Susceptible-Infectious-Critical-Recovered rumor spreading model in social networks

2021 ◽  
Author(s):  
Wang Hongmei ◽  
Qiu Liqing ◽  
Tan Kun ◽  
Cui Junwei
Keyword(s):  
Social Networks ◽  
Theoretical Analysis ◽  
Critical State ◽  
Effective Measure ◽  
Rumor Spreading ◽  
Spreading Model ◽  
Rumor Propagation ◽  
Number Formula ◽  
Simulation Results ◽  
Next Generation Matrix

As an important area of social networks, rumor spread has attracted the attention of many scholars. It aims to explore the rumor propagation, and to propose effective measures to curb the further spread of rumors. Different from some existing works, this paper believes that susceptible persons affected by rumor-refuting information will first enter the critical state, while ones who related to rumors will directly turn into the spread state. Therefore, this paper proposes a Susceptible-Infectious-Critical-Recovered (SICR) rumor model. In addition, considering that infectious persons with high levels of refuting rumors may cause emotional resonance among individuals, this model adds a connecting edge from the recovered to the infectious who are triggered by the information of refuting the rumors. First, the basic regeneration number [Formula: see text] is obtained by using the next generation matrix method. Then, the global stability of the rumor-free equilibrium [Formula: see text] and the persistence of rumor propagation are proved in detail in theoretical analysis. The simulation results show that the existence of a critical state can reduce the influence of rumors. Rumor refutation mechanism, as soon as possible to curb the spread of rumors, is an effective measure.

Rumor spreading model considering hesitating mechanism in complex social networks

2015 ◽  
Vol 437 ◽  
pp. 295-303 ◽  
Author(s):  
Ling-Ling Xia ◽  
Guo-Ping Jiang ◽  
Bo Song ◽  
Yu-Rong Song
Keyword(s):  
Social Networks ◽  
Rumor Spreading ◽  
Spreading Model

SCIR rumor propagation model with the chord mechanism in social networks

2021 ◽  
pp. 2250014
Author(s):  
Zhou Xiaodan ◽  
Qiu Liqing ◽  
Hao Tingyu
Keyword(s):  
Negative Impact ◽  
Real Life ◽  
Propagation Model ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Essential Difference ◽  
Social Phenomenon ◽  
Rumor Propagation ◽  
Number Formula ◽  
Next Generation Matrix

Rumors, as a typical social phenomenon in real life, have a negative impact on the harmony of the society. When people hear rumors, they may not resonate with rumors because they do not trust them during the process of rumors transmission. Thus, they will not spread rumors. The essential difference between chord mechanism and spreader mechanism is that spreaders will spread regardless of whether they think it is true or false. The chord needs to believe that the rumor is true in order to keep spreading it, otherwise they become immune to spreading it. Therefore, this paper proposes a new Spreader-Chord-Ignorant-Restorer (SCIR) model, which considers that the trust may affect the level of empathy. Since the level of trust affects the spread of rumors and the extent to which the immune person trusts the rumor is different, the connecting edges from the restorer to the chord and the restorer to the ignorant were added to the model. First, the basic reproductive number [Formula: see text] is derived by the next generation matrix method and thus equilibriums are obtained. Then, the global stability of the rumor-free equilibrium [Formula: see text] and the persistence of rumor propagation are proved in detail during the theoretical analysis.

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.

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.

SIHR rumor spreading model in social networks

2012 ◽  
Vol 391 (7) ◽  
pp. 2444-2453 ◽  
Author(s):  
Laijun Zhao ◽  
Jiajia Wang ◽  
Yucheng Chen ◽  
Qin Wang ◽  
Jingjing Cheng ◽  
...  
Keyword(s):  
Social Networks ◽  
Rumor Spreading ◽  
Spreading Model
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