Isolation, insertion, and reconstruction: Three strategies to intervene in rumor spread based on supernetwork model

Ruya, Tian; Liu, Yijun

doi:10.1016/j.dss.2014.09.001

Cited by 32 publications

(11 citation statements)

References 35 publications

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“…By using the attributes of supernetwork theory, a four-layer supernetwork considering user, information, psychology, and viewpoint features of online public opinion was proposed, in which the subnetworks are connected by superedges [ 19 ]. We also extended this model to the applications of opinion leader identification [ 24 ], rumor spreading intervene [ 32 ], link prediction [ 19 ], coupling mechanism analysis [ 37 ], post-informative quality evaluation [ 4 ], online water army detection [ 15 ]. Based on the empirical results of these studies, it can be confirmed that the supernetwork theory can be applied to comprehensively and effectively describe the features and their relationships of online posts in social media, which could improve the detection performance of rumors, to a large extent.…”

Section: (3) Psychology-based Featuresmentioning

confidence: 99%

A two-step rumor detection model based on the supernetwork theory about Weibo

et al. 2021

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Based on the supernetwork theory, a two-step rumor detection model was proposed. The first step was the classification of users on the basis of user-based features. In the second step, non-user-based features, including psychology-based features, content-based features, and parts of supernetwork-based features, were used to detect rumors posted by different types of users. Four machine learning methods, namely, Naive Bayes, Neural Network, Support Vector Machine, and Logistic Regression, were applied to train the classifier. Four real cases and several assessment metrics were employed to verify the effectiveness of the proposed model. Performance of the model regarding early rumor detection was also evaluated by separating the datasets according to the posting time of posts. Results showed that this model exhibited better performance in rumor detection compared to five benchmark models, mainly owing to the application of the supernetwork theory and the two-step mechanism.

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Section: (3) Psychology-based Featuresmentioning

confidence: 99%

A two-step rumor detection model based on the supernetwork theory about Weibo

et al. 2021

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“…Therefore, scholars have tried to apply the principles of super-networks to complex networks. There are two main types of super-networks: hypergraph-based super-networks and network-based super-networks [46]. The superstructure of a hypergraph-based network is a hypergraph, which pays more attention to the dynamic evolution process, so it can be used as a tool for studying the dynamic analysis of complex networks.…”

Section: Description Of Vehicle Assistance Network Evolution Model Bamentioning

confidence: 99%

Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing

Ran

Bai

2020

Sensors

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In this paper, we propose an optimization framework of vehicular fog computing and a cooperation vehicular network model. We aim to improve the performance of vehicular fog computing and solve the problem that the data of the vehicle collaborative network is difficult to obtain. This paper applies the hypergraph theory to study the underlying structure, considering the social characteristics of the vehicles and vehicle communication. Since the vehicles join the network in accordance with the Poisson process law, the model is analyzed by using Poisson stochastic process and mean field theory. This paper uses MATLAB to simulate the evolution process of cooperative networks. The results show that the vehicle's super-degree in vehicular fog computing has scale-free characteristics. Through this model, the vehicle cooperation situation can be analyzed, and the vehicle dynamics can be accurately predicted to further improve the performance of vehicular fog computing. The model can be transformed into some complex network models by adjusting the parameters. It has strong universality and has certain reference significance for the research on the related characteristics of VANETs and the theoretical research of the cooperative network.2 of 17 to higher network delays. For vehicles that are using autonomous driving in the Internet of Vehicles, high latency is likely to cause serious traffic accidents. Cellular network can provide limited enhanced communication, mainly controlled by network operators, which is inefficient from the application point of view [8]. Mobile cloud computing can bring rich computing resources to mobile users [9], but it is costly and time consuming to upload real-time information [10].Fog computing can effectively solve the above problems, by deploying a fog server with storage and computing near the user to receive and process user data [11]. The deployment location of the fog server is closer to the user, which can save a lot of data transmission time. The deployment of fog computing nodes in the connected vehicle network has become a popular trend in the research of VANETs. We finally proposed a novel concept of vehicular fog computing (VFC), which uses vehicles as infrastructure to make full use of vehicular communication and computing resources. Specifically, VFC is an architecture that has cloud-based features and provides data, computing, storage and application services to end users. VFC is also differentiated from other existing technologies by being close to end users, and having dense geographical distribution and support for mobility; its bottom layer consists of cooperative vehicles, so that the vehicle is both a fog computing provider and a vehicular fog computing user. It can use computing resources nearby and reduce the use of communication resources. In the fog computing architecture, vehicles and mobile devices are end users, and there is a fog server layer between these edge users and the cloud. The low-level equipment of vehicular fog computing vehicles has high-speed moving ch...

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“…2). The model in this study can not only depict the relationships between the elements in the public opinion formation process, but better reveal the dynamics and evolution mechanisms of the system [16][17].…”

Section: Online Public Opinion System Modelingmentioning

confidence: 99%

Meta-Synthetic Methodology: A New Way to Study Agricultural Rumor Intervention

Ruya

Liu

et al. 2016

Computer and Computing Technologies in Agriculture IX

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Intervention of online public opinion, especially agricultural rumors, has increasingly become an important issue that is related to the online society safety and reflecting the managers' social management capabilities. In this paper, meta-synthetic methodology was used to investigate the intervention mechanism of online public opinion system. General characteristics of online public opinion system were characterized qualitatively, and an opinion supernetwork model was built to analyze the intervention mechanism quantitatively. We put forward a rumor intervention mechanism including intervention timing, intervention mode, and intervention intensity. Based on system modeling and simulation, an agricultural rumor case was used to verify the practical effects of online agricultural rumor intervention. The results show that rumor intervention based on meta-synthetic methodology produces good results. This investigation will help to provide a reliable basis for the development of more sophisticated and effective rumor intervention strategies.

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Isolation, insertion, and reconstruction: Three strategies to intervene in rumor spread based on supernetwork model

Cited by 32 publications

References 35 publications

A two-step rumor detection model based on the supernetwork theory about Weibo

A two-step rumor detection model based on the supernetwork theory about Weibo

Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing

Meta-Synthetic Methodology: A New Way to Study Agricultural Rumor Intervention

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