Social network analysis can be used to assess the impact of information published on the web. The spatiotemporal impact of a certain web source on a social network can be of particular interest. We contribute a novel statistical learning algorithm for spatiotemporal impact analysis. To demonstrate our approach we analyze Twitter replies to individual news article along with their geospatial and temporal information. We then compute the multivariate spatiotemporal response pattern of all Twitter replies to information published on a given web source. This quantitative result can be interpreted with respect to a) how much impact a certain web source has on the Twitter-sphere b) where and c) when it reaches it maximal impact. We also show that the proposed approach predicts the dynamics of the social network activity better than classical trend detection methods.