Before revision:  On a dataset of Twitter cascades, we show that , compared to the state-of-art models, the proposed method improves the generalization performance on unseen data, delivers better prediction for final popularity and provides means to characterize online content from the way Twitter users discuss about it .
After revision:  On a dataset of Twitter cascades, we show that our models capture the differences between online items at the granularity of items, publishers and categories. In particular, we are able to distinguish between far-right, conspiracy, controversial and reputable online news articles based on how they diffuse through social media, achieving an F1 score of 0.945. On holdout datasets, we show that the dual mixture model provides, for reshare diffusion cascades especially unpopular ones, better generalization performance and, for online items, accurate item popularity predictions .