  Predicting the popularity of online content has attracted much attention in
the past few years. In news rooms, for instance, journalists and editors are
keen to know, as soon as possible, the articles that will bring the most
traffic into their website. The relevant literature includes a number of
approaches and algorithms to perform this forecasting. Most of the proposed
methods require monitoring the popularity of content during some time after it
is posted, before making any longer-term prediction. In this paper, we propose
a new approach for predicting the popularity of news articles before they go
online. Our approach complements existing content-based methods, and is based
on a number of observations regarding article similarity and topicality. First,
the popularity of a new article is correlated with the popularity of similar
articles of recent publication. Second, the popularity of the new article is
related to the recent historical popularity of its main topic. Based on these
observations, we use time series forecasting to predict the number of visits an
article will receive. Our experiments, conducted on a real data collection of
articles in an international news website, demonstrate the effectiveness and
efficiency of the proposed method.
