Spectator‐filter‐spreader‐stifler information propagation model with wake‐up mechanism in social networks

2020 ◽  
Author(s):  
Shuqi Liu ◽  
Chengai Sun ◽  
Liqing Qiu ◽  
Moji Wei
Keyword(s):  
Social Networks ◽  
Propagation Model ◽  
Information Propagation

scholarly journals A Bio-Inspired Defensive Rumor Confinement Strategy in Online Social Networks

2021 ◽  
Vol 33 (1) ◽  
pp. 47-70
Author(s):  
Santhoshkumar Srinivasan ◽  
Dhinesh Babu L. D.
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Propagation Model ◽  
Information Propagation ◽  
Control Approach ◽  
Rumor Propagation ◽  
Immunization Strategies ◽  
New Information ◽  
The Individual ◽  
Rumor Control

Online social networks (OSNs) are used to connect people and propagate information around the globe. Along with information propagation, rumors also penetrate across the OSNs in a massive order. Controlling the rumor propagation is utmost important to reduce the damage it causes to society. Educating the individual participants of OSNs is one of the effective ways to control the rumor faster. To educate people in OSNs, this paper proposes a defensive rumor control approach that spreads anti-rumors by the inspiration from the immunization strategies of social insects. In this approach, a new information propagation model is defined to study the defensive nature of true information against rumors. Then, an anti-rumor propagation method with a set of influential spreaders is employed to defend against the rumor. The proposed approach is compared with the existing rumor containment approaches and the results indicate that the proposed approach works well in controlling the rumors.

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.

scholarly journals Key Node Discovery Algorithm Based on Multiple Relationships and Multiple Features in Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xianyong Li ◽  
Ying Tang ◽  
Yajun Du ◽  
Yanjie Li
Keyword(s):  
Social Networks ◽  
Propagation Model ◽  
Multiple Relationships ◽  
Information Propagation ◽  
Multiple Features ◽  
Relational Networks ◽  
Discovery Algorithms ◽  
Relational Network ◽  
Single Relation ◽  
Key Nodes

The key nodes play important roles in the processes of information propagation and opinion evolution in social networks. Previous work rarely considered multiple relationships and features into key node discovery algorithms at the same time. Based on the relational networks including the forwarding network, replying network, and mentioning network in a social network, this paper first proposes an algorithm of the overlapping user relational network to extract different relational networks with same nodes. Integrated with these relational networks, a multirelationship network is established. Subsequently, a key node discovery (KND) algorithm is presented on the basis of the shortest path, degree centrality, and random walk features in the multirelationship network. The advantages of the proposed KND algorithm are proved by the SIR propagation model and the normalized discounted cumulative gain on the multirelationship networks and single-relation networks. The experiment’s results show that the proposed KND method for finding the key nodes is superior to other baseline methods on different networks.

scholarly journals Information Propagation in Online Social Network Based on Human Dynamics

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Qiang Yan ◽  
Lianren Wu ◽  
Chao Liu ◽  
Xiye Li
Keyword(s):  
Social Networks ◽  
Human Behavior ◽  
Online Social Networks ◽  
Online Social Network ◽  
Activity Patterns ◽  
Propagation Model ◽  
Limited Attention ◽  
Information Propagation ◽  
Human Dynamics ◽  
The Impact

We investigate the impact of human dynamics on the information propagation in online social networks. First, statistical properties of the human behavior are studied using the data from “Sina Microblog,” which is one of the most popular online social networks in China. We find that human activity patterns are heterogeneous and bursty and are often described by a power-law interevent time distributionP(τ)~τ−α. Second, we proposed an extended Susceptible-Infected (SI) propagation model to incorporate bursty and limited attention. We unveil how bursty human behavior and limited attention affect the information propagation in online social networks. The result in this paper can be useful for optimizing or controlling information propagation in online social networks.

An in-house communication support system based on an information propagation model utilizing social networks

2009 ◽  
Vol 92 (12) ◽  
pp. 43-49
Author(s):  
Susumu Takeuchi ◽  
Yuuichi Teranishi ◽  
Kaname Harumoto ◽  
Shinji Shimojo
Keyword(s):  
Social Networks ◽  
Support System ◽  
Propagation Model ◽  
Information Propagation ◽  
Communication Support

scholarly journals Evolutionary Game Dynamics of the Competitive Information Propagation on Social Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xue Yang ◽  
Zhiliang Zhu ◽  
Hai Yu ◽  
Yuli Zhao ◽  
Li Guo
Keyword(s):  
Social Networks ◽  
Evolutionary Game ◽  
Payoff Function ◽  
Pairwise Interaction ◽  
Propagation Model ◽  
Strategy Selection ◽  
Information Propagation ◽  
Individual Decision ◽  
Two Factors ◽  
Propagation Result

To better control the scope of information propagation and understand its dynamic characteristics, we propose an information propagation model based on evolutionary game theory. The model can simulate an individual’s strategy selection in social networks when facing two pieces of competitive information, whereby “competitive information” is defined as two pieces of information which have the opposite meaning. First, a reasonable payoff function is designed for individuals based on pairwise interaction. Second, each individual selects a friend it trusts. Third, a probability value is used to indicate whether an individual imitates the strategy of the selected friend. In the model, we consider not only the heterogeneous influence of friends’ strategies on individual decision-making in the process of communication but also the attenuation of individuals’ attention to information when information about friends is received repeatedly. The simulation results show that our model can accurately simulate the propagation of two pieces of competitive information. Furthermore, we find that the basic payoff that accrues to individuals as a result of spreading their information and the network topology are two factors that significantly influence the propagation result. The results provide effective insights into how to better control and guide public opinion.

Sign in / Sign up

Export Citation Format

Share Document