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首页> 外文期刊>Information Sciences: An International Journal >Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors
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Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors

机译：基于人类个体和社会行为，最大限度地减少多路复用在线社交网络的谣言影响

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摘要

With the growing popularity of online social networks, an environment has been set up that can spread rumors in a faster and wider manner than ever before, which can have widespread repercussions on society. Nowadays, individuals are joining multiple online social networks and rumors simultaneously propagating amongst them, thereby creating a new dimension to the problem of rumor propagation. Motivated by these facts, this paper attempts to address the rumor influence minimization in multiplex online social networks. In this work, we consider modeling the propagation process of such fictitious information as a significant step toward minimizing its influence. Thus, we analyze the individual and social behaviors in social networks; subsequently, we propose a novel rumor diffusion model, named the HISBmodel. In this model, we propose a formulation of an individual behavior towards a rumor analog to damped harmonic motion. Following this, the opinions of individuals in the propagation process are incorporated. Furthermore, the rules of rumor transmission between individuals in multiplex networks are incorporated by considering individual and social behaviors. Further, we present the HISBmodel propagation process that describes the spread of rumors in multiplex online social networks. Based on this model, we propose a truth campaign strategy in minimizing the influence of rumors in multiplex online social networks from the perspective of network inference and by exploiting the survival theory. This strategy selects the most influential nodes as soon as the rumor is detected and launches a truth campaign to raise awareness against it, so as to prevent the influence of rumors. Accordingly, we propose a greedy algorithm based on the likelihood principle, which guarantees an approximation within 63% of the optimal solution. Systematically, experiments have been conducted on real single networks crawled from Twitter, Facebook, and Slashdot as well as on multiplex networks of real online social networks (Facebook, Twitter, and YouTube). First, the results indicate the HISBmodel can reproduce all the trends of real-world rumor propagation more realistically than the models presented in the literature. Moreover, the simulations illustrate that the proposed model highlights the impact of human factors accurately in accordance with the literature. Second, compared to the methods in the literature, the experiments prove the efficiency of our strategy in minimizing the influence of rumors in the cases of single network and multiplex social network propagation. The results prove that the proposed method can capture the dynamic propagation process of the rumor and select the target nodes more accurately in order to minimize the influence of rumors. (C) 2019 Elsevier Inc. All rights reserved.
机译：随着在线社交网络的日益普及，环境已经成立，可以以比以往更快和更广泛的方式传播谣言，这可能对社会普遍存在。如今，个人正在加入多个在线社交网络和谣言，同时在它们之间传播，从而为谣言传播的问题创造了一个新的维度。这些事实的激励，本文试图解决多路复用在线社交网络中的谣言影响最小化。在这项工作中，我们考虑将这种虚构信息的传播过程建模为朝向最小化其影响力的重要一步。因此，我们分析社交网络中的个人和社会行为;随后，我们提出了一种新的谣言扩散模型，命名为HISBModel。在该模型中，我们提出了一种向谣言模拟的单个行为的制定，以阻尼谐波运动。在此之后，纳入了传播过程中个体的意见。此外，通过考虑个体和社会行为，通过了多路复用网络中的个体之间的谣言传输规则。此外，我们介绍了HABModel传播过程，该过程描述了谣言在多路复用在线社交网络中的传播。基于此模型，我们提出了一种真实性的运动战略，从网络推论的角度最大限度地降低了多路复用在线社交网络中的谣言的影响，并利用生存理论。在检测到谣言并推出真理运动以提高对其的真实运动，可以选择最有影响力的节点，以防止谣言的影响。因此，我们提出了一种基于似然原理的贪婪算法，其保证了在最佳解决方案的63％内的近似值。系统地，已经在从Twitter，Facebook和Slashdot以及真实在线社交网络（Facebook，Twitter和YouTube）的多路复用网络上进行了实验。首先，结果表明他的草编可以比文献中呈现的模型更现实地重现现实世界谣言传播的所有趋势。此外，模拟说明了所提出的模型根据文献突出了人类因素的影响。其次，与文献中的方法相比，实验证明了我们在最小化单一网络和多路复用社会网络传播中的谣言的影响方面的策略效率。结果证明，所提出的方法可以捕获谣言的动态传播过程，并更准确地选择目标节点以最小化磁解的影响。 （c）2019 Elsevier Inc.保留所有权利。

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