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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Topical Alignment in Online Social Systems

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Autor(es):
Maciel Cardoso, Felipe [1, 2] ; Meloni, Sandro [2, 3] ; Santanche, Andre [1] ; Moreno, Yamir [2, 4, 5]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[2] Univ Zaragoza, Inst Biocomputat & Phys Complex Syst, Zaragoza - Spain
[3] UIB, CSIC, Inst Cross Disciplinary Phys & Complex Syst, IFISC, Palma de Mallorca - Spain
[4] Univ Zaragoza, Dept Theoret Phys, Zaragoza - Spain
[5] ISI Fdn, Turin - Italy
Número total de Afiliações: 5
Tipo de documento: Artigo Científico
Fonte: FRONTIERS IN PHYSICS; v. 7, APR 17 2019.
Citações Web of Science: 0
Resumo

Understanding the dynamics of social interactions is crucial to comprehend human behavior. The emergence of online social media has enabled access to data regarding people relationships at a large scale. Twitter, specifically, is an information oriented network, with users sharing and consuming information. In this work, we study whether users tend to be in contact with people interested in similar topics, i.e., if they are topically aligned. To do so, we propose an approach based on the use of hashtags to extract information topics from Twitter messages and model users' interests. Our results show that, on average, users are connected with other users similar to them. Furthermore, we show that topical alignment provides interesting information that can eventually allow inferring users' connectivity. Our work, besides providing a way to assess the topical similarity of users, quantifies topical alignment among individuals, contributing to a better understanding of how complex social systems are structured. (AU)

Processo FAPESP: 15/01587-0 - Armazenagem, modelagem e análise de sistemas dinâmicos para aplicações em e-Science
Beneficiário:João Eduardo Ferreira
Linha de fomento: Auxílio à Pesquisa - Programa eScience e Data Science - Temático