News articles are extremely time sensitive by nature. There is also intense
competition among news items to propagate as widely as possible. Hence, the
task of predicting the popularity of news items on the social web is both
interesting and challenging. Prior research has dealt with predicting eventual
online popularity based on early popularity. It is most desirable, however, to
predict the popularity of items prior to their release, fostering the
possibility of appropriate decision making to modify an article and the manner
of its publication. In this paper, we construct a multi-dimensional feature
space derived from properties of an article and evaluate the efficacy of these
features to serve as predictors of online popularity. We examine both
regression and classification algorithms and demonstrate that despite
randomness in human behavior, it is possible to predict ranges of popularity on
twitter with an overall 84% accuracy. Our study also serves to illustrate the
differences between traditionally prominent sources and those immensely popular
on the social web.

本文旨在通过建立多维特征空间来分析新闻文章的性质，采用回归和分类算法预测其在线受欢迎程度，发现可在 Twitter 上以 84% 的准确率范围内预测其受欢迎度，并突显传统来源与社交网络受欢迎性的差异。