INFORMATION DIFFUSION IN FACEBOOK-LIKE SOCIAL NETWORKS UNDER INFORMATION OVERLOAD

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.

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

The Scientific World JOURNAL ◽

10.1155/2014/914907 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 7

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.

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ESTIMATING USER INFLUENCE IN ONLINE SOCIAL NETWORKS SUBJECT TO INFORMATION OVERLOAD

International Journal of Modern Physics B ◽

10.1142/s0217979214500040 ◽

2014 ◽

Vol 28 (03) ◽

pp. 1450004 ◽

Cited By ~ 9

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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Information diffusion in structured online social networks

Modern Physics Letters B ◽

10.1142/s0217984915500633 ◽

2015 ◽

Vol 29 (13) ◽

pp. 1550063 ◽

Cited By ~ 1

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.

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

International Journal of Modern Physics C ◽

10.1142/s012918312250053x ◽

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.

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Modeling and performance analysis of information diffusion under information overload in Facebook-like social networks

International Journal of Communication Systems ◽

10.1002/dac.2774 ◽

2014 ◽

Vol 27 (9) ◽

pp. 1268-1288 ◽

Cited By ~ 18

Author(s):

Pei Li ◽

Yunchuan Sun

Keyword(s):

Social Networks ◽

Performance Analysis ◽

Information Diffusion ◽

Information Overload ◽

And Performance

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Finding K-Most Influential Users in Social Networks for Information Diffusion Based on Network Structure and Different User Behavioral Patterns

2017 IEEE 14th International Conference on e-Business Engineering (ICEBE) ◽

10.1109/icebe.2017.42 ◽

2017 ◽

Author(s):

Maryam Shahsavari ◽

Alireza Hashemi Golpayegani

Keyword(s):

Social Networks ◽

Network Structure ◽

Information Diffusion ◽

Behavioral Patterns ◽

Influential Users

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Ego network structure in online social networks and its impact on information diffusion

Computer Communications ◽

10.1016/j.comcom.2015.09.028 ◽

2016 ◽

Vol 76 ◽

pp. 26-41 ◽

Cited By ~ 20

Author(s):

Valerio Arnaboldi ◽

Marco Conti ◽

Massimiliano La Gala ◽

Andrea Passarella ◽

Fabio Pezzoni

Keyword(s):

Social Networks ◽

Network Structure ◽

Online Social Networks ◽

Information Diffusion

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Hostility and Perceptions of Support in Ambiguous Social Interactions

Journal of Individual Differences ◽

10.1027/1614-0001.27.2.108 ◽

2006 ◽

Vol 27 (2) ◽

pp. 108-115 ◽

Cited By ~ 5

Author(s):

Ana-Maria Vranceanu ◽

Linda C. Gallo ◽

Laura M. Bogart

Keyword(s):

Social Support ◽

Social Networks ◽

Information Processing ◽

Social Interactions ◽

Social Information Processing ◽

Perceived Social Support ◽

Social Information ◽

Inverse Association ◽

Processing Bias ◽

Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

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