The Pulse of News in Social Media: Forecasting Popularity

TL;DR

This paper explores predicting the future popularity of news articles on social media before their release by analyzing multi-dimensional features, demonstrating an 84% accuracy in Twitter popularity prediction.

Contribution

It introduces a multi-dimensional feature space for predicting news popularity prior to publication and evaluates various algorithms for this task.

Findings

Achieved 84% accuracy in predicting Twitter popularity.

Identified key features influencing news popularity.

Compared traditional sources with social web sources.

Abstract

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.