Game Theoretic Opinion Models and Their Application in Processing Disinformation

Guo, Zhen; Cho, Jin-Hee

doi:10.1109/globecom46510.2021.9685197

Cited by 3 publications

(2 citation statements)

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“…The dominant opinions of all users in the identified modularity-based communities [9] indicate which opinion model can defend disinformation better. This work significantly extends the previous work [10] and [11] with the following additional contributions.…”

Section: Introductionsupporting

confidence: 75%

Mitigating Influence of Disinformation Propagation Using Uncertainty-Based Opinion Interactions

Guo

Cho

2023

IEEE Trans. Comput. Soc. Syst.

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For decades, the spread of disinformation in online social networks (OSNs) has been a serious social issue. Disinformation via social media can easily mislead people's beliefs toward or against an event that may mislead their behaviors based on the misbeliefs. The game theory approaches have been proposed under dynamic settings to limit the adverse influences of disinformation. It is a challenge to expand the users' game strategies from the spreading decisions to the possible opinion updating choices. This work proposes a game-theoretic opinion framework that can formulate dynamic opinions by a belief model called Subjective Logic (SL) and provide opinion updates on five types of users' interactions on OSN platforms. The opinions are updated based on user choices and user types through the game interactions among legitimate users, attackers, and a defender in an OSN. Via the extensive simulation experiments, the effectiveness of the opinion models of five decision-makers (DMs) is analyzed in terms of users believing or disbelieving disinformation in an epidemic model with parameter optimization. Our results show that while homophily-based DMs (H-DMs) introduce the highest opinion polarization, uncertainty-based DMs (U-DMs) can effectively filter untrustworthy users propagating disinformation.

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Section: Introductionsupporting

confidence: 75%

Mitigating Influence of Disinformation Propagation Using Uncertainty-Based Opinion Interactions

Guo

Cho

2023

IEEE Trans. Comput. Soc. Syst.

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“…The preliminary results of this work have been included in our prior conference paper [24]. We substantially extended our prior work [24] and made the additional contributions to this work as follows:…”

Section: Introductionmentioning

confidence: 99%

Effect of Disinformation Propagation on Opinion Dynamics: A Game Theoretic Approach

Guo

Valinejad

Cho

2022

IEEE Trans. Netw. Sci. Eng.

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Disinformation can alter or manipulate our values, opinions, and rational decisions toward any life event because disinformation, such as fake news or rumors, is propagated rapidly and broadly in online social networks (OSNs). Gametheoretic models can help people maximize the benefits from dynamic social interactions. This work presents an opinion framework formulated by repeated, incomplete information games that model OSN users' subjective opinions. The users may update their opinions using various criteria, such as uncertainty, homophily, encounter, herding, or assertion. We demonstrate how Subjective Logic, a belief model explicitly handling opinion uncertainty, can be employed to model attackers' deception strategies, users' opinion update models, and the influences of propagating disinformation through the interactions between users. Through extensive experiments, we investigated how an individual user's information processing type can introduce different impacts on the extent of disinformation propagation. We compared the performance of the five different opinion update models under OSNs characterized by two real OSN datasets. We analyzed their impact on the choices of best strategies, their utilities, and network/opinion polarization. We also examined how the player's choices of best strategies under uncertainty are different from Nash Equilibrium strategies based on correct beliefs towards their opponents' moves.

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A Game Theory Approach for Assisting Humans in Online Information-Sharing

Hirschprung

Alkoby

2022

Information

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Contemporary information-sharing environments such as Facebook offer a wide range of social and practical benefits. These environments, however, may also lead to privacy and security violations. Moreover, there is usually a trade-off between the benefits gained and the accompanying costs. Due to the uncertain nature of the information-sharing environment and the lack of technological literacy, the layperson user often fails miserably in balancing this trade-off. In this paper, we use game theory concepts to formally model this problem as a “game”, in which the players are the users and the pay-off function is a combination of the benefits and costs of the information-sharing process. We introduce a novel theoretical framework called Online Information-Sharing Assistance (OISA) to evaluate the interactive nature of the information-sharing trade-off problem. Using these theoretical foundations, we develop a set of AI agents that attempt to calculate a strategy for balancing this trade-off. Finally, as a proof of concept, we conduct an empirical study in a simulated Facebook environment in which human participants compete against OISA-based AI agents, showing that significantly higher utility can be achieved using OISA.

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Game Theoretic Opinion Models and Their Application in Processing Disinformation

Cited by 3 publications

References 12 publications

Mitigating Influence of Disinformation Propagation Using Uncertainty-Based Opinion Interactions

Mitigating Influence of Disinformation Propagation Using Uncertainty-Based Opinion Interactions

Effect of Disinformation Propagation on Opinion Dynamics: A Game Theoretic Approach

A Game Theory Approach for Assisting Humans in Online Information-Sharing

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