A NOVEL MODEL FOR RUMOR SPREADING ON SOCIAL NETWORKS WITH CONSIDERING THE INFLUENCE OF DISSENTING OPINIONS

2018 ◽  
Vol 21 (06n07) ◽  
pp. 1850011 ◽  
Author(s):  
AMIRHOSEIN BODAGHI ◽  
SAMA GOLIAEI
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Realistic Model ◽  
The Novel ◽  
Human Beings ◽  
Rumor Spreading ◽  
New Model ◽  
Sociological Factors ◽  
Novel Model ◽  
New Hypothesis

Rumor spreading is a good sample of spreading in which human beings are the main players in the spreading process. Therefore, in order to have a more realistic model of rumor spreading on online social networks, the influence of psycho-sociological factors particularly those which affect users’ reactions toward rumor/anti-rumor should be considered. To this aim, we present a new model that considers the influence of dissenting opinions on those users who have already believed in rumor/anti-rumor but have not spread the rumor/anti-rumor yet. We hypothesize that influence is a motive for the believers to spread their beliefs in rumor/anti-rumor. We derive the stochastic equations of the new model and evaluate it by using two real datasets of rumor spreading on Twitter. The evaluation results support the new hypothesis and show that the novel model which is relied on the new hypothesis is able to better represent rumor spreading.

scholarly journals Modeling and Analysis of Rumor Spreading with Social Reinforcement Mechanism

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Yuan Xu ◽  
Renjie Mei ◽  
Yujie Yang ◽  
Zhengmin Kong
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Diffusion Mechanism ◽  
Practical Significance ◽  
Propagation Mechanism ◽  
Spreading Process ◽  
Rumor Spreading ◽  
Modeling And Analysis ◽  
Reinforcement Mechanism ◽  
Theoretical Results

It is of great practical significance to figure out the propagation mechanism and outbreak condition of rumor spreading on online social networks. In our paper, we propose a multi-state reinforcement diffusion model for rumor spreading, in which the reinforcement mechanism is introduced to depict individual willingness towards rumor spreading. Multiple intermediate states are introduced to characterize the process that an individual's diffusion willingness is enhanced step by step. We study the rumor spreading process with the proposed reinforcement diffusion mechanism on two typical networks. The outbreak thresholds of rumor spreading on both two networks are obtained. Numerical simulations and Monte Carlo simulations are conducted to illustrate the spreading process and verify the correctness of theoretical results. We believe that our work will shed some light on understanding how human sociality affects the rumor spreading on online social networks.

scholarly journals Modeling the Information Diffusion of Overlapped Nodes using SFA-ICBDM

2019 ◽  
Vol 8 (2) ◽  
pp. 709-713
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Cascade Model ◽  
Recent Time ◽  
Rumor Spreading ◽  
Overlapping Communities ◽  
Network Diversity ◽  
Proposed Model ◽  
Almost All

:In recent time, online social networks like, Facebook, Twitter, and other platforms, provide functionality that allows a chunk of information migrates from one user to another over a network. Almost all the actual networks exhibit the concept of community structure. Indeed overlapping communities are very common in a complex network such as online social networks since nodes could belong to multiple communities at once. The huge size of the real-world network, diversity in users profiles and, the uncertainty in their behaviors have made modeling the information diffusion in such networks to become more and more complex and tend to be less accurate. This work pays much attention on how we can accurately predicting information diffusion cascades over social networks taking into account the role played by the overlapping nodes in the diffusion process due to its belonging to more than one community. According to that, the information diffusion is modeled in communities in which these nodes have high membership for reasons that may relate to the applications such as market optimization and rumor spreading. Our experiment made on a real social data, Digg news aggregator network on 15% of overlapped nodes, using our proposed model SFA-ICBDM described in previous work. The experimental results show that the cascade model of the overlapped nodes whether represents seed or node within cascade achieves best prediction accuracy in the community which the node belongs at more

Incremental Group-Level Popularity Prediction in Online Social Networks

2022 ◽  
Vol 22 (1) ◽  
pp. 1-26
Author(s):  
Jingjing Wang ◽  
Wenjun Jiang ◽  
Kenli Li ◽  
Guojun Wang ◽  
Keqin Li
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Low Cost ◽  
Tensor Decomposition ◽  
Group Level ◽  
Fine Grained ◽  
Cp Decomposition ◽  
Web Contents ◽  
Popularity Prediction ◽  
Novel Model

Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.

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