  We built models with Logistic Regression and linear Support Vector Machines
on a large dataset consisting of regular news articles and news from satirical
websites, and showed that such linear classifiers on a corpus with about 60,000
articles can perform with a precision of 98.7% and a recall of 95.2% on a
random test set of the news. On the other hand, when testing the classifier on
"publication sources" which are completely unknown during training, only an
accuracy of 88.2% and an F1-score of 76.3% are achieved. As another result, we
showed that the same algorithm can distinguish between news written by the news
agency itself and paid articles from customers. Here the results had an
accuracy of 99%.
