Developing Cost-Effective Rumor-Refuting Strategy Through Game-Theoretic Approach

Huang, Da-Wen; Yang, Lu‐Xing; Li, Pengdeng; Xiao, Yang; Tang, Yuan Yan

doi:10.1109/jsyst.2020.3020078

Cited by 29 publications

(12 citation statements)

References 54 publications

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“…Although misinformation is broadly a kind of information, there are some differences between the processes of information dissemination and misinformation spread. e biggest difference is that the former emphasizes a unilateral diffusion process, i.e., information dissemination models seldom consider the situation that people refuse to acquire information (see [14,[20][21][22], for some examples), whereas the latter emphasizes a competitive process between misinformation and the truth, i.e., misinformation spread models usually account for the situation that some people believe misinformation; meanwhile, some people believe the truth (see [13,15,23], for some examples). erefore, information dissemination models may not be very suitable to characterize the effect of countermeasures on containing misinformation spread.…”

Section: Misinformation Spread Modelsmentioning

confidence: 99%

“…If the influences of knowledge popularization and expert education on individuals are independent, then, at any time t ∈ [0, τ], the neutral individual v i and the misinformation-believing individual v k will transfer to the truth-believing state at the average rates of f(u 0 (t)) + 􏽐 M l�1 b il g(u l (t)) and c[f(u 0 (t)) + 􏽐 M l�1 b kl g(u l (t))], respectively. According to the modeling idea of individual-level epidemic theory [15,23,35], the evolution of individual states over time follows a continuous-time Markov chain. At any time t ∈ [0, τ], the transfer rates among the individual v i s states are as follows:…”

Section: Misinformation Spread Model Under a Cra Strategymentioning

confidence: 99%

“…We denote this network by G sf . e last one is a 100-node Facebook network obtained from [23,45]. We denote this network by G fb .…”

Section: Network Topological Structurementioning

confidence: 99%

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Containing Misinformation Spread: A Collaborative Resource Allocation Strategy for Knowledge Popularization and Expert Education

Lin-hong

Huang

Yang

2022

Security and Communication Networks

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With the prevalence of online social networks, the potential threat of misinformation has greatly enhanced. Therefore, it is significant to study how to effectively control the spread of misinformation. Publishing the truth to the public is the most effective approach to controlling the spread of misinformation. Knowledge popularization and expert education are two complementary ways to achieve that. It has been proven that if these two ways can be combined to speed up the release of the truth, the impact caused by the spread of misinformation will be dramatically reduced. However, how to reasonably allocate resources to these two ways so as to achieve a better result at a lower cost is still an open challenge. This paper provides a theoretical guidance for designing an effective collaborative resource allocation strategy. First, a novel individual-level misinformation spread model is proposed. It well characterizes the collaborative effect of the two truth-publishing ways on the containment of misinformation spread. On this basis, the expected cost of an arbitrary collaborative strategy is evaluated. Second, an optimal control problem is formulated to find effective strategies, with the expected cost as the performance index function and with the misinformation spread model as the constraint. Third, in order to solve the optimal control problem, an optimality system that specifies the necessary conditions of an optimal solution is derived. By solving the optimality system, a candidate optimal solution can be obtained. Finally, the effectiveness of the obtained candidate optimal solution is verified by a series of numerical experiments.

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Section: Misinformation Spread Modelsmentioning

confidence: 99%

Section: Misinformation Spread Model Under a Cra Strategymentioning

confidence: 99%

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Containing Misinformation Spread: A Collaborative Resource Allocation Strategy for Knowledge Popularization and Expert Education

Lin-hong

Huang

Yang

2022

Security and Communication Networks

Self Cite

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“…Etesami and Başar (2016) investigated a competitive transmission game for multi-information competition and communication over undirected networks. Recently, Huang et al (2020) introduced a differential gametheoretic model by estimating the expected net utility of rumormongers and the expected loss of rumor victims. Moreover, it is typical to explicitly model people's utility in order to understand the decision-making process under various human factors (such as cognitive behavior, psychology, neighbour influence) and popularity of information.…”

Section: Rumor Psychological Gamementioning

confidence: 99%

Rumor Transmission in Online Social Networks Under Nash Equilibrium of a Psychological Decision Game

2022

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This paper investigates rumor transmission over online social networks, such as those via Facebook or Twitter, where users liberally generate visible content to their followers, and the attractiveness of rumors varies over time and gives rise to opposition such as counter-rumors. All users in social media platforms are modeled as nodes in one of five compartments of a directed random graph: susceptible, hesitating, infected, mitigated, and recovered (SHIMR). The system is expressed with edge-based formulation and the transition dynamics are derived as a system of ordinary differential equations. We further allow individuals to decide whether to share, or disregard, or debunk the rumor so as to balance the potential gain and loss. This decision process is formulated as a game, and the condition to achieve mixed Nash equilibrium is derived. The system dynamics under equilibrium are solved and verified based on simulation results. A series of parametric analyses are conducted to investigate the factors that affect the transmission process. Insights are drawn from these results to help social media platforms design proper control strategies that can enhance the robustness of the online community against rumors.

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“…Q UANTITATIVE analysis of stochastic processes such as rumor and (mis-)information spreading over physical and online social networks (SNs) has stimulated intense research activities [1], [2]. Owing to the pervasive use of social media and the abundance of data extracted from several such networks, which for long were merely unavailable, the theoretical perception of epidemic dynamics driven by nodal interactions has refined substantially in recent years [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Co-evolution of Rumor Diffusion and Structural Stability in Signed Social Networks

Kalimzhanov¹,

Khamseh'i²,

Dadlani³

et al. 2021

Preprint

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Prediction and control of rumor diffusion in social networks (SNs) are closely tied to the underlying connectivity patterns. Contrary to most existing efforts that exclusively focus on positive social user interactions, the impact of contagion processes on the temporal evolution of signed SNs with distinctive friendly (positive) and hostile (negative) relationships yet, remains largely unexplored.In this paper, we study the interplay between social link polarity and propagation of intentionally fabricated information coupled with user alertness. In particular, we propose a novel energy model built on Heider's balance theory that relates the stochastic susceptible-alertinfected-susceptible rumor epidemic model with the structural balance of signed SNs to substantiate the trade-off between social tension and rumor spread. Moreover, the role of hostile social links in the formation of disjoint friendly clusters of alerted and infected users is analyzed. Using three real-world datasets, we further present a time-efficient algorithm to expedite the energy computation in our Monte-Carlo simulation method and show compelling insights on the effectiveness and rationality of user awareness and initial network settings in reaching structurally balanced local and global network energy states.

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Developing Cost-Effective Rumor-Refuting Strategy Through Game-Theoretic Approach

Cited by 29 publications

References 54 publications

Containing Misinformation Spread: A Collaborative Resource Allocation Strategy for Knowledge Popularization and Expert Education

Containing Misinformation Spread: A Collaborative Resource Allocation Strategy for Knowledge Popularization and Expert Education

Rumor Transmission in Online Social Networks Under Nash Equilibrium of a Psychological Decision Game

Co-evolution of Rumor Diffusion and Structural Stability in Signed Social Networks

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