Opinion formation driven by PageRank node influence on directed networks

Eom, Young-Ho; Shepelyansky, Dima L.

doi:10.1016/j.physa.2015.05.095

Cited by 34 publications

(16 citation statements)

References 28 publications

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“…15 Moreover, some researchers proposed dynamic opinion models; these researchers consider that people's opinions change over time as well as based on the surrounding environment, thus affecting the spread of a message. [16][17][18][19] Amelkin proposed a general nonlinear model of polar opinion dynamics based on several theories on sociology and social psychology. 20 In addition, some studies focus on the power of individual communication and view change 21-23 on the propagation of opinions.…”

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confidence: 99%

Network public opinion propagation model based on the influence of media and interpersonal communication

Zhang

Feng

Yang

2019

Int. J. Mod. Phys. B

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With the rapid development of the Internet, social media networks have become the primary platform for people to express their views. In addition, network public opinion has a considerable influence on society. Thus, considering the significant impact of online public opinion on society, it is necessary to study and analyze the propagation process for public opinion. In this study, we propose the Media and Interpersonal Relationship-SEIR (MI-SEIR) model based on the SEIR model. Our proposed model considers the impact of media transmission and interpersonal relationships on opinion propagation. Our MI-SEIR model divides the propagation nodes into three categories: support, neutral and opposition. There is a discussion mechanism between these nodes that represents the node’s viewpoint value evolution rule based on the node firmness, influence of nodes, quality of media coverage and parameters of infection. The state transition of nodes is decided based on the change of opinion value. Our simulation experimental results show that this model is more representative of the real propagation of online public opinion and is thus of practical significance for research and opinion analysis applications.

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confidence: 99%

Network public opinion propagation model based on the influence of media and interpersonal communication

Zhang

Feng

Yang

2019

Int. J. Mod. Phys. B

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“…The Page Ranking algorithm is based on three factors that are the number of ingoing links, linkers' predisposition for linking, and the centrality of linkers. For opinion leader detection task, many works had applied the Page Rank technique in their method 22–24 . In Reference 25, the authors proposed the SPEAR algorithm based on page ranking technique to detect opinion leaders by ranking online users.…”

Section: Related Workmentioning

confidence: 99%

Opinion leaders' detection in dynamic social networks

Oueslati

Arrami

Dhouioui

et al. 2020

Concurrency and Computation

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Summary Social media networks have revolutionized the way users interact and express their opinion. Obviously, identifying opinion leaders has a widespread applicability. For instance, by detecting leaders, companies can manipulate the public opinion. However, this task is challenging due to the complexity and the ceaseless change of the social networks structure. Yet, existing opinion leaders 'detection methods have essentially focused on static social graphs neglecting the temporal characteristics. Therefore, the necessity of identifying opinion leaders seems to be more and more crucial. In this context, we present a new approach for detecting opinion leaders based on analyzing online community interactions and dealing with the dynamic aspect of social networks. The experiments are performed on real data and the comparison of the proposed approach with commonly used approaches showed a good performance.

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“…Following the DW and HK models, some interesting extended studies had been conducted [9], [10], [11], [12], [13]. In recent years, some opinion dynamics models have been built based on social networks [14], [15], [16], [17], complex networks [18], [19], [20], dynamic networks [21], [22], [23], [24] and super-network [25].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Public Opinions in an Online and Offline Social Network

Dong

Ding

Chiclana

et al. 2021

IEEE Trans. Big Data

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with the development of the information and Internet technology, the public opinions with big data will rapidly emerge in an online-offline social network, and an inefficient management of public opinions often will lead to the security crisis for either firms or governments. To unveil the interaction mechanism among a large number of agents between the online and offline social networks, in this paper we propose the public opinion dynamics model in an online-offline social network context. Next, in the theory aspect we investigate the analytical conditions to form a consensus in the public opinion dynamics model. Furthermore, we conduct the extensive simulations to investigate how the online agents impact the dynamics of public opinion formation, and unfold that the online agents shorten the steady-state time, decrease the number of opinion clusters, and smoothen the opinion changes in the opinion dynamics. The increase in the size of the online agents often enhances these effects. The results in this paper can provide a basis for the management of the public opinions in the Internet age.

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Opinion formation driven by PageRank node influence on directed networks

Cited by 34 publications

References 28 publications

Network public opinion propagation model based on the influence of media and interpersonal communication

Network public opinion propagation model based on the influence of media and interpersonal communication

Opinion leaders' detection in dynamic social networks

Dynamics of Public Opinions in an Online and Offline Social Network

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