Maximizing Disagreement and Polarization in Social Media Networks using Double Deep Q-Learning

In this paper, we consider reinforcement learning (RL) techniques to systematically analyze and enhance the levels of disagreement and polarization within social media ecosystems. The proposed methodology employs a Double Deep Q- Learning algorithm to strategically identify individuals within the network. This identification process is aimed at selecting agents for takeover and control, thereby orchestrating a scenario that culminates in the maximization of disagreement and polarization within the network. The social media network is modeled by an asynchronous and synchronous expressed and private opinion dynamics model. The model incorporates a dual-state update mechanism: a synchronous update process for the state representing an individual's private opinion and an asynchronous update process for the state that reflects the individual's publicly expressed opinion. The RL agent's observational capacity is limited to the expressed opinions of individuals and the quantifiable metric of their followers or connections. The proposed model is analyzed for varying topologies and convergence conditions. Simulations are provided to illustrate the results.