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.

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 Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Pei Li ◽  
Wei Li ◽  
Hui Wang ◽  
Xin Zhang
Keyword(s):  
Social Networks ◽  
Information Processing ◽  
Theoretical Analysis ◽  
Information Diffusion ◽  
Information Overload ◽  
Analysis Framework ◽  
User Behaviors ◽  
Diffusion Dynamics ◽  
Different Types ◽  
Simulation Results

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations.

INFORMATION DIFFUSION IN FACEBOOK-LIKE SOCIAL NETWORKS UNDER INFORMATION OVERLOAD

2013 ◽  
Vol 24 (07) ◽  
pp. 1350047 ◽  
Author(s):  
PEI LI ◽  
KAI XING ◽  
DAPENG WANG ◽  
XIN ZHANG ◽  
HUI WANG
Keyword(s):  
Social Networks ◽  
Information Processing ◽  
Theoretical Analysis ◽  
Network Structure ◽  
Information Diffusion ◽  
Information Overload ◽  
Surprising Result ◽  
Processing Capability ◽  
Simulation Results

Research on social networks has received remarkable attention, since many people use social networks to broadcast information and stay connected with their friends. However, due to the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook-like social networks into account, and models the process of information diffusion under information overload. The term view scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated is proposed to characterize the information diffusion efficiency. Through theoretical analysis, we find that factors such as network structure and view scope number have no impact on the information diffusion efficiency, which is a surprising result. To verify the results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly.

Is Social Media Marketing Really Working?

2013 ◽  
pp. 174-193 ◽  
Author(s):  
Felipe Uribe Saavedra ◽  
Josep Rialp Criado ◽  
Joan Llonch Andreu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Business Performance ◽  
Dynamic Capabilities ◽  
Social Media Marketing ◽  
Marketing Tool ◽  
Use Of Social Media ◽  
Marketing Intensity ◽  
The Impact

Online social networks have become the fastest growing phenomenon on the Internet and firms are beginning to take advantage of them as a marketing tool. However, the strategic importance of social media marketing is not yet clear, given the novelty and the difficulty of measuring its impact on business performance. This study uses data from 191 Spanish firms from several sectors to measure the impact of the intensity of use of social media marketing on the relationship between the dynamic capabilities of market orientation and entrepreneurial orientation, and business performance. The results provide evidence of the moderating effects of social media marketing intensity on the strength of the mentioned relations and the importance of a strong and committed marketing strategy on digital social networks for businesses.

Is Social Media Marketing Really Working?

2014 ◽  
pp. 1260-1279 ◽  
Author(s):  
Felipe Uribe Saavedra ◽  
Josep Rialp Criado ◽  
Joan Llonch Andreu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Business Performance ◽  
Dynamic Capabilities ◽  
Social Media Marketing ◽  
Marketing Tool ◽  
Use Of Social Media ◽  
Marketing Intensity ◽  
The Impact

Online social networks have become the fastest growing phenomenon on the Internet and firms are beginning to take advantage of them as a marketing tool. However, the strategic importance of social media marketing is not yet clear, given the novelty and the difficulty of measuring its impact on business performance. This study uses data from 191 Spanish firms from several sectors to measure the impact of the intensity of use of social media marketing on the relationship between the dynamic capabilities of market orientation and entrepreneurial orientation, and business performance. The results provide evidence of the moderating effects of social media marketing intensity on the strength of the mentioned relations and the importance of a strong and committed marketing strategy on digital social networks for businesses.

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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