The study of opinion dynamics provides useful insights into large-scale trends in the formation and evolution of opinions in social networks and acts as a platform for rich dynamical behavior. The increased use of online social media, which allows for the rapid dissemination of information throughout a social network, makes the field more important than ever today. In particular, topics such as the propagation of false information and the formation of echo chambers and radicalized communities are relevant in today's social climate. In this Thesis I will present my research in the fields of opinion dynamics and social contagion theory. First, I discuss my work on a binary opinion model on hypergraphs where both individual agents and groups of 3 agents have opinions that evolve both through dyadic (i.e., pairwise) interactions and group memberships. This model contains parameter regimes with oscillatory and excitable dynamics that are highly sensitive to the structure of the underlying hypergraph. Second, I discuss my work on the spread of two competing beliefs in a social network where individuals have an internal opinion models their cognitive biases and modulates their likelihood of adopting one of the two beliefs. The addition of cognitive biases in the spreading process enriches its transient dynamics, facilitating behavior such as the revival of a dying belief and the overturning of an initially widespread opinion. The model is also studied with the presence of external recruitment of spreaders to examine how the intentional spread of information can lead to the eventual dominance of one of the two beliefs. Lastly, I discuss my ongoing work on the social compass model, a model of opinion depolarization. Particularly, I apply the Ott-Antonsen ansatz to successfully produce a low-dimensional description of the dynamics and study the onset of consensus for a large class of distributions of initial opinions. I also study a generalization of the model that includes community structure and external forcing.