Ego network structure in online social networks and its impact on information diffusion

2016 ◽  
Vol 76 ◽  
pp. 26-41 ◽  
Author(s):  
Valerio Arnaboldi ◽  
Marco Conti ◽  
Massimiliano La Gala ◽  
Andrea Passarella ◽  
Fabio Pezzoni
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Online Social Networks ◽  
Information Diffusion

scholarly journals Misinformation, Radicalization and Hate Through the Lens of Users

2020 ◽  
Author(s):  
Manoel Horta Ribeiro ◽  
Virgílio A. F. Almeida ◽  
Wagner Meira Jr
Keyword(s):  
Social Networks ◽  
Case Studies ◽  
Network Structure ◽  
Online Social Networks ◽  
Hate Speech ◽  
Far Right ◽  
Content Creation ◽  
Political Opinion

The popularization of Online Social Networks has changed the dynamics of content creation and consumption. In this setting, society has witnessed an amplification in phenomena such as misinformation and hate speech. This dissertation studies these issues through the lens of users. In three case studies in social networks, we: (i) provide insight on how the perception of what is misinformation is altered by political opinion; (ii) propose a methodology to study hate speech on a user-level, showing that the network structure of users can improve the detection of the phenomenon; (iii) characterize user radicalization in far-right channels on YouTube through time, showing a growing migration towards the consumption of extreme content in the platform.

scholarly journals Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12031-12040 ◽  
Author(s):  
Jiangtao Ma ◽  
Yaqiong Qiao ◽  
Guangwu Hu ◽  
Yongzhong Huang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Online Social Networks ◽  
User Profile ◽  
Social Network Structure

Information Diffusion in Social Networks

2011 ◽  
pp. 146-163 ◽  
Author(s):  
Dmitry Zinoviev
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Diffusion Processes ◽  
Small World ◽  
The Social ◽  
Strength Of Weak Ties ◽  
Strategic Position

The issue of information diffusion in small-world social networks was first systematically brought to light by Mark Granovetter in his seminal paper “The Strength of Weak Ties” in 1973 and has been an area of active academic studies in the past three decades. This chapter discusses information proliferation mechanisms in massive online social networks (MOSN). In particular, the following aspects of information diffusion processes are addressed: the role and the strategic position of influential spreaders of information; the pathways in the social networks that serve as conduits for communication and information flow; mathematical models describing proliferation processes; short-term and long-term dynamics of information diffusion, and secrecy of information diffusion.

scholarly journals Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks

2020 ◽  
Vol 34 (10) ◽  
pp. 13730-13731
Author(s):  
Ece C. Mutlu
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Complex Systems ◽  
Bayesian Networks ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Probabilistic Models ◽  
Human Interaction ◽  
Multi Agent Systems ◽  
Diffusion Dynamics

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.

Modelling multi-topic information propagation in online social networks based on resource competition

2016 ◽  
Vol 43 (3) ◽  
pp. 342-355 ◽  
Author(s):  
Liyuan Sun ◽  
Yadong Zhou ◽  
Xiaohong Guan
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Online Social Networks ◽  
Resource Competition ◽  
Propagation Model ◽  
Small Scale ◽  
Model Parameters ◽  
Information Propagation ◽  
Practical Applications ◽  
The Impact

Understanding information propagation in online social networks is important in many practical applications and is of great interest to many researchers. The challenge with the existing propagation models lies in the requirement of complete network structure, topic-dependent model parameters and topic isolated spread assumption, etc. In this paper, we study the characteristics of multi-topic information propagation based on the data collected from Sina Weibo, one of the most popular microblogging services in China. We find that the daily total amount of user resources is finite and users’ attention transfers from one topic to another. This shows evidence on the competitions between multiple dynamical topics. According to these empirical observations, we develop a competition-based multi-topic information propagation model without social network structure. This model is built based on general mechanisms of resource competitions, i.e. attracting and distracting users’ attention, and considers the interactions of multiple topics. Simulation results show that the model can effectively produce topics with temporal popularity similar to the real data. The impact of model parameters is also analysed. It is found that topic arrival rate reflects the strength of competitions, and topic fitness is significant in modelling the small scale topic propagation.

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