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Master's Dissertation
DOI
https://doi.org/10.11606/D.55.2016.tde-08112016-091004
Document
Author
Full name
Luís Fernando Dorelli de Abreu
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2016
Supervisor
Committee
Rodrigues, Francisco Aparecido (President)
Boas, Paulino Ribeiro Villas
Mata, Angélica Sousa da
Travieso, Gonzalo
Title in Portuguese
Estrutura e dinâmica de redes de informação
Keywords in Portuguese
Grafos
Redes complexas
Redes sociais
Abstract in Portuguese
O aumento na disponibilidade de dados referentes a interação entre pessoas online tornou possível o estudo o processo de propagação de informações em redes sociais com volumes de dado antes jamais pensados. Neste trabalho, utilizamos dados do site de micro-blogging Twitter juntamente com conceitos de redes complexas para entender, caracterizar e classificar processos de difusão de informação observados nessa plataforma e em redes sociais em geral. Apresentamos importantes medidas para caracterização de cascatas de informação, bem como algoritmos eficientes para o seu cálculo. Com o auxilio dessas, mostramos que é possível quantificar a influência da rede social no processo de propagação de informação. Em seguida, constatamos que a informação tende a propagar por caminhos mínimos nessa rede. Por fim, mostramos que é possível utilizar apenas a topologia da rede social, sem nenhuma informação semântica, para agrupar tópicos, e que a topologia da rede social é fortemente influenciada pelos assuntos falados nela. Apesar de nosso trabalho possuir como base um único dataset, os métodos e medidas desenvolvidos são gerais e podem ser aplicados a qualquer processo de difusão de informação e a qualquer rede complexa.
Title in English
Structure and dynamics of information networks
Keywords in English
Complex networks
Graphs
Social networks
Abstract in English
The raise in the availability of data regarding interactions between people online has opened new doors to study the process of information diffusion in social networks. In this present work, we make use of the data from the micro-blogging website Twitteralong with complex networks concepts to understand, characterize and classify information diffusion processes observed in this platform and in social networks in general. We present important measures to characterize information cascades and efficient algorithms to calculate them. With the help of these measures, we show that it is possible to quantify the influence of the social network in the process of information diffusion. After that, we show that information does tend to travel along shortest paths on Twitter. Finally, we show that the topology of the social network, without any extra semantic information, can be used to aggregate topics, and that such topology is highly influenced by the topics being discussed on it. Altough we work with only a single dataset, our methods and measures developed are general and can be applied to any process of information diffusion and any complex network.
 
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Publishing Date
2016-11-08
 
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