Characterizing user navigation and interactions in online social networks

Benevenuto, Fabrí­cio; Rodrigues, Tiago; Cha, Meeyoung; Almeida, Virgílio A. F.

doi:10.1016/j.ins.2011.12.009

Cited by 90 publications

(44 citation statements)

References 38 publications

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“…Benevenuto et al have proposed a model of OSN user behaviour that, to the best of our knowledge, is the most ambitious and comprehensive so far Benevenuto et al (2009Benevenuto et al ( , 2012. They provide, among other features, a characterisation of session timing, the frequency and type of activities performed in the OSN and the number of friends users interact with.…”

Section: Users' Activitymentioning

confidence: 99%

Towards Inferring Communication Patterns in Online Social Networks

Balsa

Pérez-Solà

Díaz

2017

ACM Trans. Internet Technol.

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The separation between the public and private spheres on online social networks is known to be at best blurred. On the one hand, previous studies have shown how it is possible to infer private attributes from publicly available data. On the other hand, no distinction exists between public and private data when we consider the ability of the OSN provider to access them. Even when OSN users go to great lengths to protect their privacy, such as by using encryption or communication obfuscation, correlations between data may render these solutions useless. In this paper, we study the relationship between private communication patterns and publicly available OSN data. Such relationship informs both privacy-invasive inferences as well as OSN communication modelling, the latter being key towards developing effective obfuscation tools. We propose an inference model based on Bayesian analysis and evaluate, using a real social network dataset, how archetypal social graph features can lead to inferences about private communication. Our results indicate that both friendship graph and public traffic data may not be informative enough to enable these inferences, with time analysis having a non-negligible impact on their precision.

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Section: Users' Activitymentioning

confidence: 99%

Towards Inferring Communication Patterns in Online Social Networks

Balsa

Pérez-Solà

Díaz

2017

ACM Trans. Internet Technol.

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“…However, navigation of social media is challenging because these worlds do not come with maps (Boguna, Krioukov & Claffy, 2008;Benevenuto et al, 2012). Users are often left to discover pages, hashtags, or subreddits of interest haphazardly, by word of mouth, following other users' "votes" or "likes," or by using a basic search feature.…”

Section: Introductionmentioning

confidence: 99%

Navigating the massive world of reddit: using backbone networks to map user interests in social media

Olson

Neal

2015

PeerJ Computer Science

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In the massive online worlds of social media, users frequently rely on organizing themselves around specific topics of interest to find and engage with like-minded people. However, navigating these massive worlds and finding topics of specific interest often proves difficult because the worlds are mostly organized haphazardly, leaving users to find relevant interests by word of mouth or using a basic search feature. Here, we report on a method using the backbone of a network to create a map of the primary topics of interest in any social network. To demonstrate the method, we build an interest map for the social news web site reddit and show how such a map could be used to navigate a social media world. Moreover, we analyze the network properties of the reddit social network and find that it has a scale-free, small-world, and modular community structure, much like other online social networks such as Facebook and Twitter. We suggest that the integration of interest maps into popular social media platforms will assist users in organizing themselves into more specific interest groups, which will help alleviate the overcrowding effect often observed in large online communities.

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“…Segundo [1,26], as redes sociais online ultrapassaram o e-mail e se tornaram a atividade mais popular online. Em média, um americano adulto passa cerca de três horas online por dia, dessas 37 minutos são em redes sociais e 33 em e-mail [1].…”

Section: Redes Sociaisunclassified

“…Um estudo sobre como os usuários navegam e interagem quando conectados em redes sociais é apresentado por [26]. Nesse trabalho, foram coletados e analisados dados sobre cliques de usuários que acessaram quatro redes: Orkut; MySpace; Hi5; e LinkedIn, a partir de um site agregador brasileiro.…”

Section: Aspectos Da Comunicação Socialunclassified

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Uso de técnicas de Computação Social para tomada de decisão de compra e venda de ações no mercado brasileiro de bolsa de valores

Alves¹

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Characterizing user navigation and interactions in online social networks

Cited by 90 publications

References 38 publications

Towards Inferring Communication Patterns in Online Social Networks

Towards Inferring Communication Patterns in Online Social Networks

Navigating the massive world of reddit: using backbone networks to map user interests in social media

Uso de técnicas de Computação Social para tomada de decisão de compra e venda de ações no mercado brasileiro de bolsa de valores

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