A quantitative model for the spread of online information

Jiang, Ping; Yan, Xing

doi:10.1007/s11135-019-00851-3

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Zhao et al introduced the SI communication model focused on emergent blog topics. The study investigates the pattern of public opinion dissemination in emergent scenarios without considering the influence of the removers [44]. And Dong et al used the SEIR model to study the rumor spreading process of Facebook [33].…”

Section: Information Dissemination Modelmentioning

confidence: 99%

Dynamic Mechanism of Destination Brand Diffusion: Agent-Based Modeling and Simulation

Deng,

Tan,

et al. 2024

Systems

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In recent years, social media has emerged as an important channel for the dissemination of destination branding. Despite the fact that the dissemination of information through social media enables a broader audience to become acquainted with destinations, the dissemination process of trending events exhibits variances. Consequently, the precise impact of the underlying mechanisms that govern the spread of information on the efficacy of disseminating destination brand trending events remains ambiguous. In an endeavor to bridge this gap, an improved SEIR model was developed in this research to investigate the dynamic dissemination mechanisms and influencing factors of destination trending events within social media. The model was applied to simulate the diffusion mechanism of destinations’ trending events. The results show that during the dissemination process of destination trending events on social media, the proportion of users affected at different stages influences the ultimate effectiveness of information propagation. In light of these insights, this research proposes a social media trending event dissemination strategy to aid in enhancing the propagation efficiency of destination brands through existing resources.

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Section: Information Dissemination Modelmentioning

confidence: 99%

Dynamic Mechanism of Destination Brand Diffusion: Agent-Based Modeling and Simulation

Deng,

Tan,

et al. 2024

Systems

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“…The results showed that the application of random or directed immunity could effectively prevent the diffusion of rumors while reducing the credibility of rumors. Jiang and Yan ( 3 ) proposed a piecewise SIR model to quantify the diffusing speed, scale and influence of online information. The simulation results showed that there was no proportional relationship between the sustained influence of a message and the number of diffusers.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Rumor Diffusion Process With the Consideration of Individual Heterogeneity: Take the Imported Food Safety Issue as an Example During the COVID-19 Pandemic

Chen

Rong

Yang

et al. 2022

Front. Public Health

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At present, rumors appear frequently in social platforms. The rumor diffusion will cause a great impact on the network order and the stability of the society. So it's necessary to study the diffusion process and develop the rumor control strategies. This article integrates three heterogeneous factors into the SEIR model and designs an individual state transition mode at first. Secondly, based on the influencing factors such as the trust degree among individuals, an individual information interaction mode is constructed. Finally, an improved SEIR model named SEIR-OM model is established, and the diffusion process of rumors are simulated and analyzed. The results show that: (1) when the average value of the interest correlation is greater, the information content deviation is lower, but the rumor diffusion range will be wider. (2) The increase of the average network degree intensifies influence of rumors, but its impact on the diffusion has a peak. (3) Adopting strategies in advance can effectively reduce the influence of rumors. In addition, the government should enforce rumor-refuting strategies right after the event. Also, the number of rumor-refuting individuals must be paid attention to. Finally, the article verifies the rationality and effectiveness of the SEIR-OM model through the real case.

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“…The epidemiology-based models (in particular SIR) are also used to study CP, which succeed to capture saturation (e.g., [12]- [14]). The current and total copies are respectively modelled as infected and (infected+recovered) populations.…”

Section: Related Workmentioning

confidence: 99%

Saturated total-population dependent branching process and viral markets

Agarwal¹,

Kavitha²

2022

Preprint

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The universal prevalence of online social networks (OSNs) is unquestionable today; the interesting posts/contents are forwarded repeatedly by the users of the OSN. Such propagation leads to the re-forwarding of the post to a subset of previous recipients, which increases as the post gets viral (reaches a large number of the users). Consequently, the effective forwards (after deleting the re-forwards) reduce, eventually leading to the saturation of the total number of copies. We study such a process using a new variant of the branching process and named it as 'Saturated total-population-dependent branching process'.We analyse the new variant using the stochastic approximation technique and derive an ODE, dependent on the expected effective forwards. Finally, we obtain deterministic trajectories which approximate the total and unread copies of the post 'asymptotically and almost surely' over any finite time window; this trajectory is entirely described by four parameters related to the network characteristics. Further, we provide theoretical expressions for the peak unread copies, maximum outreach and the life span of the post. We again observe well-known exponential growth, however, with time-varying rates. At last, we validate our theory through detailed simulations on the SNAP Twitter dataset, which illustrate a good fit. Recently, we analysed many new variants of BPs, using a new approach based on stochastic approximation techniques, e.g., attack and acquisition BP (competing viral markets [5]), and proportion dependent BP (fake news on OSNs [7]).Further modifying the said approach to address the required finite horizon analysis, we are able to analyse saturation resulting from total population dependency. Key contributions: The contribution of this paper is two fold: first, we formally analyse the STP-BP. We derive an appropriate ordinary differential equation (ODE) and its solution which (time asymptotically) almost surely approx-

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A quantitative model for the spread of online information

Cited by 7 publications

References 21 publications

Dynamic Mechanism of Destination Brand Diffusion: Agent-Based Modeling and Simulation

Dynamic Mechanism of Destination Brand Diffusion: Agent-Based Modeling and Simulation

Modeling Rumor Diffusion Process With the Consideration of Individual Heterogeneity: Take the Imported Food Safety Issue as an Example During the COVID-19 Pandemic

Saturated total-population dependent branching process and viral markets

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