Novel competitive information propagation macro mathematical model in online social network

He, Daobing; Liu, Xiaoyang

doi:10.1016/j.jocs.2020.101089

Cited by 17 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xiao et al (2019) proposed the MM-SIS (multiple information and multiplex network SIS) model to analyze the interaction between different information and different propagation paths [ 16 ]. He and Liu (2020) proposed a competitive information dissemination model, and they analyzed the competition mechanism and the information propagation evolution laws according to Markov chain theory [ 17 ]. Ivokhin et al (2018) proposed a model incorporating dynamics and mathematics to formulate the process of information spreading in people from a specific society or area [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A multiple information model incorporating limited attention and information environment

Chen

Jin

2021

PLoS ONE

View full text Add to dashboard Cite

Rapid development of intelligent information equipment accelerates the expansion of mobile social network. Speed of information spreading is gradually growing, there are lots of changes in the scale and mode of information spreading. But the basic communication network is not developed and not mature, when online information platforms breakdown sometimes it happens to be when important information appears. Therefore, the research is done to solve these occasion problems, help network information platform filter hot news and discuss the reason that hot news exists longer than other news in the Internet. In this paper, a multiple information propagation model incorporating both local information environment and people’s limited attention is proposed based on Susceptible Infected Recovered (SIR) model. Two new concepts are introduced into the model: heat rate and popular rate, to measure the local information influence power and people’s limited attention to information respectively, which are key factors determining node state transformation instead of fixed probability. In order to analyze the influence from limited attention, a situation is designed that several pieces of information are popular successively. The theoretical analysis shows that the early popular information gets more attention than the later popular information, and more attention makes it easier to spread. Besides, numerical simulation is conducted in both uniform network and scale-free network. The simulation results show that the early popular information is less vulnerable to the increase of information acceptance threshold and more sensitive to the decrease of information rejection threshold than the later popular information. Moreover, the model can also be used in the case of large amount of information transmission without adding too much complexity. Reasons are given in the research that the top hot news exists very much longer than the other ones, and latter news which have same influence as top news are hard to get the same focus. Meanwhile, results in the research can provide some ways for the other researches in the related fields. They also help related information platforms to filter and push news and referable strategies to maintain hot news.

show abstract

Section: Introductionmentioning

confidence: 99%

A multiple information model incorporating limited attention and information environment

Chen

Jin

2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Also, variants of these traditional models like SEIR (Biswas et al 2014 ), SHIR (Liu et al 2016 ), and others have been developed in the past. Recent work proposed a model named CISIR (competitive information susceptible infected recovered) for studying information dissemination in online social networks (He and Liu 2020 ). Some of the models were developed, especially for Twitter.…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Modeling information diffusion in online social networks using a modified forest-fire model

et al. 2020

View full text Add to dashboard Cite

Information dissemination has changed rapidly in recent years with the emergence of social media which provides online platforms for people worldwide to share their thoughts, activities, emotions, and build social relationships. Hence, modeling information diffusion has become an important area of research in the field of network analysis. It involves the mathematical modeling of the movement of information and study the information spread pattern. In this paper, we attempt to model information propagation in online social networks using a nature-inspired approach based on a modified forest-fire model. A slight spark can start a wildfire in a forest, and the spread of this fire depends on vegetation, weather, and topography, which may act as fuel. On similar lines, we labeled users who haven't joined the network yet as Empty, existing users as T ree, and information as F ire. The spread of information across online social networks depends upon users-followers relationships, the significance of the topic, and other such features. We introduce a novel Burnt state to the traditional forest-fire model to represent non-spreaders in the network. We validate our method on six real-world data-sets extracted from Twitter and conclude that the proposed model performs reasonably well in predicting information diffusion.

show abstract

“…He et al hypothesized that there is competition for various types of information in online social networks. They proposed the CISIR model to shed light on information competition and transmission [ 28 ]. Xiao et al considered the dynamic changes in anti-rumor information from different aspects.…”

Section: Introductionmentioning

confidence: 99%

“…The scholars mentioned above have conducted extensive research on transmitting various types of information via various channels, including the transmission of rumors [ 4 ], false information [ 5 ], competitive information [ 28 ] and malware [ 27 ]. Scholars who consider information transmission from the perspective of individuals are usually based on the attitude of transmission individuals to an event [ 22 – 24 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamical analysis and optimal control of the developed information transmission model

Kang

Hou

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

Information transmission significantly impacts social stability and technological advancement. This paper compares the phenomenon of “Super transmission” and “Asymptomatic infection” in COVID-19 transmission to information transmission. The former is similar to authoritative information transmission individuals, whereas the latter is similar to individuals with low acceptance in information transmission. It then constructs an S2EIR model with transmitter authority and individual acceptance levels. Then, it analyzes the asymptotic stability of information-free and information-existence equilibrium on a local and global scale, as well as the model’s basic reproduction number, R0. Distinguished with traditional studies, the population density function and Hamiltonian function are constructed by taking proportion of “Super transmitter” and proportion of hesitant group turning into transmitters as optimization control variables. Based on the Pontryagin maximum principle, an optimal control strategy is designed to effectively facilitate information transmission. The numerical simulation corroborates the theoretical analysis results and the system’s sensitivity to control parameter changes. The research results indicate that the authoritative “Super transmitter” has a beneficial effect on information transmission. In contrast, the “Asymptomatic infected individual” with poor individual acceptance level negatively affects information transmission.

show abstract

Novel competitive information propagation macro mathematical model in online social network

Cited by 17 publications

References 13 publications

A multiple information model incorporating limited attention and information environment

A multiple information model incorporating limited attention and information environment

Modeling information diffusion in online social networks using a modified forest-fire model

Dynamical analysis and optimal control of the developed information transmission model

Contact Info

Product

Resources

About