Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors
A Published in KBS, 2020
Author: Adil Imad Eddine Hosni and Kan Li and Sadique AhmadAdil Imad Eddine Hosni, Kan Li
Published in: Information Sciences
Abstract
The malicious rumors have tremendously attracted a more substantial number of researchers to join the fight against the propagation of these types of information in online social networks (OSNs). The spread of rumors has a severe impact on society, which can creates political conflicts, shape public opinion and weakens their trust in governments; therefore, it must be stopped as soon as it is detected. This paper investigates the problem of minimizing the influence of malicious rumors that emerge during breaking news, which are characterized by the dissemination of a large number of malicious information over a short period. Therefore, we introduce the problem of multi-rumors influence minimization (MRIM) in OSNs and propose a solution to it. To this end, we design a multi-rumor propagation model named the HISBMmodel that captures the propagation process of multi-rumors in OSNs. Moreover, we present a new formulation of an individual’s opinion toward a rumor based on a Markov chain representation, which adds a layer of realism to the proposed model. Subsequently, we propose a dynamic blocking period (DBP) approach as a solution for the MRIM problem. The main objective is to minimize both the spread and the influence of these rumors in OSNs. The proposed method selects and blocks nodes that most likely to spread a large number of rumors and support them. Different from existing methods, the proposed solution does not block nodes for an unlimited period, but this period is estimated according to the high activity of a node in an OSN. The survival theory has been exploited in this work to provide a solution formulated from the perspective of probabilistic inference of networks. Consequently, an algorithm has been proposed based on a likelihood principle to select the target nodes, which guarantees a (1−1∕e)-approximation of the optimal solution. The experimental results show that the HISBMmodel could capture the propagation of multi-rumor propagation more accurately than classical models and provides metrics to assess the impact of rumors efficiently. Moreover, the results show the outstanding performance of the proposed approach compared to the other solution in the literature. The experimental results show that in the worst-case, the DBP achieves on an average 37.66% reduction on the impact of rumors, compared to 18.46% obtained by the second-best method. However, in the best-case the performance of the proposed method reached 93.38% where second-best method achieved only 57.65% on an average. Besides, even though when the number of rumors is high, the DBP could achieve on an average 68.01% reduction on the impact of rumors.
Recommended citation:
@article{HOSNI20201458,
title = {Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors},
journal = {Information Sciences},
volume = {512},
pages = {1458-1480},
year = {2020},
issn = {0020-0255},
doi = {https://doi.org/10.1016/j.ins.2019.10.063},
url = {https://www.sciencedirect.com/science/article/pii/S002002551931031X},
author = {Adil Imad Eddine Hosni and Kan Li and Sadique Ahmad},
keywords = {Rumor influence minimization, Rumor propagation model, Multiplex online social networks, Human individual and social behaviors, Survival theory},
abstract = {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.}
}