Social networks have become a ubiquitous part of the modern world and strongly influence opinion formation in modern societies. Researchers have resorted to several models to understand this opinion formation process in social networks; among these models, the Friedkin-Johnsen (FJ) model is one of the most popular. One of the main drawbacks of the FJ model is that it only models interactions between different users; it does not take into account external influences such as when users are exposed to viral content and change their opinions based on these contents. In this project, we study how viral marketing campaigns influence the opinions of users in social networks. We develop a formal model for how viral content influences user opinions and we derive algorithms for simulating this model efficiently. We also study how viral content can lead to more polarization in the network.