  The process of predicting news stories popularity from several news sources
has become a challenge of great importance for both news producers and readers.
In this paper, we investigate methods for automatically predicting the number
of clicks on a news story during one hour. Our approach is a combination of
additive regression and bagging applied over a M5P regression tree using a
logarithmic scale (log10). The features included are social-based (social
network metadata from Facebook), content-based (automatically extracted
keyphrases, and stylometric statistics from news titles), and time-based. In
1st Sapo Data Challenge we obtained 11.99% as mean relative error value which
put us in the 4th place out of 26 participants.
