Opinion Formation

Opinion formation research investigates how individual beliefs evolve through social interaction and information exposure, aiming to understand and predict collective opinion dynamics. Current studies employ agent-based modeling, often incorporating variations of the DeGroot model or deep reinforcement learning, to explore factors like network structure, information frequency, and the influence of "super-spreaders" or adversarial actors on polarization and consensus. These models are increasingly validated against real-world data from social media and surveys, revealing insights into the interplay between individual psychology, social influence, and the spread of information. This work has implications for understanding information manipulation, designing effective public health campaigns, and mitigating the spread of misinformation online.