Modeling and Analyzing the Interaction between Network Rumors and Authoritative Information

Xia, Lingling; Jiang, Guoping; Song, Yurong; Song, Bo

doi:10.3390/e17010471

Cited by 28 publications

(8 citation statements)

References 27 publications

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“…Meng et al [13] added a new compartment into the conventional SIR model in order to catch the phenomenon of rumor spreading in real life. Xia et al [14] introduced a new class representing authorities that can diffuse authoritative information to prevent rumors from spreading. Zhao et al [15,16] modified the SIR model to integrate forgetting and remembering mechanisms with variable forgetting rate to catch rumor dissemination characteristics.…”

Section: Introductionmentioning

confidence: 99%

Characterizing super-spreading in microblog: An epidemic-based information propagation model

Liu

Bai

et al. 2016

Physica A: Statistical Mechanics and its Applications

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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 favorable for hot topics and breaking news to gain more attraction very soon than ever before, which are so-called ''super-spreading events''. In the information diffusion process of these super-spreading events, messages are passed on from one user to another and numerous individuals are influenced by a relatively small portion of users, a.k.a. super-spreaders. Acquiring an awareness of super-spreading phenomena and an understanding of patterns of wide-ranged information propagations benefits several social media data mining tasks, such as hot topic detection, predictions of information propagation, harmful information monitoring and intervention. Taking into account that super-spreading in both information diffusion and spread of a contagious disease are analogous, in this study, we build a parameterized model, the SAIR model, based on well-known epidemic models to characterize super-spreading phenomenon in tweet information propagation accompanied with super-spreaders. For the purpose of modeling information diffusion, empirical observations on a real-world Weibo dataset are statistically carried out. Both the steady-state analysis on the equilibrium and the validation on real-world Weibo dataset of the proposed model are conducted. The case study that validates the proposed model shows that the SAIR model is much more promising than the conventional SIR model in characterizing a super-spreading event of information propagation. In addition, numerical simulations are carried out and discussed to discover how sensitively the parameters affect the information propagation process.

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Section: Introductionmentioning

confidence: 99%

Characterizing super-spreading in microblog: An epidemic-based information propagation model

Liu

Bai

et al. 2016

Physica A: Statistical Mechanics and its Applications

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“…e abstraction of a complicated system is a complex network. e overall behavior or features of a complex system cannot be explained by the behavior or characteristics of its components [23]. An open complex network is a network with self-organization, selfsimilarity, attractor, small world, scale-free portion, or all that is generated by incorporating external environmental stimuli into the network with self-organization, self-similarity, attractor, small world, scale-free part, or all.…”

Section: Communication Mechanism Of Network Public Opinionmentioning

confidence: 99%

A Network Public Opinion Trend Estimation Model Using a Scale-Free Network Algorithm

A-li

2022

Mobile Information Systems

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Online public opinion is gradually becoming an important medium that influences society’s mainstream direction in today’s period of rapid development of new media and network oversight. The propagation of public opinion on the Internet has become a serious issue affecting economic and social development, as well as national security. As a result, this research investigates the evolution of public opinion and the evolutionary stability strategy of groups. Furthermore, a scale-free network is introduced to develop a network public opinion propagation model based on the scale-free network to tackle the problem that the existing public opinion propagation model cannot fully reflect the law of public opinion propagation in real life. The model incorporates components such as external environmental influences, individual personality characteristics, and interpersonal intimacy, in addition to the law of public opinion dissemination in real society. This study uses MATLAB to carry out simulation and comparison analysis in order to test the model’s performance. Simulation findings reveal that this model’s stability is around 95.6 percent, and its highest accuracy is 94.3 percent, which is greater than that of the Krause–Hegselmann and Deffuant models. The obtained forms of public opinion are richer, and the results of public opinion evolution are more in line with the process of public opinion evolution in real society, thanks to this model’s ability to visually reflect the changes in individual opinions held over time in the process of public opinion evolution. It has practical implications for future studies.

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“…Benefitting from epidemic models, a rumour spreading model proposed by Daley and Kendall categories individuals into Ignorants, Spreaders and Stiflers [22,23]. Subsequently, more models considering multiple states are proposed, such as Susceptible, Exposed, Infected, Removed (SEIR), Susceptible, Infected, Hibernator, Removed (SIHR) and Susceptible, Infected, Authoritative, Removed (SIAR) [24–26]. Despite various forms, the main focus to model diffusion process is to embody the contagious process, in which rules and states could be developed to fit with different situations.…”

Section: Related Workmentioning

confidence: 99%

An online multi-dimensional opinion dynamic model with misinformation diffusion in emergency events

Liu

Rong

2021

Journal of Information Science

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Multiple opinions, including many that are negative, are produced in emergency events. These opinions are commonly formed asynchronously based on misinformation. However, most researches on opinion dynamics involving information neglect the asynchronous process of initial opinion formation due to information diffusion. Since online social networks like Sina Weibo act as major avenues for the expression, after analysing online behaviours, an opinion dynamic model is developed with consideration of misinformation diffusion of public opinion. In this model, schemes are developed for opinion interactions in multiple dimensions by introducing characteristics of online communication as another way of opinion interactions besides communication between neighbours. Subsequently, we investigate the impacts of network structure, diffusion rate, repost rate and other factors, which provide insights into understanding online opinion dynamics during emergency events. Furthermore, we conduct simulations to determine the intervention effects of different official responses. Results show that removing comments compulsively exhibits better performance in reducing negative opinion as well as increasing the density of Spreaders. Debunking misinformation by posting early results officially which indicates the probability of the existence of misinformation may lead public opinion in time if it takes a long time to finally confirm the misinformation.

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Modeling and Analyzing the Interaction between Network Rumors and Authoritative Information

Cited by 28 publications

References 27 publications

Characterizing super-spreading in microblog: An epidemic-based information propagation model

Characterizing super-spreading in microblog: An epidemic-based information propagation model

A Network Public Opinion Trend Estimation Model Using a Scale-Free Network Algorithm

An online multi-dimensional opinion dynamic model with misinformation diffusion in emergency events

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