Yu, Honglin; Xie, Lexing; Sanner, Scott
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...[Show more]
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