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.

Information diffusion in structured online social networks

2015 ◽  
Vol 29 (13) ◽  
pp. 1550063 ◽  
Author(s):  
Pei Li ◽  
Yini Zhang ◽  
Fengcai Qiao ◽  
Hui Wang
Keyword(s):  
Social Networks ◽  
Theoretical Analysis ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
User Influence ◽  
Diffusion Dynamics ◽  
Approach Infinity ◽  
Simulation Results

Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

scholarly journals Spam Diffusion in Social Networking Media using Latent Dirichlet Allocation

2019 ◽  
Vol 8 (12) ◽  
pp. 881-885
Keyword(s):  
Online Social Networks ◽  
Topic Modeling ◽  
Information Diffusion ◽  
Latent Dirichlet Allocation ◽  
Good Accuracy ◽  
Ground Truth ◽  
Online Social Media ◽  
Diffusion Dynamics ◽  
Dirichlet Allocation

Like web spam has been a major threat to almost every aspect of the current World Wide Web, similarly social spam especially in information diffusion has led a serious threat to the utilities of online social media. To combat this challenge the significance and impact of such entities and content should be analyzed critically. In order to address this issue, this work usedTwitter as a case study and modeled the contents of information through topic modeling and coupled it with the user oriented feature to deal it with a good accuracy. Latent Dirichlet Allocation (LDA) a widely used topic modeling technique is applied to capture the latent topics from the tweets’ documents. The major contribution of this work is twofold: constructing the dataset which serves as the ground-truth for analyzing the diffusion dynamics of spam/non-spam information and analyzing the effects of topics over the diffusibility. Exhaustive experiments clearly reveal the variation in topics shared by the spam and nonspam tweets. The rise in popularity of online social networks, not only attracts legitimate users but also the spammers. Legitimate users use the services of OSNs for a good purpose i.e., maintaining the relations with friends/colleagues, sharing the information of interest, increasing the reach of their business through advertisings

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 A Trustworthy SIoT Aware Mechanism as an Enabler for Citizen Services in Smart Cities

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 918 ◽  
Author(s):  
Ateeq Ur Rehman ◽  
Rizwan Ali Naqvi ◽  
Abdul Rehman ◽  
Anand Paul ◽  
Muhammad Tariq Sadiq ◽  
...  
Keyword(s):  
Social Networks ◽  
Information And Communication Technologies ◽  
Online Social Networks ◽  
Information Technologies ◽  
Smart Cities ◽  
Trust Model ◽  
Human Interaction ◽  
Opinion Leaders ◽  
Local Cluster ◽  
Information And Communication

In the recent era, new information technologies have a significant impact on social networks. Initial integration of information and communication technologies (ICT) into city operations has promoted information city, ease of communication and principles of smart communities. Subsequently, the idea of the Internet of Things (IoT) with the specific focus of social IoT (SIoT) has contributed towards the smart cities (SC), which support the city operations with minimal human interaction. The user-generated data obtained by SIoT can be exploited to produce new useful information for creating citizen-centered smart services for SC. The aim of this research is twofold. Firstly, we used the concept of local and global trust to provide new services in SC based on popular online social networks (OSN) data used by the citizens. Secondly, the sustainability of the three different OSN is assessed. This paper investigates the social network domain with regard to the SC. Although in SC, OSN are increasing day by day, there is still an unresolved issue of trust among their users and also OSN are not much sustainable. In this research, we are analyzing the sustainability of different OSN for the SC. We employ datasets of three different social networks for our analyses. A local trust model is used to identify the central user within the local cluster while the global trust-based framework is used to identify the opinion leaders. Our analysis based on the datasets of Facebook, Twitter, and Slashdot unveil that filtration of these central-local users and opinion leaders result in the dispersion and significant reduction in a network. A novel model is being developed that outlines the relationship between local and global trust for the protection of OSN users in SC. Furthermore, the proposed mechanism uses the data posted by citizens on OSN to propose new services by mitigating the effect of untrusted users.

ESTIMATING USER INFLUENCE IN ONLINE SOCIAL NETWORKS SUBJECT TO INFORMATION OVERLOAD

2014 ◽  
Vol 28 (03) ◽  
pp. 1450004 ◽  
Author(s):  
PEI LI ◽  
YUNCHUAN SUN ◽  
YINGWEN CHEN ◽  
ZHI TIAN
Keyword(s):  
Social Networks ◽  
Information Processing ◽  
Theoretical Analysis ◽  
Online Social Networks ◽  
Information Overload ◽  
Network Size ◽  
Finite Buffer ◽  
User Influence ◽  
Diffusion Dynamics ◽  
The Impact

Online social networks have attracted remarkable attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. Due to the existence of information overload, the research on diffusion dynamics in epidemiology cannot be adopted directly to that in online social networks. In this paper, we consider diffusion dynamics in online social networks subject to information overload, and model the information-processing process of a user by a queue with a batch arrival and a finite buffer. We use the average number of times a message is processed after it is generated by a given user to characterize the user influence, which is then estimated through theoretical analysis for a given network. We validate the accuracy of our estimation by simulations, and apply the results to study the impacts of different factors on the user influence. Among the observations, we find that the impact of network size on the user influence is marginal while the user influence decreases with assortativity due to information overload, which is particularly interesting.

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