Misinformation propagation in online social networks: game theoretic and reinforcement learning approaches

Date
2022-09-30
Advisor
Instructor
Source Title
IEEE Transactions on Computational Social Systems
Print ISSN
Electronic ISSN
2329-924X
Publisher
IEEE
Volume
Issue
Pages
1 - 12
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

Misinformation in online social networks (OSNs) has been an ongoing problem, and it has been studied heavily over recent years. In this article, we use gamification to tackle misinformation propagation in OSNs. First, we construct a game based on the notion of cooperative games on graphs where the nodes of the social network are players. We use random regular networks and real networks in our simulations to show that the constructed game follows evolutionary dynamics and that the outcome of the game depends on the relation between the structural properties of the network and the benefit and cost variables defined in a cooperative game. Second, we create a game on the network level where the players control a set of nodes. We define agents whose goal is to maximize the total reward that we set up to be the number of nodes affected at the end of the game. We propose a deep reinforcement learning (RL) technique based on the multiagent deep deterministic policy gradient (MADDPG) algorithm. We test the proposed method along with well-known node selection algorithms and obtain promising results on different social networks.

Course
Other identifiers
Book Title
Keywords
Cooperative games, Misinformation propagation, Online social networks (OSNs), Reinforcement learning (RL)
Citation
Published Version (Please cite this version)