Opinion Dynamics in Gossiper-Media Networks Based on Multiagent Reinforcement Learning

In social networks, to increase the number of followers, media express their opinions through posts, articles, and published content to cater to public preferences. Meanwhile, the evolution of public opinions is affected by both the media and peers. In this work, we investigate how the interactions between media affect the dynamics of public opinions in social networks. We propose a reinforcement learning framework to model the interactions between the public (gossipers) and media agents. We model each gossiper as an individually rational agent, which updates its opinion using the bounded confidence model (BCM). Media agents are interested in maximizing the number of following gossipers competitively, and an algorithm, i.e ., WoLS-CALA, is proposed to achieve that goal. We analyze and experimentally verify that WoLS-CALA can learn Nash equilibria (NE) for games with continuous action space. In addition, the opinion dynamics of gossipers are theoretically analyzed, which shows that the existence of media will strengthen the consistency of gossipers' opinions. We then evaluate the framework in two synthetic networks, i.e ., a fully connected network and a small-world network, and one real data network from Facebook. Extensive empirical simulation reveals that our framework facilitates the consensus of opinions and confirms the theoretical analysis.