Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network

Kundu, Suman; Kajdanowicz, Tomasz; Kazienko, Przemysław; Chawla, Nitesh V.

doi:10.1109/access.2020.3029657

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Kundu et al approach the information propagation model in their work by developing a novel fuzzy relative willingness model. The diffusion model was able to successfully utilize the external influence factor, as well as the susceptibility of individual nodes to quantify human willingness [12]. While the objective of the paper differs from our research, the implementation of the external influence factor contributed to our research as we applied a similar function as well.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Influence of Twitter Bots via Agent-Based Social Simulation

2022

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Social Media is used by many as a source of information for current world events, followed by publicly sharing their sentiment about these events. However, when the shared information is not trustworthy and receives a large number of interactions, it alters the public's perception of authentic and false information, particularly when the origin of these stories comes from malicious sources. Over the past decade, there has been an influx of users on the Twitter social network, many of them automated bot accounts with the objective of participating in misinformation campaigns that heavily influence user susceptibility to fake information. This can affect public opinion on real-life matters, as previously seen in the 2020 presidential elections and the current COVID-19 epidemic, both plagued with misinformation. In this paper, we propose an agent-based social simulation environment that utilizes the social network Twitter, with the objective of evaluating how the beliefs of agents representing regular Twitter users can be influenced by malicious users scattered throughout Twitter with the sole purpose of spreading misinformation. We applied two scenarios to compare how these regular agents behave in the Twitter network, with and without malicious agents, to study how much influence malicious agents have on the general susceptibility of the regular users. To achieve this, we implemented a belief value system to measure how impressionable an agent is when encountering misinformation and how its behavior gets affected. The results indicated similar outcomes in the two scenarios as the affected belief value changed for these regular agents, exhibiting belief in the misinformation. Although the change in belief value occurred slowly, it had a profound effect when the malicious agents were present, as many more regular agents started believing in misinformation.

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Section: Related Workmentioning

confidence: 99%

Evaluating the Influence of Twitter Bots via Agent-Based Social Simulation

2022

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“…Because the distributed systems of network information mode are complex and non-linear processes which are independent, influence, interact and restrict each other, a combination of several methods can be used in this case to improve the efficiency and reliability of the whole system. Based on the actual situation and historical data, a corresponding probability distribution schema is established to estimate the number of relationships between nodes in future processing, and then these parameters are used to calculate specific values and substituted into the network to obtain the final desired prediction results, which can achieve real-time control, adjustment and tracking of unknown events or processes [15][16].…”

Section: Predictive Control Algorithmmentioning

confidence: 99%

Distributed System Coordination Predictive Control for Network Information Mode

2022

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With the rapid development of network information technology, distributed system coordinated control has become an indispensable part of the network information industry in the world today. In this paper, the concepts and classifications of the distributed system coordinated control method for network information mode are introduced first, then the problems existing in the traditional network structure are analyzed in detail. Then, from the point of view of genetic algorithm theory, a support vector machine (SVM), fuzzy logic and transfer function are established to solve the actual non-linear load dispatch, in which the data is processed by neural calculation. Finally, the improved decision table achieves the same control effect as the original model through experimental validation, and improves the system coordination performance index and stability. The experimental results show that the prediction time of this model is within the range of 4s-7s, and its stability, feasibility and prediction accuracy are up to 90%. This indicates that the distributed system coordination prediction model under network information mode performs well.

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