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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/JCES-ARDPRE
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dc.contributor.advisor1Fabricio Benevenuto de Souzapt_BR
dc.contributor.referee1Fabricio Murai Ferreirapt_BR
dc.contributor.referee2Wagner Meira Juniorpt_BR
dc.contributor.referee3Maria da Graça Campos Pimentelpt_BR
dc.creatorJohnnatan Messias Peixoto Afonsopt_BR
dc.date.accessioned2019-08-12T04:32:24Z-
dc.date.available2019-08-12T04:32:24Z-
dc.date.issued2017-06-09pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/JCES-ARDPRE-
dc.description.resumoSocial media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic aspect. Despite numerous efforts that explore demographic aspects in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this dissertation, we attempt to identify gender and race of Twitter users located in U.S. using advanced image processing algorithms from Face++. We investigate how different demographic groups (i.e. male/female, asian/black/white) connect with each other and differentiate between them regarding linguistic styles and also their interests. We quantify to what extent one group follow and interact with each other and the extent to which these connections and interactions reflect in inequalities in Twitter. We also extract linguistic features from 6 categories (affective attributes, cognitive attributes, lexical density and awareness, temporal references, social and personal concerns, and interpersonal focus) in order to identify the similarities and differences in the messages they share in Twitter. Furthermore, we extract the absolute ranking difference of top phrases between demographic groups. As a dimension of diversity, we also use the topics of interest that we retrieve from each user. Our analysis shows that users identified as white and male tend to attain higher positions in Twitter, in terms of the number of followers and number of times in other user's lists. There are clear differences in the way of writing across different demographic groups in both gender and race domains as well as in the topic of interest. We hope our effort can stimulate the development of new theories of demographic information in the online space. Therefore, we developed and deployed the Who Makes Trends? Web-based service available at http://twitter-app.mpi-sws.org/who-makes-trends/pt_BR
dc.languageInglêspt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.initialsUFMGpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectDemographicpt_BR
dc.subjectLinguistic patternspt_BR
dc.subjectTwitterpt_BR
dc.subjectGender and racept_BR
dc.subjectInequalitypt_BR
dc.subject.otherComputaçãopt_BR
dc.subject.otherRedes sociais on-linept_BR
dc.subject.otherTipologia (Linguistica)pt_BR
dc.subject.otherTwitterpt_BR
dc.subject.otherIgualdadept_BR
dc.subject.otherDados Demograficos;pt_BR
dc.titleCharacterizing Interconnections and Linguistic Patterns in Twitterpt_BR
dc.typeDissertação de Mestradopt_BR
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