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dc.contributor.advisorMenezes, Ronaldo
dc.contributor.advisorde Lima Neto, Fernando Buarque
dc.contributor.authorPacheco, Diogo
dc.date.accessioned2018-01-19T15:24:24Z
dc.date.available2018-01-19T15:24:24Z
dc.date.created2017-12
dc.date.issued2017-12
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/11141/2297
dc.descriptionThesis (Ph.D.) - Florida Institute of Technology, 2017en_US
dc.description.abstractWe live in a digital era where everyday activities are increasingly being replaced by online interactions. In addition, technology advances and data availability are changing the way we expand our knowledge about ourselves, society, and the environment. The increasing availability of data, especially social media data, has called the attention of researchers, and we have been witnessing an outbreak in studies relying on this rich source of information. However, most social media research is tuned to improve the outcomes of specific problems. Therefore, the reuse of techniques used in different areas is limited to data specialists. We propose a straightforward data-driven methodology to perform exploratory analysis of social media data by processing the unstructured stream of social data into user characterization. Emergent collective behaviors are obtained by aggregating individual characterizations. The structured representations are analyzed using Statistics and Data Science techniques. The results highlight the methodology generalization capacity, since we apply it in three different domains: (i) sports, characterizing football supporters; (ii) culture, characterizing languages; and (iii) health, characterizing organ donation awareness. Finally, the knowledge extracted from these applications (experience) serve as input to further research; we propose a measure for social disorganization using the diversity of supporters in a region, and we show language network centralities as proxy for quality of life.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCC BY-SA 4.0en_US
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/legalcodeen_US
dc.titleInformation Densification of Social Constructs via Behavior Analysis of Social Media Users - A Study on Twitteren_US
dc.typeDissertationen_US
dc.date.updated2018-01-09T20:31:09Z
thesis.degree.nameDoctorate of Philosophy in Computer Sciencesen_US
thesis.degree.levelDoctoralen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.departmentComputer Sciencesen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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