Information diffusion in OSNs

Arnaboldi, Valerio; Conti, Marco; Gala, Massimiliano La; Passarella, Andrea; Pezzoni, Fabio

doi:10.1145/2554850.2555000

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…This model of concentric circles, called "ego network model" was also confirmed for online relationships, with approximately the same number of circles and the same scaling ratio [11] as for offline relations. Thanks to online social networks, we also know that after an initial moment of growth, the ego network structure remains stable over time for the majority of the individuals [16], [17].…”

Section: A Social Ego Networkmentioning

confidence: 99%

Structural Invariants in Individuals Language Use: The “Ego Network” of Words

Ollivier¹,

Boldrini²,

Passarella³

et al. 2020

Lecture Notes in Computer Science

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The cognitive constraints that humans exhibit in their social interactions have been extensively studied by anthropologists, who have highlighted their regularities across different types of social networks. We postulate that similar regularities can be found in other cognitive processes, such as those involving language production. In order to provide preliminary evidence for this claim, we analyse a dataset containing tweets of a heterogeneous group of Twitter users (regular users and professional writers). Leveraging a methodology similar to the one used to uncover the well-established social cognitive constraints, we find that a concentric layered structure (which we call ego network of words, in analogy to the ego network of social relationships) very well captures how individuals organise the words they use. The size of the layers in this structure regularly grows (approximately 2-3 times with respect to the previous one) when moving outwards, and the two penultimate external layers consistently account for approximately 60% and 30% of the used words (the outermost layer contains 100% of the words), irrespective of the number of the total number of layers of the user.

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Section: A Social Ego Networkmentioning

confidence: 99%

Structural Invariants in Individuals Language Use: The “Ego Network” of Words

Ollivier¹,

Boldrini²,

Passarella³

et al. 2020

Lecture Notes in Computer Science

Self Cite

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“…As literature [37] shows, the above-mentioned widespread norms are correlated with cascades' properties. Most existing discovery methods of OUs rely on social connectivity information of the entire social network [21], [38], [39], but getting such information could be challenging due to users' privacy configurations.…”

Section: Definition Of Outstanding Usersmentioning

confidence: 93%

DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

Li¹,

Xu²,

Sun³

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Outstanding users (OUs) denote the influential, "core" or "bridge" users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on an entire social network's information. However, for privacy-conscious users or newly-registered users, such information is not easily accessible. To address this issue, we present DeepPick, a novel framework that considers both the generalization and specialization in the detection task of OUs. For generalization, we introduce deep neural networks to capture dynamic features of the users. For specialization, we leverage the traditional descriptive features to make use of public information about users. Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy of detection performance against the state-of-the-art.

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