A huge amount of information, created and forwarded by millions of people with various characteristics, propagates through online social networks every day. Understanding the mechanisms of information diffusion over social networks is critical to various applications, including online advertisements and website management. Differently from most existing works in this area, we investigate information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world data sets. The theoretical results are confirmed by simulations. We also test the theory on the Twitter hashtag data set. We observe that the evolutionary dynamics fit the data well and can predict future diffusion data.