How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works in this area are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the network users’ decisions, actions, and socioeconomic interactions are generally ignored in most existing works. In this chapter, we discuss an evolutionary game-theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks and uniform-degree and nonuniform-degree networks. We find that the dynamics of information diffusion over these three kinds of networks are scale-free and the same as each other when the network scale is sufficiently large. To verify the theoretical analysis, we perform simulations of the information diffusion over synthetic networks and real-world Facebook networks. Moreover, we conduct an experiment on the Twitter hashtag data set, which shows that the game-theoretic model well fits and predicts information diffusion over real social networks.