Characteristic analysis of information propagation pattern in online social network

2013 ◽  
Vol 33 (1) ◽  
pp. 105-107 ◽  
Author(s):  
Jia HAN ◽  
Ruliang XIAO ◽  
Yao HU ◽  
Tao TANG ◽  
Lina FANG
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Information Propagation ◽  
Characteristic Analysis ◽  
Propagation Pattern

Viral information propagation in the Digg online social network

2014 ◽  
Vol 415 ◽  
pp. 87-94 ◽  
Author(s):  
Mark Freeman ◽  
James McVittie ◽  
Iryna Sivak ◽  
Jianhong Wu
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Information Propagation

scholarly journals Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

2020 ◽  
Vol 12 (7) ◽  
pp. 3064 ◽  
Author(s):  
Tai Huynh ◽  
Hien Nguyen ◽  
Ivan Zelinka ◽  
Dac Dinh ◽  
Xuan Hau Pham
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Marketing Strategy ◽  
Online Social Network ◽  
Modern Method ◽  
Information Propagation ◽  
The Social ◽  
User Data ◽  
The Impact ◽  
Positive Results

Influencer marketing is a modern method that uses influential users to approach goal customers easily and quickly. An online social network is a useful platform to detect the most effective influencer for a brand. Thus, we have an issue: how can we extract user data to determine an influencer? In this paper, a model for representing a social network based on users, tags, and the relationships among them, called the SNet model, is presented. A graph-based approach for computing the impact of users and the speed of information propagation, and measuring the favorite brand of a user and sharing the similar brand characteristics, called a passion point, is proposed. Therefore, we consider two main influential measures, including the extent of the influence on other people by the relationships between users and the concern to user’s tags, and the tag propagation through social pulse on the social network. Based on these, the problem of determining the influencer of a specific brand on a social network is solved. The results of this method are used to run the influencer marketing strategy in practice and have obtained positive results.

Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network

2019 ◽  
Vol 63 (11) ◽  
pp. 1689-1703 ◽  
Author(s):  
Xiaoyang Liu ◽  
Daobing He
Keyword(s):  
Neural Network ◽  
Public Opinion ◽  
Social Network ◽  
Information Dissemination ◽  
Online Social Network ◽  
Information Propagation ◽  
Propagation Algorithm ◽  
Information Interaction ◽  
Opinion Evolution ◽  
Network Public Opinion

Abstract This paper proposes a new information dissemination and opinion evolution IPNN (Information Propagation Neural Network) model based on artificial neural network. The feedforward network, feedback network and dynamic evolution algorithms are designed and implemented. Firstly, according to the ‘six degrees separation’ theory of information dissemination, a seven-layer neural network underlying framework with input layer, propagation layer and termination layer is constructed; secondly, the information sharing and information interaction evolution process between nodes are described by using the event information forward propagation algorithm, opinion difference reverse propagation algorithm; finally, the external factors of online social network information dissemination is considered, the impact of external behavior patterns is measured by media public opinion guidance and network structure dynamic update operations. Simulation results show that the proposed new mathematical model reveals the relationship between the state of micro-network nodes and the evolution of macro-network public opinion. It accurately depicts the internal information interaction mechanism and diffusion mechanism in online social network. Furthermore, it reveals the process of network public opinion formation and the nature of public opinion explosion in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution.

scholarly journals Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyang Liu ◽  
Chao Liu ◽  
Xiaoping Zeng
Keyword(s):  
Mathematical Modeling ◽  
Social Network ◽  
Dynamic Method ◽  
Online Social Network ◽  
Propagation Model ◽  
Information Propagation ◽  
Public Event ◽  
Network Nodes ◽  
Event Information ◽  
Simulation Results

Emergency public event arises everyday on social network. The information propagation of emergency public event (favorable and harmful) is researched. The dynamics of a susceptible-infected-susceptible and susceptible-infected-removed epidemic models incorporated with information propagation of emergency public event are studied. In particular, we investigate the propagation model and the infection spreading pattern using nonlinear dynamic method and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective network nodes to mimic existing scenarios in online social network. The simulation results reveal that the inclusion of multiple infective node achieved stability and equilibrium in the proposed information propagation model.

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