We propose a new methodology for recommending interesting news to users by exploiting the information in their twitter persona. We model relevance between users and news articles using a mix of signals drawn from the news stream and from twitter: the profile of the social neighborhood of the users, the content of their own tweet stream, and topic popularity in the news and in the whole twitter-land. We validate our approach on a real-world dataset of approximately 40k articles coming from Yahoo! News and one month of crawled twitter data. We train our model using a learning-to-rank approach and support-vector machines. The train and test set are drawn from Yahoo! toolbar log data. We heuristically identify 3 214 users of twitter in the log and use their clicks on news articles to train our system. Our methodology is able to predict with good accuracy the news articles clicked by the users and rank them higher than other news articles. The results show that the combination of various signals from real-time web and microblogging platforms can be a useful resource to understand user behavior.