Misinformation Propagation in Online Social Networks: Game Theoretic and Reinforcement Learning Approaches

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.

Automated summary

This article presents a study on using gamification to tackle misinformation propagation in online social networks (OSNs). A game based on cooperative games on graphs is constructed, and its evolutionary dynamics are shown through simulations. Additionally, a deep reinforcement learning technique using the multiagent deep deterministic policy gradient algorithm is proposed to maximize rewards.