Characterizing the Life Cycle of Online News Stories Using Social Media Reactions

TL;DR

This study analyzes the life cycle of online news stories by combining website visitation data with social media reactions, enabling early and accurate predictions of article popularity and shelf-life.

Contribution

It introduces a hybrid observation method that characterizes article classes and predicts future visitation patterns using early social media reactions.

Findings

Social media reactions help predict future visits early and accurately.

Early social media data (10-20 minutes) suffices for accurate traffic modeling.

The method improves early shelf-life prediction of news stories.

Abstract

This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories.