2019
DOI: 10.1016/j.physa.2019.121479
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A double-identity rumor spreading model

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Cited by 25 publications
(14 citation statements)
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“…Li et al (2015) identified the invariant characteristic that the followers' count of users obeys a power-law distribution with an exponent almost equal to 2 by empirically studying 10 million user profiles on the largest Chinese microblog, Sina Weibo, and 41.7 million profiles on Twitter [36]. Moreover, rumors are known to spread faster in scale-free networks, which are also called BA networks, than in small-world networks [37].…”
Section: Health Status Subnetworkmentioning
confidence: 99%
“…Li et al (2015) identified the invariant characteristic that the followers' count of users obeys a power-law distribution with an exponent almost equal to 2 by empirically studying 10 million user profiles on the largest Chinese microblog, Sina Weibo, and 41.7 million profiles on Twitter [36]. Moreover, rumors are known to spread faster in scale-free networks, which are also called BA networks, than in small-world networks [37].…”
Section: Health Status Subnetworkmentioning
confidence: 99%
“…Creator's identity is primarily explained in the context of individuals dealing with particular areas: profit-or nonprofit oriented organization creator (Fauchart & Gruber, 2011;Giacomin et al, 2007), classical artse.g., literature creator (Ottery, 2006), music creator (Tillay & Chapman, 2019), new artse.g., anime creator (Reysen et al, 2020), social media content creator (Arriagada & Ibáñez, 2020;Maynard, 2021;Mehta & Kaye, 2019), religious institution creator (Jones & Massa, 2013), fake-news or rumor creator (Dong et al, 2019). Academics emphasize the fluctuating contexts and necessity for regulation to these deviations.…”
Section: Creator's Identitymentioning
confidence: 99%
“…Thus the prevalent phenomena can be understood from the same ground as Figure (1) and 1when we classify the people into two groups of society for the introducer of innovation and its adopter and consider the interacting duality of those societies. Thus the burst phenomenon including the prevalence, in general, can be treated as the interacting phenomenon taking place between the systems with the duality, by which methodology we can understand the epidemic diffusion, and the prevalence of attitude, thoughts and rumors [29][30][31][32][33][34]. Figure 1, the innovators correspond to the external events, the early adopters to the primary, real field, and the remainder to the secondary, cyber field.…”
Section: Burst In the Cyber World And Prevalence Phenomenamentioning
confidence: 99%
“…When the direct contact between people is restricted in the real world owing to the exchange of information in the cyber world, the introduction of such a model seems to be effective for preventing the expansion of epidemics in case of their prevalence. Improving such a type of dual model, Wang et al [39], Dong et al [31], Yi et al [32], and Wang et al [33] have introduced dual type models where agents have different characteristics in the two worlds from each other to discuss the diffusion of epidemics and rumors. Although in these models the topological structure of the network was assumed unchanged with time, Lee et al [34] assumed in their epidemic model the characteristics of agents changing with the propagation of epidemics, which induce the change of network structure, that is the topology in the real world.…”
Section: Introductionmentioning
confidence: 99%