Rumor Spreading Model with Trust Mechanism in Complex Social Networks

Wang, Yaqi; Yang, Xiaoyuan; Han, Yiliang; Wang, Xuan

doi:10.1088/0253-6102/59/4/21

Cited by 115 publications

(73 citation statements)

References 26 publications

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“…Furthermore, we find that the time required for the rumor to reach the max ( ) I t on the WS network is nearly twice as long as the corresponding time on the BA network. Thus, due to the propagation rules of SIAR model, BA network is more prone to the spreading of rumors than the WS network, which is in good agreement with the results in [14] and [20]. It also can be seen from Figures 5 and 6 that with the increase of β , the peak value max ( ) I t and the final rumor size denoted with ( ) R ∞ will decrease.…”

Section: S(t)i(t)a(t)r(t) (B) S(t) I(t) A(t) R(t)supporting

confidence: 84%

“…The process of SIAR rumor-spreading is shown in Figure 1. The third rule conforms to the hypothesis [1,[13][14][15][16][17][18][19][20][21] that an active spreader (i.e., infected individual) stops spreading the rumor because he learns that it has lost its "news value"; if this happens as soon as he meets another individual (that is, spreader or stifler or authoritative individual) knowing or refuting the rumor, then transitions from infected state (I) to removed state (R) occur as a result of II, IA, and IR encounters. The above assumptions are derived from the rumor diffusion mechanism of online social network and different people's attitudes to rumors.…”

Section: Siar Rumor-spreading Modelsupporting

confidence: 68%

“…Thus, rumors and authoritative information spread in the same network. However, the above studies [10][11][12] did not provide a realistic description of this situation, and many other works [13][14][15][16][17][18][19][20][21] are mainly involved the diffusion of a single rumor. In order to provide a relatively realistic description of the interaction between rumors and authoritative information, we introduce a new SIAR model of rumor-spreading on complex networks considering the hysteresis phenomenon of authoritative information which lags behind the rumor.…”

Section: Introductionmentioning

confidence: 97%

“…Trpevski et al [10] suggested an alternate model, where two rumors with different probabilities of acceptance spread among nodes. Some scholars [11] further investigated the rumor spreading model with skepticism mechanism and analyzed the stability of corresponding mean-field equations. A model combining the spreading stage and the interaction stage for opinions [12] was proposed to illustrate the process of dispelling a rumor.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Analyzing the Interaction between Network Rumors and Authoritative Information

Xia

Jiang

Song

et al. 2015

Entropy

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Abstract:In this paper, we propose a novel two-stage rumor spreading SusceptibleInfected-Authoritative-Removed (SIAR) model for complex homogeneous and heterogeneous networks. The interaction Markov chains (IMC) mean-field equations based on the SIAR model are derived to describe the dynamic interaction between the rumors and authoritative information. We use a Monte Carlo simulation method to characterize the dynamics of the Susceptible-Infected-Removed (SIR) and SIAR models, showing that the SIAR model with consideration of authoritative information gives a more realistic description of propagation features of rumors than the SIR model. The simulation results demonstrate that the critical threshold λc of the SIAR model has the tiniest increase than the threshold of SIR model. The sooner the authoritative information is introduced, the less negative impact the rumors will bring. We also get the result that heterogeneous networks are more prone to the spreading of rumors. Additionally, the inhibition of rumor spreading, as one of the characteristics of the new SIAR model itself, is instructive for later studies on the rumor spreading models and the controlling strategies.

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Section: S(t)i(t)a(t)r(t) (B) S(t) I(t) A(t) R(t)supporting

confidence: 84%

Section: Siar Rumor-spreading Modelsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Modeling and Analyzing the Interaction between Network Rumors and Authoritative Information

Xia

Jiang

Song

et al. 2015

Entropy

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show abstract

“…2 Some of the most representative studies published in this area in the last few years include Zanette [5], Thompson et al [6], Nekovee et al [7], Huo et al [8], Zhao et al [9] and Wang et al [10].…”

Section: Network Dynamicsmentioning

confidence: 99%

Sentiment cycles in discrete-time homogeneous networks

Gomes

2015

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• The paper approaches sentiment transition in a complex network.• Agents are classified in neutral, optimists and pessimists.• In continuous-time, the model delivers a stable steady-state outcome.• In discrete-time, stability holds under a homogeneous network of degree one.• Endogenous cycles emerge in discrete-time for a connectivity degree larger than one. a r t i c l e i n f o t r a c tConsider a network connecting individual agents that are endowed with distinct sentiments or 'views of the world'. Specifically, assume that each node in the network contains an agent that, at a given period t, can be found in one of five states: sentiment neutrality, exuberant optimism, non-exuberant optimism, exuberant pessimism and nonexuberant pessimism. Local interaction rules, similar to those one encounters in rumor propagation models, make agents change their sentiment as they contact with others. Under a continuous-time framework, the proposed setting delivers a stable fixed-point equilibrium, meaning that the shares of agents in each sentiment category will converge to constant steady-state levels. The inspection of the same structure of analysis in discretetime indicates that the stability outcome continues to hold when the connectivity degree is equal to 1. However, this result might change as one considers higher-order connectivity. In this last case, persistent endogenous waves of optimism and pessimism emerge under a reasonable parameterization of the model.

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

Liu

Sun

Qiu

et al. 2020

Concurrency and Computation

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Summary Nowadays, social network platforms play a significant role in daily life, allowing people to hear what is going on around the world without leaving home. The research on the internal transformation of information propagation is critical, which can help restrain the propagation of fake information or rumors and promote the widespread dissemination of effective information. To better understand the transition of states in information propagation, this article considers a new filter state that does not transmit information temporarily and establishes a spectator‐filter‐spreader‐stifler (SFIR) model. Mainly, to explore the dynamics of information propagation, the article introduces two different wake‐up mechanisms including the spontaneous awakening of the filter and the infection of the leader spreaders to the passive stifler, which relies on the proportion of current spreaders as well as the doubt degree on information. Afterward, based on the average field equations built, the nonpropagation equilibrium point E0 and propagation equilibrium point E1 are obtained by theoretical analysis. Moreover, the simulation results reveal that spontaneous wake‐up mechanism accelerates the information propagation while leader spreaders infection increases the scope of information propagation, which can provide theoretical support for controlling information propagation more effectively.

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Rumor Spreading Model with Trust Mechanism in Complex Social Networks

Cited by 115 publications

References 26 publications

Modeling and Analyzing the Interaction between Network Rumors and Authoritative Information

Modeling and Analyzing the Interaction between Network Rumors and Authoritative Information

Sentiment cycles in discrete-time homogeneous networks

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

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