Social Media Multidimensional Analysis for Intelligent Health Surveillance
Impacto
Scholar |
Otros documentos de la autoría: Aramburu Cabo, María José; Berlanga Llavori, Rafael; Lanza Cruz, Indira Lázara
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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INVESTIGACIONMetadatos
Título
Social Media Multidimensional Analysis for Intelligent Health SurveillanceFecha de publicación
2020-03-28Editor
MDPICita bibliográfica
ARAMBURU, María José; BERLANGA, Rafael; LANZA, Indira. Social Media Multidimensional Analysis for Intelligent Health Surveillance. International Journal of Environmental Research and Public Health, 2020, 17.7: 2289.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/1660-4601/17/7/2289Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have ... [+]
Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems. [-]
Proyecto de investigación
the Spanish Ministry of Industry and Commerce (grant numberTIN2017-88805-R) ; Universitat Jaume I (pre-doctoral grant PREDOC/2017/28)Derechos de acceso
info:eu-repo/semantics/openAccess
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- LSI_Articles [362]
- ICC_Articles [427]
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