We propose an approach inspired by the diffusion of innovations theory to model and characterize fake news sharing in social media through the lens of the different levels of influential factors (users, networks, and news). We address the problem of predicting fake news sharing as a classification task and demonstrate the potentials of the proposed features by achieving an AUROC of 0.97 and an average precision of 0.88, consistently outperforming baseline models with a higher margin (about 30% of AUROC). Also, we show that news-based features are the most effective at predicting real and fake news sharing, followed by the user- and network-based features.