Twitter-driven YouTube views: Beyond individual influencers

Abstract

This paper proposes a novel method to predict increases in YouTube viewcount driven from the Twitter social network. Specifically, we aim to predict two types of viewcount increases: a sudden increase in viewcount (named as JUMP), and the viewcount shortly after the upload of a new video (named as EARLY). Experiments on hundreds of thousands of videos and millions of tweets show that Twitter-derived features alone can predict whether a video will be in the top 5% for EARLY popularity with 0.7 Precision@100. Furthermore, our results reveal that while individual influence is indeed important for predicting how Twitter drives YouTube views, it is a diversity of interest from the most active to the least active Twitter users mentioning a video (measured by the variation in their total activity) that is most informative for both Jump and Early prediction. In summary, by going beyond features that quantify individual influence and additionally leveraging collective features of activity variation, we are able to obtain an effective cross-network predictor of Twitter-driven YouTube views.