str_mv Redes sociales en línea
Big data
Procesamiento de la información
description With 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.
publishDate 2015
dc.date.issued.none.fl_str_mv 2015
dc.date.accessioned.none.fl_str_mv 2019-08-02T21:56:29Z
dc.date.available.none.fl_str_mv 2019-08-02T21:56:29Z
dc.type.spa.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.local.spa.fl_str_mv artículo
dc.identifier.issn.none.fl_str_mv 1613-0073
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12495/1588
dc.identifier.instname.spa.fl_str_mv instname:Universidad El Bosque
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad El Bosque
dc.identifier.repourl.none.fl_str_mv repourl:https://repositorio.unbosque.edu.co
identifier_str_mv 1613-0073
instname:Universidad El Bosque
reponame:Repositorio Institucional Universidad El Bosque
repourl:https://repositorio.unbosque.edu.co
url http://hdl.handle.net/20.500.12495/1588
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofseries.spa.fl_str_mv CEUR Workshop Proceedings, 1613-0073, Vol.1478, 2nd Annual International Symposium on Information Management and Big Data, 2015 p. 83-84
dc.relation.uri.none.fl_str_mv https://www.semanticscholar.org/paper/Behavior-of-Symptoms-on-Twitter-Salcedo-Le%C3%B3n/62f2305b9f261cd4f6f62c71f4963091dc3cf9ac
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Acceso cerrado
dc.rights.accessrights.none.fl_str_mv http://purl.org/coar/access_right/c_abf308
dc.rights.creativecommons.none.fl_str_mv 2015
rights_invalid_str_mv Acceso cerrado
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2015
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv SIMBig
dc.publisher.journal.spa.fl_str_mv CEUR Workshop Proceedings
institution Universidad El Bosque
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spelling Salcedo, DennisLeon, Alejandro2019-08-02T21:56:29Z2019-08-02T21:56:29Z20151613-0073http://hdl.handle.net/20.500.12495/1588instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coapplication/pdfengSIMBigCEUR Workshop ProceedingsCEUR Workshop Proceedings, 1613-0073, Vol.1478, 2nd Annual International Symposium on Information Management and Big Data, 2015 p. 83-84https://www.semanticscholar.org/paper/Behavior-of-Symptoms-on-Twitter-Salcedo-Le%C3%B3n/62f2305b9f261cd4f6f62c71f4963091dc3cf9acBehavior of symptoms on twitterarticleartículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Information managementSocial networking (online)Levenshtein distanceRedes sociales en líneaBig dataProcesamiento de la informaciónWith 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. 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