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

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.