2016
DOI: 10.1016/j.physa.2016.07.022
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Characterizing super-spreading in microblog: An epidemic-based information propagation model

Abstract: h i g h l i g h t s• Information propagation on OSNs is studied considering super-spreading phenomenon. • A SAIR model is proposed to characterize the information propagation with Weibo data.• Super-spreaders in information propagation on OSNs are identified and characterized. • The sensitivity of parameters depicting super-spreading phenomenon is analyzed. a b s t r a c t As the microblogging services are becoming more prosperous in everyday life for users on Online Social Networks (OSNs), it is more favorabl… Show more

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Cited by 66 publications
(37 citation statements)
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“…At present, the epidemic model was proposed based on the complex network theory, which has been widely used in various fields [12,[20][21][22][23]. Studies on the epidemic model have been extensively used in biological and ecological applications [24][25][26].…”
Section: Complexitymentioning
confidence: 99%
“…At present, the epidemic model was proposed based on the complex network theory, which has been widely used in various fields [12,[20][21][22][23]. Studies on the epidemic model have been extensively used in biological and ecological applications [24][25][26].…”
Section: Complexitymentioning
confidence: 99%
“…A lot of models have been proposed to characterize this process, in which the most classical models are epidemic spreading models, such as the SIS model and SIR model, as a piece of information can be transmitted from one individual to another which is the similar pattern as the epidemic spreading [3,4]. Besides, perhaps the most commonly used models are the independent cascade model where the information flows over the network through cascade and the threshold model (including the linear threshold model and general threshold models) established based on the assumption that the neighbors play significant roles for the diffusion process [2,3,4]. Liu et al build the SAIR model based on well-known epidemic models to characterize super-spreading phenomenon in tweet information dissemination accompanied with super-spreaders [4].…”
Section: Related Workmentioning
confidence: 99%
“…Besides, perhaps the most commonly used models are the independent cascade model where the information flows over the network through cascade and the threshold model (including the linear threshold model and general threshold models) established based on the assumption that the neighbors play significant roles for the diffusion process [2,3,4]. Liu et al build the SAIR model based on well-known epidemic models to characterize super-spreading phenomenon in tweet information dissemination accompanied with super-spreaders [4]. Zhang et al compared and evaluated the available models and algorithms to respectively investigate their physical roles and optimization designs [3].…”
Section: Related Workmentioning
confidence: 99%
“…Through analytical and simulations over homogeneous and heterogeneous networks, the authors found that this new group would reduce the maximum of rumor influence, and postpone the terminal time of the diffusion. Liu et al considered the existence of the "superspreaders" in the networks, whose spreading speed was much faster, and introduced a corresponding new group into the classic SIR model (Liu et al, 2016). The validation on realworld Weibo dataset of the proposed model was conducted and showed that this improved SIR model was much more promising than the classic SIR model in characterizing a superspreading event of information propagation.…”
Section: Introductionmentioning
confidence: 99%