- Luís Marujo, M. Bugalho, J. Neto, A. Gershman, J. Carbonell. 2013. Hourly Traffic Prediction of News Stories. Abstract: ABSTRACT 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 (log 10 ). 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 1 st Sapo Data Challenge we obtained 11.99% as mean relative error value which put us in the 4 th place out of 26 participants. Categories and Subject Descriptors H.3.3 [ Information Search and Retrieval ]: Information filtering General Terms Algorithms , Measurement , Experimentation . Keywords Prediction, News, Clicks, Sapo Challenge, Traffic 1. INTRODUCTION
