The Dynamics of Opinion Evolution in Gossiper-Media Model with WoLS-CALA Learning

In social networks, media outlets such as TV, newspapers, blogs adjust their opinions to cater to the public's interest to increase the number of followers. Meanwhile, the evolution of the public's opinions are affected by both the media and the peers they interact with. In this work, we investigate how the interactions between mainstream media affect the dynamics of the public's opinions in social networks. We propose a reinforcement learning framework to model the interactions between the public ( aka gossipers) and the media agents. We model each gossiper as an individually rational agent, which updates its opinion using the Bounded Confidence Model (BCM). Each media agent is interested in maximizing the number of following gossipers competitively, and an adaptive WoLS-CALA (Win or Learn Slow Continuous Action Learning Automaton) algorithm is proposed to achieve that goal. We theoretically prove that WoLS-CALA can learn to Nash equilibria for two-agent games with continuous action space. Besides, the opinion dynamics of both gossipers and media are theoretically analyzed. Extensive empirical simulation reveals the opinion dynamics of our framework facilitates the consensus of opinions and confirms the theoretical analysis.