Characterizing Interconnections and Linguistic Patterns in Twitter

Johnnatan Messias Peixoto Afonso

Use este identificador para citar ou linkar para este item: http://hdl.handle.net/1843/JCES-ARDPRE

Tipo:	Dissertação de Mestrado
Título:	Characterizing Interconnections and Linguistic Patterns in Twitter
Autor(es):	Johnnatan Messias Peixoto Afonso
Primeiro Orientador:	Fabricio Benevenuto de Souza
Primeiro membro da banca :	Fabricio Murai Ferreira
Segundo membro da banca:	Wagner Meira Junior
Terceiro membro da banca:	Maria da Graça Campos Pimentel
Resumo:	Social 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/
Assunto:	Computação Redes sociais on-line Tipologia (Linguistica) Twitter Igualdade Dados Demograficos;
Idioma:	Inglês
Editor:	Universidade Federal de Minas Gerais
Sigla da Instituição:	UFMG
Tipo de Acesso:	Acesso Aberto
URI:	http://hdl.handle.net/1843/JCES-ARDPRE
Data do documento:	9-Jun-2017
Aparece nas coleções:	Dissertações de Mestrado

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